From d29c1b044652b26c8a9002138c17dc4c4b52f5bc Mon Sep 17 00:00:00 2001 From: QingChuanWS <2754693100@qq.com> Date: Wed, 27 Jan 2021 00:55:04 +0800 Subject: [PATCH 1/6] =?UTF-8?q?=E5=88=A0=E9=99=A4m55=E5=BA=93=EF=BC=8C?= =?UTF-8?q?=E5=AE=8C=E5=96=84md=E6=96=87=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...TFlite_Micro_Demo移植参考指南（Keil版）.md | 4 +- .../TencentOS-tiny Person Detection Demo.md | 23 - .../TencentOS_tiny.uvoptx | 1179 ++ .../TencentOS_tiny.uvprojx | 49 +- .../ai/tflite_micro/ARM_CortexM55_lib/LICENSE | 203 - .../tensorflow/core/public/version.h | 139 - .../tensorflow/lite/c/builtin_op_data.h | 472 - .../tensorflow/lite/c/common.h | 936 - .../tensorflow/lite/core/api/error_reporter.h | 59 - .../lite/core/api/flatbuffer_conversions.h | 253 - .../tensorflow/lite/core/api/op_resolver.h | 48 - .../tensorflow/lite/core/api/profiler.h | 194 - .../tensorflow/lite/core/api/tensor_utils.h | 28 - 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a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md @@ -191,7 +191,7 @@ void task1(void *arg) ### 1. tflite_micro组件加入到keil工程 -由于NUCLEO-L496ZG芯片中的内核为ARM Cortex M4，所以本次我们可以直接使用ARM Cortex M4版本的tensorflow_lite_micro.lib库来简化tflite_micro搭建流程。 +由于 NUCLEO-L496ZG 芯片中的内核为 ARM Cortex M4，所以本次我们可以直接使用 ARM Cortex M4 版本的tensorflow_lite_micro.lib 库来简化 tflite_micro 搭建流程。 #### 1.1 在project中加入新的文件夹tensorflow @@ -237,7 +237,7 @@ TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro -其中宏`NUCLEO_STM32L496ZG`是指定Nucleo STM32L496的hlpuart1为系统printf函数的输出串口，具体定义在Nucleo STM32L496的BSP文件夹中的`mcu_init.c`中。 +其中宏`NUCLEO_STM32L496ZG`是指定 Nucleo STM32L496 的 hlpuart1 为系统 printf 函数的输出串口，具体定义在 Nucleo STM32L496 的 BSP 文件夹中的`mcu_init.c`中。 ### 2. 编写Person_Detection 任务函数 diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS-tiny Person Detection Demo.md b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS-tiny Person Detection Demo.md deleted file mode 100644 index 8c487e88..00000000 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS-tiny Person Detection Demo.md +++ /dev/null @@ -1,23 +0,0 @@ -## TencentOS-tiny_Person_Detection_Demo - -### 1. 目录结构： - -- TencentOS-tiny\board\NUCLEO_STM32L496ZG\BSP\Hardware : **外设驱动代码** -- TencentOS-tiny\examples\tflitemicro_person_detection : **Demo任务函数** -- TencentOS-tiny\board\NUCLEO_STM32L496ZG\KEIL\tflitemicro_person_detection : **keil工程** -- TencentOS-tiny\components\tflite_micro\tensorflow : **tflite_micro代码** - -### 2. 完成的工作： - -- 使用STM32CubeMX，选择与TOS同版本的固件库重新生成外设初始化代码； -- TOS、摄像头和LCD工作都正常工作； -- tflite_micro 以component的形式加到工程 -- retarget.c引入工程，并通过宏进行选择 -- example中行人检测demo已经可以正常工作 - -### 3. 未完成的工作： - -- 变量名、函数名还没有按照TOS的风格完全统一 -- keil移植指南 -- tflite_micro用户指南 - diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx new file mode 100644 index 00000000..205a4f04 --- /dev/null +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx @@ -0,0 +1,1179 @@ + + + + 1.0 + +
### uVision Project, (C) Keil Software
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+ + + tos/cmsis_os + 0 + 0 + 0 + 0 + + 7 + 64 + 1 + 0 + 0 + 0 + ..\..\..\..\osal\cmsis_os\cmsis_os.c + cmsis_os.c + 0 + 0 + + + + + hal + 0 + 0 + 0 + 0 + + 8 + 65 + 1 + 0 + 0 + 0 + ..\..\BSP\Hardware\Src\delay.c + delay.c + 0 + 0 + + + 8 + 66 + 1 + 0 + 0 + 0 + ..\..\BSP\Hardware\Src\lcd_2inch4.c + lcd_2inch4.c + 0 + 0 + + + 8 + 67 + 1 + 0 + 0 + 0 + ..\..\BSP\Hardware\Src\lcd_config.c + lcd_config.c + 0 + 0 + + + 8 + 68 + 1 + 0 + 0 + 0 + ..\..\BSP\Hardware\Src\ov2640.c + ov2640.c + 0 + 0 + + + 8 + 69 + 1 + 0 + 0 + 0 + ..\..\BSP\Hardware\Src\sccb.c + sccb.c + 0 + 0 + + + + + examples + 0 + 0 + 0 + 0 + + 9 + 70 + 1 + 0 + 0 + 0 + ..\..\..\..\examples\tflitemicro_person_detection\tflitemicro_person_detection.c + tflitemicro_person_detection.c + 0 + 0 + + + + + tensorflow + 0 + 0 + 0 + 0 + + 10 + 71 + 4 + 0 + 0 + 0 + ..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib + tensorflow_lite_micro_M4.lib + 0 + 0 + + + 10 + 72 + 8 + 0 + 0 + 0 + .\tflu_person_detection\detection_responder.cc + detection_responder.cc + 0 + 0 + + + 10 + 73 + 8 + 0 + 0 + 0 + .\tflu_person_detection\image_provider.cc + image_provider.cc + 0 + 0 + + + 10 + 74 + 8 + 0 + 0 + 0 + .\tflu_person_detection\main_functions.cc + main_functions.cc + 0 + 0 + + + 10 + 75 + 8 + 0 + 0 + 0 + .\tflu_person_detection\model_settings.cc + model_settings.cc + 0 + 0 + + + 10 + 76 + 8 + 0 + 0 + 0 + .\tflu_person_detection\person_detect_model_data.cc + person_detect_model_data.cc + 0 + 0 + + + + + ::CMSIS + 0 + 0 + 0 + 1 + + + diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx index 21576550..a1efeae7 100644 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx @@ -16,8 +16,8 @@ STM32L496ZGTx STMicroelectronics - Keil.STM32L4xx_DFP.2.4.0 - http://www.keil.com/pack/ + Keil.STM32L4xx_DFP.2.5.0 + https://www.keil.com/pack/ IRAM(0x20000000-0x2004FFFF) IROM(0x8000000-0x80FFFFF) CLOCK(8000000) FPU2 CPUTYPE("Cortex-M4") @@ -137,7 +137,7 @@ 4107 1 - STLink\ST-LINKIII-KEIL_SWO.dll + BIN\UL2CM3.DLL @@ -779,24 +779,9 @@ tensorflow - person_detect_model_data.cc - 8 - .\tflu_person_detection\person_detect_model_data.cc - - - model_settings.cc - 8 - .\tflu_person_detection\model_settings.cc - - - main_functions.cc - 8 - .\tflu_person_detection\main_functions.cc - - - image_provider.cc - 8 - .\tflu_person_detection\image_provider.cc + tensorflow_lite_micro_M4.lib + 4 + ..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib detection_responder.cc @@ -804,14 +789,24 @@ .\tflu_person_detection\detection_responder.cc - retarget.c - 1 - ..\..\..\..\components\ai\tflite_micro\KEIL\retarget.c + image_provider.cc + 8 + .\tflu_person_detection\image_provider.cc - tensorflow_lite_micro_M4.lib - 4 - ..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib + main_functions.cc + 8 + .\tflu_person_detection\main_functions.cc + + + model_settings.cc + 8 + .\tflu_person_detection\model_settings.cc + + + person_detect_model_data.cc + 8 + .\tflu_person_detection\person_detect_model_data.cc diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/LICENSE b/components/ai/tflite_micro/ARM_CortexM55_lib/LICENSE deleted file mode 100644 index 40f8c347..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/LICENSE +++ /dev/null @@ -1,203 +0,0 @@ -Copyright 2019 The TensorFlow Authors. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/core/public/version.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/core/public/version.h deleted file mode 100644 index 431784a5..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/core/public/version.h +++ /dev/null @@ -1,139 +0,0 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CORE_PUBLIC_VERSION_H_ -#define TENSORFLOW_CORE_PUBLIC_VERSION_H_ - -// TensorFlow uses semantic versioning, see http://semver.org/. - -// Also update tensorflow/tensorflow.bzl and -// tensorflow/tools/pip_package/setup.py -#define TF_MAJOR_VERSION 2 -#define TF_MINOR_VERSION 4 -#define TF_PATCH_VERSION 0 - -// TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", -// "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "" - -#define TF_STR_HELPER(x) #x -#define TF_STR(x) TF_STR_HELPER(x) - -// e.g. "0.5.0" or "0.6.0-alpha". -#define TF_VERSION_STRING \ - (TF_STR(TF_MAJOR_VERSION) "." TF_STR(TF_MINOR_VERSION) "." TF_STR( \ - TF_PATCH_VERSION) TF_VERSION_SUFFIX) - -// GraphDef compatibility versions (the versions field in graph.proto). -// -// Each graph has producer and min_consumer versions, and each -// consumer has its own version and a min_producer. In addition, graphs can -// mark specific consumer versions as bad (to prevent bugs from executing). -// A consumer will execute a graph if the consumer's version is at least the -// graph's min_consumer, the graph's producer version is at least the consumer's -// min_producer, and the consumer version isn't specifically disallowed by the -// graph. -// -// By default, newly created graphs have producer version TF_GRAPH_DEF_VERSION -// min_consumer TF_GRAPH_DEF_MIN_CONSUMER, and no other bad consumer versions. -// -// Version history: -// -// 0. Graphs created before GraphDef versioning -// 1. First real version (2dec2015) -// 2. adjust_contrast only takes float, doesn't perform clamping (11dec2015) -// 3. Remove TileGrad, since it was equivalent to reduce_sum (30dec2015) -// 4. When support for this version is removed, we can safely make AttrValue -// parsing more strict with respect to empty list values (see -// 111635679, 7jan2016). -// 5. Graphs are wholly-validated during Session::Create() (7jan2016). -// 6. TensorFlow is scalar strict within Google (27jan2016). -// 7. Remove TopK in favor of TopKV2 (5feb2016). -// 8. Replace RandomCrop from C++ with pure Python (5feb2016). -// 9. Deprecate batch_norm_with_global_normalization (16feb2016). -// 10. Deprecate conv3d_backprop_{filter,input} (10jun2016). -// 11. Deprecate {batch}_self_adjoint_eig (3aug2016). -// 12. Graph consumers understand the node_def field of FunctionDef (22aug2016). -// 13. Deprecate multiple batch linear algebra ops (9sep2016). -// 14. Deprecate batch_matrix_* ops. (10sep2016). -// 15. Deprecate batch_fft_* ops. (14sep2016). -// 16. Deprecate tensor_array (v1) ops in favor of v2 (10nov2016). -// 17. Deprecate inv (11nov2016). -// 17. Expose reverse_v2 (10nov2016) -// 18. Add VariableV2 (30nov2016) -// 19. Deprecated ops created by models moved out of core SkipGram, NegTrain. -// (08dec2016) -// 20. Catch all version 1.0 changes to Python API generation. SplitV is now -// used for tf.split, ReverseV2 is now used by tf.reverse, ConcatV2 is -// now used by tf.concat. Graphs use flooring -// division and mod semantics. TensorArrayV3. (12dec2016) -// Also considered the version for when it is required for reduction -// ops' indices to be scalar or vector, and not higher rank. -// Some earlier graph def versions allowed this. -// 21. Dropped FunctionDef.Node support, switched to node_def introduced -// in version 12. (11jan2017) -// 22. Placeholder now can specify and enforce scalar and partial -// shapes, particularly when restoring a graph from GraphDef -// produced at version 22 or later. (04/10/2016) -// 23. Remove NonMaxSuppression in favor of NonMaxSuppressionV2. -// 24. Deprecate lookup ops (v1) ops in favor of v2 (30may2017) -// 25. Deprecate stack (v1) ops in favor of v2 (2017/6/15). -// 25. Deprecate RandomPoisson (v1) ops in favor of v2 (2017/10/25). -// 26. Add a bool 'stripped_default_attrs' to MetaInfoDef indicating -// whether default-valued attrs have been stripped from the nodes in the -// GraphDef. (7dec2017) -// 27. Deprecate TensorArray ops v2 in favor of v3 and deprecated io_ops -// deprecated in favor of V2 ops. (2018/01/23) -// 28. Deprecate MatrixExponential op in favor of Python implementation. -// (2018/08/21). -// (2019/02/15). Added `control_ret` field to FunctionDef proto, and -// `control_output` field to OpDef proto. -// 29. Deprecate StatefulStandardNormal op in favor of StatefulStandardNormalV2. -// (2019/03/25). -// (2019/04/17). Added `arg_attr` field to FunctionDefProto. -// 30. (2019/05/09) First date based GraphDef version. GraphDef -// versions advance by 1 each day after this point. - -#define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0 -#define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0 -#define TF_GRAPH_DEF_VERSION 485 // Updated: 2020/8/6 - -// Checkpoint compatibility versions (the versions field in SavedSliceMeta). -// -// The checkpoint versions have the same semantics as GraphDef versions, but the -// numbering scheme is separate. We have no plans to ever deprecate checkpoint -// versions, but it's good to have this in place in case we ever need to. -// -// Version history: -// -// 0. Checkpoints saved before checkpoint versioning. -// 1. First real version (10feb2015). -#define TF_CHECKPOINT_VERSION_MIN_PRODUCER 0 -#define TF_CHECKPOINT_VERSION_MIN_CONSUMER 0 -#define TF_CHECKPOINT_VERSION 1 - -/// Version query functions (defined in generated version_info.cc) - -// Host compiler version (declared elsewhere to be __VERSION__) -extern const char* tf_compiler_version(); -// The git commit designator when tensorflow was built -// If no git repository, this will be "internal". -extern const char* tf_git_version(); -// Value of the _GLIBCXX_USE_CXX11_ABI flag, or 0 if it's not set. -extern int tf_cxx11_abi_flag(); -// Returns 1 if build is monolithic, or 0 otherwise. -extern int tf_monolithic_build(); - -#endif // TENSORFLOW_CORE_PUBLIC_VERSION_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/builtin_op_data.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/builtin_op_data.h deleted file mode 100644 index e205f075..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/builtin_op_data.h +++ /dev/null @@ -1,472 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ -#define TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ - -#include - -#include "tensorflow/lite/c/common.h" - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -// TfLiteReshapeParams can't have dynamic data so we fix the maximum possible -// number of dimensions. -#define TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT 8 - -// TODO(aselle): Consider using "if this then that" for testing. - -// Useful placeholder to put in otherwise empty structs to avoid size warnings. -typedef struct { - char dummy; -} EmptyStructPlaceholder; - -// IMPORTANT: All new members of structs must be added at the end to ensure -// backwards compatibility. - -// Possible padding types (for convolutions) -typedef enum { - kTfLitePaddingUnknown = 0, - kTfLitePaddingSame, - kTfLitePaddingValid, -} TfLitePadding; - -typedef enum { - kTfLiteMirrorPaddingUnknown = 0, - kTfLiteMirrorPaddingReflect, - kTfLiteMirrorPaddingSymmetric, -} TfLiteMirrorPaddingMode; - -// TODO(b/130259536): We should move this out of builtin_op_data. -typedef struct { - int width; - int height; - int width_offset; - int height_offset; -} TfLitePaddingValues; - -typedef struct { - TfLiteMirrorPaddingMode mode; -} TfLiteMirrorPaddingParams; - -// Possible fused activation functions. -// TODO(aselle): rename to TfLiteActivation -typedef enum { - kTfLiteActNone = 0, - kTfLiteActRelu, - kTfLiteActReluN1To1, // min(max(-1, x), 1) - kTfLiteActRelu1 = kTfLiteActReluN1To1, // kTfLiteActRelu1 will be deprecated. - kTfLiteActRelu6, // min(max(0, x), 6) - kTfLiteActTanh, - kTfLiteActSignBit, - kTfLiteActSigmoid, -} TfLiteFusedActivation; - -typedef struct { - // Parameters for CONV_2D version 1. - TfLitePadding padding; - int stride_width; - int stride_height; - TfLiteFusedActivation activation; - - // Parameters for CONV_2D version 2. - // Note: Version 2 supports dilation values not equal to 1. - int dilation_width_factor; - int dilation_height_factor; -} TfLiteConvParams; - -typedef struct { - TfLitePadding padding; - int stride_width; - int stride_height; - int filter_width; - int filter_height; - TfLiteFusedActivation activation; - struct { - TfLitePaddingValues padding; - } computed; -} TfLitePoolParams; - -typedef struct { - // Parameters for DepthwiseConv version 1 or above. - TfLitePadding padding; - int stride_width; - int stride_height; - // `depth_multiplier` is redundant. It's used by CPU kernels in - // TensorFlow 2.0 or below, but ignored in versions above. - // - // The information can be deduced from the shape of input and the shape of - // weights. Since the TFLiteConverter toolchain doesn't support partially - // specified shapes, relying on `depth_multiplier` stops us from supporting - // graphs with dynamic shape tensors. - // - // Note: Some of the delegates (e.g. NNAPI, GPU) are still relying on this - // field. - int depth_multiplier; - TfLiteFusedActivation activation; - // Parameters for DepthwiseConv version 2 or above. - int dilation_width_factor; - int dilation_height_factor; -} TfLiteDepthwiseConvParams; - -typedef struct { - int rank; - TfLiteFusedActivation activation; - - // Parameter for SVDF version 4. - bool asymmetric_quantize_inputs; -} TfLiteSVDFParams; - -typedef struct { - TfLiteFusedActivation activation; - - // Parameter for RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteRNNParams; - -typedef struct { - bool time_major; - TfLiteFusedActivation activation; - - // Parameter for Sequence RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteSequenceRNNParams; - -typedef struct { - bool time_major; - TfLiteFusedActivation activation; - bool merge_outputs; - - // Parameter for Bidirectional RNN verison 3. - bool asymmetric_quantize_inputs; -} TfLiteBidirectionalSequenceRNNParams; - -typedef enum { - kTfLiteFullyConnectedWeightsFormatDefault = 0, - kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8 = 1, -} TfLiteFullyConnectedWeightsFormat; - -typedef struct { - // Parameters for FullyConnected version 1 or above. - TfLiteFusedActivation activation; - - // Parameters for FullyConnected version 2 or above. - TfLiteFullyConnectedWeightsFormat weights_format; - - // Parameters for FullyConnected version 5 or above. - // If set to true, then the number of dimensions in the input and the output - // tensors are the same. Furthermore, all but the last dimension of the input - // and output shapes will be equal. - bool keep_num_dims; - - // Parameters for FullyConnected version 7 or above. - // If set to true and the weights are quantized, then non constant inputs - // are quantized at evaluation time with asymmetric quantization. - bool asymmetric_quantize_inputs; -} TfLiteFullyConnectedParams; - -typedef enum { - kTfLiteLshProjectionUnknown = 0, - kTfLiteLshProjectionSparse = 1, - kTfLiteLshProjectionDense = 2, -} TfLiteLSHProjectionType; - -typedef struct { - TfLiteLSHProjectionType type; -} TfLiteLSHProjectionParams; - -typedef struct { - float beta; -} TfLiteSoftmaxParams; - -typedef struct { - int axis; - TfLiteFusedActivation activation; -} TfLiteConcatenationParams; - -typedef struct { - TfLiteFusedActivation activation; - // Parameter added for the version 4. - bool pot_scale_int16; -} TfLiteAddParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteSpaceToBatchNDParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteBatchToSpaceNDParams; - -typedef struct { - bool adj_x; - bool adj_y; -} TfLiteBatchMatMulParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteMulParams; - -typedef struct { - TfLiteFusedActivation activation; - // Parameter added for the version 5. - bool pot_scale_int16; -} TfLiteSubParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteDivParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteL2NormParams; - -typedef struct { - int radius; - float bias; - float alpha; - float beta; -} TfLiteLocalResponseNormParams; - -typedef enum { - kTfLiteLSTMFullKernel = 0, - kTfLiteLSTMBasicKernel -} TfLiteLSTMKernelType; - -typedef struct { - // Parameters for LSTM version 1. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // Parameters for LSTM version 2. - // kTfLiteLSTMBasicKernel is only supported in version 2 or above. - TfLiteLSTMKernelType kernel_type; - - // Parameters for LSTM version 4. - bool asymmetric_quantize_inputs; -} TfLiteLSTMParams; - -typedef struct { - // Parameters needed for the underlying LSTM. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // If set to true then the first dimension is time, otherwise batch. - bool time_major; - - // Parameter for unidirectional sequence RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteUnidirectionalSequenceLSTMParams; - -typedef struct { - // Parameters supported by version 1: - // Parameters inherited for the LSTM kernel. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // If true, store the outputs of both directions in the first output. - bool merge_outputs; - - // Parameters supported by version 2: - // If set to true then the first dimension is time, otherwise batch. - bool time_major; - - // Parameters supported by version 4: - // If set to true, then hybrid ops use asymmetric quantization for inputs. - bool asymmetric_quantize_inputs; -} TfLiteBidirectionalSequenceLSTMParams; - -typedef struct { - bool align_corners; - // half_pixel_centers assumes pixels are of half the actual dimensions, and - // yields more accurate resizes. Corresponds to the same argument for the - // original TensorFlow op in TF2.0. - bool half_pixel_centers; -} TfLiteResizeBilinearParams; - -typedef struct { - bool align_corners; - bool half_pixel_centers; -} TfLiteResizeNearestNeighborParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLitePadParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLitePadV2Params; - -typedef struct { - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT]; - int num_dimensions; -} TfLiteReshapeParams; - -typedef struct { - int ngram_size; - int max_skip_size; - bool include_all_ngrams; -} TfLiteSkipGramParams; - -typedef struct { - int block_size; -} TfLiteSpaceToDepthParams; - -typedef struct { - int block_size; -} TfLiteDepthToSpaceParams; - -typedef struct { - TfLiteType in_data_type; - TfLiteType out_data_type; -} TfLiteCastParams; - -typedef enum { - kTfLiteCombinerTypeSum = 0, - kTfLiteCombinerTypeMean = 1, - kTfLiteCombinerTypeSqrtn = 2, -} TfLiteCombinerType; - -typedef struct { - TfLiteCombinerType combiner; -} TfLiteEmbeddingLookupSparseParams; - -typedef struct { - int axis; -} TfLiteGatherParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteTransposeParams; - -typedef struct { - bool keep_dims; -} TfLiteReducerParams; - -typedef struct { - int num_splits; -} TfLiteSplitParams; - -typedef struct { - int num_splits; -} TfLiteSplitVParams; - -typedef struct { - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int squeeze_dims[8]; - int num_squeeze_dims; -} TfLiteSqueezeParams; - -typedef struct { - int begin_mask; - int end_mask; - int ellipsis_mask; - int new_axis_mask; - int shrink_axis_mask; -} TfLiteStridedSliceParams; - -typedef struct { - TfLiteType output_type; -} TfLiteArgMaxParams; - -typedef struct { - TfLiteType output_type; -} TfLiteArgMinParams; - -typedef struct { - TfLitePadding padding; - int stride_width; - int stride_height; -} TfLiteTransposeConvParams; - -typedef struct { - bool validate_indices; -} TfLiteSparseToDenseParams; - -typedef struct { - TfLiteType out_type; -} TfLiteShapeParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteRankParams; - -typedef struct { - // Parameters supported by version 1: - float min; - float max; - int num_bits; - - // Parameters supported by version 2: - bool narrow_range; -} TfLiteFakeQuantParams; - -typedef struct { - int values_count; - int axis; -} TfLitePackParams; - -typedef struct { - int axis; -} TfLiteOneHotParams; - -typedef struct { - int num; - int axis; -} TfLiteUnpackParams; - -typedef struct { - float alpha; -} TfLiteLeakyReluParams; - -typedef struct { - TfLiteType index_out_type; -} TfLiteUniqueParams; - -typedef struct { - int seq_dim; - int batch_dim; -} TfLiteReverseSequenceParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteMatrixDiagParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteMatrixSetDiagParams; - -typedef struct { - int then_subgraph_index; - int else_subgraph_index; -} TfLiteIfParams; - -typedef struct { - int cond_subgraph_index; - int body_subgraph_index; -} TfLiteWhileParams; - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus - -#endif // TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/common.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/common.h deleted file mode 100644 index 7ef173c7..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/common.h +++ /dev/null @@ -1,936 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This file defines common C types and APIs for implementing operations, -// delegates and other constructs in TensorFlow Lite. The actual operations and -// delegates can be defined using C++, but the interface between the interpreter -// and the operations are C. -// -// Summary of abstractions -// TF_LITE_ENSURE - Self-sufficient error checking -// TfLiteStatus - Status reporting -// TfLiteIntArray - stores tensor shapes (dims), -// TfLiteContext - allows an op to access the tensors -// TfLiteTensor - tensor (a multidimensional array) -// TfLiteNode - a single node or operation -// TfLiteRegistration - the implementation of a conceptual operation. -// TfLiteDelegate - allows delegation of nodes to alternative backends. -// -// Some abstractions in this file are created and managed by Interpreter. -// -// NOTE: The order of values in these structs are "semi-ABI stable". New values -// should be added only to the end of structs and never reordered. - -#ifndef TENSORFLOW_LITE_C_COMMON_H_ -#define TENSORFLOW_LITE_C_COMMON_H_ - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -typedef enum TfLiteStatus { - kTfLiteOk = 0, - kTfLiteError = 1, - kTfLiteDelegateError = 2 -} TfLiteStatus; - -// The list of external context types known to TF Lite. This list exists solely -// to avoid conflicts and to ensure ops can share the external contexts they -// need. Access to the external contexts is controlled by one of the -// corresponding support files. -typedef enum TfLiteExternalContextType { - kTfLiteEigenContext = 0, // include eigen_support.h to use. - kTfLiteGemmLowpContext = 1, // include gemm_support.h to use. - kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support. - kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use. - kTfLiteMaxExternalContexts = 4 -} TfLiteExternalContextType; - -// Forward declare so dependent structs and methods can reference these types -// prior to the struct definitions. -struct TfLiteContext; -struct TfLiteDelegate; -struct TfLiteRegistration; - -// An external context is a collection of information unrelated to the TF Lite -// framework, but useful to a subset of the ops. TF Lite knows very little -// about about the actual contexts, but it keeps a list of them, and is able to -// refresh them if configurations like the number of recommended threads -// change. -typedef struct TfLiteExternalContext { - TfLiteExternalContextType type; - TfLiteStatus (*Refresh)(struct TfLiteContext* context); -} TfLiteExternalContext; - -#define kTfLiteOptionalTensor (-1) - -// Fixed size list of integers. Used for dimensions and inputs/outputs tensor -// indices -typedef struct TfLiteIntArray { - int size; -// gcc 6.1+ have a bug where flexible members aren't properly handled -// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c -#if (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ - __GNUC_MINOR__ >= 1) || \ - defined(HEXAGON) - int data[0]; -#else - int data[]; -#endif -} TfLiteIntArray; - -// Given the size (number of elements) in a TfLiteIntArray, calculate its size -// in bytes. -int TfLiteIntArrayGetSizeInBytes(int size); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a array of a given `size` (uninitialized entries). -// This returns a pointer, that you must free using TfLiteIntArrayFree(). -TfLiteIntArray* TfLiteIntArrayCreate(int size); -#endif - -// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise. -int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b); - -// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise. -int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size, - const int b_data[]); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a copy of an array passed as `src`. -// You are expected to free memory with TfLiteIntArrayFree -TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src); - -// Free memory of array `a`. -void TfLiteIntArrayFree(TfLiteIntArray* a); -#endif // TF_LITE_STATIC_MEMORY - -// Fixed size list of floats. Used for per-channel quantization. -typedef struct TfLiteFloatArray { - int size; -// gcc 6.1+ have a bug where flexible members aren't properly handled -// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c -// This also applies to the toolchain used for Qualcomm Hexagon DSPs. -#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ - __GNUC_MINOR__ >= 1 - float data[0]; -#else - float data[]; -#endif -} TfLiteFloatArray; - -// Given the size (number of elements) in a TfLiteFloatArray, calculate its size -// in bytes. -int TfLiteFloatArrayGetSizeInBytes(int size); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a array of a given `size` (uninitialized entries). -// This returns a pointer, that you must free using TfLiteFloatArrayFree(). -TfLiteFloatArray* TfLiteFloatArrayCreate(int size); - -// Free memory of array `a`. -void TfLiteFloatArrayFree(TfLiteFloatArray* a); -#endif // TF_LITE_STATIC_MEMORY - -// Since we must not depend on any libraries, define a minimal subset of -// error macros while avoiding names that have pre-conceived meanings like -// assert and check. - -// Try to make all reporting calls through TF_LITE_KERNEL_LOG rather than -// calling the context->ReportError function directly, so that message strings -// can be stripped out if the binary size needs to be severely optimized. -#ifndef TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_KERNEL_LOG(context, ...) \ - do { \ - (context)->ReportError((context), __VA_ARGS__); \ - } while (false) - -#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) \ - do { \ - if ((context) != nullptr) { \ - (context)->ReportError((context), __VA_ARGS__); \ - } \ - } while (false) -#else // TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_KERNEL_LOG(context, ...) -#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) -#endif // TF_LITE_STRIP_ERROR_STRINGS - -// Check whether value is true, and if not return kTfLiteError from -// the current function (and report the error string msg). -#define TF_LITE_ENSURE_MSG(context, value, msg) \ - do { \ - if (!(value)) { \ - TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \ - return kTfLiteError; \ - } \ - } while (0) - -// Check whether the value `a` is true, and if not return kTfLiteError from -// the current function, while also reporting the location of the error. -#define TF_LITE_ENSURE(context, a) \ - do { \ - if (!(a)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s was not true.", __FILE__, \ - __LINE__, #a); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_STATUS(a) \ - do { \ - const TfLiteStatus s = (a); \ - if (s != kTfLiteOk) { \ - return s; \ - } \ - } while (0) - -// Check whether the value `a == b` is true, and if not return kTfLiteError from -// the current function, while also reporting the location of the error. -// `a` and `b` may be evaluated more than once, so no side effects or -// extremely expensive computations should be done. -// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes. -#define TF_LITE_ENSURE_EQ(context, a, b) \ - do { \ - if ((a) != (b)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%d != %d)", __FILE__, \ - __LINE__, #a, #b, (a), (b)); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_TYPES_EQ(context, a, b) \ - do { \ - if ((a) != (b)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%s != %s)", __FILE__, \ - __LINE__, #a, #b, TfLiteTypeGetName(a), \ - TfLiteTypeGetName(b)); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_OK(context, status) \ - do { \ - const TfLiteStatus s = (status); \ - if ((s) != kTfLiteOk) { \ - return s; \ - } \ - } while (0) - -// Define TFL_CAPI_EXPORT macro to export a function properly with a shared -// library. -#ifdef SWIG -#define TFL_CAPI_EXPORT -#else -#if defined(_WIN32) -#ifdef TFL_COMPILE_LIBRARY -#define TFL_CAPI_EXPORT __declspec(dllexport) -#else -#define TFL_CAPI_EXPORT __declspec(dllimport) -#endif // TFL_COMPILE_LIBRARY -#else -#define TFL_CAPI_EXPORT __attribute__((visibility("default"))) -#endif // _WIN32 -#endif // SWIG - -// Single-precision complex data type compatible with the C99 definition. -typedef struct TfLiteComplex64 { - float re, im; // real and imaginary parts, respectively. -} TfLiteComplex64; - -// Double-precision complex data type compatible with the C99 definition. -typedef struct TfLiteComplex128 { - double re, im; // real and imaginary parts, respectively. -} TfLiteComplex128; - -// Half precision data type compatible with the C99 definition. -typedef struct TfLiteFloat16 { - uint16_t data; -} TfLiteFloat16; - -// Types supported by tensor -typedef enum { - kTfLiteNoType = 0, - kTfLiteFloat32 = 1, - kTfLiteInt32 = 2, - kTfLiteUInt8 = 3, - kTfLiteInt64 = 4, - kTfLiteString = 5, - kTfLiteBool = 6, - kTfLiteInt16 = 7, - kTfLiteComplex64 = 8, - kTfLiteInt8 = 9, - kTfLiteFloat16 = 10, - kTfLiteFloat64 = 11, - kTfLiteComplex128 = 12, -} TfLiteType; - -// Return the name of a given type, for error reporting purposes. -const char* TfLiteTypeGetName(TfLiteType type); - -// SupportedQuantizationTypes. -typedef enum TfLiteQuantizationType { - // No quantization. - kTfLiteNoQuantization = 0, - // Affine quantization (with support for per-channel quantization). - // Corresponds to TfLiteAffineQuantization. - kTfLiteAffineQuantization = 1, -} TfLiteQuantizationType; - -// Structure specifying the quantization used by the tensor, if-any. -typedef struct TfLiteQuantization { - // The type of quantization held by params. - TfLiteQuantizationType type; - // Holds a reference to one of the quantization param structures specified - // below. - void* params; -} TfLiteQuantization; - -// Legacy. Will be deprecated in favor of TfLiteAffineQuantization. -// If per-layer quantization is specified this field will still be populated in -// addition to TfLiteAffineQuantization. -// Parameters for asymmetric quantization. Quantized values can be converted -// back to float using: -// real_value = scale * (quantized_value - zero_point) -typedef struct TfLiteQuantizationParams { - float scale; - int32_t zero_point; -} TfLiteQuantizationParams; - -// Parameters for asymmetric quantization across a dimension (i.e per output -// channel quantization). -// quantized_dimension specifies which dimension the scales and zero_points -// correspond to. -// For a particular value in quantized_dimension, quantized values can be -// converted back to float using: -// real_value = scale * (quantized_value - zero_point) -typedef struct TfLiteAffineQuantization { - TfLiteFloatArray* scale; - TfLiteIntArray* zero_point; - int32_t quantized_dimension; -} TfLiteAffineQuantization; - -/* A union of pointers that points to memory for a given tensor. */ -typedef union TfLitePtrUnion { - /* Do not access these members directly, if possible, use - * GetTensorData(tensor) instead, otherwise only access .data, as other - * members are deprecated. */ - int32_t* i32; - int64_t* i64; - float* f; - TfLiteFloat16* f16; - double* f64; - char* raw; - const char* raw_const; - uint8_t* uint8; - bool* b; - int16_t* i16; - TfLiteComplex64* c64; - TfLiteComplex128* c128; - int8_t* int8; - /* Only use this member. */ - void* data; -} TfLitePtrUnion; - -// Memory allocation strategies. -// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated. -// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence, -// and available during eval. -// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and -// only available during eval. -// * kTfLiteDynamic: Allocated during eval, or for string tensors. -// * kTfLitePersistentRo: Allocated and populated during prepare. This is -// useful for tensors that can be computed during prepare and treated -// as constant inputs for downstream ops (also in prepare). -typedef enum TfLiteAllocationType { - kTfLiteMemNone = 0, - kTfLiteMmapRo, - kTfLiteArenaRw, - kTfLiteArenaRwPersistent, - kTfLiteDynamic, - kTfLitePersistentRo, -} TfLiteAllocationType; - -// The delegates should use zero or positive integers to represent handles. -// -1 is reserved from unallocated status. -typedef int TfLiteBufferHandle; -enum { - kTfLiteNullBufferHandle = -1, -}; - -// Storage format of each dimension in a sparse tensor. -typedef enum TfLiteDimensionType { - kTfLiteDimDense = 0, - kTfLiteDimSparseCSR, -} TfLiteDimensionType; - -// Metadata to encode each dimension in a sparse tensor. -typedef struct TfLiteDimensionMetadata { - TfLiteDimensionType format; - int dense_size; - TfLiteIntArray* array_segments; - TfLiteIntArray* array_indices; -} TfLiteDimensionMetadata; - -// Parameters used to encode a sparse tensor. For detailed explanation of each -// field please refer to lite/schema/schema.fbs. -typedef struct TfLiteSparsity { - TfLiteIntArray* traversal_order; - TfLiteIntArray* block_map; - TfLiteDimensionMetadata* dim_metadata; - int dim_metadata_size; -} TfLiteSparsity; - -// An tensor in the interpreter system which is a wrapper around a buffer of -// data including a dimensionality (or NULL if not currently defined). -#ifndef TF_LITE_STATIC_MEMORY -typedef struct TfLiteTensor { - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. NOTE: the product of elements of `dims` - // and the element datatype size should be equal to `bytes` below. - TfLiteIntArray* dims; - // Quantization information. - TfLiteQuantizationParams params; - // How memory is mapped - // kTfLiteMmapRo: Memory mapped read only. - // i.e. weights - // kTfLiteArenaRw: Arena allocated read write memory - // (i.e. temporaries, outputs). - TfLiteAllocationType allocation_type; - // The number of bytes required to store the data of this Tensor. I.e. - // (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if - // type is kTfLiteFloat32 and dims = {3, 2} then - // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24. - size_t bytes; - - // An opaque pointer to a tflite::MMapAllocation - const void* allocation; - - // Null-terminated name of this tensor. - const char* name; - - // The delegate which knows how to handle `buffer_handle`. - // WARNING: This is an experimental interface that is subject to change. - struct TfLiteDelegate* delegate; - - // An integer buffer handle that can be handled by `delegate`. - // The value is valid only when delegate is not null. - // WARNING: This is an experimental interface that is subject to change. - TfLiteBufferHandle buffer_handle; - - // If the delegate uses its own buffer (e.g. GPU memory), the delegate is - // responsible to set data_is_stale to true. - // `delegate->CopyFromBufferHandle` can be called to copy the data from - // delegate buffer. - // WARNING: This is an // experimental interface that is subject to change. - bool data_is_stale; - - // True if the tensor is a variable. - bool is_variable; - - // Quantization information. Replaces params field above. - TfLiteQuantization quantization; - - // Parameters used to encode a sparse tensor. - // This is optional. The field is NULL if a tensor is dense. - // WARNING: This is an experimental interface that is subject to change. - TfLiteSparsity* sparsity; - - // Optional. Encodes shapes with unknown dimensions with -1. This field is - // only populated when unknown dimensions exist in a read-write tensor (i.e. - // an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and - // `dims_signature` contains [1, -1, -1, 3]). - const TfLiteIntArray* dims_signature; -} TfLiteTensor; - -// A structure representing an instance of a node. -// This structure only exhibits the inputs, outputs and user defined data, not -// other features like the type. -typedef struct TfLiteNode { - // Inputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* inputs; - - // Outputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* outputs; - - // intermediate tensors to this node expressed as indices into the simulator's - // tensors. - TfLiteIntArray* intermediates; - - // Temporary tensors uses during the computations. This usually contains no - // tensors, but ops are allowed to change that if they need scratch space of - // any sort. - TfLiteIntArray* temporaries; - - // Opaque data provided by the node implementer through `Registration.init`. - void* user_data; - - // Opaque data provided to the node if the node is a builtin. This is usually - // a structure defined in builtin_op_data.h - void* builtin_data; - - // Custom initial data. This is the opaque data provided in the flatbuffer. - // WARNING: This is an experimental interface that is subject to change. - const void* custom_initial_data; - int custom_initial_data_size; - - // The pointer to the delegate. This is non-null only when the node is - // created by calling `interpreter.ModifyGraphWithDelegate`. - // WARNING: This is an experimental interface that is subject to change. - struct TfLiteDelegate* delegate; -} TfLiteNode; -#else // defined(TF_LITE_STATIC_MEMORY)? -// NOTE: This flag is opt-in only at compile time. -// -// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct -// contains only the minimum fields required to initialize and prepare a micro -// inference graph. The fields in this struct have been ordered from -// largest-to-smallest for optimal struct sizeof. -// -// This struct does not use: -// - allocation -// - buffer_handle -// - data_is_stale -// - delegate -// - dims_signature -// - name -// - sparsity -typedef struct TfLiteTensor { - // TODO(b/155784997): Consider consolidating these quantization fields: - // Quantization information. Replaces params field above. - TfLiteQuantization quantization; - - // Quantization information. - TfLiteQuantizationParams params; - - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. NOTE: the product of elements of `dims` - // and the element datatype size should be equal to `bytes` below. - TfLiteIntArray* dims; - - // The number of bytes required to store the data of this Tensor. I.e. - // (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if - // type is kTfLiteFloat32 and dims = {3, 2} then - // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24. - size_t bytes; - - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; - - // How memory is mapped - // kTfLiteMmapRo: Memory mapped read only. - // i.e. weights - // kTfLiteArenaRw: Arena allocated read write memory - // (i.e. temporaries, outputs). - TfLiteAllocationType allocation_type; - - // True if the tensor is a variable. - bool is_variable; -} TfLiteTensor; - -// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains -// only the minimum fields required to represent a node. -// -// This struct does not use: -// - delegate -// - intermediates -// - temporaries -typedef struct TfLiteNode { - // Inputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* inputs; - - // Outputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* outputs; - - // Opaque data provided by the node implementer through `Registration.init`. - void* user_data; - - // Opaque data provided to the node if the node is a builtin. This is usually - // a structure defined in builtin_op_data.h - void* builtin_data; - - // Custom initial data. This is the opaque data provided in the flatbuffer. - // WARNING: This is an experimental interface that is subject to change. - const void* custom_initial_data; - int custom_initial_data_size; -} TfLiteNode; -#endif // TF_LITE_STATIC_MEMORY - -// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount -// of information required for a kernel to run during TfLiteRegistration::Eval. -// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM -// builds with this flag by default internally. -typedef struct TfLiteEvalTensor { - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. - TfLiteIntArray* dims; - - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; -} TfLiteEvalTensor; - -#ifndef TF_LITE_STATIC_MEMORY -// Free data memory of tensor `t`. -void TfLiteTensorDataFree(TfLiteTensor* t); - -// Free quantization data. -void TfLiteQuantizationFree(TfLiteQuantization* quantization); - -// Free sparsity parameters. -void TfLiteSparsityFree(TfLiteSparsity* sparsity); - -// Free memory of tensor `t`. -void TfLiteTensorFree(TfLiteTensor* t); - -// Set all of a tensor's fields (and free any previously allocated data). -void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, - TfLiteQuantizationParams quantization, char* buffer, - size_t size, TfLiteAllocationType allocation_type, - const void* allocation, bool is_variable, - TfLiteTensor* tensor); - -// Resize the allocated data of a (dynamic) tensor. Tensors with allocation -// types other than kTfLiteDynamic will be ignored. -void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); -#endif // TF_LITE_STATIC_MEMORY - -// WARNING: This is an experimental interface that is subject to change. -// -// Currently, TfLiteDelegateParams has to be allocated in a way that it's -// trivially destructable. It will be stored as `builtin_data` field in -// `TfLiteNode` of the delegate node. -// -// See also the `CreateDelegateParams` function in `interpreter.cc` details. -typedef struct TfLiteDelegateParams { - struct TfLiteDelegate* delegate; - TfLiteIntArray* nodes_to_replace; - TfLiteIntArray* input_tensors; - TfLiteIntArray* output_tensors; -} TfLiteDelegateParams; - -typedef struct TfLiteContext { - // Number of tensors in the context. - size_t tensors_size; - - // The execution plan contains a list of the node indices in execution - // order. execution_plan->size is the current number of nodes. And, - // execution_plan->data[0] is the first node that needs to be run. - // TfLiteDelegates can traverse the current execution plan by iterating - // through each member of this array and using GetNodeAndRegistration() to - // access details about a node. i.e. - // TfLiteIntArray* execution_plan; - // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan)); - // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) { - // int node_index = execution_plan->data[exec_index]; - // TfLiteNode* node; - // TfLiteRegistration* reg; - // context->GetNodeAndRegistration(context, node_index, &node, ®); - // } - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context, - TfLiteIntArray** execution_plan); - - // An array of tensors in the interpreter context (of length `tensors_size`) - TfLiteTensor* tensors; - - // opaque full context ptr (an opaque c++ data structure) - void* impl_; - - // Request memory pointer be resized. Updates dimensions on the tensor. - // NOTE: ResizeTensor takes ownership of newSize. - TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor, - TfLiteIntArray* new_size); - // Request that an error be reported with format string msg. - void (*ReportError)(struct TfLiteContext*, const char* msg, ...); - - // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If - // non-null, the value pointed to by `first_new_tensor_index` will be set to - // the index of the first new tensor. - TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add, - int* first_new_tensor_index); - - // Get a Tensor node by node_index. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetNodeAndRegistration)( - struct TfLiteContext*, int node_index, TfLiteNode** node, - struct TfLiteRegistration** registration); - - // Replace ops with one or more stub delegate operations. This function - // does not take ownership of `nodes_to_replace`. - TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)( - struct TfLiteContext*, struct TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate); - - // Number of threads that are recommended to subsystems like gemmlowp and - // eigen. - int recommended_num_threads; - - // Access external contexts by type. - // WARNING: This is an experimental interface that is subject to change. - TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*, - TfLiteExternalContextType); - // Set the value of a external context. Does not take ownership of the - // pointer. - // WARNING: This is an experimental interface that is subject to change. - void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType, - TfLiteExternalContext*); - - // Flag for allowing float16 precision for FP32 calculation. - // default: false. - // WARNING: This is an experimental API and subject to change. - bool allow_fp32_relax_to_fp16; - - // Pointer to the op-level profiler, if set; nullptr otherwise. - void* profiler; - - // Allocate persistent buffer which has the same life time as the interpreter. - // Returns nullptr on failure. - // The memory is allocated from heap for TFL, and from tail in TFLM. - // This method is only available in Init or Prepare stage. - // WARNING: This is an experimental interface that is subject to change. - void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes); - - // Allocate a buffer which will be deallocated right after invoke phase. - // The memory is allocated from heap in TFL, and from volatile arena in TFLM. - // This method is only available in invoke stage. - // NOTE: If possible use RequestScratchBufferInArena method to avoid memory - // allocation during inference time. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes, - void** ptr); - - // Request a scratch buffer in the arena through static memory planning. - // This method is only available in Prepare stage and the buffer is allocated - // by the interpreter between Prepare and Eval stage. In Eval stage, - // GetScratchBuffer API can be used to fetch the address. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx, - size_t bytes, int* buffer_idx); - - // Get the scratch buffer pointer. - // This method is only available in Eval stage. - // WARNING: This is an experimental interface that is subject to change. - void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx); - - // Resize the memory pointer of the `tensor`. This method behaves the same as - // `ResizeTensor`, except that it makes a copy of the shape array internally - // so the shape array could be deallocated right afterwards. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx, - TfLiteTensor* tensor, int dims, - const int* shape); - - // This method provides a preview of post-delegation partitioning. Each - // TfLiteDelegateParams in the referenced array corresponds to one instance of - // the delegate kernel. - // Example usage: - // - // TfLiteIntArray* nodes_to_replace = ...; - // TfLiteDelegateParams* params_array; - // int num_partitions = 0; - // TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning( - // context, delegate, nodes_to_replace, ¶ms_array, &num_partitions)); - // for (int idx = 0; idx < num_partitions; idx++) { - // const auto& partition_params = params_array[idx]; - // ... - // } - // - // NOTE: The context owns the memory referenced by partition_params_array. It - // will be cleared with another call to PreviewDelegateParitioning, or after - // TfLiteDelegateParams::Prepare returns. - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*PreviewDelegatePartitioning)( - struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace, - TfLiteDelegateParams** partition_params_array, int* num_partitions); - - // Returns a TfLiteTensor struct for a given index. - // WARNING: This is an experimental interface that is subject to change. - // WARNING: This method may not be available on all platforms. - TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context, - int tensor_idx); - - // Returns a TfLiteEvalTensor struct for a given index. - // WARNING: This is an experimental interface that is subject to change. - // WARNING: This method may not be available on all platforms. - TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context, - int tensor_idx); -} TfLiteContext; - -typedef struct TfLiteRegistration { - // Initializes the op from serialized data. - // If a built-in op: - // `buffer` is the op's params data (TfLiteLSTMParams*). - // `length` is zero. - // If custom op: - // `buffer` is the op's `custom_options`. - // `length` is the size of the buffer. - // - // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer - // or an instance of a struct). - // - // The returned pointer will be stored with the node in the `user_data` field, - // accessible within prepare and invoke functions below. - // NOTE: if the data is already in the desired format, simply implement this - // function to return `nullptr` and implement the free function to be a no-op. - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - - // The pointer `buffer` is the data previously returned by an init invocation. - void (*free)(TfLiteContext* context, void* buffer); - - // prepare is called when the inputs this node depends on have been resized. - // context->ResizeTensor() can be called to request output tensors to be - // resized. - // - // Returns kTfLiteOk on success. - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - - // Execute the node (should read node->inputs and output to node->outputs). - // Returns kTfLiteOk on success. - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - - // profiling_string is called during summarization of profiling information - // in order to group executions together. Providing a value here will cause a - // given op to appear multiple times is the profiling report. This is - // particularly useful for custom ops that can perform significantly - // different calculations depending on their `user-data`. - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - - // Builtin codes. If this kernel refers to a builtin this is the code - // of the builtin. This is so we can do marshaling to other frameworks like - // NN API. - // Note: It is the responsibility of the registration binder to set this - // properly. - int32_t builtin_code; - - // Custom op name. If the op is a builtin, this will be null. - // Note: It is the responsibility of the registration binder to set this - // properly. - // WARNING: This is an experimental interface that is subject to change. - const char* custom_name; - - // The version of the op. - // Note: It is the responsibility of the registration binder to set this - // properly. - int version; -} TfLiteRegistration; - -// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the -// values should be 1, 2, 4, 8, ...etc. -typedef enum TfLiteDelegateFlags { - kTfLiteDelegateFlagsNone = 0, - // The flag is set if the delegate can handle dynamic sized tensors. - // For example, the output shape of a `Resize` op with non-constant shape - // can only be inferred when the op is invoked. - // In this case, the Delegate is responsible for calling - // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling - // `ResizeTensor` when invoking the op. - // - // If the delegate isn't capable to handle dynamic tensors, this flag need - // to be set to false. - kTfLiteDelegateFlagsAllowDynamicTensors = 1, - - // This flag can be used by delegates (that allow dynamic tensors) to ensure - // applicable tensor shapes are automatically propagated in the case of tensor - // resizing. - // This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors - // of a delegate kernel will have correct shapes before its Prepare() method - // is called. The runtime leverages TFLite builtin ops in the original - // execution plan to propagate shapes. - // - // A few points to note: - // 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is - // false, this one is redundant since the delegate kernels are re-initialized - // every time tensors are resized. - // 2. Enabling this flag adds some overhead to AllocateTensors(), since extra - // work is required to prepare the original execution plan. - // 3. This flag requires that the original execution plan only have ops with - // valid registrations (and not 'dummy' custom ops like with Flex). - // WARNING: This feature is experimental and subject to change. - kTfLiteDelegateFlagsRequirePropagatedShapes = 2 -} TfLiteDelegateFlags; - -// WARNING: This is an experimental interface that is subject to change. -typedef struct TfLiteDelegate { - // Data that delegate needs to identify itself. This data is owned by the - // delegate. The delegate is owned in the user code, so the delegate is - // responsible for doing this when it is destroyed. - void* data_; - - // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the - // delegate a view of the current graph through TfLiteContext*. It typically - // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels() - // to ask the TensorFlow lite runtime to create macro-nodes to represent - // delegated subgraphs of the original graph. - TfLiteStatus (*Prepare)(TfLiteContext* context, - struct TfLiteDelegate* delegate); - - // Copy the data from delegate buffer handle into raw memory of the given - // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as - // long as it follows the rules for kTfLiteDynamic tensors, in which case this - // cannot be null. - TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle buffer_handle, - TfLiteTensor* tensor); - - // Copy the data from raw memory of the given 'tensor' to delegate buffer - // handle. This can be null if the delegate doesn't use its own buffer. - TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle buffer_handle, - TfLiteTensor* tensor); - - // Free the Delegate Buffer Handle. Note: This only frees the handle, but - // this doesn't release the underlying resource (e.g. textures). The - // resources are either owned by application layer or the delegate. - // This can be null if the delegate doesn't use its own buffer. - void (*FreeBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle* handle); - - // Bitmask flags. See the comments in `TfLiteDelegateFlags`. - int64_t flags; -} TfLiteDelegate; - -// Build a 'null' delegate, with all the fields properly set to their default -// values. -TfLiteDelegate TfLiteDelegateCreate(); - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus -#endif // TENSORFLOW_LITE_C_COMMON_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/error_reporter.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/error_reporter.h deleted file mode 100644 index 05839a61..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/error_reporter.h +++ /dev/null @@ -1,59 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ -#define TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ - -#include - -namespace tflite { - -/// A functor that reports error to supporting system. Invoked similar to -/// printf. -/// -/// Usage: -/// ErrorReporter foo; -/// foo.Report("test %d", 5); -/// or -/// va_list args; -/// foo.Report("test %d", args); // where args is va_list -/// -/// Subclass ErrorReporter to provide another reporting destination. -/// For example, if you have a GUI program, you might redirect to a buffer -/// that drives a GUI error log box. -class ErrorReporter { - public: - virtual ~ErrorReporter() {} - virtual int Report(const char* format, va_list args) = 0; - int Report(const char* format, ...); - int ReportError(void*, const char* format, ...); -}; - -} // namespace tflite - -// You should not make bare calls to the error reporter, instead use the -// TF_LITE_REPORT_ERROR macro, since this allows message strings to be -// stripped when the binary size has to be optimized. If you are looking to -// reduce binary size, define TF_LITE_STRIP_ERROR_STRINGS when compiling and -// every call will be stubbed out, taking no memory. -#ifndef TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_REPORT_ERROR(reporter, ...) \ - do { \ - static_cast(reporter)->Report(__VA_ARGS__); \ - } while (false) -#else // TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_REPORT_ERROR(reporter, ...) -#endif // TF_LITE_STRIP_ERROR_STRINGS - -#endif // TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/flatbuffer_conversions.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/flatbuffer_conversions.h deleted file mode 100644 index aaeb98c0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/flatbuffer_conversions.h +++ /dev/null @@ -1,253 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ -#define TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ - -// These functions transform codes and data structures that are defined in the -// flatbuffer serialization format into in-memory values that are used by the -// runtime API and interpreter. - -#include -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -// Interface class for builtin data allocations. -class BuiltinDataAllocator { - public: - virtual void* Allocate(size_t size, size_t alignment_hint) = 0; - virtual void Deallocate(void* data) = 0; - - // Allocate a structure, but make sure it is a POD structure that doesn't - // require constructors to run. The reason we do this, is that Interpreter's C - // extension part will take ownership so destructors will not be run during - // deallocation. - template - T* AllocatePOD() { - // TODO(b/154346074): Change this to is_trivially_destructible when all - // platform targets support that properly. - static_assert(std::is_pod::value, "Builtin data structure must be POD."); - void* allocated_memory = this->Allocate(sizeof(T), alignof(T)); - return new (allocated_memory) T; - } - - virtual ~BuiltinDataAllocator() {} -}; - -// Parse the appropriate data out of the op. -// -// This handles builtin data explicitly as there are flatbuffer schemas. -// If it returns kTfLiteOk, it passes the data out with `builtin_data`. The -// calling function has to pass in an allocator object, and this allocator -// will be called to reserve space for the output data. If the calling -// function's allocator reserves memory on the heap, then it's the calling -// function's responsibility to free it. -// If it returns kTfLiteError, `builtin_data` will be `nullptr`. -TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -// Converts the tensor data type used in the flat buffer to the representation -// used by the runtime. -TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, - ErrorReporter* error_reporter); - -TfLiteStatus ParseAbs(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseCeil(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseConcatenation(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseCos(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseDepthwiseConv2D(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseDequantize(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseFloor(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseFullyConnected(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseGreater(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseGreaterEqual(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseHardSwish(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseL2Normalization(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLess(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseLessEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLog(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseLogicalAnd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogicalNot(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogicalOr(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogistic(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseMaximum(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseMinimum(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseNeg(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseNotEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePad(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePadV2(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePrelu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseQuantize(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRelu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRelu6(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseResizeNearestNeighbor(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseRound(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRsqrt(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSin(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseStridedSlice(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseTanh(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/op_resolver.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/op_resolver.h deleted file mode 100644 index 1294b7b8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/op_resolver.h +++ /dev/null @@ -1,48 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_ -#define TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -/// Abstract interface that returns TfLiteRegistrations given op codes or custom -/// op names. This is the mechanism that ops being referenced in the flatbuffer -/// model are mapped to executable function pointers (TfLiteRegistrations). -class OpResolver { - public: - /// Finds the op registration for a builtin operator by enum code. - virtual const TfLiteRegistration* FindOp(tflite::BuiltinOperator op, - int version) const = 0; - /// Finds the op registration of a custom operator by op name. - virtual const TfLiteRegistration* FindOp(const char* op, - int version) const = 0; - virtual ~OpResolver() {} -}; - -// Handles the logic for converting between an OperatorCode structure extracted -// from a flatbuffer and information about a registered operator -// implementation. -TfLiteStatus GetRegistrationFromOpCode(const OperatorCode* opcode, - const OpResolver& op_resolver, - ErrorReporter* error_reporter, - const TfLiteRegistration** registration); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/profiler.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/profiler.h deleted file mode 100644 index 897efbe1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/profiler.h +++ /dev/null @@ -1,194 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_PROFILER_H_ -#define TENSORFLOW_LITE_CORE_API_PROFILER_H_ - -#include - -namespace tflite { - -// A simple utility for enabling profiled event tracing in TensorFlow Lite. -class Profiler { - public: - // As certain Profiler instance might be only interested in certain event - // types, we define each event type value to allow a Profiler to use - // bitmasking bitwise operations to determine whether an event should be - // recorded or not. - enum class EventType { - // Default event type, the metadata field has no special significance. - DEFAULT = 1, - - // The event is an operator invocation and the event_metadata field is the - // index of operator node. - OPERATOR_INVOKE_EVENT = 2, - - // The event is an invocation for an internal operator of a TFLite delegate. - // The event_metadata field is the index of operator node that's specific to - // the delegate. - DELEGATE_OPERATOR_INVOKE_EVENT = 4, - - // The event is a recording of runtime instrumentation such as the overall - // TFLite runtime status, the TFLite delegate status (if a delegate - // is applied), and the overall model inference latency etc. - // Note, the delegate status and overall status are stored as separate - // event_metadata fields. In particular, the delegate status is encoded - // as DelegateStatus::full_status(). - GENERAL_RUNTIME_INSTRUMENTATION_EVENT = 8, - }; - - virtual ~Profiler() {} - - // Signals the beginning of an event and returns a handle to the profile - // event. The `event_metadata1` and `event_metadata2` have different - // interpretations based on the actual Profiler instance and the `event_type`. - // For example, as for the 'SubgraphAwareProfiler' defined in - // lite/core/subgraph.h, when the event_type is OPERATOR_INVOKE_EVENT, - // `event_metadata1` represents the index of a TFLite node, and - // `event_metadata2` represents the index of the subgraph that this event - // comes from. - virtual uint32_t BeginEvent(const char* tag, EventType event_type, - int64_t event_metadata1, - int64_t event_metadata2) = 0; - // Similar w/ the above, but `event_metadata2` defaults to 0. - uint32_t BeginEvent(const char* tag, EventType event_type, - int64_t event_metadata) { - return BeginEvent(tag, event_type, event_metadata, /*event_metadata2*/ 0); - } - - // Signals an end to the specified profile event with 'event_metadata's, This - // is useful when 'event_metadata's are not available when the event begins - // or when one wants to overwrite the 'event_metadata's set at the beginning. - virtual void EndEvent(uint32_t event_handle, int64_t event_metadata1, - int64_t event_metadata2) {} - // Signals an end to the specified profile event. - virtual void EndEvent(uint32_t event_handle) = 0; - - // Appends an event of type 'event_type' with 'tag' and 'event_metadata' - // which started at 'start' and ended at 'end' - // Note: - // In cases were ProfileSimmarizer and tensorflow::StatsCalculator are used - // they assume the value is in "usec", if in any case subclasses - // didn't put usec, then the values are not meaningful. - // TODO karimnosseir: Revisit and make the function more clear. - void AddEvent(const char* tag, EventType event_type, uint64_t start, - uint64_t end, int64_t event_metadata) { - AddEvent(tag, event_type, start, end, event_metadata, - /*event_metadata2*/ 0); - } - - virtual void AddEvent(const char* tag, EventType event_type, uint64_t start, - uint64_t end, int64_t event_metadata1, - int64_t event_metadata2) {} - - protected: - friend class ScopedProfile; -}; - -// Adds a profile event to `profiler` that begins with the construction -// of the object and ends when the object goes out of scope. -// The lifetime of tag should be at least the lifetime of `profiler`. -// `profiler` may be null, in which case nothing is profiled. -class ScopedProfile { - public: - ScopedProfile(Profiler* profiler, const char* tag, - Profiler::EventType event_type = Profiler::EventType::DEFAULT, - int64_t event_metadata = 0) - : profiler_(profiler), event_handle_(0) { - if (profiler) { - event_handle_ = profiler_->BeginEvent(tag, event_type, event_metadata); - } - } - - ~ScopedProfile() { - if (profiler_) { - profiler_->EndEvent(event_handle_); - } - } - - protected: - Profiler* profiler_; - uint32_t event_handle_; -}; - -class ScopedOperatorProfile : public ScopedProfile { - public: - ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index) - : ScopedProfile(profiler, tag, Profiler::EventType::OPERATOR_INVOKE_EVENT, - static_cast(node_index)) {} -}; - -class ScopedDelegateOperatorProfile : public ScopedProfile { - public: - ScopedDelegateOperatorProfile(Profiler* profiler, const char* tag, - int node_index) - : ScopedProfile(profiler, tag, - Profiler::EventType::DELEGATE_OPERATOR_INVOKE_EVENT, - static_cast(node_index)) {} -}; - -class ScopedRuntimeInstrumentationProfile : public ScopedProfile { - public: - ScopedRuntimeInstrumentationProfile(Profiler* profiler, const char* tag) - : ScopedProfile( - profiler, tag, - Profiler::EventType::GENERAL_RUNTIME_INSTRUMENTATION_EVENT, -1) {} - - void set_runtime_status(int64_t delegate_status, int64_t interpreter_status) { - if (profiler_) { - delegate_status_ = delegate_status; - interpreter_status_ = interpreter_status; - } - } - - ~ScopedRuntimeInstrumentationProfile() { - if (profiler_) { - profiler_->EndEvent(event_handle_, delegate_status_, interpreter_status_); - } - } - - private: - int64_t delegate_status_; - int64_t interpreter_status_; -}; - -} // namespace tflite - -#define TFLITE_VARNAME_UNIQ_IMPL(name, ctr) name##ctr -#define TFLITE_VARNAME_UNIQ(name, ctr) TFLITE_VARNAME_UNIQ_IMPL(name, ctr) - -#define TFLITE_SCOPED_TAGGED_DEFAULT_PROFILE(profiler, tag) \ - tflite::ScopedProfile TFLITE_VARNAME_UNIQ(_profile_, __COUNTER__)( \ - (profiler), (tag)) - -#define TFLITE_SCOPED_TAGGED_OPERATOR_PROFILE(profiler, tag, node_index) \ - tflite::ScopedOperatorProfile TFLITE_VARNAME_UNIQ(_profile_, __COUNTER__)( \ - (profiler), (tag), (node_index)) - -#define TFLITE_SCOPED_DELEGATE_OPERATOR_PROFILE(profiler, tag, node_index) \ - tflite::ScopedDelegateOperatorProfile TFLITE_VARNAME_UNIQ( \ - _profile_, __COUNTER__)((profiler), (tag), (node_index)) - -#define TFLITE_ADD_RUNTIME_INSTRUMENTATION_EVENT( \ - profiler, tag, delegate_status, interpreter_status) \ - do { \ - if (!profiler) { \ - const auto handle = profiler->BeginEvent( \ - tag, Profiler::EventType::GENERAL_RUNTIME_INSTRUMENTATION_EVENT, \ - delegate_status, interpreter_status); \ - profiler->EndEvent(handle); \ - } \ - } while (false); - -#endif // TENSORFLOW_LITE_CORE_API_PROFILER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/tensor_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/tensor_utils.h deleted file mode 100644 index 9f1cf94a..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/core/api/tensor_utils.h +++ /dev/null @@ -1,28 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -// Resets a variable tensor to the default value. -TfLiteStatus ResetVariableTensor(TfLiteTensor* tensor); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/common.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/common.h deleted file mode 100644 index 66a2d977..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/common.h +++ /dev/null @@ -1,956 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ - -#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK -#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK -#endif -#endif - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/optimized/neon_check.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -constexpr int kReverseShift = -1; - -inline void GetActivationMinMax(FusedActivationFunctionType ac, - float* output_activation_min, - float* output_activation_max) { - switch (ac) { - case FusedActivationFunctionType::kNone: - *output_activation_min = std::numeric_limits::lowest(); - *output_activation_max = std::numeric_limits::max(); - break; - case FusedActivationFunctionType::kRelu: - *output_activation_min = 0.f; - *output_activation_max = std::numeric_limits::max(); - break; - case FusedActivationFunctionType::kRelu1: - *output_activation_min = -1.f; - *output_activation_max = 1.f; - break; - case FusedActivationFunctionType::kRelu6: - *output_activation_min = 0.f; - *output_activation_max = 6.f; - break; - } -} - -template -inline T ActivationFunctionWithMinMax(T x, T output_activation_min, - T output_activation_max) { - using std::max; - using std::min; - return min(max(x, output_activation_min), output_activation_max); -} - -// Legacy function, left for compatibility only. -template -float ActivationFunction(float x) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - return ActivationFunctionWithMinMax(x, output_activation_min, - output_activation_max); -} - -inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, - const float* bias_data, int array_size, - float* array_data) { - // Note: see b/132215220: in May 2019 we thought it would be OK to replace - // this with the Eigen one-liner: - // return (array.colwise() + bias).cwiseMin(clamp_max).cwiseMin(clamp_max). - // This turned out to severely regress performance: +4ms (i.e. 8%) on - // MobileNet v2 / 1.0 / 224. So we keep custom NEON code for now. - TFLITE_DCHECK_EQ((array_size % bias_size), 0); -#ifdef USE_NEON - float* array_ptr = array_data; - float* array_end_ptr = array_ptr + array_size; - const auto clamp_min_vec = vdupq_n_f32(clamp_min); - const auto clamp_max_vec = vdupq_n_f32(clamp_max); - for (; array_ptr != array_end_ptr; array_ptr += bias_size) { - int i = 0; - for (; i <= bias_size - 16; i += 16) { - auto b0 = vld1q_f32(bias_data + i); - auto b1 = vld1q_f32(bias_data + i + 4); - auto b2 = vld1q_f32(bias_data + i + 8); - auto b3 = vld1q_f32(bias_data + i + 12); - auto a0 = vld1q_f32(array_ptr + i); - auto a1 = vld1q_f32(array_ptr + i + 4); - auto a2 = vld1q_f32(array_ptr + i + 8); - auto a3 = vld1q_f32(array_ptr + i + 12); - auto x0 = vaddq_f32(a0, b0); - auto x1 = vaddq_f32(a1, b1); - auto x2 = vaddq_f32(a2, b2); - auto x3 = vaddq_f32(a3, b3); - x0 = vmaxq_f32(clamp_min_vec, x0); - x1 = vmaxq_f32(clamp_min_vec, x1); - x2 = vmaxq_f32(clamp_min_vec, x2); - x3 = vmaxq_f32(clamp_min_vec, x3); - x0 = vminq_f32(clamp_max_vec, x0); - x1 = vminq_f32(clamp_max_vec, x1); - x2 = vminq_f32(clamp_max_vec, x2); - x3 = vminq_f32(clamp_max_vec, x3); - vst1q_f32(array_ptr + i, x0); - vst1q_f32(array_ptr + i + 4, x1); - vst1q_f32(array_ptr + i + 8, x2); - vst1q_f32(array_ptr + i + 12, x3); - } - for (; i <= bias_size - 4; i += 4) { - auto b = vld1q_f32(bias_data + i); - auto a = vld1q_f32(array_ptr + i); - auto x = vaddq_f32(a, b); - x = vmaxq_f32(clamp_min_vec, x); - x = vminq_f32(clamp_max_vec, x); - vst1q_f32(array_ptr + i, x); - } - for (; i < bias_size; i++) { - array_ptr[i] = ActivationFunctionWithMinMax(array_ptr[i] + bias_data[i], - clamp_min, clamp_max); - } - } -#else // not NEON - for (int array_offset = 0; array_offset < array_size; - array_offset += bias_size) { - for (int i = 0; i < bias_size; i++) { - array_data[array_offset + i] = ActivationFunctionWithMinMax( - array_data[array_offset + i] + bias_data[i], clamp_min, clamp_max); - } - } -#endif -} - -inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp( - int32_t x, int32_t quantized_multiplier, int left_shift) { - using gemmlowp::RoundingDivideByPOT; - using gemmlowp::SaturatingRoundingDoublingHighMul; - return RoundingDivideByPOT( - SaturatingRoundingDoublingHighMul(x, quantized_multiplier), -left_shift); -} - -inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne( - int32_t x, int32_t quantized_multiplier, int left_shift) { - using gemmlowp::SaturatingRoundingDoublingHighMul; - return SaturatingRoundingDoublingHighMul(x * (1 << left_shift), - quantized_multiplier); -} - -inline int32_t MultiplyByQuantizedMultiplier(int32_t x, - int32_t quantized_multiplier, - int shift) { - using gemmlowp::RoundingDivideByPOT; - using gemmlowp::SaturatingRoundingDoublingHighMul; - int left_shift = shift > 0 ? shift : 0; - int right_shift = shift > 0 ? 0 : -shift; - return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul( - x * (1 << left_shift), quantized_multiplier), - right_shift); -} - -inline int32_t MultiplyByQuantizedMultiplier(int64_t x, - int32_t quantized_multiplier, - int shift) { - // Inputs: - // - quantized_multiplier has fixed point at bit 31 - // - shift is -31 to +7 (negative for right shift) - // - // Assumptions: The following input ranges are assumed - // - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1) - // - scaling is chosen so final scaled result fits in int32_t - // - input x is in the range -(1<<47) <= x < (1<<47) - assert(quantized_multiplier >= 0); - assert(shift >= -31 && shift < 8); - - int32_t reduced_multiplier = (quantized_multiplier + (1 << 15)) >> 16; - int total_shift = 15 - shift; - x = (x * (int64_t)reduced_multiplier) + ((int64_t)1 << (total_shift - 1)); - int32_t result = x >> total_shift; - return result; -} - -template -int CountLeadingZeros(T integer_input) { - static_assert(std::is_unsigned::value, - "Only unsigned integer types handled."); -#if defined(__GNUC__) - return integer_input ? __builtin_clz(integer_input) - : std::numeric_limits::digits; -#else - if (integer_input == 0) { - return std::numeric_limits::digits; - } - - const T one_in_leading_positive = static_cast(1) - << (std::numeric_limits::digits - 1); - int leading_zeros = 0; - while (integer_input < one_in_leading_positive) { - integer_input <<= 1; - ++leading_zeros; - } - return leading_zeros; -#endif -} - -template -inline int CountLeadingSignBits(T integer_input) { - static_assert(std::is_signed::value, "Only signed integer types handled."); -#if defined(__GNUC__) && !defined(__clang__) - return integer_input ? __builtin_clrsb(integer_input) - : std::numeric_limits::digits; -#else - using U = typename std::make_unsigned::type; - return integer_input >= 0 - ? CountLeadingZeros(static_cast(integer_input)) - 1 - : integer_input != std::numeric_limits::min() - ? CountLeadingZeros(2 * static_cast(-integer_input) - 1) - : 0; -#endif -} - -// Use "count leading zeros" helper functions to do a fast Floor(log_2(x)). -template -inline Integer FloorLog2(Integer n) { - static_assert(std::is_integral::value, ""); - static_assert(std::is_signed::value, ""); - static_assert(sizeof(Integer) == 4 || sizeof(Integer) == 8, ""); - TFLITE_CHECK_GT(n, 0); - if (sizeof(Integer) == 4) { - return 30 - CountLeadingSignBits(n); - } else { - return 62 - CountLeadingSignBits(n); - } -} - -// generate INT16 LUT for function(), e.g., table exp(x) and 1/(1+x) used in -// softmax -inline void gen_lut(const std::function& func, double min, - double max, int16_t* table, const int num) { - // size of table should equal to num + 1 - // last element only for slope calculation - double step = (max - min) / (num - 1); - double half_step = step / 2.0; - for (int i = 0; i < num - 1; i++) { - double sample_val = TfLiteRound(func(min + i * step) * 32768.0); - double midpoint_interp_val = - TfLiteRound((func(min + (i + 1) * step) * 32768.0 + - TfLiteRound(func(min + i * step) * 32768.0)) / - 2.0); - double midpoint_val = - TfLiteRound(func(min + i * step + half_step) * 32768.0); - double midpoint_err = midpoint_interp_val - midpoint_val; - double bias = TfLiteRound(midpoint_err / 2.0); - table[i] = std::min(std::max(sample_val - bias, -32768.0), 32767.0); - } - table[num - 1] = - std::min(std::max(TfLiteRound(func(max) * 32768.0), -32768.0), 32767.0); -} - -// int16_t func table lookup, e.g., lookup exp() and 1/(1+x) used in softmax -inline int16_t generic_int16_table_lookup(int16_t value, const int16_t* lut) { - // 512 base value, lut[513] only for calculate slope - uint16_t index = static_cast(256 + (value >> 7)); - assert(index < 512 && "LUT index out of range."); - int16_t offset = value & 0x7f; - - // base and slope are Q0.15 - int16_t base = lut[index]; - int16_t slope = lut[index + 1] - lut[index]; - - // Q0.15 * Q0.7 = Q0.22 - // Round and convert from Q0.22 to Q0.15 - int32_t delta = (static_cast(slope) * offset + 64) >> 7; - - // Q0.15 + Q0.15 - return base + delta; -} - -// Table of sigmoid(i/24) at 0.16 format - 256 elements. - -// We use combined sigmoid and tanh look-up table, since -// tanh(x) = 2*sigmoid(2*x) -1. -// Both functions are symmetric, so the LUT table is only needed -// for the absolute value of the input. -static const uint16_t sigmoid_table_uint16[256] = { - 32768, 33451, 34133, 34813, 35493, 36169, 36843, 37513, 38180, 38841, 39498, - 40149, 40794, 41432, 42064, 42688, 43304, 43912, 44511, 45102, 45683, 46255, - 46817, 47369, 47911, 48443, 48964, 49475, 49975, 50464, 50942, 51409, 51865, - 52311, 52745, 53169, 53581, 53983, 54374, 54755, 55125, 55485, 55834, 56174, - 56503, 56823, 57133, 57433, 57724, 58007, 58280, 58544, 58800, 59048, 59288, - 59519, 59743, 59959, 60168, 60370, 60565, 60753, 60935, 61110, 61279, 61441, - 61599, 61750, 61896, 62036, 62172, 62302, 62428, 62549, 62666, 62778, 62886, - 62990, 63090, 63186, 63279, 63368, 63454, 63536, 63615, 63691, 63765, 63835, - 63903, 63968, 64030, 64090, 64148, 64204, 64257, 64308, 64357, 64405, 64450, - 64494, 64536, 64576, 64614, 64652, 64687, 64721, 64754, 64786, 64816, 64845, - 64873, 64900, 64926, 64950, 64974, 64997, 65019, 65039, 65060, 65079, 65097, - 65115, 65132, 65149, 65164, 65179, 65194, 65208, 65221, 65234, 65246, 65258, - 65269, 65280, 65291, 65301, 65310, 65319, 65328, 65337, 65345, 65352, 65360, - 65367, 65374, 65381, 65387, 65393, 65399, 65404, 65410, 65415, 65420, 65425, - 65429, 65433, 65438, 65442, 65445, 65449, 65453, 65456, 65459, 65462, 65465, - 65468, 65471, 65474, 65476, 65479, 65481, 65483, 65485, 65488, 65489, 65491, - 65493, 65495, 65497, 65498, 65500, 65501, 65503, 65504, 65505, 65507, 65508, - 65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517, 65517, 65518, - 65519, 65520, 65520, 65521, 65522, 65522, 65523, 65523, 65524, 65524, 65525, - 65525, 65526, 65526, 65526, 65527, 65527, 65528, 65528, 65528, 65529, 65529, - 65529, 65529, 65530, 65530, 65530, 65530, 65531, 65531, 65531, 65531, 65531, - 65532, 65532, 65532, 65532, 65532, 65532, 65533, 65533, 65533, 65533, 65533, - 65533, 65533, 65533, 65534, 65534, 65534, 65534, 65534, 65534, 65534, 65534, - 65534, 65534, 65535}; - -// TODO(b/77858996): Add these to gemmlowp. -template -IntegerType SaturatingAddNonGemmlowp(IntegerType a, IntegerType b) { - static_assert(std::is_same::value, "unimplemented"); - return a; -} - -template <> -inline std::int32_t SaturatingAddNonGemmlowp(std::int32_t a, std::int32_t b) { - std::int64_t a64 = a; - std::int64_t b64 = b; - std::int64_t sum = a64 + b64; - return static_cast(std::min( - static_cast(std::numeric_limits::max()), - std::max( - static_cast(std::numeric_limits::min()), - sum))); -} - -template -gemmlowp::FixedPoint SaturatingAddNonGemmlowp( - gemmlowp::FixedPoint a, - gemmlowp::FixedPoint b) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingAddNonGemmlowp(a.raw(), b.raw())); -} - -template -IntegerType SaturatingSub(IntegerType a, IntegerType b) { - static_assert(std::is_same::value, "unimplemented"); - return a; -} - -template <> -inline std::int16_t SaturatingSub(std::int16_t a, std::int16_t b) { - std::int32_t a32 = a; - std::int32_t b32 = b; - std::int32_t diff = a32 - b32; - return static_cast( - std::min(static_cast(32767), - std::max(static_cast(-32768), diff))); -} - -template <> -inline std::int32_t SaturatingSub(std::int32_t a, std::int32_t b) { - std::int64_t a64 = a; - std::int64_t b64 = b; - std::int64_t diff = a64 - b64; - return static_cast(std::min( - static_cast(std::numeric_limits::max()), - std::max( - static_cast(std::numeric_limits::min()), - diff))); -} - -template -gemmlowp::FixedPoint SaturatingSub( - gemmlowp::FixedPoint a, - gemmlowp::FixedPoint b) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingSub(a.raw(), b.raw())); -} -// End section to be moved to gemmlowp. - -template -IntegerType SaturatingRoundingMultiplyByPOTParam(IntegerType x, int exponent) { - if (exponent == 0) { - return x; - } - using ScalarIntegerType = - typename gemmlowp::FixedPointRawTypeTraits::ScalarRawType; - const IntegerType min = - gemmlowp::Dup(std::numeric_limits::min()); - const IntegerType max = - gemmlowp::Dup(std::numeric_limits::max()); - const int ScalarIntegerTypeBits = 8 * sizeof(ScalarIntegerType); - - const std::int32_t threshold = - ((1 << (ScalarIntegerTypeBits - 1 - exponent)) - 1); - const IntegerType positive_mask = - gemmlowp::MaskIfGreaterThan(x, gemmlowp::Dup(threshold)); - const IntegerType negative_mask = - gemmlowp::MaskIfLessThan(x, gemmlowp::Dup(-threshold)); - - IntegerType result = gemmlowp::ShiftLeft(x, exponent); - result = gemmlowp::SelectUsingMask(positive_mask, max, result); - result = gemmlowp::SelectUsingMask(negative_mask, min, result); - return result; -} - -// If we want to leave IntegerBits fixed, then multiplication -// by a power of two has to be saturating/rounding, not exact anymore. -template -gemmlowp::FixedPoint -SaturatingRoundingMultiplyByPOTParam( - gemmlowp::FixedPoint a, int exponent) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); -} - -// Convert int32_t multiplier to int16_t with rounding. -inline void DownScaleInt32ToInt16Multiplier(int32_t multiplier_int32_t, - int16_t* multiplier_int16_t) { - TFLITE_DCHECK_GE(multiplier_int32_t, 0); - static constexpr int32_t kRoundingOffset = 1 << 15; - if (multiplier_int32_t >= - std::numeric_limits::max() - kRoundingOffset) { - *multiplier_int16_t = std::numeric_limits::max(); - return; - } - const int32_t result = (multiplier_int32_t + kRoundingOffset) >> 16; - TFLITE_DCHECK_LE(result << 16, multiplier_int32_t + kRoundingOffset); - TFLITE_DCHECK_GT(result << 16, multiplier_int32_t - kRoundingOffset); - *multiplier_int16_t = result; - TFLITE_DCHECK_EQ(*multiplier_int16_t, result); -} - -// Minimum output bits to accommodate log of maximum input range. It actually -// does not matter if one considers, say, [-64,64] or [-64,64). -// -// For example, run this through Octave: -// [0:127; ... -// ceil(log(abs( log(2.^(0:127))+1 ))/log(2)); ... -// ceil(log(abs( log(2.^(0:127))+1 ))/log(2))] -constexpr int min_log_x_output_bits(int input_bits) { - return input_bits > 90 ? 7 - : input_bits > 44 ? 6 - : input_bits > 21 ? 5 - : input_bits > 10 ? 4 - : input_bits > 4 ? 3 - : input_bits > 1 ? 2 - : 1; -} - -// Although currently the name of this function says that it cannot handle -// values less than 1, in practice it can handle as low as 1/x_max, where -// x_max is the largest representable input. In other words, the output range -// is symmetric. -template -inline gemmlowp::FixedPoint -log_x_for_x_greater_than_or_equal_to_1_impl( - gemmlowp::FixedPoint input_val) { - // assert(__builtin_clz(0u) >= std::numeric_limits::digits - 1); - // assert(__builtin_clz(0u) <= std::numeric_limits::digits); - using FixedPoint0 = gemmlowp::FixedPoint; - // The reason for accumulating the result with an extra bit of headroom is - // that z_pow_2_adj * log_2 might be saturated, and adding num_scaled * - // recip_denom will otherwise introduce an error. - static constexpr int kAccumIntegerBits = OutputIntegerBits + 1; - using FixedPointAccum = gemmlowp::FixedPoint; - - const FixedPoint0 log_2 = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1488522236, std::log(2.0)); - const FixedPoint0 sqrt_sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1805811301, std::sqrt(std::sqrt(0.5))); - const FixedPoint0 sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1518500250, std::sqrt(0.5)); - const FixedPoint0 one_quarter = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(FixedPoint0, 536870912, 1.0 / 4.0); - - const FixedPoint0 alpha_n = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 117049297, 11.0 / 240.0 * std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_d = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 127690142, 1.0 / 20.0 * std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_i = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1057819769, - 2.0 / std::sqrt(std::sqrt(2.0)) - std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_f = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 638450708, 1.0 / 4.0 * std::sqrt(std::sqrt(2.0))); - - const FixedPointAccum shifted_quarter = - gemmlowp::Rescale(one_quarter); - - // Reinterpret the input value as Q0.31, because we will figure out the - // required shift "ourselves" instead of using, say, Rescale. - FixedPoint0 z_a = FixedPoint0::FromRaw(input_val.raw()); - // z_a_pow_2 = input_integer_bits - z_a_headroom; - int z_a_headroom_plus_1 = CountLeadingZeros(static_cast(z_a.raw())); - FixedPoint0 r_a_tmp = - SaturatingRoundingMultiplyByPOTParam(z_a, (z_a_headroom_plus_1 - 1)); - const int32_t r_a_raw = - SaturatingRoundingMultiplyByPOTParam((r_a_tmp * sqrt_half).raw(), 1); - // z_pow_2_adj = max(z_pow_2_a - 0.75, z_pow_2_b - 0.25); - // z_pow_2_adj = max(InputIntegerBits - z_a_headroom_plus_1 + 0.25, - // InputIntegerBits - z_b_headroom - 0.25); - const FixedPointAccum z_a_pow_2_adj = SaturatingAddNonGemmlowp( - FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( - InputIntegerBits - z_a_headroom_plus_1, 31 - kAccumIntegerBits)), - shifted_quarter); - - // z_b is treated like z_a, but premultiplying by sqrt(0.5). - FixedPoint0 z_b = z_a * sqrt_half; - int z_b_headroom = CountLeadingZeros(static_cast(z_b.raw())) - 1; - const int32_t r_b_raw = - SaturatingRoundingMultiplyByPOTParam(z_a.raw(), z_b_headroom); - const FixedPointAccum z_b_pow_2_adj = SaturatingSub( - FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( - InputIntegerBits - z_b_headroom, 31 - kAccumIntegerBits)), - shifted_quarter); - - const FixedPoint0 r = FixedPoint0::FromRaw(std::min(r_a_raw, r_b_raw)); - const FixedPointAccum z_pow_2_adj = FixedPointAccum::FromRaw( - std::max(z_a_pow_2_adj.raw(), z_b_pow_2_adj.raw())); - - const FixedPoint0 p = gemmlowp::RoundingHalfSum(r, sqrt_sqrt_half); - FixedPoint0 q = r - sqrt_sqrt_half; - q = q + q; - - const FixedPoint0 common_sq = q * q; - const FixedPoint0 num = q * r + q * common_sq * alpha_n; - const FixedPoint0 denom_minus_one_0 = - p * (alpha_i + q + alpha_d * common_sq) + alpha_f * q; - const FixedPoint0 recip_denom = - one_over_one_plus_x_for_x_in_0_1(denom_minus_one_0); - - const FixedPointAccum num_scaled = gemmlowp::Rescale(num); - return gemmlowp::Rescale(z_pow_2_adj * log_2 + - num_scaled * recip_denom); -} - -template -inline gemmlowp::FixedPoint -log_x_for_x_greater_than_or_equal_to_1( - gemmlowp::FixedPoint input_val) { - static_assert( - OutputIntegerBits >= min_log_x_output_bits(InputIntegerBits), - "Output integer bits must be sufficient to accommodate logs of inputs."); - return log_x_for_x_greater_than_or_equal_to_1_impl( - input_val); -} - -inline int32_t GetReciprocal(int32_t x, int x_integer_digits, - int* num_bits_over_unit) { - int headroom_plus_one = CountLeadingZeros(static_cast(x)); - // This is the number of bits to the left of the binary point above 1.0. - // Consider x=1.25. In that case shifted_scale=0.8 and - // no later adjustment will be needed. - *num_bits_over_unit = x_integer_digits - headroom_plus_one; - const int32_t shifted_sum_minus_one = - static_cast((static_cast(x) << headroom_plus_one) - - (static_cast(1) << 31)); - - gemmlowp::FixedPoint shifted_scale = - gemmlowp::one_over_one_plus_x_for_x_in_0_1( - gemmlowp::FixedPoint::FromRaw(shifted_sum_minus_one)); - return shifted_scale.raw(); -} - -inline void GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift, - int32_t* output_inv_sqrt, - int* output_shift) { - TFLITE_DCHECK_GE(input, 0); - if (input <= 1) { - // Handle the input value 1 separately to avoid overflow in that case - // in the general computation below (b/143972021). Also handle 0 as if it - // were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid - // but rare/unrealistic input value. We can expect both to occur in some - // incompletely trained models, but probably not in fully trained models. - *output_inv_sqrt = std::numeric_limits::max(); - *output_shift = 0; - return; - } - TFLITE_DCHECK_GT(input, 1); - *output_shift = 11; - while (input >= (1 << 29)) { - input /= 4; - ++*output_shift; - } - const unsigned max_left_shift_bits = - CountLeadingZeros(static_cast(input)) - 1; - const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2; - const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1; - *output_shift -= left_shift_bit_pairs; - input <<= 2 * left_shift_bit_pairs; - TFLITE_DCHECK_GE(input, (1 << 27)); - TFLITE_DCHECK_LT(input, (1 << 29)); - using gemmlowp::FixedPoint; - using gemmlowp::Rescale; - using gemmlowp::SaturatingRoundingMultiplyByPOT; - // Using 3 integer bits gives us enough room for the internal arithmetic in - // this Newton-Raphson iteration. - using F3 = FixedPoint; - using F0 = FixedPoint; - const F3 fixedpoint_input = F3::FromRaw(input >> 1); - const F3 fixedpoint_half_input = - SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input); - const F3 fixedpoint_half_three = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5); - // Newton-Raphson iteration - // Naive unoptimized starting guess: x = 1 - F3 x = F3::One(); - // Naive unoptimized number of iterations: 5 - for (int i = 0; i < 5; i++) { - const F3 x3 = Rescale<3>(x * x * x); - x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3); - } - const F0 fixedpoint_half_sqrt_2 = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.); - x = x * fixedpoint_half_sqrt_2; - *output_inv_sqrt = x.raw(); - if (*output_shift < 0) { - *output_inv_sqrt <<= -*output_shift; - *output_shift = 0; - } - // Convert right shift (right is positive) to left shift. - *output_shift *= reverse_shift; -} - -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -inline int SubscriptToIndex(const NdArrayDesc<5>& desc, int indexes[5]) { - return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] + - indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] + - indexes[4] * desc.strides[4]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - -// Copies dims to desc, calculating strides. -template -inline void CopyDimsToDesc(const RuntimeShape& input_shape, - NdArrayDesc* desc_out) { - int desc_stride = 1; - for (int i = N - 1; i >= 0; --i) { - desc_out->extents[i] = input_shape.Dims(i); - desc_out->strides[i] = desc_stride; - desc_stride *= input_shape.Dims(i); - } -} - -template -inline void NdArrayDescsForElementwiseBroadcast( - const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, - NdArrayDesc* desc0_out, NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); - auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); - - // Copy dims to desc, calculating strides. - CopyDimsToDesc(extended_input0_shape, desc0_out); - CopyDimsToDesc(extended_input1_shape, desc1_out); - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = extended_input0_shape.Dims(i); - const int extent1 = extended_input1_shape.Dims(i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - -template -inline void NdArrayDescsForElementwiseBroadcast( - const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, - const RuntimeShape& input2_shape, NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out, NdArrayDesc* desc2_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - TFLITE_DCHECK(desc2_out != nullptr); - - auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); - auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); - auto extended_input2_shape = RuntimeShape::ExtendedShape(N, input2_shape); - - // Copy dims to desc, calculating strides. - CopyDimsToDesc(extended_input0_shape, desc0_out); - CopyDimsToDesc(extended_input1_shape, desc1_out); - CopyDimsToDesc(extended_input2_shape, desc2_out); - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = extended_input0_shape.Dims(i); - const int extent1 = extended_input1_shape.Dims(i); - const int extent2 = extended_input2_shape.Dims(i); - - int extent = extent0; - if (extent1 != 1) extent = extent1; - if (extent2 != 1) extent = extent2; - - TFLITE_DCHECK(extent0 == 1 || extent0 == extent); - TFLITE_DCHECK(extent1 == 1 || extent1 == extent); - TFLITE_DCHECK(extent2 == 1 || extent2 == extent); - - if (!(extent0 == extent1 && extent1 == extent2)) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent; - } - if (extent1 == 1) { - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent; - } - if (extent2 == 1) { - desc2_out->strides[i] = 0; - desc2_out->extents[i] = extent; - } - } - } -} - -// Detailed implementation of NDOpsHelper, the indexes must be a zero array. -// This implementation is equivalent to N nested loops. Ex, if N=4, it can be -// re-writen as: -// for (int b = 0; b < output.extents[0]; ++b) { -// for (int y = 0; y < output.extents[1]; ++y) { -// for (int x = 0; x < output.extents[2]; ++x) { -// for (int c = 0; c < output.extents[3]; ++c) { -// calc({b,y,x,c}); -// } -// } -// } -// } -template -typename std::enable_if::type NDOpsHelperImpl( - const NdArrayDesc& output, const Calc& calc, int indexes[N]) { - for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { - NDOpsHelperImpl(output, calc, indexes); - } -} - -template -typename std::enable_if::type NDOpsHelperImpl( - const NdArrayDesc& output, const Calc& calc, int indexes[N]) { - for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { - calc(indexes); - } -} - -// Execute the calc function in the innermost iteration based on the shape of -// the output. The calc function should take a single argument of type int[N]. -template -inline void NDOpsHelper(const NdArrayDesc& output, const Calc& calc) { - int indexes[N] = {0}; - NDOpsHelperImpl(output, calc, indexes); -} -// Copied from gemmlowp::RoundDown when we dropped direct dependency on -// gemmlowp. -// -// Returns the runtime argument rounded down to the nearest multiple of -// the fixed Modulus. -template -Integer RoundDown(Integer i) { - return i - (i % Modulus); -} - -// Copied from gemmlowp::RoundUp when we dropped direct dependency on -// gemmlowp. -// -// Returns the runtime argument rounded up to the nearest multiple of -// the fixed Modulus. -template -Integer RoundUp(Integer i) { - return RoundDown(i + Modulus - 1); -} - -// Copied from gemmlowp::CeilQuotient when we dropped direct dependency on -// gemmlowp. -// -// Returns the quotient a / b rounded up ('ceil') to the nearest integer. -template -Integer CeilQuotient(Integer a, Integer b) { - return (a + b - 1) / b; -} - -// This function is a copy of gemmlowp::HowManyThreads, copied when we dropped -// the direct dependency of internal/optimized/ on gemmlowp. -// -// It computes a reasonable number of threads to use for a GEMM of shape -// (rows, cols, depth). -// -// TODO(b/131910176): get rid of this function by switching each call site -// to its own more sensible logic for its own workload. -template -inline int LegacyHowManyThreads(int max_num_threads, int rows, int cols, - int depth) { - // Early-exit in the default case where multi-threading is disabled. - if (max_num_threads == 1) { - return 1; - } - - // Ensure that each thread has KernelRows rows to process, if at all possible. - int thread_count = std::min(max_num_threads, rows / KernelRows); - - // Limit the number of threads according to the overall size of the problem. - if (thread_count > 1) { - // Empirically determined value. - static constexpr std::uint64_t min_cubic_size_per_thread = 64 * 1024; - - // We can only multiply two out of three sizes without risking overflow - const std::uint64_t cubic_size = - std::uint64_t(rows) * std::uint64_t(cols) * std::uint64_t(depth); - - thread_count = std::min( - thread_count, static_cast(cubic_size / min_cubic_size_per_thread)); - } - - if (thread_count < 1) { - thread_count = 1; - } - - assert(thread_count > 0 && thread_count <= max_num_threads); - return thread_count; -} - -template -void optimized_ops_preload_l1_stream(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 0 means read */ 0, /* 0 means no locality */ 0); -#else - (void)ptr; -#endif -} - -template -void optimized_ops_preload_l1_keep(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 0 means read */ 0, /* 3 means high locality */ 3); -#else - (void)ptr; -#endif -} - -template -void optimized_ops_prefetch_write_l1_keep(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 1 means write */ 1, /* 3 means high locality */ 3); -#else - (void)ptr; -#endif -} - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/compatibility.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/compatibility.h deleted file mode 100644 index 61becad3..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/compatibility.h +++ /dev/null @@ -1,112 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ - -#include - -#include "tensorflow/lite/kernels/op_macros.h" - -#ifndef TFLITE_DCHECK -#define TFLITE_DCHECK(condition) (condition) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_EQ -#define TFLITE_DCHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_NE -#define TFLITE_DCHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_GE -#define TFLITE_DCHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_GT -#define TFLITE_DCHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_LE -#define TFLITE_DCHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_LT -#define TFLITE_DCHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -// TODO(ahentz): Clean up: We should stick to the DCHECK versions. -#ifndef TFLITE_CHECK -#define TFLITE_CHECK(condition) (condition) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_EQ -#define TFLITE_CHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_NE -#define TFLITE_CHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_GE -#define TFLITE_CHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_GT -#define TFLITE_CHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_LE -#define TFLITE_CHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_LT -#define TFLITE_CHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TF_LITE_STATIC_MEMORY -// TODO(b/162019032): Consider removing these type-aliases. -using int8 = std::int8_t; -using uint8 = std::uint8_t; -using int16 = std::int16_t; -using uint16 = std::uint16_t; -using int32 = std::int32_t; -using uint32 = std::uint32_t; -#endif // !defined(TF_LITE_STATIC_MEMORY) - -// TFLITE_DEPRECATED() -// -// Duplicated from absl/base/macros.h to avoid pulling in that library. -// Marks a deprecated class, struct, enum, function, method and variable -// declarations. The macro argument is used as a custom diagnostic message (e.g. -// suggestion of a better alternative). -// -// Example: -// -// class TFLITE_DEPRECATED("Use Bar instead") Foo {...}; -// TFLITE_DEPRECATED("Use Baz instead") void Bar() {...} -// -// Every usage of a deprecated entity will trigger a warning when compiled with -// clang's `-Wdeprecated-declarations` option. This option is turned off by -// default, but the warnings will be reported by clang-tidy. -#if defined(__clang__) && __cplusplus >= 201103L -#define TFLITE_DEPRECATED(message) __attribute__((deprecated(message))) -#endif - -#ifndef TFLITE_DEPRECATED -#define TFLITE_DEPRECATED(message) -#endif - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/cppmath.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/cppmath.h deleted file mode 100644 index 24a3aec8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/cppmath.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ - -#include - -namespace tflite { - -#if defined(TF_LITE_USE_GLOBAL_CMATH_FUNCTIONS) || \ - (defined(__ANDROID__) && !defined(__NDK_MAJOR__)) || defined(ARDUINO) || \ - defined(__ZEPHYR__) -#define TF_LITE_GLOBAL_STD_PREFIX -#else -#define TF_LITE_GLOBAL_STD_PREFIX std -#endif - -#define DECLARE_STD_GLOBAL_SWITCH1(tf_name, std_name) \ - template \ - inline T tf_name(const T x) { \ - return TF_LITE_GLOBAL_STD_PREFIX::std_name(x); \ - } - -DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/max.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/max.h deleted file mode 100644 index c1810027..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/max.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ - -#include - -namespace tflite { - -#if defined(TF_LITE_USE_GLOBAL_MAX) || defined(__ZEPHYR__) -inline float TfLiteMax(const float& x, const float& y) { - return std::max(x, y); -} -#else -template -inline T TfLiteMax(const T& x, const T& y) { - return std::fmax(x, y); -} -#endif - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/min.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/min.h deleted file mode 100644 index 62035dcc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/min.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ - -#include - -namespace tflite { - -#if defined(TF_LITE_USE_GLOBAL_MIN) || defined(__ZEPHYR__) -inline float TfLiteMin(const float& x, const float& y) { - return std::min(x, y); -} -#else -template -inline T TfLiteMin(const T& x, const T& y) { - return std::fmin(x, y); -} -#endif - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/optimized/neon_check.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/optimized/neon_check.h deleted file mode 100644 index bbf745ce..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/optimized/neon_check.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ - -#if defined(__ARM_NEON__) || defined(__ARM_NEON) -#define USE_NEON -#include -#endif - -#if defined __GNUC__ && defined __SSE4_1__ && !defined TF_LITE_DISABLE_X86_NEON -#define USE_NEON -#include "NEON_2_SSE.h" -#endif - -// NEON_OR_PORTABLE(SomeFunc, args) calls NeonSomeFunc(args) if USE_NEON is -// defined, PortableSomeFunc(args) otherwise. -#ifdef USE_NEON -// Always use Neon code -#define NEON_OR_PORTABLE(funcname, ...) Neon##funcname(__VA_ARGS__) - -#else -// No NEON available: Use Portable code -#define NEON_OR_PORTABLE(funcname, ...) Portable##funcname(__VA_ARGS__) - -#endif // defined(USE_NEON) - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/quantization_util.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/quantization_util.h deleted file mode 100644 index 0ee914b0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/quantization_util.h +++ /dev/null @@ -1,292 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -// Given the min and max values of a float array, return -// reasonable quantization parameters to use for this array. -template -QuantizationParams ChooseQuantizationParams(double rmin, double rmax, - bool narrow_range) { - const T qmin = std::numeric_limits::min() + (narrow_range ? 1 : 0); - const T qmax = std::numeric_limits::max(); - const double qmin_double = qmin; - const double qmax_double = qmax; - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - TFLITE_CHECK_LE(rmin, 0.); - TFLITE_CHECK_GE(rmax, 0.); - if (rmin == rmax) { - // Special case where the min,max range is a point. Should be {0}. - TFLITE_CHECK_EQ(rmin, 0.); - TFLITE_CHECK_EQ(rmax, 0.); - QuantizationParams quantization_params; - quantization_params.zero_point = 0; - quantization_params.scale = 0.; - return quantization_params; - } - - // General case. - // - // First determine the scale. - const double scale = (rmax - rmin) / (qmax_double - qmin_double); - - // Zero-point computation. - // First the initial floating-point computation. The zero-point can be - // determined from solving an affine equation for any known pair - // (real value, corresponding quantized value). - // We know two such pairs: (rmin, qmin) and (rmax, qmax). - // The arithmetic error on the zero point computed from either pair - // will be roughly machine_epsilon * (sum of absolute values of terms) - // so we want to use the variant that adds the smaller terms. - const double zero_point_from_min = qmin_double - rmin / scale; - const double zero_point_from_max = qmax_double - rmax / scale; - const double zero_point_from_min_error = - std::abs(qmin_double) + std::abs(rmin / scale); - const double zero_point_from_max_error = - std::abs(qmax_double) + std::abs(rmax / scale); - - const double zero_point_double = - zero_point_from_min_error < zero_point_from_max_error - ? zero_point_from_min - : zero_point_from_max; - - // Now we need to nudge the zero point to be an integer - // (our zero points are integer, and this is motivated by the requirement - // to be able to represent the real value "0" exactly as a quantized value, - // which is required in multiple places, for example in Im2col with SAME - // padding). - T nudged_zero_point = 0; - if (zero_point_double < qmin_double) { - nudged_zero_point = qmin; - } else if (zero_point_double > qmax_double) { - nudged_zero_point = qmax; - } else { - nudged_zero_point = static_cast(round(zero_point_double)); - } - // The zero point should always be in the range of quantized value, - // [qmin, qmax]. - TFLITE_CHECK_GE(nudged_zero_point, qmin); - TFLITE_CHECK_LE(nudged_zero_point, qmax); - - // Finally, store the result nudged quantization params. - QuantizationParams quantization_params; - quantization_params.zero_point = nudged_zero_point; - quantization_params.scale = scale; - return quantization_params; -} - -template -QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { - return ChooseQuantizationParams(rmin, rmax, false); -} - -// Converts a floating-point number to an integer. For all inputs x where -// static_cast(x) is legal according to the C++ standard, the result -// is identical to that cast (i.e. the result is x with its fractional part -// truncated whenever that is representable as IntOut). -// -// static_cast would cause undefined behavior for the following cases, which -// have well-defined behavior for this function: -// -// 1. If x is NaN, the result is zero. -// -// 2. If the truncated form of x is above the representable range of IntOut, -// the result is std::numeric_limits::max(). -// -// 3. If the truncated form of x is below the representable range of IntOut, -// the result is std::numeric_limits::min(). -// -// Note that cases #2 and #3 cover infinities as well as finite numbers. -// -// The range of FloatIn must include the range of IntOut, otherwise -// the results are undefined. -// TODO(sfeuz): Replace by absl::SafeCast once available. -template -IntOut SafeCast(FloatIn x) { - static_assert(!std::numeric_limits::is_integer, - "FloatIn is integer"); - static_assert(std::numeric_limits::is_integer, - "IntOut is not integer"); - static_assert(std::numeric_limits::radix == 2, "IntOut is base 2"); - - // Special case NaN, for which the logic below doesn't work. - if (std::isnan(x)) { - return 0; - } - - // Negative values all clip to zero for unsigned results. - if (!std::numeric_limits::is_signed && x < 0) { - return 0; - } - - // Handle infinities. - if (std::isinf(x)) { - return x < 0 ? std::numeric_limits::min() - : std::numeric_limits::max(); - } - - // Set exp such that x == f * 2^exp for some f with |f| in [0.5, 1.0), - // unless x is zero in which case exp == 0. Note that this implies that the - // magnitude of x is strictly less than 2^exp. - int exp = 0; - std::frexp(x, &exp); - - // Let N be the number of non-sign bits in the representation of IntOut. If - // the magnitude of x is strictly less than 2^N, the truncated version of x - // is representable as IntOut. The only representable integer for which this - // is not the case is kMin for signed types (i.e. -2^N), but that is covered - // by the fall-through below. - if (exp <= std::numeric_limits::digits) { - return x; - } - - // Handle numbers with magnitude >= 2^N. - return x < 0 ? std::numeric_limits::min() - : std::numeric_limits::max(); -} - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of NEGATIVE its exponent --- -// this is intended as a RIGHT-shift. -// -// Restricted to the case where the multiplier < 1 (and non-negative). -void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift); - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of its exponent. -// -// Restricted to the case where the multiplier > 1. -void QuantizeMultiplierGreaterThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift); - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of its exponent. -// -// Handles an arbitrary positive multiplier. The 'shift' output-value is -// basically the 'floating-point exponent' of the multiplier: -// Negative for a right-shift (when the multiplier is <1), positive for a -// left-shift (when the multiplier is >1) -void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, - int* shift); - -// Splits a double input value into a returned fraction, and a shift value from -// the exponent, using only bitwise and integer operations to support -// microcontrollers and other environments without floating-point support. -// -// This is designed to be a replacement for how std::frexp() is used within the -// QuantizeMultiplier() function, and so has a different signature than the -// standard version, returning a 64-bit integer rather than a double. This -// result has a maximum value of 1<<31, with the fraction expressed as a -// proportion of that maximum. -// -// std::frexp() returns NaNs and infinities unmodified, but since we're -// returning integers that can't represent those values, instead we return -// a shift of std::numeric_limits::max() for all bad numbers, with an int64 -// result of 0 for NaNs, std:numeric_limits::max() for +INFINITY, and -// std::numeric_limits::min() for -INFINITY. Denormalized inputs will -// result in return values that end up truncating some bits at the end, -// reflecting the loss of precision inherent in denormalization. -int64_t IntegerFrExp(double input, int* shift); - -// Converts an integer fraction in the format produced by IntegerFrExp (where -// 0x40000000 is 1.0) and an exponent shift (between -1022 and +1022) into an -// IEEE binary64 double format result. The implementation uses only integer and -// bitwise operators, so no floating point hardware support or emulation is -// needed. This is here so quantized operations can run non-time-critical -// preparation calculations on microcontrollers and other platforms without -// float support. -double DoubleFromFractionAndShift(int64_t fraction, int shift); - -// Performs a multiplication of two numbers in double format, using only integer -// and bitwise instructions. This is aimed at supporting housekeeping functions -// for quantized operations on microcontrollers without floating-point hardware. -double IntegerDoubleMultiply(double a, double b); - -// Returns -1 if a is less than b, 0 if a and b are equal, and +1 if a is -// greater than b. It is implemented using only integer and logical instructions -// so that it can be easily run on microcontrollers for quantized operations. -int IntegerDoubleCompare(double a, double b); - -// This first creates a multiplier in a double equivalent of -// Q(input_integer_bits).(31-input_integer_bits) representation, with extra -// precision in the double's fractional bits. It then splits the result into -// significand and exponent. -void PreprocessSoftmaxScaling(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, int* left_shift); -// Like PreprocessSoftmaxScaling, but inverse scaling factors also calculated. -void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, - int* left_shift, - int32_t* reverse_scaling_divisor, - int* reverse_scaling_left_shift); -// Calculate the largest input that will result in a within-bounds intermediate -// result within MultiplyByQuantizedMultiplierGreaterThanOne. In other words, -// it must not overflow before we reduce the value by multiplication by the -// input multiplier. The negative radius is used as the minimum difference in -// Softmax. -int CalculateInputRadius(int input_integer_bits, int input_left_shift, - int total_signed_bits = 31); - -// Nudges a min/max quantization range to ensure zero is zero. -// Gymnastics with nudged zero point is to ensure that real zero maps to -// an integer, which is required for e.g. zero-padding in convolutional layers. -// Outputs nudged_min, nudged_max, nudged_scale. -void NudgeQuantizationRange(const float min, const float max, - const int quant_min, const int quant_max, - float* nudged_min, float* nudged_max, - float* nudged_scale); - -// Fake quantizes (quantizes and dequantizes) input_data using the scale, -// nudged_min, and nudged_max from NudgeQuantizationRange. This matches the code -// in TensorFlow's FakeQuantizeWithMinMaxVarsFunctor. -void FakeQuantizeArray(const float nudged_scale, const float nudged_min, - const float nudged_max, const float* input_data, - float* output_data, const float size); - -// If x is approximately a power of two (with any positive or negative -// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise -// returns false. -bool CheckedLog2(const float x, int* log2_result); - -// Decomposes an array of double multipliers into a Q0.31 int32 representation -// of its significand, and shift representation of its exponent. -// -// Handles an arbitrary multiplier. The 'shift' output-value is -// basically the 'floating-point exponent' of the multiplier: -// Negative for a right-shift (when the multiplier is <1), positive for a -// left-shift (when the multiplier is >1) -void QuantizeMultiplierArray(const double* effective_scales, size_t size, - int32_t* effective_scale_significand, - int* effective_shift); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/add.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/add.h deleted file mode 100644 index 5be7ab4d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/add.h +++ /dev/null @@ -1,454 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { - -namespace reference_ops { - -template -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], params.quantized_activation_min, - params.quantized_activation_max); - } -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const float* input1_data, - const RuntimeShape& input2_shape, const float* input2_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; i++) { - auto x = input1_data[i] + input2_data[i]; - output_data[i] = ActivationFunctionWithMinMax( - x, params.float_activation_min, params.float_activation_max); - } -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. - -// This function is used for 8-bit as well as for 16-bit, but the accumulator -// is 32-bit for both cases. The overflow does not happen due to the -// choice of the shift (20 or 15, accordingly - see add.cc for more comments). -template -inline void AddElementwise(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data) { - TFLITE_DCHECK_GT(params.input1_offset, -std::numeric_limits::max()); - TFLITE_DCHECK_GT(params.input2_offset, -std::numeric_limits::max()); - TFLITE_DCHECK_LT(params.input1_offset, std::numeric_limits::max()); - TFLITE_DCHECK_LT(params.input2_offset, std::numeric_limits::max()); - - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -// Scalar-broadcast add that can be used for inner loop of more general -// broadcast add, so that, for example, scalar-broadcast with batch will still -// be fast. -inline void AddScalarBroadcast(int size, const ArithmeticParams& params, - uint8_t input1_data, const uint8_t* input2_data, - uint8_t* output_data) { - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - - const int32_t input1_val = params.input1_offset + input1_data; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - for (int i = 0; i < size; ++i) { - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void AddGeneralParamScale(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int16_t* input1_data, - const RuntimeShape& input2_shape, - const int16_t* input2_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - int max_value = std::numeric_limits::max(); - - TFLITE_DCHECK_GT(params.input1_offset, -max_value); - TFLITE_DCHECK_GT(params.input2_offset, -max_value); - TFLITE_DCHECK_LT(params.input1_offset, max_value); - TFLITE_DCHECK_LT(params.input2_offset, max_value); - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int16_t* input1_data, - const RuntimeShape& input2_shape, const int16_t* input2_data, - const RuntimeShape& output_shape, int16_t* output_data, - bool pot_scale = true) { - if (!pot_scale) { - AddGeneralParamScale(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); - return; - } - - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - - const int input1_shift = params.input1_shift; - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - const int16_t output_activation_min = params.quantized_activation_min; - const int16_t output_activation_max = params.quantized_activation_max; - - TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); - TFLITE_DCHECK_LE(input1_shift, 0); - TFLITE_DCHECK_LE(params.input2_shift, 0); - const int16_t* not_shift_input = - input1_shift == 0 ? input1_data : input2_data; - const int16_t* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_right_shift = - input1_shift == 0 ? -params.input2_shift : -input1_shift; - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - using F0 = gemmlowp::FixedPoint; - - F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = F0::FromRaw( - gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); - F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); - const int16_t raw_output = result.raw(); - const int16_t clamped_output = std::min( - output_activation_max, std::max(output_activation_min, raw_output)); - output_data[i] = clamped_output; - } -} - -// TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -inline void BroadcastAdd4DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, - float* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - output_data[Offset(extended_output_shape, b, y, x, c)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, b, y, x, c)] + - input2_data[SubscriptToIndex(desc2, b, y, x, c)], - params.float_activation_min, params.float_activation_max); - } - } - } - } -} - -inline void BroadcastAdd4DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int32_t* input1_data, - const RuntimeShape& input2_shape, - const int32_t* input2_data, - const RuntimeShape& output_shape, - int32_t* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - output_data[Offset(extended_output_shape, b, y, x, c)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, b, y, x, c)] + - input2_data[SubscriptToIndex(desc2, b, y, x, c)], - params.quantized_activation_min, - params.quantized_activation_max); - } - } - } - } -} - -// This function is used for 8-bit as well as for 16-bit, but the accumulator -// is 32-bit for both cases. The overflow does not happen due to the -// choice of the shift (20 or 15, accordingly - see add.cc for more comments). -template -inline void BroadcastAdd4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - const int32_t input1_val = - params.input1_offset + - input1_data[SubscriptToIndex(desc1, b, y, x, c)]; - const int32_t input2_val = - params.input2_offset + - input2_data[SubscriptToIndex(desc2, b, y, x, c)]; - const int32_t shifted_input1_val = - input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = - input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, - params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, - params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[Offset(extended_output_shape, b, y, x, c)] = - static_cast(clamped_output); - } - } - } - } -} - -inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, - const RuntimeShape& unswitched_input1_shape, - const uint8_t* unswitched_input1_data, - const RuntimeShape& unswitched_input2_shape, - const uint8_t* unswitched_input2_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - ArithmeticParams switched_params = unswitched_params; - switched_params.input1_offset = unswitched_params.input2_offset; - switched_params.input1_multiplier = unswitched_params.input2_multiplier; - switched_params.input1_shift = unswitched_params.input2_shift; - switched_params.input2_offset = unswitched_params.input1_offset; - switched_params.input2_multiplier = unswitched_params.input1_multiplier; - switched_params.input2_shift = unswitched_params.input1_shift; - - const bool use_unswitched = - unswitched_params.broadcast_category == - tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; - - const ArithmeticParams& params = - use_unswitched ? unswitched_params : switched_params; - const uint8_t* input1_data = - use_unswitched ? unswitched_input1_data : unswitched_input2_data; - const uint8_t* input2_data = - use_unswitched ? unswitched_input2_data : unswitched_input1_data; - - // Fivefold nested loops. The second input resets its position for each - // iteration of the second loop. The first input resets its position at the - // beginning of the fourth loop. The innermost loop is an elementwise add of - // sections of the arrays. - uint8_t* output_data_ptr = output_data; - const uint8_t* input1_data_ptr = input1_data; - const uint8_t* input2_data_reset = input2_data; - // In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared - // between input shapes. y3 for input 1 is always broadcast, and so the - // dimension there is 1, whereas optionally y1 might be broadcast for input 2. - // Put another way, - // input1.shape.FlatSize = y0 * y1 * y2 * y4, - // input2.shape.FlatSize = y0 * y2 * y3 * y4. - int y0 = params.broadcast_shape[0]; - int y1 = params.broadcast_shape[1]; - int y2 = params.broadcast_shape[2]; - int y3 = params.broadcast_shape[3]; - int y4 = params.broadcast_shape[4]; - if (y4 > 1) { - // General fivefold pattern, with y4 > 1 so there is a non-broadcast inner - // dimension. - for (int i0 = 0; i0 < y0; ++i0) { - const uint8_t* input2_data_ptr; - for (int i1 = 0; i1 < y1; ++i1) { - input2_data_ptr = input2_data_reset; - for (int i2 = 0; i2 < y2; ++i2) { - for (int i3 = 0; i3 < y3; ++i3) { - AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, - output_data_ptr); - input2_data_ptr += y4; - output_data_ptr += y4; - } - // We have broadcast y4 of input1 data y3 times, and now move on. - input1_data_ptr += y4; - } - } - // We have broadcast y2*y3*y4 of input2 data y1 times, and now move on. - input2_data_reset = input2_data_ptr; - } - } else { - // Special case of y4 == 1, in which the innermost loop is a single element - // and can be combined with the next (y3) as an inner broadcast. - // - // Note that this handles the case of pure scalar broadcast when - // y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar - // broadcast with batch (as y2 > 1). - // - // NOTE The process is the same as the above general case except simplified - // for y4 == 1 and the loop over y3 is contained within the - // AddScalarBroadcast function. - for (int i0 = 0; i0 < y0; ++i0) { - const uint8_t* input2_data_ptr; - for (int i1 = 0; i1 < y1; ++i1) { - input2_data_ptr = input2_data_reset; - for (int i2 = 0; i2 < y2; ++i2) { - AddScalarBroadcast(y3, params, *input1_data_ptr, input2_data_ptr, - output_data_ptr); - input2_data_ptr += y3; - output_data_ptr += y3; - input1_data_ptr += 1; - } - } - input2_data_reset = input2_data_ptr; - } - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h deleted file mode 100644 index e6f34fd7..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h +++ /dev/null @@ -1,68 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -template -void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, - const T3* input2_data, const RuntimeShape& output_shape, - T2* output_data, const Cmp& cmp) { - TFLITE_DCHECK_GT(input1_shape.DimensionsCount(), 0); - TFLITE_DCHECK_EQ(input1_shape.DimensionsCount() - 1, - output_shape.DimensionsCount()); - int axis = input2_data[0]; - if (axis < 0) { - axis += input1_shape.DimensionsCount(); - } - const int axis_size = input1_shape.Dims(axis); - - int outer_size = 1; - for (int i = 0; i < axis; ++i) { - TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i)); - outer_size *= input1_shape.Dims(i); - } - - int inner_size = 1; - const int dims_count = input1_shape.DimensionsCount(); - for (int i = axis + 1; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1)); - inner_size *= input1_shape.Dims(i); - } - for (int outer = 0; outer < outer_size; ++outer) { - for (int inner = 0; inner < inner_size; ++inner) { - auto min_max_value = input1_data[outer * axis_size * inner_size + inner]; - T2 min_max_index = 0; - for (int i = 1; i < axis_size; ++i) { - const auto& curr_value = - input1_data[(outer * axis_size + i) * inner_size + inner]; - if (cmp(curr_value, min_max_value)) { - min_max_value = curr_value; - min_max_index = static_cast(i); - } - } - output_data[outer * inner_size + inner] = min_max_index; - } - } -} -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/binary_function.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/binary_function.h deleted file mode 100644 index 51d9e2b7..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/binary_function.h +++ /dev/null @@ -1,84 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// TODO(ycling): Refactoring. Remove BroadcastLogical and use the more -// generalized and efficient BroadcastBinaryFunction. -// -// Also appears to duplicate MinimumMaximum. -// -// R: Result type. T1: Input 1 type. T2: Input 2 type. -template -inline void BroadcastBinaryFunction4DSlow( - const RuntimeShape& unextended_input1_shape, const T1* input1_data, - const RuntimeShape& unextended_input2_shape, const T2* input2_data, - const RuntimeShape& unextended_output_shape, R* output_data, - R (*func)(T1, T2)) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - - for (int b = 0; b < output_shape.Dims(0); ++b) { - for (int y = 0; y < output_shape.Dims(1); ++y) { - for (int x = 0; x < output_shape.Dims(2); ++x) { - for (int c = 0; c < output_shape.Dims(3); ++c) { - auto out_idx = Offset(output_shape, b, y, x, c); - auto in1_idx = SubscriptToIndex(desc1, b, y, x, c); - auto in2_idx = SubscriptToIndex(desc2, b, y, x, c); - auto in1_val = input1_data[in1_idx]; - auto in2_val = input2_data[in2_idx]; - output_data[out_idx] = func(in1_val, in2_val); - } - } - } - } -} - -// R: Result type. T1: Input 1 type. T2: Input 2 type. -// TODO(renjieliu): Refactor other binary functions to use this one. -template -inline void BinaryFunction(const RuntimeShape& input1_shape, - const T1* input1_data, - const RuntimeShape& input2_shape, - const T2* input2_data, - const RuntimeShape& output_shape, R* output_data, - R (*func)(T1, T2)) { - const int flat_size = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = func(input1_data[i], input2_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/ceil.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/ceil.h deleted file mode 100644 index 66d1dc35..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/ceil.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline void Ceil(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = std::ceil(input_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/comparisons.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/comparisons.h deleted file mode 100644 index 49844ab1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/comparisons.h +++ /dev/null @@ -1,334 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/string_util.h" - -namespace tflite { - -namespace reference_ops { - -template -inline bool EqualFn(T lhs, T rhs) { - return lhs == rhs; -} - -template -inline bool NotEqualFn(T lhs, T rhs) { - return lhs != rhs; -} - -template -inline bool GreaterFn(T lhs, T rhs) { - return lhs > rhs; -} -template -inline bool GreaterEqualFn(T lhs, T rhs) { - return lhs >= rhs; -} -template -inline bool LessFn(T lhs, T rhs) { - return lhs < rhs; -} -template -inline bool LessEqualFn(T lhs, T rhs) { - return lhs <= rhs; -} - -inline bool StringRefEqualFn(const StringRef& lhs, const StringRef& rhs) { - if (lhs.len != rhs.len) return false; - for (int i = 0; i < lhs.len; ++i) { - if (lhs.str[i] != rhs.str[i]) return false; - } - return true; -} - -inline bool StringRefNotEqualFn(const StringRef& lhs, const StringRef& rhs) { - return !StringRefEqualFn(lhs, rhs); -} - -template -using ComparisonFn = bool (*)(T, T); - -template F> -inline void ComparisonImpl( - const ComparisonParams& op_params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { - const int64_t flatsize = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int64_t i = 0; i < flatsize; ++i) { - output_data[i] = F(input1_data[i], input2_data[i]); - } -} - -inline void ComparisonStringImpl(bool (*F)(const StringRef&, const StringRef&), - const RuntimeShape& input1_shape, - const TfLiteTensor* input1, - const RuntimeShape& input2_shape, - const TfLiteTensor* input2, - const RuntimeShape& output_shape, - bool* output_data) { - const int64_t flatsize = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int64_t i = 0; i < flatsize; ++i) { - const auto lhs = GetString(input1, i); - const auto rhs = GetString(input2, i); - output_data[i] = F(lhs, rhs); - } -} - -template F> -inline void Comparison(const ComparisonParams& op_params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, bool* output_data) { - ComparisonImpl(op_params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); -} - -template F> -inline void ComparisonWithScaling( - const ComparisonParams& op_params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { - int left_shift = op_params.left_shift; - int32_t input1_offset = op_params.input1_offset; - int32_t input1_multiplier = op_params.input1_multiplier; - int input1_shift = op_params.input1_shift; - int32_t input2_offset = op_params.input2_offset; - int32_t input2_multiplier = op_params.input2_multiplier; - int input2_shift = op_params.input2_shift; - - const int64_t flatsize = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int64_t i = 0; i < flatsize; ++i) { - const int32_t input1_val = input1_offset + input1_data[i]; - const int32_t input2_val = input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << left_shift); - const int32_t shifted_input2_val = input2_val * (1 << left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, input2_shift); - output_data[i] = F(scaled_input1_val, scaled_input2_val); - } -} - -struct BroadcastComparison4DSlowCommon { - const RuntimeShape output_shape; - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; -}; - -inline BroadcastComparison4DSlowCommon BroadcastComparison4DSlowPreprocess( - const RuntimeShape& unextended_input1_shape, - const RuntimeShape& unextended_input2_shape, - const RuntimeShape& unextended_output_shape) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - return {RuntimeShape::ExtendedShape(4, unextended_output_shape), desc1, - desc2}; -} - -template F> -inline void BroadcastComparison4DSlowImpl( - const ComparisonParams& op_params, - const RuntimeShape& unextended_input1_shape, const T* input1_data, - const RuntimeShape& unextended_input2_shape, const T* input2_data, - const RuntimeShape& unextended_output_shape, bool* output_data) { - const BroadcastComparison4DSlowCommon dims = - BroadcastComparison4DSlowPreprocess(unextended_input1_shape, - unextended_input2_shape, - unextended_output_shape); - - for (int b = 0; b < dims.output_shape.Dims(0); ++b) { - for (int y = 0; y < dims.output_shape.Dims(1); ++y) { - for (int x = 0; x < dims.output_shape.Dims(2); ++x) { - for (int c = 0; c < dims.output_shape.Dims(3); ++c) { - output_data[Offset(dims.output_shape, b, y, x, c)] = - F(input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)], - input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]); - } - } - } - } -} - -inline void BroadcastComparison4DSlowStringImpl( - bool (*F)(const StringRef&, const StringRef&), - const RuntimeShape& unextended_input1_shape, const TfLiteTensor* input1, - const RuntimeShape& unextended_input2_shape, const TfLiteTensor* input2, - const RuntimeShape& unextended_output_shape, bool* output_data) { - const BroadcastComparison4DSlowCommon dims = - BroadcastComparison4DSlowPreprocess(unextended_input1_shape, - unextended_input2_shape, - unextended_output_shape); - - for (int b = 0; b < dims.output_shape.Dims(0); ++b) { - for (int y = 0; y < dims.output_shape.Dims(1); ++y) { - for (int x = 0; x < dims.output_shape.Dims(2); ++x) { - for (int c = 0; c < dims.output_shape.Dims(3); ++c) { - const auto lhs = - GetString(input1, SubscriptToIndex(dims.desc1, b, y, x, c)); - const auto rhs = - GetString(input2, SubscriptToIndex(dims.desc2, b, y, x, c)); - output_data[Offset(dims.output_shape, b, y, x, c)] = F(lhs, rhs); - } - } - } - } -} - -template F> -inline void BroadcastComparison4DSlow(const ComparisonParams& op_params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, - bool* output_data) { - BroadcastComparison4DSlowImpl(op_params, input1_shape, input1_data, - input2_shape, input2_data, - output_shape, output_data); -} - -template F> -inline void BroadcastComparison4DSlowWithScaling( - const ComparisonParams& op_params, - const RuntimeShape& unextended_input1_shape, const T* input1_data, - const RuntimeShape& unextended_input2_shape, const T* input2_data, - const RuntimeShape& unextended_output_shape, bool* output_data) { - const BroadcastComparison4DSlowCommon dims = - BroadcastComparison4DSlowPreprocess(unextended_input1_shape, - unextended_input2_shape, - unextended_output_shape); - - int left_shift = op_params.left_shift; - int32_t input1_offset = op_params.input1_offset; - int32_t input1_multiplier = op_params.input1_multiplier; - int input1_shift = op_params.input1_shift; - int32_t input2_offset = op_params.input2_offset; - int32_t input2_multiplier = op_params.input2_multiplier; - int input2_shift = op_params.input2_shift; - - for (int b = 0; b < dims.output_shape.Dims(0); ++b) { - for (int y = 0; y < dims.output_shape.Dims(1); ++y) { - for (int x = 0; x < dims.output_shape.Dims(2); ++x) { - for (int c = 0; c < dims.output_shape.Dims(3); ++c) { - const int32_t input1_val = - input1_offset + - input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)]; - const int32_t input2_val = - input2_offset + - input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]; - const int32_t shifted_input1_val = input1_val * (1 << left_shift); - const int32_t shifted_input2_val = input2_val * (1 << left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, input2_shift); - output_data[Offset(dims.output_shape, b, y, x, c)] = - F(scaled_input1_val, scaled_input2_val); - } - } - } - } -} - -#define TFLITE_COMPARISON_OP(name) \ - inline void name(const ComparisonParams& op_params, \ - const RuntimeShape& input1_shape, const float* input1_data, \ - const RuntimeShape& input2_shape, const float* input2_data, \ - const RuntimeShape& output_shape, bool* output_data) { \ - Comparison(op_params, input1_shape, input1_data, input2_shape, \ - input2_data, output_shape, output_data); \ - } \ - template \ - inline void name##NoScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - ComparisonImpl(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, output_shape, \ - output_data); \ - } \ - template \ - inline void name##WithScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - ComparisonWithScaling(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name##NoScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlowImpl( \ - op_params, input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - inline void Broadcast4DSlow##name( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const float* input1_data, const RuntimeShape& input2_shape, \ - const float* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlow(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name##WithScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlowWithScaling( \ - op_params, input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ - } -TFLITE_COMPARISON_OP(Equal); -TFLITE_COMPARISON_OP(NotEqual); -TFLITE_COMPARISON_OP(Greater); -TFLITE_COMPARISON_OP(GreaterEqual); -TFLITE_COMPARISON_OP(Less); -TFLITE_COMPARISON_OP(LessEqual); -#undef TFLITE_COMPARISON_OP - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/concatenation.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/concatenation.h deleted file mode 100644 index 25959793..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/concatenation.h +++ /dev/null @@ -1,140 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -template -inline void Concatenation(const ConcatenationParams& params, - const RuntimeShape* const* input_shapes, - const Scalar* const* input_data, - const RuntimeShape& output_shape, - Scalar* output_data) { - int axis = params.axis; - int inputs_count = params.inputs_count; - const int concat_dimensions = output_shape.DimensionsCount(); - TFLITE_DCHECK_LT(axis, concat_dimensions); - - int64_t concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); - for (int j = 0; j < concat_dimensions; j++) { - if (j != axis) { - MatchingDim(*input_shapes[i], j, output_shape, j); - } - } - concat_size += input_shapes[i]->Dims(axis); - } - TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); - int64_t outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= output_shape.Dims(i); - } - // For all input arrays, - // FlatSize() = outer_size * Dims(axis) * base_inner_size; - int64_t base_inner_size = 1; - for (int i = axis + 1; i < concat_dimensions; ++i) { - base_inner_size *= output_shape.Dims(i); - } - - Scalar* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; - const Scalar* input_ptr = input_data[i] + k * copy_size; - memcpy(output_ptr, input_ptr, copy_size * sizeof(Scalar)); - output_ptr += copy_size; - } - } -} - -// TODO(prabhumk): This is the same as the optimized implementation. -// TODO(prabhumk): The quantized implementation of concatentation isn't fully -// quantized as it takes scale as a floating point value. This should be fixed -// when optimizng this routine further. -inline void ConcatenationWithScaling(const ConcatenationParams& params, - const RuntimeShape* const* input_shapes, - const uint8_t* const* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - int axis = params.axis; - const int32_t* input_zeropoint = params.input_zeropoint; - const float* input_scale = params.input_scale; - int inputs_count = params.inputs_count; - const int32_t output_zeropoint = params.output_zeropoint; - const float output_scale = params.output_scale; - - const int concat_dimensions = output_shape.DimensionsCount(); - TFLITE_DCHECK_LT(axis, concat_dimensions); - - int64_t concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); - for (int j = 0; j < concat_dimensions; j++) { - if (j != axis) { - MatchingDim(*input_shapes[i], j, output_shape, j); - } - } - concat_size += input_shapes[i]->Dims(axis); - } - TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); - int64_t outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= output_shape.Dims(i); - } - // For all input arrays, - // FlatSize() = outer_size * Dims(axis) * base_inner_size; - int64_t base_inner_size = 1; - for (int i = axis + 1; i < concat_dimensions; ++i) { - base_inner_size *= output_shape.Dims(i); - } - - const float inverse_output_scale = 1.f / output_scale; - uint8_t* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; - const uint8_t* input_ptr = input_data[i] + k * copy_size; - if (input_zeropoint[i] == output_zeropoint && - input_scale[i] == output_scale) { - memcpy(output_ptr, input_ptr, copy_size); - } else { - const float scale = input_scale[i] * inverse_output_scale; - const float bias = -input_zeropoint[i] * scale; - for (int j = 0; j < copy_size; ++j) { - const int32_t value = static_cast(tflite::TfLiteRound( - input_ptr[j] * scale + bias)) + - output_zeropoint; - output_ptr[j] = static_cast( - std::max(std::min(255, value), 0)); - } - } - output_ptr += copy_size; - } - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/conv.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/conv.h deleted file mode 100644 index d4bf46a8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/conv.h +++ /dev/null @@ -1,262 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ - -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/internal/common.h" - - - -namespace tflite { - -namespace reference_ops { - - -inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& filter_shape, - const float* filter_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data, const RuntimeShape& im2col_shape, - float* im2col_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - float total = 0.f; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - float input_value = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - float filter_value = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - total += (input_value * filter_value); - } - } - } - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[out_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - ActivationFunctionWithMinMax(total + bias_value, - output_activation_min, - output_activation_max); - } - } - } - } -} - -inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data, const RuntimeShape& im2col_shape, - uint8_t* im2col_data, void* cpu_backend_context) { - (void)cpu_backend_context; // only used in optimized code. - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - acc += - (filter_val + filter_offset) * (input_val + input_offset); - } - } - } - } - if (bias_data) { - acc += bias_data[out_channel]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, - output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(acc); - } - } - } - } -} - -inline void HybridConvPerChannel( - const ConvParams& params, float* scaling_factors_ptr, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const float* bias_data, - const RuntimeShape& output_shape, float* output_data, - const RuntimeShape& im2col_shape, int8_t* im2col_data, - const float* per_channel_scale, int32_t* input_offset) { - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - acc += filter_val * (input_val - input_offset[batch]); - } - } - } - } - float acc_float = - acc * per_channel_scale[out_channel] * scaling_factors_ptr[batch]; - if (bias_data) { - acc_float += bias_data[out_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - ActivationFunctionWithMinMax(acc_float, output_activation_min, - output_activation_max); - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite - - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h deleted file mode 100644 index 0cecb16b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h +++ /dev/null @@ -1,100 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -inline void DepthwiseConv( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& filter_shape, - const float* filter_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int b = 0; b < batches; ++b) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int ic = 0; ic < input_depth; ++ic) { - for (int m = 0; m < depth_multiplier; m++) { - const int oc = m + ic * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - float total = 0.f; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - float input_value = - input_data[Offset(input_shape, b, in_y, in_x, ic)]; - float filter_value = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, oc)]; - total += (input_value * filter_value); - } - } - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[oc]; - } - output_data[Offset(output_shape, b, out_y, out_x, oc)] = - ActivationFunctionWithMinMax(total + bias_value, - output_activation_min, - output_activation_max); - } - } - } - } - } -} - -} // end namespace reference_ops -} // end namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h deleted file mode 100644 index 20bf83df..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h +++ /dev/null @@ -1,297 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -// Used in tests and template parameters to control which version of depthwise -// convolution is called. Primarily for reference code, and specializations -// forced in tests. -enum class DepthwiseConvImplementation { - // Run all tests against kUseStandardEntry even if also testing another - // kernel, since we need to be sure that the main DepthwiseConv() function in - // optimized_ops.h dispatches to a correctly-executing kernel. - kNone = 0, // The "default" option: use the normal - // DepthwiseConv kernel (entry) function. - kUseGenericKernel, // Forced use of generic kernel. - kUseNeon3x3, // 3x3 kernel that uses NEON when available. - kUseNeon3x3DotProduct, // 3x3 kernel that uses dot-product enabled NEON - // when available. - kUseCModel3x3DotProduct, // 3x3 kernel, reference C model that is intended - // to match overall design NEON code. - kUseUnwound3x3DotProduct, // 3x3 kernel, reference C model with unwound loops - // and some arrays. - kUseIntrinsics3x3DotProduct, // 3x3 kernel using NEON intrinsics. -}; - -// Category of depthwise convolution output rounding. -enum class DepthwiseConvOutputRounding { - kNone = 0, // Invalid: specific method must be specified. - kAwayFromZero, // Original method: exact halves rounded away from zero. - kUpward, // Halves towards +infinity: adds 0.5 before truncate. - // This is where a future kNearestEven would be placed. -}; - -// Category of depthwise convolution depth multiplication. -enum class DepthwiseConvDepthMultiplication { - kNoMultiplication = 0, // Depth multiplier = 1. - kUnitInputDepth, // Input depth = 1, output depth = depth multiplier. -}; - -namespace reference_ops { -namespace depthwise_conv { - -template -inline int32_t DepthwiseConvRound(int32_t x, int32_t quantized_multiplier, - int shift) { - TFLITE_DCHECK_NE(output_rounding, DepthwiseConvOutputRounding::kNone); - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - using gemmlowp::SaturatingRoundingDoublingHighMul; - const int left_shift = shift > 0 ? shift : 0; - const int right_shift = shift > 0 ? 0 : -shift; - const int rounding_offset = right_shift > 0 ? 1 << (right_shift - 1) : 0; - return (SaturatingRoundingDoublingHighMul(x * (1 << left_shift), - quantized_multiplier) + - rounding_offset) >> - right_shift; -} - -template -struct DepthwiseConvBasicKernel { - static inline void Run( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int b = 0; b < batches; ++b) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int ic = 0; ic < input_depth; ++ic) { - for (int m = 0; m < depth_multiplier; m++) { - const int oc = m + ic * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = - in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - int32_t input_val = - input_data[Offset(input_shape, b, in_y, in_x, ic)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, oc)]; - acc += (filter_val + filter_offset) * - (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[oc]; - } - acc = DepthwiseConvRound(acc, output_multiplier, - output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, b, out_y, out_x, oc)] = - static_cast(acc); - } - } - } - } - } - } - - // TODO(b/148596273): Reconcile reference versions, perhaps with common - // MultiplyByQuantizedMultiplier or DepthwiseConvRound function. - static inline void RunPerChannel( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - const int32_t* output_multiplier = params.output_multiplier_per_channel; - const int32_t* output_shift = params.output_shift_per_channel; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = - in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we - // force real value of 0.0 be represented by a quantized - // value. This guarantees that the input_offset is a int8_t, - // even though it is represented using int32_t. int32_t += - // int8_t - // * (int8_t - int8_t) so the highest value we can get from - // each accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold - // as long as the filter size (filter_y * filter_x * - // in_channel) does not exceed 2^16, which is the case in - // all the models we have seen so far. - acc += filter_val * (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - acc = DepthwiseConvRound( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = static_cast(acc); - } - } - } - } - } - } -}; - -} // namespace depthwise_conv - -inline void DepthwiseConv( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - return depthwise_conv::DepthwiseConvBasicKernel< - DepthwiseConvOutputRounding::kAwayFromZero>::Run(params, input_shape, - input_data, filter_shape, - filter_data, bias_shape, - bias_data, output_shape, - output_data); -} - -} // namespace reference_ops -} // end namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/dequantize.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/dequantize.h deleted file mode 100644 index b90951f9..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/dequantize.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ - -#include - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// Dequantizes into a float without rounding. -template -inline void Dequantize(const tflite::DequantizationParams& op_params, - const RuntimeShape& input_shape, - const InputT* input_data, - const RuntimeShape& output_shape, OutputT* output_data) { - int32_t zero_point = op_params.zero_point; - const double scale = op_params.scale; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const int32_t val = input_data[i]; - const OutputT result = static_cast(scale * (val - zero_point)); - output_data[i] = result; - } -} - -// Dequantizes per-channel quantized tensor to float. -template -inline void PerChannelDequantize( - const tflite::PerChannelDequantizationParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, float* output_data) { - // Ensure flat size is same. - MatchingFlatSize(input_shape, output_shape); - - const int32_t* zero_point = op_params.zero_point; - const float* scale = op_params.scale; - const int32_t quantized_dimension = op_params.quantized_dimension; - const int32_t num_dims = input_shape.DimensionsCount(); - const int32_t* dims_data = input_shape.DimsData(); - std::vector current_dim(num_dims, 0); - - do { - size_t offset = - ReducedOutputOffset(num_dims, reinterpret_cast(dims_data), - current_dim.data(), 0, nullptr); - const int channel = current_dim[quantized_dimension]; - const int32_t val = input_data[offset]; - const float result = - static_cast(scale[channel] * (val - zero_point[channel])); - output_data[offset] = result; - } while (NextIndex(num_dims, reinterpret_cast(dims_data), - current_dim.data())); -} - -} // namespace reference_ops - -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/floor.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/floor.h deleted file mode 100644 index 0693fd42..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/floor.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline void Floor(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - int offset = i; - output_data[offset] = std::floor(input_data[offset]); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/fully_connected.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/fully_connected.h deleted file mode 100644 index 39a9cd02..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/fully_connected.h +++ /dev/null @@ -1,320 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& weights_shape, - const float* weights_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data) { - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - // TODO(benoitjacob): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dims_count = output_shape.DimensionsCount(); - const int weights_dims_count = weights_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dims_count - 1); - const int output_depth = MatchingDim(weights_shape, weights_dims_count - 2, - output_shape, output_dims_count - 1); - const int accum_depth = weights_shape.Dims(weights_dims_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - float total = 0.f; - for (int d = 0; d < accum_depth; ++d) { - total += input_data[b * accum_depth + d] * - weights_data[out_c * accum_depth + d]; - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[out_c]; - } - output_data[out_c + output_depth * b] = ActivationFunctionWithMinMax( - total + bias_value, output_activation_min, output_activation_max); - } - } -} - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - // TODO(benoitjacob): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - int32_t acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += (filter_val + filter_offset) * (input_val + input_offset); - } - if (bias_data) { - acc += bias_data[out_c]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[out_c + output_depth * b] = static_cast(acc); - } - } -} - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(output_offset, 0); - // TODO(benoitjacob): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum = bias_data[out_c]; - // Accumulation loop. - for (int d = 0; d < accum_depth; ++d) { - int16_t input_val = input_data[b * accum_depth + d] + input_offset; - int16_t filter_val = - filter_data[out_c * accum_depth + d] + filter_offset; - accum += filter_val * input_val; - } - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The quantized - // multiplier and shift here have been pre-computed offline - // (e.g. by toco). - accum = - MultiplyByQuantizedMultiplier(accum, output_multiplier, output_shift); - // Saturate, cast to int16_t, and store to output array. - accum = std::max(accum, output_activation_min - output_offset); - accum = std::min(accum, output_activation_max - output_offset); - accum += output_offset; - output_data[out_c + output_depth * b] = accum; - } - } -} - -inline void ShuffledFullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& weights_shape, - const uint8_t* shuffled_weights_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data, uint8_t* shuffled_input_workspace_data) { - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); - TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); - TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); - // TODO(benoitjacob): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int weights_dim_count = weights_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(weights_shape, weights_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = weights_shape.Dims(weights_dim_count - 1); - TFLITE_DCHECK((accum_depth % 16) == 0); - TFLITE_DCHECK((output_depth % 4) == 0); - - // Shuffling and xoring of input activations into the workspace buffer - uint8_t* shuffled_input_workspace_ptr = shuffled_input_workspace_data; - if (batches == 1) { - for (int i = 0; i < accum_depth; i++) { - shuffled_input_workspace_data[i] = input_data[i] ^ 0x80; - } - } else if (batches == 4) { - for (int c = 0; c < accum_depth; c += 16) { - for (int b = 0; b < 4; b++) { - const uint8_t* src_data_ptr = input_data + b * accum_depth + c; - for (int j = 0; j < 16; j++) { - uint8_t src_val = *src_data_ptr++; - // Flip the sign bit, so that the kernel will only need to - // reinterpret these uint8_t values as int8_t, getting for free the - // subtraction of the zero_point value 128. - uint8_t dst_val = src_val ^ 0x80; - *shuffled_input_workspace_ptr++ = dst_val; - } - } - } - } else { - TFLITE_DCHECK(false); - return; - } - - // Actual computation - if (batches == 1) { - int16_t* output_ptr = output_data; - // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) - // so that just reinterpreting them as int8_t values is equivalent to - // subtracting 128 from them, thus implementing for free the subtraction of - // the zero_point value 128. - const int8_t* shuffled_weights_ptr = - reinterpret_cast(shuffled_weights_data); - // Likewise, we preshuffled and pre-xored the input data above. - const int8_t* shuffled_input_data = - reinterpret_cast(shuffled_input_workspace_data); - for (int c = 0; c < output_depth; c += 4) { - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum[4] = {0}; - // Accumulation loop. - for (int d = 0; d < accum_depth; d += 16) { - for (int i = 0; i < 4; i++) { - for (int j = 0; j < 16; j++) { - int8_t input_val = shuffled_input_data[d + j]; - int8_t weights_val = *shuffled_weights_ptr++; - accum[i] += weights_val * input_val; - } - } - } - for (int i = 0; i < 4; i++) { - // Add bias value - int32_t acc = accum[i] + bias_data[c + i]; - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The quantized - // multiplier and shift here have been pre-computed offline - // (e.g. by toco). - acc = - MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - // Saturate, cast to int16_t, and store to output array. - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_ptr[c + i] = acc; - } - } - } else if (batches == 4) { - int16_t* output_ptr = output_data; - // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) - // so that just reinterpreting them as int8_t values is equivalent to - // subtracting 128 from them, thus implementing for free the subtraction of - // the zero_point value 128. - const int8_t* shuffled_weights_ptr = - reinterpret_cast(shuffled_weights_data); - // Likewise, we preshuffled and pre-xored the input data above. - const int8_t* shuffled_input_data = - reinterpret_cast(shuffled_input_workspace_data); - for (int c = 0; c < output_depth; c += 4) { - const int8_t* shuffled_input_ptr = shuffled_input_data; - // Accumulation loop. - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum[4][4]; - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - accum[i][b] = 0; - } - } - for (int d = 0; d < accum_depth; d += 16) { - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - for (int j = 0; j < 16; j++) { - int8_t input_val = shuffled_input_ptr[16 * b + j]; - int8_t weights_val = shuffled_weights_ptr[16 * i + j]; - accum[i][b] += weights_val * input_val; - } - } - } - shuffled_input_ptr += 64; - shuffled_weights_ptr += 64; - } - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - // Add bias value - int32_t acc = accum[i][b] + bias_data[c + i]; - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The - // quantized multiplier and shift here have been pre-computed offline - // (e.g. by toco). - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, - output_shift); - // Saturate, cast to int16_t, and store to output array. - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_ptr[b * output_depth + c + i] = acc; - } - } - } - } else { - TFLITE_DCHECK(false); - return; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/hard_swish.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/hard_swish.h deleted file mode 100644 index cda1b5cf..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/hard_swish.h +++ /dev/null @@ -1,166 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_ - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -inline int16_t SaturatingLeftShift(int16_t value, int amount) { - int32_t result = static_cast(value) * (1 << amount); - result = std::min(result, std::numeric_limits::max()); - result = std::max(result, std::numeric_limits::min()); - return result; -} - -// Similar to ARM instruction SQDMULH. -// Similar to gemmlowp::SaturatingRoundingDoublingHighMul except -// rounding to zero instead of to nearest (SQRDMULH). -inline std::int16_t SaturatingDoublingHighMul(std::int16_t a, std::int16_t b) { - bool overflow = a == b && a == std::numeric_limits::min(); - std::int32_t a_32(a); - std::int32_t b_32(b); - std::int32_t ab_32 = a_32 * b_32; - std::int16_t ab_x2_high16 = static_cast((ab_32) / (1 << 15)); - return overflow ? std::numeric_limits::max() : ab_x2_high16; -} - -template -inline void HardSwish(const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("ReferenceHardSwish/Float"); - auto matching_size = MatchingFlatSize(input_shape, output_shape); - const T* in_end = input_data + matching_size; - for (; input_data < in_end; input_data++, output_data++) { - const float in = *input_data; - *output_data = - in * std::min(static_cast(6), std::max(static_cast(0), in + 3)) / - 6; - } -} - -template -inline void HardSwish(const HardSwishParams& params, - const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("ReferenceHardSwish/Quantized"); - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const int16_t input_value = input_data[i] - params.input_zero_point; - // Left-shift as much as we can without overflow/saturation to put - // significant bits in the high bits of our 16-bit fixedpoint values, so - // that fixed-point approximate computations below are as accurate as - // possible. - const int16_t input_value_on_hires_input_scale = input_value * (1 << 7); - // Compute the input value on essentially the output scale, just not - // right-shifted yet. This is the value that we'll use in the (x >= +3) - // case, and that in the general case we'll multiply against the "relu-ish" - // fixed-point multiplier in [0, 1]. - const int16_t input_value_on_preshift_output_scale = - gemmlowp::SaturatingRoundingDoublingHighMul( - input_value_on_hires_input_scale, - params.output_multiplier_fixedpoint_int16); - // Now compute the "relu-ish multiplier". In the (-3 <= x <= +3) case, that - // is just an affine rescaling of x from [-3, 3] to [0, 1]. In the general - // case, it is just that plus saturation at the boundaries of [-3, 3]. - // First, we rescale from [-3, 3] to [-1, 1], saturating. - // That is done by rescaling the input value with a fixed-point multiplier - // (reluish_multiplier_fixedpoint) and bit-shift such that we represent - // that input value on the scale where the real value 3.0f is represented - // by the quantized value 32768. (+32768 is actually not representable as - // int16_t, so this saturates at +32767, and that is seen empirically to be - // a negligible contribution to numerical error/bias). - // - // This code is careful to correctly implement any magnitude of multiplier, - // involving either a right shift or a left shift, with correct saturation - // behavior in the left-shift case. This forces this code to be more - // complicated, but is necessary for real applications: a partially - // trained quantized MobileNet v3-small model that motivated this code - // exhibits some large [min, max] range boundaries, of the order of - // magnitude of 10 or 100 depending on layers. - // - // The next few lines are basically just an ordinary - // MultiplyByQuantizedMultiplier, except that we are more careful here - // about the fine details of saturation when left-shifting, because here - // overflow in left-shift is a common case, not an anomaly as - // MultiplyByQuantizedMultiplier assumes. - int16_t reluish_value = input_value_on_hires_input_scale; - // Shift left, saturating, as much as we can while ensuring that this - // saturation will not contribute to the result. That is, left shift amount - // reduced by 1. - if (params.reluish_multiplier_exponent > 0) { - reluish_value = SaturatingLeftShift( - reluish_value, params.reluish_multiplier_exponent - 1); - } - // Apply the fixed-point multiplier, dividing the value by a divisor - // ranging in [1, 2]. - reluish_value = gemmlowp::SaturatingRoundingDoublingHighMul( - reluish_value, params.reluish_multiplier_fixedpoint_int16); - // Apply the last bit of left-shift. Thus, in the left-shifting case, if - // any saturation affects the result, it is happening here --- any - // saturation having occurred above is overwritten here, not affecting the - // result. - if (params.reluish_multiplier_exponent > 0) { - reluish_value = SaturatingLeftShift(reluish_value, 1); - } - // Shift right, in the right-shifting case. - if (params.reluish_multiplier_exponent < 0) { - reluish_value = gemmlowp::RoundingDivideByPOT( - reluish_value, -params.reluish_multiplier_exponent); - } - // At this point we have rescaled the value into a 16bit fixedpoint - // reluish_value in [-1, 1]. - // We now convert that to a 16bit fixedpoint value in [0, 1]. - reluish_value = (reluish_value + (1 << 15)) >> 1; - // Use of SaturatingDoublingHighMul here is important to cancel the biases - // from the above SaturatingRoundingDoublingHighMul. - // - // On a partially trained MobileNet-v3-small, - // - // | bias on | ImageNet - // | quantized | Top-1 - // Operation used here | values | accuracy (50k) - // --------------------------------------+------------+----------- - // SaturatingDoublingHighMul | -0.0024 | 58.920 - // SaturatingRoundingDoublingHighMul | -0.0067 | 58.064 - // - // In activations_test, this is covered by this testcase: - // QuantizedActivationsOpTest.HardSwishBias - // - const int16_t preshift_output_value = SaturatingDoublingHighMul( - reluish_value, input_value_on_preshift_output_scale); - // We were so far operating on the pre-shift output scale. Now we finally - // apply that output shift, arriving at the final output scale. - int16_t output_value = gemmlowp::RoundingDivideByPOT( - preshift_output_value, -params.output_multiplier_exponent); - output_value += params.output_zero_point; - output_value = - std::min(output_value, std::numeric_limits::max()); - output_value = - std::max(output_value, std::numeric_limits::min()); - output_data[i] = output_value; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/add.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/add.h deleted file mode 100644 index 2af6f373..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/add.h +++ /dev/null @@ -1,145 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void CheckArithmeticParams(const ArithmeticParams& params) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - // Input offset is negative input zero point. Activation tensors are - // asymmetric quantized so they span the full int8 range. - TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); - TFLITE_DCHECK_GE(-params.input2_offset, std::numeric_limits::min()); - TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); - TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits::max()); -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. -inline void AddElementwise(int size, const ArithmeticParams& params, - const int8_t* input1_data, const int8_t* input2_data, - int8_t* output_data) { - CheckArithmeticParams(params); - - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int8_t* input1_data, - const RuntimeShape& input2_shape, const int8_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - CheckArithmeticParams(params); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void BroadcastAdd4DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int8_t* input1_data, - const RuntimeShape& input2_shape, - const int8_t* input2_data, - const RuntimeShape& output_shape, - int8_t* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - const int32_t input1_val = - params.input1_offset + - input1_data[SubscriptToIndex(desc1, b, y, x, c)]; - const int32_t input2_val = - params.input2_offset + - input2_data[SubscriptToIndex(desc2, b, y, x, c)]; - const int32_t shifted_input1_val = - input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = - input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, - params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, - params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[Offset(extended_output_shape, b, y, x, c)] = - static_cast(clamped_output); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h deleted file mode 100644 index f4bcb2bd..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h +++ /dev/null @@ -1,217 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -// Fixed-point per-channel-quantization convolution reference kernel. -inline void ConvPerChannel( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - const int32_t input_offset = params.input_offset; // r = s(q - Z) - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int32_t output_offset = params.output_offset; - - // Set min and max value of the output. - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Consistency check. - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Check dimensions of the tensors. - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we force - // real value of 0.0 be represented by a quantized value. This - // guarantees that the input_offset is a int8_t, even though - // it is represented using int32_t. int32_t += int8_t * - // (int8_t - int8_t) so the highest value we can get from each - // accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold as - // long as the filter size (filter_y * filter_x * in_channel) - // does not exceed 2^16, which is the case in all the models - // we have seen so far. - // TODO(jianlijianli): Add a check to make sure the - // accumulator depth is smaller than 2^16. - acc += filter_val * (input_val + input_offset); - } - } - } - } - - if (bias_data) { - acc += bias_data[out_channel]; - } - acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(acc); - } - } - } - } -} - -// Fixed-point per-channel-quantization convolution reference kernel. -// 16-bit data and 8-bit filter -inline void ConvPerChannel( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const std::int64_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - // Get parameters. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - - // Set min and max value of the output. - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Consistency check. - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Check dimensions of the tensors. - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - std::int64_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - // Accumulate with 64 bits accumulator. - // int64_t += int8_t * int16_t so the highest value we can - // get from each accumulation is [-127, 127] * ([-32768, - // 32767] - - // [-32768, 32767]), which is [-8322945, 8322945]. - // log2(8322945) = 22.99. - acc += filter_val * input_val; - } - } - } - } - if (bias_data) { - acc += bias_data[out_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(scaled_acc); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h deleted file mode 100644 index 6f54e47f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h +++ /dev/null @@ -1,289 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { -inline void DepthwiseConvPerChannel( - const DepthwiseParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we force - // real value of 0.0 be represented by a quantized value. This - // guarantees that the input_offset is a int8_t, even though - // it is represented using int32_t. int32_t += int8_t * - // (int8_t - int8_t) so the highest value we can get from each - // accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold as - // long as the filter size (filter_y * filter_x * in_channel) - // does not exceed 2^16, which is the case in all the models - // we have seen so far. - // TODO(jianlijianli): Add a check to make sure the - // accumulator depth is smaller than 2^16. - acc += filter_val * (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = static_cast(acc); - } - } - } - } - } -} - -inline void DepthwiseConvPerChannel( - const DepthwiseParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const std::int64_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - // Get parameters. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - std::int64_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 64 bits accumulator. - // We assume maximum of 2^16 accumulations as with the 8-bit - // case so actually the value in the accumulator should not - // exceed 40 bits - acc += static_cast(filter_val) * - static_cast(input_val); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = - static_cast(scaled_acc); - } - } - } - } - } -} - -inline void DepthwiseConvHybridPerChannel( - const DepthwiseParams& params, float* scaling_factors_ptr, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const float* bias_data, - const RuntimeShape& output_shape, float* output_data, - const float* per_channel_scale, int32_t* input_offset) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int bias_depth = bias_shape.FlatSize(); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_depth, output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - acc += filter_val * (input_val - input_offset[batch]); - } - } - } - float acc_float = static_cast(acc); - acc_float *= - per_channel_scale[output_channel] * scaling_factors_ptr[batch]; - if (bias_data && output_channel < bias_depth) { - acc_float += bias_data[output_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = - ActivationFunctionWithMinMax(acc_float, output_activation_min, - output_activation_max); - } - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h deleted file mode 100644 index 2bc3e794..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h +++ /dev/null @@ -1,108 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = output_shape.Dims(0); - const int output_depth = output_shape.Dims(1); - TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - int32_t acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += (filter_val + filter_offset) * (input_val + input_offset); - } - if (bias_data) { - acc += bias_data[out_c]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[out_c + output_depth * b] = static_cast(acc); - } - } -} - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int64_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - const int32_t filter_offset = params.weights_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = output_shape.Dims(0); - const int output_depth = output_shape.Dims(1); - TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - int64_t acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += (filter_val + filter_offset) * input_val; - } - if (bias_data) { - acc += bias_data[out_c]; - } - int32_t acc_scaled = - MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc_scaled = std::max(acc_scaled, output_activation_min); - acc_scaled = std::min(acc_scaled, output_activation_max); - output_data[out_c + output_depth * b] = static_cast(acc_scaled); - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h deleted file mode 100644 index 31f2de98..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void L2Normalization(int32_t input_zero_point, int32_t outer_size, - int32_t depth, const int8_t* input_data, - int8_t* output_data) { - static constexpr int8_t kMinInt8 = std::numeric_limits::min(); - static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); - // The output scale must be in sync with Prepare(). - // Output is in 1/128 scale so the actual output range is nudged from [-1, 1] - // to [-1, 127/128]. - static constexpr int32_t kOutputScale = 7; - for (int outer_index = 0; outer_index < outer_size; ++outer_index) { - // int32_t = (int8_t - int8_t) ^ 2. - // ([-128, 127] - [-128, 127]) ^ 2 = [0, (2^8 - 1)^2] so the accumulator is - // safe from overflowing in at least 2^16 steps. - int32_t acc = 0; - for (int inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input = - input_data[depth * outer_index + inner_index] - input_zero_point; - acc += input * input; - } - int32_t inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplierExp(acc, kReverseShift, &inv_l2norm_multiplier, - &inv_l2norm_shift); - - for (int inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input = - input_data[depth * outer_index + inner_index] - input_zero_point; - - // Rescale and downcast. Rescale is folded into the division. - int32_t output_in_q24 = MultiplyByQuantizedMultiplier( - input, inv_l2norm_multiplier, inv_l2norm_shift + kOutputScale); - output_in_q24 = - std::min(static_cast(kMaxInt8), - std::max(static_cast(kMinInt8), output_in_q24)); - output_data[depth * outer_index + inner_index] = - static_cast(output_in_q24); - } - } -} -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h deleted file mode 100644 index e315683c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h +++ /dev/null @@ -1,99 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ - -#include -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void Logistic(int32_t input_zero_point, int32_t input_range_radius, - int32_t input_multiplier, int32_t input_left_shift, - int32_t input_size, const int8_t* input_data, - int8_t* output_data) { - // Integer bits must be in sync with Prepare() function. - static constexpr int32_t kInputIntegerBits = 4; - static constexpr int32_t kOutputIntegerBits = 8; - static constexpr int8_t kMinInt8 = std::numeric_limits::min(); - static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); - static constexpr int32_t kOutputZeroPoint = -128; - - for (int i = 0; i < input_size; ++i) { - const int32_t input = - static_cast(input_data[i]) - input_zero_point; - if (input <= -input_range_radius) { - output_data[i] = kMinInt8; - } else if (input >= input_range_radius) { - output_data[i] = kMaxInt8; - } else { - const int32_t input_in_q4 = MultiplyByQuantizedMultiplier( - input, input_multiplier, input_left_shift); - using FixedPoint4 = gemmlowp::FixedPoint; - const int32_t output_in_q0 = - gemmlowp::logistic(FixedPoint4::FromRaw(input_in_q4)).raw(); - - // Rescale and downcast. - using gemmlowp::RoundingDivideByPOT; - int32_t output_in_q23 = - RoundingDivideByPOT(output_in_q0, 31 - kOutputIntegerBits); - output_in_q23 = std::min(std::max(output_in_q23 + kOutputZeroPoint, - static_cast(kMinInt8)), - static_cast(kMaxInt8)); - output_data[i] = static_cast(output_in_q23); - } - } -} - -inline void Logistic(int32_t input_multiplier, int32_t input_size, - const int16_t* ptr_input_data, int16_t* ptr_output_data) { - // We use the LUT for sigmoid and take into account, that - // tanh(x) = 2*sigmoid(2*x) - 1 - - int32_t input_data_mul = (input_multiplier > 0) ? input_multiplier : 1; - - for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) { - int32_t input_data = (*ptr_input_data) * input_data_mul; - - // Scale by 3/4 to expand range [-8,8]->[-10.7,10.7] and - // we do interpolation on unsigned values. - uint32_t abs_input_data = 3 * abs(input_data); - - // We divide by 2 power of 9, because - // we need to divide by 2 in power of 7 for - // the input conversion + 1/4 from the scale above. - uint8_t uh = abs_input_data >> 9; - uint32_t ua = sigmoid_table_uint16[uh]; - uint32_t ub = sigmoid_table_uint16[uh + 1]; - uint32_t ut = abs_input_data & 0x1ff; - - // Interpolation is done using the fractional bit. - uint32_t result = (ua << 9) + ut * (ub - ua); - - result = (input_data >= 0) ? (result + (1 << 9)) - : ((1 << (16 + 9)) - result + (1 << 9) - 1); - - // Back to 16-bit. - result >>= 10; - - *ptr_output_data = result; - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h deleted file mode 100644 index b80838aa..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h +++ /dev/null @@ -1,131 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ - -#include "fixedpoint/fixedpoint.h" -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -template -inline void MulElementwise(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data) { - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[i] = static_cast(clamped_output); - } -} - -template -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - ruy::profiler::ScopeLabel label("Mul/8bit"); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - MulElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -// Mul with 16 bit inputs and int8_t outputs. -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int16_t* input1_data, - const RuntimeShape& input2_shape, const int16_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - ruy::profiler::ScopeLabel label("Mul/Int16Int8"); - int32_t output_offset = params.output_offset; - int32_t output_activation_min = params.quantized_activation_min; - int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - using F0 = gemmlowp::FixedPoint; - - F0 unclamped_result = - F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); - int16_t rescaled_result = - gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); - int16_t clamped_result = std::min( - output_activation_max - output_offset, rescaled_result); - clamped_result = std::max(output_activation_min - output_offset, - clamped_result); - output_data[i] = output_offset + clamped_result; - } -} - -template -inline void BroadcastMul4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("BroadcastMul4DSlow"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - // The input shapes are extended as part of NdArrayDesc initialization. - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - const int32_t input1_val = - params.input1_offset + - input1_data[SubscriptToIndex(desc1, b, y, x, c)]; - const int32_t input2_val = - params.input2_offset + - input2_data[SubscriptToIndex(desc2, b, y, x, c)]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = std::min( - params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[Offset(extended_output_shape, b, y, x, c)] = - static_cast(clamped_output); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h deleted file mode 100644 index 17944bc4..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h +++ /dev/null @@ -1,258 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ - -#include -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const int8_t* input_data, - const RuntimeShape& output_shape, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - // Round to the closest integer value. - acc = acc > 0 ? (acc + filter_count / 2) / filter_count - : (acc - filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& output_shape, - int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, - std::numeric_limits::min()); - TFLITE_DCHECK_LE(params.quantized_activation_max, - std::numeric_limits::max()); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int8_t max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} - -inline void AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const int16_t* input_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - // Round to the closest integer value. - acc = acc > 0 ? (acc + filter_count / 2) / filter_count - : (acc - filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, - std::numeric_limits::min()); - TFLITE_DCHECK_LE(params.quantized_activation_max, - std::numeric_limits::max()); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int16_t max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h deleted file mode 100644 index baae65ab..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h +++ /dev/null @@ -1,106 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { -namespace reference_integer_ops { - -inline void Tanh(int32_t input_zero_point, int32_t input_range_radius, - int32_t input_multiplier, int32_t input_shift, - int32_t input_size, const int8_t* input_data, - int8_t* output_data) { - // Integer bits must be in sync with Prepare() function. - static constexpr int32_t kInputIntegerBits = 4; - static constexpr int32_t kOutputScale = 7; - static constexpr int32_t kMinInt8 = std::numeric_limits::min(); - static constexpr int32_t kMaxInt8 = std::numeric_limits::max(); - using F4 = gemmlowp::FixedPoint; - - for (int i = 0; i < input_size; ++i) { - const int32_t input = - static_cast(input_data[i]) - input_zero_point; - if (input <= -input_range_radius) { - output_data[i] = kMinInt8; - } else if (input >= input_range_radius) { - output_data[i] = kMaxInt8; - } else { - const int32_t input_in_q4 = - MultiplyByQuantizedMultiplier(input, input_multiplier, input_shift); - const int32_t output_in_q0 = - gemmlowp::tanh(F4::FromRaw(input_in_q4)).raw(); - - // Rescale and downcast. - using gemmlowp::RoundingDivideByPOT; - int32_t output_in_q24 = - RoundingDivideByPOT(output_in_q0, 31 - kOutputScale); - output_in_q24 = std::min(std::max(output_in_q24, kMinInt8), kMaxInt8); - output_data[i] = static_cast(output_in_q24); - } - } -} - -inline void Tanh(int32_t input_multiplier, int32_t input_left_shift, - int32_t input_size, const int16_t* ptr_input_data, - int16_t* ptr_output_data) { - // We use the LUT for sigmoid and take into account, that - // tanh(x) = 2*sigmoid(2*x) - 1 - - int32_t input_data_mul = (input_multiplier > 0) ? input_multiplier : 1; - - for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) { - int32_t input_data = (*ptr_input_data) * input_data_mul; - - if (input_left_shift == 1) { - input_data <<= 1; - } - - // Scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. - uint32_t abs_input_data = 3 * abs(input_data); - uint32_t uh = abs_input_data >> 8; - int32_t result; - - if (uh >= 255) { - // Saturate to maximum. - result = 0xFFFF << 8; - } else { - uint32_t ua = sigmoid_table_uint16[uh]; - uint32_t ub = sigmoid_table_uint16[uh + 1]; - - uint8_t ut = abs_input_data & 0xFF; - - result = (ua << 8) + ut * (ub - ua); - } - - result = (input_data >= 0) - ? (result - (1 << (14 + 9)) + (1 << (9 - 2))) - : (-result + (1 << (14 + 9)) + (1 << (9 - 2)) - 1); - - // Convert back to 16-bit. - result >>= (9 - 1); - - *ptr_output_data = result; - } -} - -} // namespace reference_integer_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/l2normalization.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/l2normalization.h deleted file mode 100644 index 7587d2b5..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/l2normalization.h +++ /dev/null @@ -1,90 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline void L2Normalization(const tflite::L2NormalizationParams& op_params, - const RuntimeShape& input_shape, - const float* input_data, - const RuntimeShape& output_shape, - float* output_data, float epsilon = 1e-6) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - for (int i = 0; i < outer_size; ++i) { - float squared_l2_norm = 0; - for (int c = 0; c < depth; ++c) { - const float val = input_data[depth * i + c]; - squared_l2_norm += val * val; - } - float l2_norm = std::sqrt(squared_l2_norm); - l2_norm = std::max(l2_norm, epsilon); - for (int c = 0; c < depth; ++c) { - output_data[depth * i + c] = input_data[depth * i + c] / l2_norm; - } - } -} - -inline void L2Normalization(const tflite::L2NormalizationParams& op_params, - const RuntimeShape& input_shape, - const uint8_t* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int32_t input_zero_point = op_params.input_zero_point; - - for (int i = 0; i < outer_size; ++i) { - int32_t square_l2_norm = 0; - for (int c = 0; c < depth; c++) { - int32_t diff = input_data[depth * i + c] - input_zero_point; - square_l2_norm += diff * diff; - } - int32_t inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift, - &inv_l2norm_multiplier, &inv_l2norm_shift); - for (int c = 0; c < depth; c++) { - int32_t diff = input_data[depth * i + c] - input_zero_point; - int32_t rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( - 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); - int32_t unclamped_output_val = 128 + rescaled_diff; - int32_t output_val = - std::min(static_cast(255), - std::max(static_cast(0), unclamped_output_val)); - output_data[depth * i + c] = static_cast(output_val); - } - } -} - -} // namespace reference_ops -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/logistic.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/logistic.h deleted file mode 100644 index 64b7133b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/logistic.h +++ /dev/null @@ -1,132 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite { -namespace reference_ops { - -inline void Logistic(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const float cutoff_upper = 16.619047164916992188f; - const float cutoff_lower = -9.f; - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // Rational for using approximation in reference kernel. - // 0. This approximation gives enough precision for float. - // 1. This works around an issue on an embedded chipset where exp() does not - // return correctly as expected - exp(x) should return inf when overflown - // not 1.701417 IEEE 754 defines representation for inf. - // 2. This will speed up calculation and is matching the behavior in the - // optimized kernels. (check the definition of scalar_logistic_op) - - for (int i = 0; i < flat_size; i++) { - float val = input_data[i]; - float result; - if (val > cutoff_upper) { - result = 1.0f; - } else if (val < cutoff_lower) { - result = std::exp(val); - } else { - result = 1.f / (1.f + std::exp(-val)); - } - output_data[i] = result; - } -} - -// Convenience version that allows, for example, generated-code calls to be -// uniform between data types. -inline void Logistic(const LogisticParams&, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - // Drop params: not needed. - Logistic(input_shape, input_data, output_shape, output_data); -} - -inline void Logistic(const LogisticParams& params, - const RuntimeShape& input_shape, const int16_t* input_data, - const RuntimeShape& output_shape, int16_t* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - // This is the return type of math functions such as tanh, logistic, - // whose range is in [-1, 1]. - using F0 = gemmlowp::FixedPoint; - // F3 uses 3 integer bits, range [-8, 8], the input range expected here. - using F3 = gemmlowp::FixedPoint; - - const F3 input = F3::FromRaw(input_data[i]); - F0 output = gemmlowp::logistic(input); - output_data[i] = output.raw(); - } -} - -// Quantized int8_t logistic activation. Cheats by dequantizing and -// requantizing around the floating point logistic method. This implementation -// is slow on platforms without a floating point unit. - -// TODO(b/141211002): Delete this int8_t implementation once we can reuse the -// approach used in TFLite for int8_t Logistic. -inline void Logistic(const RuntimeShape& input_shape, const int8_t* input_data, - float input_scale, int input_zero_point, - const RuntimeShape& output_shape, int8_t* output_data, - float output_scale, int output_zero_point) { - const float cutoff_upper = 16.619047164916992188f; - const float cutoff_lower = -9.f; - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // Rational for using approximation in reference kernel. - // 0. This approximation gives enough precision for float. - // 1. This works around an issue on an embedded chipset where exp() does not - // return correctly as expected - exp(x) should return inf when overflown - // not 1.701417 IEEE 754 defines representation for inf. - // 2. This will speed up calculation and is matching the behavior in the - // optimized kernels. (check the definition of scalar_logistic_op) - - for (int i = 0; i < flat_size; i++) { - // Dequantize. - float val = - static_cast((input_data[i] - input_zero_point) * input_scale); - float result; - if (val > cutoff_upper) { - result = 1.0f; - } else if (val < cutoff_lower) { - result = std::exp(val); - } else { - result = 1.f / (1.f + std::exp(-val)); - } - // Requantize - int8_t output = - static_cast(result / output_scale + output_zero_point); - output_data[i] = output; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/maximum_minimum.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/maximum_minimum.h deleted file mode 100644 index cd11b419..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/maximum_minimum.h +++ /dev/null @@ -1,64 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -template -void MaximumMinimumBroadcastSlow(const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const T* input2_data, - const RuntimeShape& unextended_output_shape, - T* output_data, Op op) { - // Uses element-wise calculation if broadcast is not required. - if (unextended_input1_shape == unextended_input2_shape) { - const int flat_size = - MatchingElementsSize(unextended_input1_shape, unextended_input2_shape, - unextended_output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = op(input1_data[i], input2_data[i]); - } - } else { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); - - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast( - unextended_input1_shape, unextended_input2_shape, &desc1, &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), - &output_desc); - - auto maxmin_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - op(input1_data[SubscriptToIndex(desc1, indexes)], - input2_data[SubscriptToIndex(desc2, indexes)]); - }; - NDOpsHelper(output_desc, maxmin_func); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/mul.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/mul.h deleted file mode 100644 index 0578b81b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/mul.h +++ /dev/null @@ -1,166 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite { - -namespace reference_ops { - -// Element-wise mul that can often be used for inner loop of broadcast Mul as -// well as the non-broadcast Mul. -inline void MulElementwise(int size, const ArithmeticParams& params, - const uint8_t* input1_data, - const uint8_t* input2_data, uint8_t* output_data) { - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[i] = static_cast(clamped_output); - } -} - -template -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, &output_activation_max); - - const int flat_size = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] * input2_data[i], output_activation_min, - output_activation_max); - } -} - -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - - MulElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void BroadcastMul4DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const uint8_t* input1_data, - const RuntimeShape& input2_shape, - const uint8_t* input2_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - const int32_t input1_val = - params.input1_offset + - input1_data[SubscriptToIndex(desc1, b, y, x, c)]; - const int32_t input2_val = - params.input2_offset + - input2_data[SubscriptToIndex(desc2, b, y, x, c)]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = std::min( - params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[Offset(extended_output_shape, b, y, x, c)] = - static_cast(clamped_output); - } - } - } - } -} - -template -void BroadcastMul4DSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const T* input2_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, &output_activation_max); - - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < output_shape.Dims(0); ++b) { - for (int y = 0; y < output_shape.Dims(1); ++y) { - for (int x = 0; x < output_shape.Dims(2); ++x) { - for (int c = 0; c < output_shape.Dims(3); ++c) { - output_data[Offset(output_shape, b, y, x, c)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, b, y, x, c)] * - input2_data[SubscriptToIndex(desc2, b, y, x, c)], - output_activation_min, output_activation_max); - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/neg.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/neg.h deleted file mode 100644 index e127883f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/neg.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -template -inline void Negate(const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = -input_data[i]; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pad.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pad.h deleted file mode 100644 index 2a040cef..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pad.h +++ /dev/null @@ -1,162 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// TFLite Pad supports activation tensors with up to 4 dimensions. -constexpr int PadKernelMaxDimensionCount() { return 4; } - -// There are two versions of pad: Pad and PadV2. In PadV2 there is a second -// scalar input that provides the padding value. Therefore pad_value_ptr can be -// equivalent to a simple input1_data. For Pad, it should point to a zero -// value. -// -// Note that two typenames are required, so that T=P=int32_t is considered a -// specialization distinct from P=int32_t. -template -inline void PadImpl(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - const RuntimeShape ext_input_shape = - RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), input_shape); - const RuntimeShape ext_output_shape = - RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), output_shape); - TFLITE_DCHECK_LE(op_params.left_padding_count, PadKernelMaxDimensionCount()); - TFLITE_DCHECK_LE(op_params.right_padding_count, PadKernelMaxDimensionCount()); - - // Runtime calls are currently fixed at 4 dimensions. Copy inputs so we can - // pad them to 4 dims (yes, we are "padding the padding"). - int left_padding_copy[PadKernelMaxDimensionCount()]; - for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { - left_padding_copy[i] = 0; - } - for (int i = 0; i < op_params.left_padding_count; ++i) { - left_padding_copy[i + PadKernelMaxDimensionCount() - - op_params.left_padding_count] = op_params.left_padding[i]; - } - int right_padding_copy[PadKernelMaxDimensionCount()]; - for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { - right_padding_copy[i] = 0; - } - for (int i = 0; i < op_params.right_padding_count; ++i) { - right_padding_copy[i + PadKernelMaxDimensionCount() - - op_params.right_padding_count] = - op_params.right_padding[i]; - } - - const int output_batch = ext_output_shape.Dims(0); - const int output_height = ext_output_shape.Dims(1); - const int output_width = ext_output_shape.Dims(2); - const int output_depth = ext_output_shape.Dims(3); - - const int left_b_padding = left_padding_copy[0]; - const int left_h_padding = left_padding_copy[1]; - const int left_w_padding = left_padding_copy[2]; - const int left_d_padding = left_padding_copy[3]; - - const int right_b_padding = right_padding_copy[0]; - const int right_h_padding = right_padding_copy[1]; - const int right_w_padding = right_padding_copy[2]; - const int right_d_padding = right_padding_copy[3]; - - const T pad_value = *pad_value_ptr; - - const T* in_ptr = input_data; - T* out_ptr = output_data; - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - if (out_b < left_b_padding || - out_b >= output_batch - right_b_padding || - out_h < left_h_padding || - out_h >= output_height - right_h_padding || - out_w < left_w_padding || - out_w >= output_width - right_w_padding || - out_d < left_d_padding || - out_d >= output_depth - right_d_padding) { - *out_ptr++ = pad_value; - } else { - *out_ptr++ = *in_ptr++; - } - } - } - } - } -} - -template -inline void Pad(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -// The second (pad-value) input can be int32_t when, say, the first is uint8_t. -template -inline void Pad(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const int32_t* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - const T converted_pad_value = static_cast(*pad_value_ptr); - PadImpl(op_params, input_shape, input_data, &converted_pad_value, - output_shape, output_data); -} - -// This version avoids conflicting template matching. -template <> -inline void Pad(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, const int32_t* input_data, - const int32_t* pad_value_ptr, const RuntimeShape& output_shape, - int32_t* output_data) { - PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -template -inline void PadImageStyle(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, - const RuntimeShape& output_shape, T* output_data) { - Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -template -inline void PadImageStyle(const tflite::PadParams& op_params, - const RuntimeShape& input_shape, - const float* input_data, const P* pad_value_ptr, - const RuntimeShape& output_shape, - float* output_data) { - Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pooling.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pooling.h deleted file mode 100644 index 0872f521..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/pooling.h +++ /dev/null @@ -1,297 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -inline void AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float total = 0.f; - float filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - total += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - const float average = total / filter_count; - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(average, params.float_activation_min, - params.float_activation_max); - } - } - } - } -} - -inline void AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const uint8_t* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - acc = (acc + filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } -} - -inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float sum_squares = 0.f; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - const float val = - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - sum_squares += val * val; - filter_count++; - } - } - const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(l2pool_result, - params.float_activation_min, - params.float_activation_max); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(max, params.float_activation_min, - params.float_activation_max); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, 0); - TFLITE_DCHECK_LE(params.quantized_activation_max, 255); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - uint8_t max = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/prelu.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/prelu.h deleted file mode 100644 index 02db5174..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/prelu.h +++ /dev/null @@ -1,109 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// Broadcast prelu to output_shape for quantized uint8_t/int8_t data. -template -inline void BroadcastPrelu4DSlow( - const PreluParams& params, const RuntimeShape& input_shape, - const T* input_data, const RuntimeShape& alpha_shape, const T* alpha_data, - const RuntimeShape& output_shape, T* output_data) { - TFLITE_DCHECK_LE(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(alpha_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 4); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input_shape, alpha_shape, &desc1, &desc2); - - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - int output_index = Offset(extended_output_shape, b, y, x, c); - int input_index = SubscriptToIndex(desc1, b, y, x, c); - const int32_t input_value = - params.input_offset + input_data[input_index]; - int32_t output_value; - if (input_value >= 0) { - output_value = MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_1, params.output_shift_1); - } else { - auto alpha_index = SubscriptToIndex(desc2, b, y, x, c); - const int32_t alpha_value = - params.alpha_offset + alpha_data[alpha_index]; - - output_value = MultiplyByQuantizedMultiplier( - input_value * alpha_value, params.output_multiplier_2, - params.output_shift_2); - } - output_value += params.output_offset; - - const int32_t quantized_min = std::numeric_limits::min(); - const int32_t quantized_max = std::numeric_limits::max(); - const int32_t clamped_output = - std::min(quantized_max, std::max(quantized_min, output_value)); - output_data[output_index] = static_cast(clamped_output); - } - } - } - } -} - -template -inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape, - const T* input_data, const RuntimeShape& alpha_shape, - const T* alpha_data, const RuntimeShape& output_shape, - T* output_data) { - const int32_t quantized_min = std::numeric_limits::min(); - const int32_t quantized_max = std::numeric_limits::max(); - - const int flat_size = - MatchingElementsSize(input_shape, alpha_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const int32_t input_value = params.input_offset + input_data[i]; - int32_t output_value; - if (input_value >= 0) { - output_value = MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_1, params.output_shift_1); - } else { - const int32_t alpha_value = params.alpha_offset + alpha_data[i]; - - output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value, - params.output_multiplier_2, - params.output_shift_2); - } - output_value += params.output_offset; - - const int32_t clamped_output = - std::min(quantized_max, std::max(quantized_min, output_value)); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h deleted file mode 100644 index 40f779c5..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h +++ /dev/null @@ -1,138 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// Consolidates dimensions in broadcast inputs, checks for five-fold pattern. -// -// For example, if sequence of dimensions of one input is -// ..., 1, 3, 1, 7, 9, 5,... and the other is ..., 2, 3, 1, 7, 1, 1, ... -// we can consolidate these as -// ..., 1, 3*7, 9*5, ... and 2, 3*7, 1. -// -// The category is updated in the less-frequent case of shapes that are -// not suited to a fivefold-loop broadcast. -// -// Falls back to generic pattern when it does not know how to process properly. -// -// Returns true iff there is some sort of broadcast, which includes five-fold -// patterns and falling back to generic broadcast. -inline bool ProcessBroadcastShapes(const RuntimeShape& shape0, - const RuntimeShape& shape1, - tflite::ArithmeticParams* params) { - const int dims_count = - std::max(shape0.DimensionsCount(), shape1.DimensionsCount()); - - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - RuntimeShape scalar_shape(dims_count, 1); - - auto extended_shape0 = RuntimeShape::ExtendedShape(dims_count, shape0); - auto extended_shape1 = RuntimeShape::ExtendedShape(dims_count, shape1); - - // Check for "exact" match, implicitly accepting any scalar shapes. - if (extended_shape0 == extended_shape1) { - params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; - return false; - } - - for (int i = dims_count - 1; i >= 0; --i) { - if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) { - continue; - } else if (extended_shape0.Dims(i) == 1) { - params->broadcast_category = - BroadcastableOpCategory::kFirstInputBroadcastsFast; - break; - } else if (extended_shape1.Dims(i) == 1) { - params->broadcast_category = - BroadcastableOpCategory::kSecondInputBroadcastsFast; - break; - } else { - // This case is erroneous: there is a dimension that does not match and - // is not a broadcast from one shape to the other. - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - return true; - } - } - - if (params->broadcast_category != - BroadcastableOpCategory::kFirstInputBroadcastsFast && - params->broadcast_category != - BroadcastableOpCategory::kSecondInputBroadcastsFast) { - // This is unreachable because at least one else clause in the above loop - // must be reached. - TFLITE_DCHECK(false); - params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; - return false; - } - - // From this point it is assumed contractually that corresponding dimensions - // in shape0 and shape1 are either (a) equal or (b) one or other equals 1. - const bool swap_inputs = params->broadcast_category == - BroadcastableOpCategory::kSecondInputBroadcastsFast; - const RuntimeShape* shape_a = - swap_inputs ? &extended_shape1 : &extended_shape0; - const RuntimeShape* shape_b = - swap_inputs ? &extended_shape0 : &extended_shape1; - - int i = dims_count - 1; - params->broadcast_shape[0] = 1; - params->broadcast_shape[1] = 1; - params->broadcast_shape[2] = 1; - params->broadcast_shape[3] = 1; - params->broadcast_shape[4] = 1; - // y_0 is greedy: include dims if both or neither equal 1: in other words, - // test for equality rather than (shape_a->Dims(i) != 1). - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[4] *= shape_b->Dims(i); - --i; - } - // Here either input_a or input_b has dim of 1 (if i >= 0). If it is input_b - // that has the unit dimension, the next two loops are not entered. - while (i >= 0 && shape_a->Dims(i) == 1) { - params->broadcast_shape[3] *= shape_b->Dims(i); - --i; - } - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[2] *= shape_a->Dims(i); - --i; - } - // Here either input_a or input_b has dim of 1 (if i >= 0). - while (i >= 0 && shape_b->Dims(i) == 1) { - params->broadcast_shape[1] *= shape_a->Dims(i); - --i; - } - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[0] *= shape_b->Dims(i); - --i; - } - - // Rarer case is when the broadcast dimensions cannot be handled by a fivefold - // loop. - if (i >= 0) { - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - } - return true; -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/quantize.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/quantize.h deleted file mode 100644 index 6f3f9aeb..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/quantize.h +++ /dev/null @@ -1,55 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -template -inline void AffineQuantize(const tflite::QuantizationParams& op_params, - const RuntimeShape& input_shape, - const InputT* input_data, - const RuntimeShape& output_shape, - OutputT* output_data) { - const int32_t zero_point = op_params.zero_point; - const double scale = op_params.scale; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - static constexpr int32_t min_val = std::numeric_limits::min(); - static constexpr int32_t max_val = std::numeric_limits::max(); - - for (int i = 0; i < flat_size; i++) { - const InputT val = input_data[i]; - int32_t unclamped = - static_cast(TfLiteRound(val / static_cast(scale))) + - zero_point; - int32_t clamped = std::min(std::max(unclamped, min_val), max_val); - output_data[i] = clamped; - } -} - -} // namespace reference_ops - -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/reduce.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/reduce.h deleted file mode 100644 index 597d015d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/reduce.h +++ /dev/null @@ -1,405 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/max.h" -#include "tensorflow/lite/kernels/internal/min.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -// A generic reduce method that can be used for reduce_sum, reduce_mean, etc. -// This method iterates through input data and reduce elements along the -// dimensions given in axis. -template -inline bool Reduce(const In* input_data, const int* input_dims, - const int* output_dims, const int input_num_dims, - const int output_num_dims, const int* axis, - const int num_axis, int* input_iter, - Out reducer(const Out current, const In in), - Out* output_data) { - // Reset input iterator. - for (int idx = 0; idx < input_num_dims; ++idx) { - input_iter[idx] = 0; - } - // Iterate through input_data. - do { - size_t input_offset = - ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, - input_iter, num_axis, axis); - output_data[output_offset] = - reducer(output_data[output_offset], input_data[input_offset]); - } while (NextIndex(input_num_dims, input_dims, input_iter)); - return true; -} - -// This method parses the input 'axis' to remove duplicates and handle negative -// values, and returns a valid 'out_axis' -inline bool ResolveAxis(const int num_dims, const int* axis, - const int64_t num_axis, int* out_axis, - int* out_num_axis) { - *out_num_axis = 0; // Just in case. - // Short-circuit axis resolution for scalars; the axis will go unused. - if (num_dims == 0) { - return true; - } - // o(n^2) is fine since out_num_axis should be really small, mostly <= 4 - for (int64_t idx = 0; idx < num_axis; ++idx) { - // Handle negative index. A positive index 'p_idx' can be represented as a - // negative index 'n_idx' as: n_idx = p_idx-num_dims - // eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1] */ - int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx]; - TFLITE_DCHECK(current >= 0 && current < num_dims); - bool is_dup = false; - for (int j = 0; j < *out_num_axis; ++j) { - if (out_axis[j] == current) { - is_dup = true; - break; - } - } - if (!is_dup) { - out_axis[*out_num_axis] = current; - *out_num_axis += 1; - } - } - return true; -} - -// This method expects that output_data has been initialized. -template -inline bool ReduceSumImpl(const In* input_data, const int* input_dims, - const int* output_dims, const int input_num_dims, - const int output_num_dims, const int* axis, - const int num_axis, int* input_iter, - Out* output_data) { - auto reducer = [](const Out current, const In in) -> Out { - const Out actual_in = static_cast(in); - return current + actual_in; - }; - return Reduce(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, axis, num_axis, input_iter, reducer, - output_data); -} - -template -inline bool InitTensorDataForReduce(const int* dims, const int num_dims, - const T init_value, T* data) { - size_t num_elements = 1; - for (int idx = 0; idx < num_dims; ++idx) { - size_t current = static_cast(dims[idx]); - // Overflow prevention. - if (num_elements > std::numeric_limits::max() / current) { - return false; - } - num_elements *= current; - } - for (size_t idx = 0; idx < num_elements; ++idx) { - data[idx] = init_value; - } - return true; -} - -// Computes the generic value (i.e., sum/max/min/prod) of elements across -// dimensions given in axis. It needs to pass in init_value and reducer. -template -inline bool ReduceGeneric(const T* input_data, const int* input_dims, - const int input_num_dims, T* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int64_t num_axis_dimensions, - bool keep_dims, int* temp_index, int* resolved_axis, - T init_value, - T reducer(const T current, const T in)) { - // Reset output data. - if (!InitTensorDataForReduce(output_dims, output_num_dims, init_value, - output_data)) { - return false; - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - return Reduce(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, reducer, output_data); -} - -// Computes the mean of elements across dimensions given in axis. -// It does so in two stages, first calculates the sum of elements along the axis -// then divides it by the number of element in axis. -template -inline bool Mean(const T* input_data, const int* input_dims, - const int input_num_dims, T* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int num_axis_dimensions, bool keep_dims, - int* temp_index, int* resolved_axis, U* temp_sum) { - ruy::profiler::ScopeLabel label("Mean"); - // Reset output data. - size_t num_outputs = 1; - for (int idx = 0; idx < output_num_dims; ++idx) { - size_t current = static_cast(output_dims[idx]); - // Overflow prevention. - if (num_outputs > std::numeric_limits::max() / current) { - return false; - } - num_outputs *= current; - } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = T(); - temp_sum[idx] = U(); - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, temp_sum)) { - return false; - } - - // Calculate mean by dividing output_data by num of aggregated element. - U num_elements_in_axis = 1; - for (int idx = 0; idx < num_resolved_axis; ++idx) { - size_t current = static_cast(input_dims[resolved_axis[idx]]); - // Overflow prevention. - if (current > (std::numeric_limits::max() / num_elements_in_axis)) { - return false; - } - num_elements_in_axis *= current; - } - - if (num_elements_in_axis > 0) { - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = - static_cast(temp_sum[idx] / static_cast(num_elements_in_axis)); - } - } - return true; -} - -template -inline void Mean(const tflite::MeanParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("Mean4D"); - - // Current implementation only supports dimension equals 4 and simultaneous - // reduction over width and height. - TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4); - TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - const int output_batch = output_shape.Dims(0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int output_depth = output_shape.Dims(3); - - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - - TFLITE_CHECK_EQ(op_params.axis_count, 2); - TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) || - (op_params.axis[0] == 2 && op_params.axis[1] == 1)); - TFLITE_CHECK_EQ(output_height, 1); - TFLITE_CHECK_EQ(output_width, 1); - - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - float value = 0; - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - value += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)]; - } - } - output_data[Offset(output_shape, out_b, 0, 0, out_d)] = - value / (input_width * input_height); - } - } -} - -inline void Mean(const tflite::MeanParams& op_params, - const RuntimeShape& unextended_input_shape, - const uint8_t* input_data, int32_t input_zero_point, - float input_scale, const RuntimeShape& unextended_output_shape, - uint8_t* output_data, int32_t output_zero_point, - float output_scale) { - ruy::profiler::ScopeLabel label("Mean4D/Uint8"); - - // Current implementation only supports dimension equals 4 and simultaneous - // reduction over width and height. - TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4); - TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - const int output_batch = output_shape.Dims(0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int output_depth = output_shape.Dims(3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const float num_elements_in_axis = input_width * input_height; - - TFLITE_CHECK_EQ(op_params.axis_count, 2); - TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) || - (op_params.axis[0] == 2 && op_params.axis[1] == 1)); - TFLITE_CHECK_EQ(output_height, 1); - TFLITE_CHECK_EQ(output_width, 1); - - constexpr int32_t kMinValue = std::numeric_limits::min(); - constexpr int32_t kMaxValue = std::numeric_limits::max(); - - int32_t bias = - output_zero_point - - static_cast(input_zero_point * input_scale / output_scale); - double real_scale = - static_cast(input_scale / (num_elements_in_axis * output_scale)); - - int32_t multiplier; - int shift; - QuantizeMultiplier(real_scale, &multiplier, &shift); - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - int32_t acc = 0; - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - acc += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)]; - } - } - acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift); - acc += bias; - acc = std::min(std::max(acc, kMinValue), kMaxValue); - output_data[Offset(output_shape, out_b, 0, 0, out_d)] = - static_cast(acc); - } - } -} - -// Computes the mean of elements across dimensions given in axis. -// It does so in two stages, first calculates the sum of elements along the axis -// then divides it by the number of element in axis for quantized values. -template -inline bool QuantizedMeanOrSum(const T* input_data, int32_t input_zero_point, - float input_scale, const int* input_dims, - const int input_num_dims, T* output_data, - int32_t output_zero_point, float output_scale, - const int* output_dims, - const int output_num_dims, const int* axis, - const int num_axis_dimensions, bool keep_dims, - int* temp_index, int* resolved_axis, U* temp_sum, - bool compute_sum) { - const bool uint8_case = std::is_same::value; - const bool int16_case = std::is_same::value; - if (uint8_case) { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Uint8" : "Mean/Uint8"); - } else if (int16_case) { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int16" : "Mean/Int16"); - } else { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int8" : "Mean/Int8"); - } - // Reset output data. - size_t num_outputs = 1; - for (int idx = 0; idx < output_num_dims; ++idx) { - size_t current = static_cast(output_dims[idx]); - // Overflow prevention. - if (num_outputs > std::numeric_limits::max() / current) { - return false; - } - num_outputs *= current; - } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = T(); - temp_sum[idx] = U(); - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, temp_sum)) { - return false; - } - - // Calculate mean by dividing output_data by num of aggregated element. - U num_elements_in_axis = 1; - for (int idx = 0; idx < num_resolved_axis; ++idx) { - size_t current = static_cast(input_dims[resolved_axis[idx]]); - // Overflow prevention. - if (current > (std::numeric_limits::max() / num_elements_in_axis)) { - return false; - } - num_elements_in_axis *= current; - } - - if (num_elements_in_axis > 0) { - const float scale = input_scale / output_scale; - if (compute_sum) { - // TODO(b/116341117): Eliminate float and do this completely in 8bit. - const float bias = - -input_zero_point * scale * num_elements_in_axis + 0.5f; - for (size_t idx = 0; idx < num_outputs; ++idx) { - const U value = - static_cast(TfLiteRound(temp_sum[idx] * scale + bias)) + - output_zero_point; - output_data[idx] = static_cast(value); - } - } else { - const float bias = -input_zero_point * scale + 0.5f; - for (size_t idx = 0; idx < num_outputs; ++idx) { - float float_mean = static_cast(temp_sum[idx]) / - static_cast(num_elements_in_axis); - float result = TfLiteMin( - TfLiteRound(float_mean * scale + bias) + output_zero_point, - static_cast(std::numeric_limits::max())); - result = TfLiteMax(result, - static_cast(std::numeric_limits::min())); - output_data[idx] = static_cast(result); - } - } - } - return true; -} - -} // namespace reference_ops - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/requantize.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/requantize.h deleted file mode 100644 index 32e32ed0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/requantize.h +++ /dev/null @@ -1,67 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace reference_ops { - -template -inline void Requantize(const input_type* input_data, int32_t size, - int32_t effective_scale_multiplier, - int32_t effective_scale_shift, int32_t input_zeropoint, - int32_t output_zeropoint, output_type* output_data) { - ruy::profiler::ScopeLabel label("Requantize"); - const bool same_scale = - (effective_scale_multiplier == 1 << 30 && effective_scale_shift == 1); - if (same_scale) { - const bool mixed_type_int8_uint8 = - std::is_same::value && - std::is_same::value; - const bool mixed_type_uint8_int8 = - std::is_same::value && - std::is_same::value; - const int32_t zero_point_diff = input_zeropoint - output_zeropoint; - // Fast path to do requantization for the case when just a shift of 128 is - // needed. - if ((mixed_type_int8_uint8 && zero_point_diff == -128) || - (mixed_type_uint8_int8 && zero_point_diff == 128)) { - for (int i = 0; i < size; ++i) { - output_data[i] = input_data[i] ^ 0x80; - } - } - } - static constexpr int32_t kMinOutput = std::numeric_limits::min(); - static constexpr int32_t kMaxOutput = std::numeric_limits::max(); - for (int i = 0; i < size; ++i) { - const int32_t input = input_data[i] - input_zeropoint; - const int32_t output = - MultiplyByQuantizedMultiplier(input, effective_scale_multiplier, - effective_scale_shift) + - output_zeropoint; - const int32_t clamped_output = - std::max(std::min(output, kMaxOutput), kMinOutput); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h deleted file mode 100644 index 95550abc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ - -#include - -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline int32_t GetNearestNeighbor(const int input_value, - const int32_t input_size, - const int32_t output_size, - const bool align_corners, - const bool half_pixel_centers) { - const float scale = - (align_corners && output_size > 1) - ? (input_size - 1) / static_cast(output_size - 1) - : input_size / static_cast(output_size); - const float offset = half_pixel_centers ? 0.5f : 0.0f; - int32_t output_value = std::min( - align_corners - ? static_cast(TfLiteRound((input_value + offset) * scale)) - : static_cast(std::floor((input_value + offset) * scale)), - input_size - 1); - if (half_pixel_centers) { - output_value = std::max(static_cast(0), output_value); - } - return output_value; -} - -template -inline void ResizeNearestNeighbor( - const tflite::ResizeNearestNeighborParams& op_params, - const RuntimeShape& unextended_input_shape, const T* input_data, - const RuntimeShape& output_size_shape, const int32_t* output_size_data, - const RuntimeShape& unextended_output_shape, T* output_data) { - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); - int32_t input_height = input_shape.Dims(1); - int32_t input_width = input_shape.Dims(2); - int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); - - // The Tensorflow version of this op allows resize on the width and height - // axis only. - TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2); - int32_t output_height = output_size_data[0]; - int32_t output_width = output_size_data[1]; - - const int col_offset = input_shape.Dims(3); - const int row_offset = input_shape.Dims(2) * col_offset; - const int batch_offset = input_shape.Dims(1) * row_offset; - - const T* input_ptr = input_data; - T* output_ptr = output_data; - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - int32_t in_y = GetNearestNeighbor(y, input_height, output_height, - op_params.align_corners, - op_params.half_pixel_centers); - const T* y_input_ptr = input_ptr + in_y * row_offset; - for (int x = 0; x < output_width; ++x) { - int32_t in_x = GetNearestNeighbor(x, input_width, output_width, - op_params.align_corners, - op_params.half_pixel_centers); - const T* x_input_ptr = y_input_ptr + in_x * col_offset; - memcpy(output_ptr, x_input_ptr, depth * sizeof(T)); - output_ptr += depth; - } - } - input_ptr += batch_offset; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/round.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/round.h deleted file mode 100644 index 9bd8f3f2..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/round.h +++ /dev/null @@ -1,51 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline float RoundToNearest(float value) { - auto floor_val = std::floor(value); - auto diff = value - floor_val; - if ((diff < 0.5f) || - ((diff == 0.5f) && (static_cast(floor_val) % 2 == 0))) { - return floor_val; - } else { - return floor_val = floor_val + 1.0f; - } -} - -inline void Round(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - // Note that this implementation matches that of tensorFlow tf.round - // and corresponds to the bankers rounding method. - // cfenv (for fesetround) is not yet supported universally on Android, so - // using a work around. - output_data[i] = RoundToNearest(input_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/softmax.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/softmax.h deleted file mode 100644 index b035b433..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/softmax.h +++ /dev/null @@ -1,228 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ - -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite { -namespace reference_ops { - -inline void Softmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - // Find max element value which we'll use to ensure numerical stability - // taking advantage of the following equality: - // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C)) - float max = std::numeric_limits::lowest(); - for (int c = 0; c < depth; ++c) { - max = std::max(max, input_data[i * depth + c]); - } - - // Compute sum. - float sum = 0.f; - for (int c = 0; c < depth; ++c) { - sum += std::exp((input_data[i * depth + c] - max) * - static_cast(params.beta)); - } - - // Compute result. - for (int c = 0; c < depth; ++c) { - output_data[i * depth + c] = std::exp((input_data[i * depth + c] - max) * - static_cast(params.beta)) / - sum; - } - } -} - -// Quantized softmax with int8_t/uint8_t input and int8_t/uint8_t/int16_t -// output. -template -inline void Softmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, const InputT* input_data, - const RuntimeShape& output_shape, OutputT* output_data) { - const int32_t input_beta_multiplier = params.input_multiplier; - const int32_t input_beta_left_shift = params.input_left_shift; - const int diff_min = params.diff_min; - // The representation chosen for the input to the exp() function is Q5.26. - // We need to leave extra space since values that we skip might be as large as - // -32 before multiplying by input_beta_multiplier, and therefore as large as - // -16 afterwards. Note that exp(-8) is definitely not insignificant to - // accumulation, but exp(-16) definitely is. - static const int kScaledDiffIntegerBits = 5; - static const int kAccumulationIntegerBits = 12; - using FixedPointScaledDiff = - gemmlowp::FixedPoint; - using FixedPointAccum = - gemmlowp::FixedPoint; - using FixedPoint0 = gemmlowp::FixedPoint; - - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - InputT max_in_row = std::numeric_limits::min(); - for (int c = 0; c < depth; ++c) { - max_in_row = std::max(max_in_row, input_data[i * depth + c]); - } - - FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - sum_of_exps = sum_of_exps + gemmlowp::Rescale( - exp_on_negative_values(scaled_diff_f8)); - } - } - - int num_bits_over_unit; - FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal( - sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit)); - - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - - FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); - int32_t unsat_output = gemmlowp::RoundingDivideByPOT( - (shifted_scale * exp_in_0).raw(), - num_bits_over_unit + 31 - (sizeof(OutputT) * 8)); - - const int32_t shifted_output = - unsat_output + - static_cast(std::numeric_limits::min()); - - output_data[i * depth + c] = static_cast(std::max( - std::min(shifted_output, - static_cast(std::numeric_limits::max())), - static_cast(std::numeric_limits::min()))); - } else { - output_data[i * depth + c] = std::numeric_limits::min(); - } - } - } -} - -// Quantized softmax with int16_t input and int16_t output. -inline void SoftmaxInt16(const SoftmaxParams& params, - const RuntimeShape& input_shape, - const int16_t* input_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - // Find the largest element - int16_t max_in_row = std::numeric_limits::min(); - for (int c = 0; c < depth; ++c) { - max_in_row = std::max(max_in_row, input_data[i * depth + c]); - } - - // Compute exp(input - max_input) - std::vector exp_result_Q015(depth); - for (int c = 0; c < depth; ++c) { - int32_t input_diff = input_data[i * depth + c] - max_in_row; - // scale the input_diff such that [-65535, 0] correspond to [-10.0, 0.0] - int32_t scaled_diff = MultiplyByQuantizedMultiplier( - input_diff, params.input_multiplier, params.input_left_shift); - // recenter to [-32768, 32767] - int32_t sym_scaled_diff = scaled_diff + 32767; - int16_t sat_sym_scaled_diff = - std::min(std::max(sym_scaled_diff, static_cast(-32768)), - static_cast(32767)); - // apply the exp() LUT activation function - exp_result_Q015[c] = - generic_int16_table_lookup(sat_sym_scaled_diff, params.exp_lut); - } - - // sum_of_exps is a Q16.15 fixed point format. - int32_t sum_of_exps = 0; - for (int c = 0; c < depth; ++c) { - // Q16.15 + Q0.15 - sum_of_exps += exp_result_Q015[c]; - } - - // Compute the reciprocal 1/sum_of_exps - uint8_t headroom_plus_one = - CountLeadingZeros(static_cast(sum_of_exps)); - int32_t shifted_sum = - ((static_cast(sum_of_exps) << (headroom_plus_one - 1)) + - (1 << 13)) >> - 14; - // since the LUT computes 1/(1 + x) we need to first compute x = (sum - 1). - // also, the LUT expects a symmetrical input, so we must also recenter x - // from [0, 65535] to [-32768, 32767]. - int32_t sym_shifted_sum = shifted_sum + (-((1 << 15) + (1 << 16))); - int16_t sat_sym_shifted_sum = static_cast( - std::min(std::max(sym_shifted_sum, static_cast(-32768)), - static_cast(32767))); - // apply 1/(1 + x) LUT activation function - int16_t reciprocal_scale_Q015 = generic_int16_table_lookup( - sat_sym_shifted_sum, params.one_over_one_plus_x_lut); - - // Rescale the exp_result with reciprocal - // range of output is [0, 32767] correspond to [0.0, 1.0] - for (int c = 0; c < depth; ++c) { - uint8_t right_shift = 31 - headroom_plus_one; - int64_t round = 1 << (right_shift - 1); - int32_t result = (static_cast(exp_result_Q015[c]) * - static_cast(reciprocal_scale_Q015) + - round) >> - right_shift; - output_data[i * depth + c] = static_cast( - std::min(std::max(result, static_cast(0)), - static_cast(32767))); - } - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/strided_slice.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/strided_slice.h deleted file mode 100644 index 8b6f0c13..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/strided_slice.h +++ /dev/null @@ -1,94 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/strided_slice_logic.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { -template -inline void StridedSlice(const tflite::StridedSliceParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - using strided_slice::LoopCondition; - using strided_slice::StartForAxis; - using strided_slice::StopForAxis; - // Note that the output_shape is not used herein. - tflite::StridedSliceParams params_copy = op_params; - - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 5); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 5); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(5, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(5, unextended_output_shape); - - // Reverse and pad to 5 dimensions because that is what the runtime code - // requires (ie. all shapes must be 5D and are given backwards). - strided_slice::StridedSlicePadIndices(¶ms_copy, 5); - - const int start_0 = StartForAxis(params_copy, input_shape, 0); - const int stop_0 = StopForAxis(params_copy, input_shape, 0, start_0); - const int start_1 = StartForAxis(params_copy, input_shape, 1); - const int stop_1 = StopForAxis(params_copy, input_shape, 1, start_1); - const int start_2 = StartForAxis(params_copy, input_shape, 2); - const int stop_2 = StopForAxis(params_copy, input_shape, 2, start_2); - const int start_3 = StartForAxis(params_copy, input_shape, 3); - const int stop_3 = StopForAxis(params_copy, input_shape, 3, start_3); - const int start_4 = StartForAxis(params_copy, input_shape, 4); - const int stop_4 = StopForAxis(params_copy, input_shape, 4, start_4); - - T* out_ptr = output_data; - for (int offset_0 = start_0 * input_shape.Dims(1), - end_0 = stop_0 * input_shape.Dims(1), - step_0 = params_copy.strides[0] * input_shape.Dims(1); - !LoopCondition(offset_0, end_0, params_copy.strides[0]); - offset_0 += step_0) { - for (int offset_1 = (offset_0 + start_1) * input_shape.Dims(2), - end_1 = (offset_0 + stop_1) * input_shape.Dims(2), - step_1 = params_copy.strides[1] * input_shape.Dims(2); - !LoopCondition(offset_1, end_1, params_copy.strides[1]); - offset_1 += step_1) { - for (int offset_2 = (offset_1 + start_2) * input_shape.Dims(3), - end_2 = (offset_1 + stop_2) * input_shape.Dims(3), - step_2 = params_copy.strides[2] * input_shape.Dims(3); - !LoopCondition(offset_2, end_2, params_copy.strides[2]); - offset_2 += step_2) { - for (int offset_3 = (offset_2 + start_3) * input_shape.Dims(4), - end_3 = (offset_2 + stop_3) * input_shape.Dims(4), - step_3 = params_copy.strides[3] * input_shape.Dims(4); - !LoopCondition(offset_3, end_3, params_copy.strides[3]); - offset_3 += step_3) { - for (int offset_4 = offset_3 + start_4, end_4 = offset_3 + stop_4; - !LoopCondition(offset_4, end_4, params_copy.strides[4]); - offset_4 += params_copy.strides[4]) { - *out_ptr++ = input_data[offset_4]; - } - } - } - } - } -} -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/sub.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/sub.h deleted file mode 100644 index b27f251d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/sub.h +++ /dev/null @@ -1,516 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ - -#include - -#include -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -namespace reference_ops { - -inline void SubNonBroadcast(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, - float* output_data) { - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], params.float_activation_min, - params.float_activation_max); - } -} - -inline void SubNonBroadcast(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int32_t* input1_data, - const RuntimeShape& input2_shape, - const int32_t* input2_data, - const RuntimeShape& output_shape, - int32_t* output_data) { - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], params.quantized_activation_min, - params.quantized_activation_max); - } -} - -// TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(b/151345101): BroadcastSub is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -template -inline void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, - float* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/float"); - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, indexes)] - - input2_data[SubscriptToIndex(desc2, indexes)], - params.float_activation_min, params.float_activation_max); - }; - NDOpsHelper(output_desc, sub_func); -} - -template -inline void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const uint8_t* input1_data, - const RuntimeShape& input2_shape, - const uint8_t* input2_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/uint8_t"); - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - const int32_t input1_val = - params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; - const int32_t input2_val = - params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[SubscriptToIndex(output_desc, indexes)] = - static_cast(clamped_output); - }; - NDOpsHelper(output_desc, sub_func); -} - -template -inline void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int32_t* input1_data, - const RuntimeShape& input2_shape, - const int32_t* input2_data, - const RuntimeShape& output_shape, - int32_t* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/int32_t"); - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, indexes)] - - input2_data[SubscriptToIndex(desc2, indexes)], - params.quantized_activation_min, params.quantized_activation_max); - }; - NDOpsHelper(output_desc, sub_func); -} - -template -inline void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int8_t* input1_data, - const RuntimeShape& input2_shape, - const int8_t* input2_data, - const RuntimeShape& output_shape, - int8_t* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/int8_t"); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - const int32_t input1_val = - params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; - const int32_t input2_val = - params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[SubscriptToIndex(output_desc, indexes)] = - static_cast(clamped_output); - }; - NDOpsHelper(output_desc, sub_func); -} - -template -void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int64_t* input1_data, - const RuntimeShape& input2_shape, - const int64_t* input2_data, - const RuntimeShape& output_shape, int64_t* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/int64_t"); - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, indexes)] - - input2_data[SubscriptToIndex(desc2, indexes)], - params.int64_activation_min, params.int64_activation_max); - }; - NDOpsHelper(output_desc, sub_func); -} - -template -void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/templated"); - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - auto sub_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, indexes)] - - input2_data[SubscriptToIndex(desc2, indexes)], - params.quantized_activation_min, params.quantized_activation_max); - }; - NDOpsHelper(output_desc, sub_func); -} - -// Element-wise Sub that can often be used for inner loop of broadcast sub as -// well as the non-broadcast sub. -inline void SubElementwise(int size, const ArithmeticParams& params, - const uint8_t* input1_data, - const uint8_t* input2_data, uint8_t* output_data) { - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. -inline void SubElementwise(int size, const ArithmeticParams& params, - const int8_t* input1_data, const int8_t* input2_data, - int8_t* output_data) { - const int32_t int8_max_value = std::numeric_limits::max(); - TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value); - TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value); - TFLITE_DCHECK_LE(params.input1_offset, int8_max_value); - TFLITE_DCHECK_LE(params.input2_offset, int8_max_value); - - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Sub(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - SubElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Sub(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int8_t* input1_data, - const RuntimeShape& input2_shape, const int8_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - const int32_t int8_max_value = std::numeric_limits::max(); - TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value); - TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value); - TFLITE_DCHECK_LE(params.input1_offset, int8_max_value); - TFLITE_DCHECK_LE(params.input2_offset, int8_max_value); - SubElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -template -void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, - T* output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - output_data[Offset(extended_output_shape, b, y, x, c)] = - input1_data[SubscriptToIndex(desc1, b, y, x, c)] - - input2_data[SubscriptToIndex(desc2, b, y, x, c)]; - } - } - } - } -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - int32_t* activation_min, - int32_t* activation_max) { - *activation_min = params.quantized_activation_min; - *activation_max = params.quantized_activation_max; -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - float* activation_min, float* activation_max) { - *activation_min = params.float_activation_min; - *activation_max = params.float_activation_max; -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - int64_t* activation_min, - int64_t* activation_max) { - *activation_min = params.int64_activation_min; - *activation_max = params.int64_activation_max; -} - -template -inline void SubWithActivation( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("SubWithActivation"); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - T activation_min, activation_max; - SetActivationMinMax(params, &activation_min, &activation_max); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], activation_min, activation_max); - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/tanh.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/tanh.h deleted file mode 100644 index 3a05c474..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/tanh.h +++ /dev/null @@ -1,129 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite { -namespace reference_ops { - -inline void Tanh(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - float val = input_data[i]; - float result = std::tanh(val); - output_data[i] = result; - } -} - -// Convenience version that allows, for example, generated-code calls to be -// uniform between data types. -inline void Tanh(const TanhParams&, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - // Drop params: not needed. - Tanh(input_shape, input_data, output_shape, output_data); -} - -inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& output_shape, - int16_t* output_data) { - const int input_left_shift = params.input_left_shift; - // Support for shifts is limited until we have a parameterized version of - // SaturatingRoundingMultiplyByPOT(). - TFLITE_DCHECK_GE(input_left_shift, 0); - TFLITE_DCHECK_LE(input_left_shift, 1); - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // F0 uses 0 integer bits, range [-1, 1]. - // This is the return type of math functions such as tanh, logistic, - // whose range is in [-1, 1]. - using F0 = gemmlowp::FixedPoint; - // F3 uses 3 integer bits, range [-8, 8], the input range expected here. - using F3 = gemmlowp::FixedPoint; - - if (input_left_shift == 0) { - for (int i = 0; i < flat_size; i++) { - F3 input = F3::FromRaw(input_data[i]); - F0 output = gemmlowp::tanh(input); - output_data[i] = output.raw(); - } - } else { - for (int i = 0; i < flat_size; i++) { - F3 input = F3::FromRaw( - gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i])); - F0 output = gemmlowp::tanh(input); - output_data[i] = output.raw(); - } - } -} - -inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int32_t input_zero_point = params.input_zero_point; - const int32_t input_range_radius = params.input_range_radius; - const int32_t input_multiplier = params.input_multiplier; - const int input_left_shift = params.input_left_shift; - const int32_t output_zero_point = 128; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const uint8_t input_val_u8 = input_data[i]; - const int32_t input_val_centered = - static_cast(input_val_u8) - input_zero_point; - uint8_t output_val; - if (input_val_centered <= -input_range_radius) { - output_val = 0; - } else if (input_val_centered >= input_range_radius) { - output_val = 255; - } else { - const int32_t input_val_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_val_centered, input_multiplier, input_left_shift); - using FixedPoint4 = gemmlowp::FixedPoint; - using FixedPoint0 = gemmlowp::FixedPoint; - const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); - const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); - // Convert from Q0.31 to Q24.7. - using gemmlowp::RoundingDivideByPOT; - int32_t output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); - output_val_s32 += output_zero_point; - if (output_val_s32 == 256) { - output_val_s32 = 255; - } - // Reinterpret as Q0.7, encoded in uint8_t. - TFLITE_DCHECK_GE(output_val_s32, 0); - TFLITE_DCHECK_LE(output_val_s32, 255); - output_val = static_cast(output_val_s32); - } - output_data[i] = output_val; - } -} - -} // namespace reference_ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/strided_slice_logic.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/strided_slice_logic.h deleted file mode 100644 index d9b5acbb..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/strided_slice_logic.h +++ /dev/null @@ -1,204 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ - -#include -#include -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace strided_slice { - -// Use until std::clamp() is available from C++17. -inline int Clamp(const int v, const int lo, const int hi) { - TFLITE_DCHECK(!(hi < lo)); - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -inline void StridedSlicePadIndices(tflite::StridedSliceParams* p, - int dim_count) { - // Add indices and mask bits to fully include extra dimensions - TFLITE_CHECK_LE(dim_count, 5); - TFLITE_CHECK_GE(dim_count, p->start_indices_count); - TFLITE_CHECK_EQ(p->start_indices_count, p->stop_indices_count); - TFLITE_CHECK_EQ(p->stop_indices_count, p->strides_count); - - const int pad_count = dim_count - p->start_indices_count; - - // Pad indices at start, so move arrays by pad_count. - for (int i = p->start_indices_count - 1; i >= 0; --i) { - p->strides[i + pad_count] = p->strides[i]; - p->start_indices[i + pad_count] = p->start_indices[i]; - p->stop_indices[i + pad_count] = p->stop_indices[i]; - } - for (int i = 0; i < pad_count; ++i) { - p->start_indices[i] = 0; - p->stop_indices[i] = 1; - p->strides[i] = 1; - } - - // Pad masks with 0s or 1s as required. - p->shrink_axis_mask <<= pad_count; - p->ellipsis_mask <<= pad_count; - p->new_axis_mask <<= pad_count; - p->begin_mask <<= pad_count; - p->end_mask <<= pad_count; - p->begin_mask |= (1 << pad_count) - 1; - p->end_mask |= (1 << pad_count) - 1; - - p->start_indices_count = dim_count; - p->stop_indices_count = dim_count; - p->strides_count = dim_count; -} - -// Return the index for the first element along that axis. This index will be a -// positive integer between [0, axis_size - 1] that can be used to index -// directly into the data. -inline int StartForAxis(const tflite::StridedSliceParams& params, - const RuntimeShape& input_shape, int axis) { - const auto begin_mask = params.begin_mask; - const auto* start_indices = params.start_indices; - const auto* strides = params.strides; - const int axis_size = input_shape.Dims(axis); - if (axis_size == 0) { - return 0; - } - // Begin with the specified index. - int start = start_indices[axis]; - - // begin_mask override - if (begin_mask & 1 << axis) { - if (strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - if (start < 0) { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -// Return the "real" index for the end of iteration along that axis. This is an -// "end" in the traditional C sense, in that it points to one past the last -// element. ie. So if you were iterating through all elements of a 1D array of -// size 4, this function would return 4 as the stop, because it is one past the -// "real" indices of 0, 1, 2 & 3. -inline int StopForAxis(const tflite::StridedSliceParams& params, - const RuntimeShape& input_shape, int axis, - int start_for_axis) { - const auto end_mask = params.end_mask; - const auto shrink_axis_mask = params.shrink_axis_mask; - const auto* stop_indices = params.stop_indices; - const auto* strides = params.strides; - const int axis_size = input_shape.Dims(axis); - if (axis_size == 0) { - return 0; - } - - // Begin with the specified index - const bool shrink_axis = shrink_axis_mask & (1 << axis); - int stop = stop_indices[axis]; - - // When shrinking an axis, the end position does not matter (and can be - // incorrect when negative indexing is used, see Issue #19260). Always use - // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has - // already been adjusted for negative indices. - if (shrink_axis) { - stop = start_for_axis + 1; - } - - // end_mask override - if (end_mask & (1 << axis)) { - if (strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -inline bool LoopCondition(int index, int stop, int stride) { - // True when we have reached the end of an axis and should loop. - return stride > 0 ? index >= stop : index <= stop; -} - -inline tflite::StridedSliceParams BuildStridedSliceParams( - int begin_mask, int end_mask, int shrink_axis_mask, - const std::vector& start_indices, const std::vector& stop_indices, - const std::vector& strides) { - tflite::StridedSliceParams op_params; - const int dims_count = start_indices.size(); - - op_params.start_indices_count = dims_count; - op_params.stop_indices_count = dims_count; - op_params.strides_count = dims_count; - for (int i = 0; i < dims_count; ++i) { - op_params.start_indices[i] = start_indices[i]; - op_params.stop_indices[i] = stop_indices[i]; - op_params.strides[i] = strides[i]; - } - - op_params.begin_mask = begin_mask; - op_params.ellipsis_mask = 0; - op_params.end_mask = end_mask; - op_params.new_axis_mask = 0; - op_params.shrink_axis_mask = shrink_axis_mask; - - return op_params; -} - -} // namespace strided_slice - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor.h deleted file mode 100644 index 905552fc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor.h +++ /dev/null @@ -1,147 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/string_util.h" - -namespace tflite { - -inline RuntimeShape GetTensorShape(std::vector data) { - return RuntimeShape(data.size(), data.data()); -} - -// A list of tensors in a format that can be used by kernels like split and -// concatenation. -template -class VectorOfTensors { - public: - // Build with the tensors in 'tensor_list'. - VectorOfTensors(const TfLiteContext& context, - const TfLiteIntArray& tensor_list) { - int num_tensors = tensor_list.size; - - all_data_.reserve(num_tensors); - all_shape_.reserve(num_tensors); - all_shape_ptr_.reserve(num_tensors); - - for (int i = 0; i < num_tensors; ++i) { - TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; - all_data_.push_back(GetTensorData(t)); - all_shape_.push_back(GetTensorShape(t)); - } - - // Taking the pointer from inside a std::vector is only OK if the vector is - // never modified, so we populate all_shape in the previous loop and then we - // are free to grab iterators here. - for (int i = 0; i < num_tensors; ++i) { - all_shape_ptr_.push_back(&all_shape_[i]); - } - } - // Return a pointer to the data pointers of all tensors in the list. For - // example: - // float* const* f = v.data(); - // f[0][1] is the second element of the first tensor. - T* const* data() const { return all_data_.data(); } - - // Return a pointer the shape pointers of all tensors in the list. For - // example: - // const RuntimeShape* const* d = v.dims(); - // dims[1] are the dimensions of the second tensor in the list. - const RuntimeShape* const* shapes() const { return all_shape_ptr_.data(); } - - private: - std::vector all_data_; - std::vector all_shape_; - std::vector all_shape_ptr_; -}; - -// A list of quantized tensors in a format that can be used by kernels like -// split and concatenation. -class VectorOfQuantizedTensors : public VectorOfTensors { - public: - // Build with the tensors in 'tensor_list'. - VectorOfQuantizedTensors(const TfLiteContext& context, - const TfLiteIntArray& tensor_list) - : VectorOfTensors(context, tensor_list) { - for (int i = 0; i < tensor_list.size; ++i) { - TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; - zero_point_.push_back(t->params.zero_point); - scale_.push_back(t->params.scale); - } - } - - const float* scale() const { return scale_.data(); } - const int32_t* zero_point() const { return zero_point_.data(); } - - private: - std::vector zero_point_; - std::vector scale_; -}; - -// Writes randomly accessed values from `input` sequentially into `output`. -template -class SequentialTensorWriter { - public: - SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output) { - input_data_ = GetTensorData(input); - output_ptr_ = GetTensorData(output); - } - SequentialTensorWriter(const T* input_data, T* output_data) - : input_data_(input_data), output_ptr_(output_data) {} - - void Write(int position) { *output_ptr_++ = input_data_[position]; } - void WriteN(int position, int len) { - memcpy(output_ptr_, &input_data_[position], sizeof(T) * len); - output_ptr_ += len; - } - - private: - const T* input_data_; - T* output_ptr_; -}; - -// String ops are not yet supported on platforms w/ static memory. -#ifndef TF_LITE_STATIC_MEMORY -template <> -class SequentialTensorWriter { - public: - SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output) - : input_(input), output_(output) {} - ~SequentialTensorWriter() { buffer_.WriteToTensor(output_, nullptr); } - - void Write(int position) { this->WriteN(position, 1); } - void WriteN(int position, int len) { - for (int i = 0; i < len; i++) { - buffer_.AddString(GetString(input_, position + i)); - } - } - - private: - const TfLiteTensor* input_; - TfLiteTensor* output_; - DynamicBuffer buffer_; -}; -#endif // TF_LITE_STATIC_MEMORY - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor_ctypes.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor_ctypes.h deleted file mode 100644 index f1d3e17f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/tensor_ctypes.h +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -template -inline T* GetTensorData(TfLiteTensor* tensor) { - return tensor != nullptr ? reinterpret_cast(tensor->data.raw) : nullptr; -} - -template -inline const T* GetTensorData(const TfLiteTensor* tensor) { - return tensor != nullptr ? reinterpret_cast(tensor->data.raw) - : nullptr; -} - -inline RuntimeShape GetTensorShape(const TfLiteTensor* tensor) { - if (tensor == nullptr) { - return RuntimeShape(); - } - - TfLiteIntArray* dims = tensor->dims; - const int dims_size = dims->size; - const int32_t* dims_data = reinterpret_cast(dims->data); - return RuntimeShape(dims_size, dims_data); -} - -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/types.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/types.h deleted file mode 100644 index 9db742dd..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/types.h +++ /dev/null @@ -1,1153 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ - -#include -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite { - -enum class FusedActivationFunctionType : uint8_t { - kNone, - kRelu6, - kRelu1, - kRelu -}; -enum class PaddingType : uint8_t { kNone, kSame, kValid }; - -struct PaddingValues { - int16_t width; - int16_t height; - // offset is used for calculating "remaining" padding, for example, `width` - // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is - // 1 + 1 = 2. - int16_t width_offset; - // Same as width_offset except it's over the height dimension. - int16_t height_offset; -}; - -// This enumeration allows for non-default formats for the weights array -// of a fully-connected operator, allowing the use of special optimized -// runtime paths. -enum class FullyConnectedWeightsFormat : uint8_t { - // Default format (flat 2D layout, the inner contiguous dimension - // is input_depth, the outer non-contiguous dimension is output_depth) - kDefault, - // Summary: optimized layout for fast CPU runtime implementation, - // aimed specifically at ARM CPUs at the moment, and specialized for - // 8-bit quantized layers. - // - // The use case we're concerned with here is: 8-bit quantization, - // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in - // a key application that drove this), very small batch size (e.g. 1 -- 4). - // - // Even with 8-bit quantization of weights, the performance of memory - // accesses to the weights can become the dominant issue when - // the batch size is small, so each weight value is used in only a few - // arithmetic ops, i.e. the fully-connected node has a low arithmetic - // intensity. The specific issues that arise are of three kinds: - // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory - // bound. That's the "good" issue to run into. - // (2) One may run into sub-optimal pre-fetching: the data hasn't been - // prefetched into the cache by the time we need it. - // (3) One may run into cache aliasing: multiple values that are - // pre-fetched, alias each other in the L1 cache (which typically - // has only 4-way set associativity in ARM CPUs) and thus evict - // each other before we get to using them. - // - // The point of this shuffling is to avoid issues (2) and (3) so that - // we get as fast as possible given only the hard constraint (1). - // This is achieved by turning the difficulty into a solution: the - // difficulty, that each value loaded from memory is used only in - // one kernel iteration, making this operation memory-intensive, hints at - // the solution, of shuffling the weights so that they are stored in the - // exact order as the kernel needs to load them, so that the memory - // accesses made by the kernel are trivial. This solves (2) because the - // trivial memory access pattern allows the CPU's automatic prefetching - // to perform very well (no need even for preload instructions), and this - // solves (3) because the values being loaded concurrently are now - // contiguous in the address space, thus don't alias each other in the cache. - // - // On ARM, we typically want our kernel to process a 4x16 block of weights - // at a time, because: - // - 16 is the number of bytes in a NEON register. - // - 4 is how many rows we need to handle concurrently in the kernel in - // order to have sufficient mutual independence of instructions to - // maximize arithmetic throughput. - // - // Finally, the 'Int8' part in the name refers to the fact that this - // weights format has each weights value encoded as a signed int8_t value, - // even if the data type of the weights buffer is uint8_t. This is intended - // to save runtime kernels the effort to have to XOR the top bit of these - // bytes before using them in signed arithmetic, see this file for more - // explanations on the 'signed int8_t trick' in matrix multiplication kernels: - // - // tensorflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc - // - kShuffled4x16Int8, -}; - -// Quantization parameters, determining the mapping of quantized values -// to real values (i.e. determining how quantized values are mathematically -// interpreted). -// -// The correspondence is as follows: -// -// real_value = scale * (quantized_value - zero_point); -// -// In other words, zero_point designates which quantized value corresponds to -// the real 0 value, and scale designates the difference between the real values -// corresponding to consecutive quantized values differing by 1. -struct QuantizationParams { - int32_t zero_point = 0; - double scale = 0.0; -}; - -inline bool operator==(const QuantizationParams& qp1, - const QuantizationParams& qp2) { - return qp1.zero_point == qp2.zero_point && qp1.scale == qp2.scale; -} - -template -struct Dims { - int sizes[N]; - int strides[N]; -}; - -class RuntimeShape { - public: - // Shapes with dimensions up to 5 are stored directly in the structure, while - // larger shapes are separately allocated. - static constexpr int kMaxSmallSize = 5; - - RuntimeShape& operator=(RuntimeShape const&) = delete; - - RuntimeShape() : size_(0) {} - - explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { - if (dimensions_count > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - dims_pointer_ = new int32_t[dimensions_count]; -#endif // TF_LITE_STATIC_MEMORY - } - } - - RuntimeShape(int shape_size, int32_t value) : size_(0) { - Resize(shape_size); - for (int i = 0; i < shape_size; ++i) { - SetDim(i, value); - } - } - - RuntimeShape(int dimensions_count, const int32_t* dims_data) : size_(0) { - ReplaceWith(dimensions_count, dims_data); - } - - RuntimeShape(const std::initializer_list init_list) : size_(0) { - BuildFrom(init_list); - } - - // Avoid using this constructor. We should be able to delete it when C++17 - // rolls out. - RuntimeShape(RuntimeShape const& other) : size_(other.DimensionsCount()) { - if (size_ > kMaxSmallSize) { - dims_pointer_ = new int32_t[size_]; - } - std::memcpy(DimsData(), other.DimsData(), sizeof(int32_t) * size_); - } - - bool operator==(const RuntimeShape& comp) const { - return this->size_ == comp.size_ && - std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32_t)) == - 0; - } - - ~RuntimeShape() { - if (size_ > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - delete[] dims_pointer_; -#endif // TF_LITE_STATIC_MEMORY - } - } - - inline int32_t DimensionsCount() const { return size_; } - inline int32_t Dims(int i) const { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - return size_ > kMaxSmallSize ? dims_pointer_[i] : dims_[i]; - } - inline void SetDim(int i, int32_t val) { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - if (size_ > kMaxSmallSize) { - dims_pointer_[i] = val; - } else { - dims_[i] = val; - } - } - - inline int32_t* DimsData() { - return size_ > kMaxSmallSize ? dims_pointer_ : dims_; - } - inline const int32_t* DimsData() const { - return size_ > kMaxSmallSize ? dims_pointer_ : dims_; - } - // The caller must ensure that the shape is no bigger than 5-D. - inline const int32_t* DimsDataUpTo5D() const { return dims_; } - - inline void Resize(int dimensions_count) { - if (size_ > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - delete[] dims_pointer_; -#endif // TF_LITE_STATIC_MEMORY - } - size_ = dimensions_count; - if (dimensions_count > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - dims_pointer_ = new int32_t[dimensions_count]; -#endif // TF_LITE_STATIC_MEMORY - } - } - - inline void ReplaceWith(int dimensions_count, const int32_t* dims_data) { - Resize(dimensions_count); - int32_t* dst_dims = DimsData(); - std::memcpy(dst_dims, dims_data, dimensions_count * sizeof(int32_t)); - } - - template - inline void BuildFrom(const T& src_iterable) { - const int dimensions_count = - std::distance(src_iterable.begin(), src_iterable.end()); - Resize(dimensions_count); - int32_t* data = DimsData(); - for (auto it : src_iterable) { - *data = it; - ++data; - } - } - - // This will probably be factored out. Old code made substantial use of 4-D - // shapes, and so this function is used to extend smaller shapes. Note that - // (a) as Dims<4>-dependent code is eliminated, the reliance on this should be - // reduced, and (b) some kernels are stricly 4-D, but then the shapes of their - // inputs should already be 4-D, so this function should not be needed. - inline static RuntimeShape ExtendedShape(int new_shape_size, - const RuntimeShape& shape) { - return RuntimeShape(new_shape_size, shape, 1); - } - - inline void BuildFrom(const std::initializer_list init_list) { - BuildFrom>(init_list); - } - - // Returns the total count of elements, that is the size when flattened into a - // vector. - inline int FlatSize() const { - int buffer_size = 1; - const int* dims_data = reinterpret_cast(DimsData()); - for (int i = 0; i < size_; i++) { - buffer_size *= dims_data[i]; - } - return buffer_size; - } - - bool operator!=(const RuntimeShape& comp) const { return !((*this) == comp); } - - private: - // For use only by ExtendedShape(), written to guarantee (return-value) copy - // elision in C++17. - // This creates a shape padded to the desired size with the specified value. - RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) - : size_(0) { - // If the following check fails, it is likely because a 4D-only kernel is - // being used with an array of larger dimension count. - TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); - Resize(new_shape_size); - const int size_increase = new_shape_size - shape.DimensionsCount(); - for (int i = 0; i < size_increase; ++i) { - SetDim(i, pad_value); - } - std::memcpy(DimsData() + size_increase, shape.DimsData(), - sizeof(int32_t) * shape.DimensionsCount()); - } - - int32_t size_; - union { - int32_t dims_[kMaxSmallSize]; - int32_t* dims_pointer_; - }; -}; - -// Converts inference-style shape to legacy tflite::Dims<4>. -inline tflite::Dims<4> ToRuntimeDims(const tflite::RuntimeShape& array_shape) { - tflite::Dims<4> result; - const int dimensions_count = array_shape.DimensionsCount(); - TFLITE_CHECK_LE(dimensions_count, 4); - int cum_prod = 1; - for (int i = 0; i < 4; i++) { - const int new_dim = - (i < dimensions_count) ? array_shape.Dims(dimensions_count - 1 - i) : 1; - result.sizes[i] = new_dim; - result.strides[i] = cum_prod; - cum_prod *= new_dim; - } - return result; -} - -// TODO(b/80418076): Move to legacy ops file, update invocations. -inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { - return RuntimeShape( - {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(const int num_dims, const int* dims, int* current) { - if (num_dims == 0) { - return false; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(current != nullptr); - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) { - int current_val = current[idx] + carry; - TFLITE_DCHECK_GE(dims[idx], current_val); - if (dims[idx] == current_val) { - current[idx] = 0; - } else { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int* dims, - const int* index, const int num_axis, - const int* axis) { - if (num_dims == 0) { - return 0; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(index != nullptr); - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { - if (idx == axis[axis_idx]) { - is_axis = true; - break; - } - } - } - if (!is_axis) { - offset = offset * static_cast(dims[idx]) + - static_cast(index[idx]); - } - } - return offset; -} - -inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), 4); - const int* dims_data = reinterpret_cast(shape.DimsDataUpTo5D()); - TFLITE_DCHECK(i0 >= 0 && i0 < dims_data[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < dims_data[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < dims_data[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < dims_data[3]); - return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; -} - -inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < dims.sizes[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < dims.sizes[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < dims.sizes[3]); - return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + - i3 * dims.strides[3]; -} - -inline int Offset(const Dims<4>& dims, int* index) { - return Offset(dims, index[0], index[1], index[2], index[3]); -} - -inline int Offset(const RuntimeShape& shape, int* index) { - return Offset(shape, index[0], index[1], index[2], index[3]); -} - -// Get array size, DCHECKing that the dim index is in range. -// -// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() -// already performs this check. -template -int ArraySize(const Dims& array, int index) { - TFLITE_DCHECK(index >= 0 && index < N); - return array.sizes[index]; -} - -// Get common array size, DCHECKing that they all agree. -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return ArraySize(array1, index1); -} - -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2, Args... args) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return MatchingArraySize(array1, index1, args...); -} - -// Get common shape dim, DCHECKing that they all agree. -inline int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return shape1.Dims(index1); -} - -template -int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2, Args... args) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return MatchingDim(shape1, index1, args...); -} - -// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). -template -inline int FlatSize(const Dims& dims) { - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= dims.sizes[i]; - } - return flat_size; -} - -TFLITE_DEPRECATED("Prefer FlatSize.") -inline int RequiredBufferSizeForDims(const Dims<4>& dims) { - return FlatSize(dims); -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - return size_1; -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - const int size_3 = check_shape_1.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - TFLITE_CHECK_EQ(size_2, size_3); - return size_1; -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return shape.FlatSize(); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return FlatSize(dims); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -template -inline int FlatSizeSkipDim(const Dims& dims, int skip_dim) { - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < N); - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims.sizes[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return FlatSizeSkipDim(dims, skip_dim); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2, - check_dims_3); -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { - const int dims_count = shape.DimensionsCount(); - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); - const auto* dims_data = shape.DimsData(); - int flat_size = 1; - for (int i = 0; i < dims_count; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims_data[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return FlatSizeSkipDim(shape, skip_dim); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, - check_shape_3); -} - -template -bool IsPackedWithoutStrides(const Dims& dims) { - int expected_stride = 1; - for (int d = 0; d < N; d++) { - if (dims.strides[d] != expected_stride) return false; - expected_stride *= dims.sizes[d]; - } - return true; -} - -template -void ComputeStrides(Dims* dims) { - dims->strides[0] = 1; - for (int d = 1; d < N; d++) { - dims->strides[d] = dims->strides[d - 1] * dims->sizes[d - 1]; - } -} - -enum class BroadcastableOpCategory : uint8_t { - kNone, - kNonBroadcast, // Matching input shapes. - kFirstInputBroadcastsFast, // Fivefold nested loops. - kSecondInputBroadcastsFast, // Fivefold nested loops. - kGenericBroadcast, // Fall-back. -}; - -struct MinMax { - float min; - float max; -}; -static_assert(sizeof(MinMax) == 8, ""); - -struct ActivationParams { - FusedActivationFunctionType activation_type; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; -}; - -struct ReluParams : public ActivationParams { - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; -}; - -// Styles of resizing op usages. For example, kImageStyle can be used with a Pad -// op for pattern-specific optimization. -enum class ResizingCategory : uint8_t { - kNone, - kImageStyle, // 4D, operating on inner dimensions, say {0, a, b, 0}. - kGenericResize, -}; - -// For Add, Sub, Mul ops. -struct ArithmeticParams { - // Shape dependent / common to data / op types. - BroadcastableOpCategory broadcast_category; - // uint8_t inference params. - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // Add / Sub, not Mul, uint8_t inference params. - int left_shift; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_multiplier; - int input2_shift; - - // TODO(b/158622529): Union the following activation params. - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - // int64_t activation params. - int64_t int64_activation_min; - int64_t int64_activation_max; - - // Processed output dimensions. - // Let input "a" be the one that broadcasts in the faster-changing dimension. - // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and - // {b0, b1, b2, b3, b4}, - // broadcast_shape[4] = b0 = a0. - // broadcast_shape[3] = b1; a1 = 1. - // broadcast_shape[2] = b2 = a2. - // broadcast_shape[1] = a3; b3 = 1. - // broadcast_shape[0] = b4 = a4. - int broadcast_shape[5]; -}; - -struct ConcatenationParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16_t inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct ComparisonParams { - // uint8_t inference params. - int left_shift; - int32_t input1_offset; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_offset; - int32_t input2_multiplier; - int input2_shift; - // Shape dependent / common to inference types. - bool is_broadcast; -}; - -struct ConvParams { - PaddingType padding_type; - PaddingValues padding_values; - // TODO(starka): This was just "stride", so check that width+height is OK. - int16_t stride_width; - int16_t stride_height; - int16_t dilation_width_factor; - int16_t dilation_height_factor; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct DepthToSpaceParams { - int32_t block_size; -}; - -struct DepthwiseParams { - PaddingType padding_type; - PaddingValues padding_values; - int16_t stride_width; - int16_t stride_height; - int16_t dilation_width_factor; - int16_t dilation_height_factor; - int16_t depth_multiplier; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - const int32_t* output_multiplier_per_channel; - const int32_t* output_shift_per_channel; -}; - -struct DequantizationParams { - double scale; - int32_t zero_point; -}; - -struct PerChannelDequantizationParams { - const float* scale; - const int32_t* zero_point; - int32_t quantized_dimension; -}; - -struct FakeQuantParams { - MinMax minmax; - int32_t num_bits; -}; - -struct FullyConnectedParams { - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - // Mark the operands as cacheable if they are unchanging, e.g. weights. - bool lhs_cacheable; - bool rhs_cacheable; - FullyConnectedWeightsFormat weights_format; -}; - -struct GatherParams { - int16_t axis; -}; - -struct L2NormalizationParams { - // uint8_t inference params. - int32_t input_zero_point; -}; - -struct LocalResponseNormalizationParams { - int32_t range; - double bias; - double alpha; - double beta; -}; - -struct HardSwishParams { - // zero_point of the input activations. - int16_t input_zero_point; - // zero_point of the output activations. - int16_t output_zero_point; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // "relu-ish scale", which 3.0/32768. - // See the implementation of HardSwishPrepare. - int16_t reluish_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int reluish_multiplier_exponent; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // output scale. - // See the implementation of HardSwishPrepare. - int16_t output_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int output_multiplier_exponent; -}; - -struct LogisticParams { - // uint8_t inference params. - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -struct LstmCellParams { - int32_t weights_zero_point; - int32_t accum_multiplier; - int accum_shift; - int state_integer_bits; -}; - -struct MeanParams { - int8_t axis_count; - int16_t axis[4]; -}; - -struct PackParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16_t inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct PadParams { - int8_t left_padding_count; - int32_t left_padding[4]; - int8_t right_padding_count; - int32_t right_padding[4]; - ResizingCategory resizing_category; -}; - -struct PreluParams { - int32_t input_offset; - int32_t alpha_offset; - int32_t output_offset; - int32_t output_multiplier_1; - int output_shift_1; - int32_t output_multiplier_2; - int output_shift_2; -}; - -struct PoolParams { - FusedActivationFunctionType activation; - PaddingType padding_type; - PaddingValues padding_values; - int stride_height; - int stride_width; - int filter_height; - int filter_width; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct ReshapeParams { - int8_t shape_count; - int32_t shape[4]; -}; - -struct ResizeBilinearParams { - bool align_corners; - // half_pixel_centers assumes pixels are of half the actual dimensions, and - // yields more accurate resizes. Corresponds to the same argument for the - // original TensorFlow op in TF2.0. - bool half_pixel_centers; -}; - -struct ResizeNearestNeighborParams { - bool align_corners; - bool half_pixel_centers; -}; - -struct SliceParams { - int8_t begin_count; - int32_t begin[4]; - int8_t size_count; - int32_t size[4]; -}; - -struct SoftmaxParams { - // beta is not really used (not a Tensorflow parameter) and not implemented - // for LogSoftmax. - double beta; - // uint8_t inference params. Used even when beta defaults to 1.0. - int32_t input_multiplier; - int32_t input_left_shift; - // Reverse scaling is only used by LogSoftmax. - int32_t reverse_scaling_divisor; - int32_t reverse_scaling_right_shift; - int diff_min; - int32_t zero_point; - float scale; - float* table; - int16_t* exp_lut; - int16_t* one_over_one_plus_x_lut; - uint8_t* uint8_table1; - uint8_t* uint8_table2; -}; - -struct SpaceToBatchParams { - // "Zero" padding for uint8_t means padding with the output offset. - int32_t output_offset; -}; - -struct SpaceToDepthParams { - int32_t block_size; -}; - -struct SplitParams { - // Graphs that split into, say, 2000 nodes are encountered. The indices in - // OperatorEdges are of type uint16_t. - uint16_t num_split; - int16_t axis; -}; - -struct SqueezeParams { - int8_t squeeze_dims_count; - int32_t squeeze_dims[4]; -}; - -struct StridedSliceParams { - int8_t start_indices_count; - int32_t start_indices[5]; - int8_t stop_indices_count; - int32_t stop_indices[5]; - int8_t strides_count; - int32_t strides[5]; - - int16_t begin_mask; - int16_t ellipsis_mask; - int16_t end_mask; - int16_t new_axis_mask; - int16_t shrink_axis_mask; -}; - -struct TanhParams { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -struct TransposeParams { - int8_t perm_count; - int32_t perm[5]; -}; - -struct UnpackParams { - uint16_t num_split; - int16_t axis; -}; - -struct LeakyReluParams { - float alpha; - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier_alpha; - int32_t output_shift_alpha; - int32_t output_multiplier_identity; - int32_t output_shift_identity; -}; - -template -inline void SetActivationParams(float min, float max, P* params) { - params->float_activation_min = min; - params->float_activation_max = max; -} - -template -inline void SetActivationParams(int32_t min, int32_t max, P* params) { - params->quantized_activation_min = min; - params->quantized_activation_max = max; -} - -template -inline void SetActivationParams(int64_t min, int64_t max, P* params) { - params->int64_activation_min = min; - params->int64_activation_max = max; -} - -template -inline void GetActivationParams(const P& params, int32_t* min, int32_t* max) { - *min = params.quantized_activation_min; - *max = params.quantized_activation_max; -} - -template -inline void GetActivationParams(const P& params, float* min, float* max) { - *min = params.float_activation_min; - *max = params.float_activation_max; -} - -template -inline void GetActivationParams(const P& params, int64_t* min, int64_t* max) { - *min = params.int64_activation_min; - *max = params.int64_activation_max; -} -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/kernel_util.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/kernel_util.h deleted file mode 100644 index 0d6aa8fc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/kernel_util.h +++ /dev/null @@ -1,196 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ -#define TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ - -#include - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -// A fair number of functions in this header have historically been inline. -// It is ok to change functions to not be inline if the latency with -// benchmark_model for MobileNet + MobileBERT is unaffected. If such a change is -// made, move the newly non-inlined function declarations to the top of this -// header file. -const TfLiteTensor* GetInput(const TfLiteContext* context, - const TfLiteNode* node, int index); - -// Note: You must check if result is not null: -// TfLiteTensor* my_tensor = GetVariableInput(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -TfLiteTensor* GetVariableInput(TfLiteContext* context, const TfLiteNode* node, - int index); - -TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node, - int index); - -const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context, - const TfLiteNode* node, int index); - -inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; } -inline int SizeOfDimension(const TfLiteTensor* t, int dim) { - return t->dims->data[dim]; -} - -#ifndef TF_LITE_STATIC_MEMORY -inline TfLiteTensor* GetTemporary(TfLiteContext* context, - const TfLiteNode* node, int index) { - return &context->tensors[node->temporaries->data[index]]; -} -inline const TfLiteTensor* GetIntermediates(TfLiteContext* context, - const TfLiteNode* node, int index) { - return &context->tensors[node->intermediates->data[index]]; -} -inline int NumIntermediates(const TfLiteNode* node) { - return node->intermediates->size; -} -#endif // TF_LITE_STATIC_MEMORY -inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; } -inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; } - -inline int64_t NumElements(const TfLiteIntArray* dims) { - int64_t count = 1; - for (int i = 0; i < dims->size; ++i) { - count *= dims->data[i]; - } - return count; -} - -inline int64_t NumElements(const TfLiteTensor* t) { - return NumElements(t->dims); -} - -// Determines whether tensor is constant. -// TODO(b/138199592): Introduce new query which checks for constant OR -// persistent-read-only, which would be useful for most tensor kernels that -// are potentially dynamic based on the input tensor value availability at the -// time of prepare. -inline bool IsConstantTensor(const TfLiteTensor* tensor) { - return tensor->allocation_type == kTfLiteMmapRo; -} - -// Determines whether tensor is dynamic. Note that a tensor can be non-const and -// not dynamic. This function specifically checks for a dynamic tensor. -inline bool IsDynamicTensor(const TfLiteTensor* tensor) { - return tensor->allocation_type == kTfLiteDynamic; -} - -// Sets tensor to dynamic. -inline void SetTensorToDynamic(TfLiteTensor* tensor) { - if (tensor->allocation_type != kTfLiteDynamic) { - tensor->allocation_type = kTfLiteDynamic; - tensor->data.raw = nullptr; - } -} - -// Sets tensor to persistent and read-only. -inline void SetTensorToPersistentRo(TfLiteTensor* tensor) { - if (tensor->allocation_type != kTfLitePersistentRo) { - tensor->allocation_type = kTfLitePersistentRo; - tensor->data.raw = nullptr; - } -} - -// Determines whether it is a hybrid op - one that has float inputs and -// quantized weights. -inline bool IsHybridOp(const TfLiteTensor* input, const TfLiteTensor* weight) { - return ((weight->type == kTfLiteUInt8 || weight->type == kTfLiteInt8) && - input->type == kTfLiteFloat32); -} - -// Check dimensionality match and populate OpData for Conv and DepthwiseConv. -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int* per_channel_shift); - -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int* per_channel_shift, int num_channels); - -// Calculates the multiplication factor for a quantized convolution (or -// quantized depthwise convolution) involving the given tensors. Returns an -// error if the scales of the tensors are not compatible. -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - const TfLiteTensor* bias, - TfLiteTensor* output, - double* multiplier); - -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - TfLiteTensor* output, - double* multiplier); - -// Calculates the useful quantized range of an activation layer given its -// activation tensor. -TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, - TfLiteFusedActivation activation, - TfLiteTensor* output, - int32_t* act_min, - int32_t* act_max); - -// Calculates the useful range of an activation layer given its activation -// tensor.a -template -void CalculateActivationRange(TfLiteFusedActivation activation, - T* activation_min, T* activation_max) { - if (activation == kTfLiteActRelu) { - *activation_min = 0; - *activation_max = std::numeric_limits::max(); - } else if (activation == kTfLiteActRelu6) { - *activation_min = 0; - *activation_max = 6; - } else if (activation == kTfLiteActReluN1To1) { - *activation_min = -1; - *activation_max = 1; - } else { - *activation_min = std::numeric_limits::lowest(); - *activation_max = std::numeric_limits::max(); - } -} - -// Return true if the given tensors have the same shape. -bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2); - -// Calculates the output_shape that is necessary for element-wise operations -// with broadcasting involving the two input tensors. -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteIntArray** output_shape); - -// Calculates the output_shape that is necessary for element-wise operations -// with broadcasting involving the three input tensors. -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - const TfLiteTensor* input3, - TfLiteIntArray** output_shape); -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/op_macros.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/op_macros.h deleted file mode 100644 index 22a17634..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/op_macros.h +++ /dev/null @@ -1,83 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ -#define TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ - -// If we're on a platform without standard IO functions, fall back to a -// non-portable function. -#ifdef TF_LITE_MCU_DEBUG_LOG - -#include "tensorflow/lite/micro/debug_log.h" - -#define DEBUG_LOG(x) \ - do { \ - DebugLog(x); \ - } while (0) - -inline void InfiniteLoop() { - DEBUG_LOG("HALTED\n"); - while (1) { - } -} - -#define TFLITE_ABORT InfiniteLoop(); - -#else // TF_LITE_MCU_DEBUG_LOG - -#include -#include - -#define DEBUG_LOG(x) \ - do { \ - printf("%s", (x)); \ - } while (0) - -// Report Error for unsupported type by op 'op_name' and returns kTfLiteError. -#define TF_LITE_UNSUPPORTED_TYPE(context, type, op_name) \ - do { \ - TF_LITE_KERNEL_LOG((context), "%s:%d Type %s is unsupported by op %s.", \ - __FILE__, __LINE__, TfLiteTypeGetName(type), \ - (op_name)); \ - return kTfLiteError; \ - } while (0) - -#define TFLITE_ABORT abort() - -#endif // TF_LITE_MCU_DEBUG_LOG - -#ifdef NDEBUG -#define TFLITE_ASSERT_FALSE (static_cast(0)) -#else -#define TFLITE_ASSERT_FALSE TFLITE_ABORT -#endif - -#define TF_LITE_FATAL(msg) \ - do { \ - DEBUG_LOG(msg); \ - DEBUG_LOG("\nFATAL\n"); \ - TFLITE_ABORT; \ - } while (0) - -#define TF_LITE_ASSERT(x) \ - do { \ - if (!(x)) TF_LITE_FATAL(#x); \ - } while (0) - -#define TF_LITE_ASSERT_EQ(x, y) \ - do { \ - if ((x) != (y)) TF_LITE_FATAL(#x " didn't equal " #y); \ - } while (0) - -#endif // TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/padding.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/padding.h deleted file mode 100644 index 1116b1da..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/padding.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_ -#define TENSORFLOW_LITE_KERNELS_PADDING_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" - -namespace tflite { - -// TODO(renjieliu): Migrate others to use ComputePaddingWithLeftover. -inline int ComputePadding(int stride, int dilation_rate, int in_size, - int filter_size, int out_size) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2; - return padding > 0 ? padding : 0; -} - -// It's not guaranteed that padding is symmetric. It's important to keep -// offset for algorithms need all paddings. -inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size, - int filter_size, int out_size, - int* offset) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - int total_padding = - ((out_size - 1) * stride + effective_filter_size - in_size); - total_padding = total_padding > 0 ? total_padding : 0; - *offset = total_padding % 2; - return total_padding / 2; -} - -// Matching GetWindowedOutputSize in TensorFlow. -inline int ComputeOutSize(TfLitePadding padding, int image_size, - int filter_size, int stride, int dilation_rate = 1) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - switch (padding) { - case kTfLitePaddingSame: - return (image_size + stride - 1) / stride; - case kTfLitePaddingValid: - return (image_size + stride - effective_filter_size) / stride; - default: - return 0; - } -} - -inline TfLitePaddingValues ComputePaddingHeightWidth( - int stride_height, int stride_width, int dilation_rate_height, - int dilation_rate_width, int in_height, int in_width, int filter_height, - int filter_width, TfLitePadding padding, int* out_height, int* out_width) { - *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width, - dilation_rate_width); - *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height, - dilation_rate_height); - - TfLitePaddingValues padding_values; - int offset = 0; - padding_values.height = - ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height, - filter_height, *out_height, &offset); - padding_values.height_offset = offset; - padding_values.width = - ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width, - filter_width, *out_width, &offset); - padding_values.width_offset = offset; - return padding_values; -} -} // namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_PADDING_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/all_ops_resolver.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/all_ops_resolver.h deleted file mode 100644 index e8105b96..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/all_ops_resolver.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" - -namespace tflite { - -// The magic number in the template parameter is the maximum number of ops that -// can be added to AllOpsResolver. It can be increased if needed. And most -// applications that care about the memory footprint will want to directly use -// MicroMutableOpResolver and have an application specific template parameter. -// The examples directory has sample code for this. -class AllOpsResolver : public MicroMutableOpResolver<128> { - public: - AllOpsResolver(); - - private: - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.h deleted file mode 100644 index ce34426c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.h +++ /dev/null @@ -1,22 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_ -#define TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_ - -extern const unsigned char g_keyword_scrambled_model_data[]; -extern const unsigned int g_keyword_scrambled_model_data_length; - -#endif // TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/compatibility.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/compatibility.h deleted file mode 100644 index 49acb28f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/compatibility.h +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ -#define TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ - -// C++ will automatically create class-specific delete operators for virtual -// objects, which by default call the global delete function. For embedded -// applications we want to avoid this, and won't be calling new/delete on these -// objects, so we need to override the default implementation with one that does -// nothing to avoid linking in ::delete(). -// This macro needs to be included in all subclasses of a virtual base class in -// the private section. -#ifdef TF_LITE_STATIC_MEMORY -#define TF_LITE_REMOVE_VIRTUAL_DELETE \ - void operator delete(void* p) {} -#else -#define TF_LITE_REMOVE_VIRTUAL_DELETE -#endif - -#endif // TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/debug_log.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/debug_log.h deleted file mode 100644 index 1004ab9f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/debug_log.h +++ /dev/null @@ -1,23 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ -#define TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ - -// This function should be implemented by each target platform, and provide a -// way for strings to be output to some text stream. For more information, see -// tensorflow/lite/micro/debug_log.cc. -extern "C" void DebugLog(const char* s); - -#endif // TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h deleted file mode 100644 index aadad3be..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Provides an interface to take an action based on the output from the person -// detection model. - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -// Called every time the results of a person detection run are available. The -// `person_score` has the numerical confidence that the captured image contains -// a person, and `no_person_score` has the numerical confidence that the image -// does not contain a person. Typically if person_score > no person score, the -// image is considered to contain a person. This threshold may be adjusted for -// particular applications. -void RespondToDetection(tflite::ErrorReporter* error_reporter, - int8_t person_score, int8_t no_person_score); - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h deleted file mode 100644 index 4d18c4d6..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -// This is an abstraction around an image source like a camera, and is -// expected to return 8-bit sample data. The assumption is that this will be -// called in a low duty-cycle fashion in a low-power application. In these -// cases, the imaging sensor need not be run in a streaming mode, but rather can -// be idled in a relatively low-power mode between calls to GetImage(). The -// assumption is that the overhead and time of bringing the low-power sensor out -// of this standby mode is commensurate with the expected duty cycle of the -// application. The underlying sensor may actually be put into a streaming -// configuration, but the image buffer provided to GetImage should not be -// overwritten by the driver code until the next call to GetImage(); -// -// The reference implementation can have no platform-specific dependencies, so -// it just returns a static image. For real applications, you should -// ensure there's a specialized implementation that accesses hardware APIs. -TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width, - int image_height, int channels, int8_t* image_data, - uint8_t * hardware_input); - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h deleted file mode 100644 index 8361e318..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_ - -#include "tensorflow/lite/c/common.h" - -// Initializes all data needed for the example. The name is important, and needs -// to be setup() for Arduino compatibility. -extern "C" void person_detect_init(); - -// Runs one iteration of data gathering and inference. This should be called -// repeatedly from the application code. The name needs to be loop() for Arduino -// compatibility. -extern "C" int person_detect(uint8_t * hardware_input); - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h deleted file mode 100644 index f6c968e9..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_ - -// Keeping these as constant expressions allow us to allocate fixed-sized arrays -// on the stack for our working memory. - -// All of these values are derived from the values used during model training, -// if you change your model you'll need to update these constants. -constexpr int kNumCols = 96; -constexpr int kNumRows = 96; -constexpr int kNumChannels = 1; - -constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels; - -constexpr int kCategoryCount = 2; -constexpr int kPersonIndex = 1; -constexpr int kNotAPersonIndex = 0; -extern const char* kCategoryLabels[kCategoryCount]; - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h deleted file mode 100644 index 5d1b59ff..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This is a standard TensorFlow Lite model file that has been converted into a -// C data array, so it can be easily compiled into a binary for devices that -// don't have a file system. It was created using the command: -// xxd -i person_detect.tflite > person_detect_model_data.cc - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_ - -extern const unsigned char g_person_detect_model_data[]; -extern const int g_person_detect_model_data_len; - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/activation_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/activation_utils.h deleted file mode 100644 index 95ecc26d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/activation_utils.h +++ /dev/null @@ -1,57 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/max.h" -#include "tensorflow/lite/kernels/internal/min.h" - -namespace tflite { -namespace ops { -namespace micro { - -// Returns the floating point value for a fused activation: -inline float ActivationValFloat(TfLiteFusedActivation act, float a) { - switch (act) { - case kTfLiteActNone: - return a; - case kTfLiteActRelu: - return TfLiteMax(0.0f, a); - case kTfLiteActReluN1To1: - return TfLiteMax(-1.0f, TfLiteMin(a, 1.0f)); - case kTfLiteActRelu6: - return TfLiteMax(0.0f, TfLiteMin(a, 6.0f)); - case kTfLiteActTanh: - return std::tanh(a); - case kTfLiteActSignBit: - return std::signbit(a); - case kTfLiteActSigmoid: - return 1.0f / (1.0f + std::exp(-a)); - } - return 0.0f; // To indicate an unsupported activation (i.e. when a new fused - // activation is added to the enum and not handled here). -} - -} // namespace micro -} // namespace ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_runner.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_runner.h deleted file mode 100644 index 45d107e7..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_runner.h +++ /dev/null @@ -1,83 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/simple_memory_allocator.h" - -namespace tflite { -namespace micro { - -// Helper class to perform a simulated kernel (i.e. TfLiteRegistration) lifecyle -// (init, prepare, invoke). All internal allocations are handled by this class. -// Simply pass in the registration, list of required tensors, inputs array, -// outputs array, and any pre-builtin data. Calling Invoke() will automatically -// walk the kernl and outputs will be ready on the the TfLiteTensor output -// provided during construction. -class KernelRunner { - public: - KernelRunner(const TfLiteRegistration& registration, TfLiteTensor* tensors, - int tensors_size, TfLiteIntArray* inputs, - TfLiteIntArray* outputs, void* builtin_data, - ErrorReporter* error_reporter); - - // Calls init and prepare on the kernel (i.e. TfLiteRegistration) struct. Any - // exceptions will be reported through the error_reporter and returned as a - // status code here. - TfLiteStatus InitAndPrepare(const char* init_data = nullptr); - - // Calls init, prepare, and invoke on a given TfLiteRegistration pointer. - // After successful invoke, results will be available in the output tensor as - // passed into the constructor of this class. - TfLiteStatus Invoke(); - - protected: - static TfLiteTensor* GetTensor(const struct TfLiteContext* context, - int tensor_index); - static TfLiteEvalTensor* GetEvalTensor(const struct TfLiteContext* context, - int tensor_index); - static void* AllocatePersistentBuffer(TfLiteContext* context, size_t bytes); - static TfLiteStatus RequestScratchBufferInArena(TfLiteContext* context, - size_t bytes, - int* buffer_index); - static void* GetScratchBuffer(TfLiteContext* context, int buffer_index); - static void ReportOpError(struct TfLiteContext* context, const char* format, - ...); - - private: - static constexpr int kNumScratchBuffers_ = 5; - - static constexpr int kKernelRunnerBufferSize_ = 10000; - static uint8_t kKernelRunnerBuffer_[kKernelRunnerBufferSize_]; - - SimpleMemoryAllocator* allocator_ = nullptr; - const TfLiteRegistration& registration_; - TfLiteTensor* tensors_ = nullptr; - ErrorReporter* error_reporter_ = nullptr; - - TfLiteContext context_ = {}; - TfLiteNode node_ = {}; - - int scratch_buffer_count_ = 0; - uint8_t* scratch_buffers_[kNumScratchBuffers_]; -}; - -} // namespace micro -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_util.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_util.h deleted file mode 100644 index 530e52df..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/kernel_util.h +++ /dev/null @@ -1,83 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { -namespace micro { - -// Returns a mutable tensor for a given input index. is_variable must be checked -// during prepare when the full TfLiteTensor is available. -inline TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context, - const TfLiteNode* node, - int index) { - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(node != nullptr); - return context->GetEvalTensor(context, node->inputs->data[index]); -} - -// Returns the TfLiteEvalTensor struct for a given input index in a node. -inline const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context, - const TfLiteNode* node, int index) { - return GetMutableEvalInput(context, node, index); -} - -// Returns the TfLiteEvalTensor struct for a given output index in a node. -inline TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context, - const TfLiteNode* node, int index) { - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(node != nullptr); - return context->GetEvalTensor(context, node->outputs->data[index]); -} - -// Returns data for a TfLiteEvalTensor struct. -template -T* GetTensorData(TfLiteEvalTensor* tensor) { - return tensor != nullptr ? reinterpret_cast(tensor->data.raw) : nullptr; -} - -// Returns const data for a TfLiteEvalTensor struct. -template -const T* GetTensorData(const TfLiteEvalTensor* tensor) { - TFLITE_DCHECK(tensor != nullptr); - return reinterpret_cast(tensor->data.raw); -} - -// Returns the shape of a TfLiteEvalTensor struct. -inline const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor) { - if (tensor == nullptr) { - return RuntimeShape(); - } - TfLiteIntArray* dims = tensor->dims; - const int dims_size = dims->size; - const int32_t* dims_data = reinterpret_cast(dims->data); - return RuntimeShape(dims_size, dims_data); -} - -// Return true if the given tensors have the same shape. -bool HaveSameShapes(const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2); - -} // namespace micro -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_ops.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_ops.h deleted file mode 100644 index 7e63f342..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_ops.h +++ /dev/null @@ -1,92 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite { -namespace ops { -namespace micro { - -// Forward declaration of all micro op kernel registration methods. These -// registrations are included with the standard `BuiltinOpResolver`. -// -// This header is particularly useful in cases where only a subset of ops are -// needed. In such cases, the client can selectively add only the registrations -// their model requires, using a custom `(Micro)MutableOpResolver`. Selective -// registration in turn allows the linker to strip unused kernels. - -TfLiteRegistration Register_ABS(); -TfLiteRegistration Register_ADD(); -TfLiteRegistration Register_ARG_MAX(); -TfLiteRegistration Register_ARG_MIN(); -TfLiteRegistration Register_AVERAGE_POOL_2D(); -TfLiteRegistration Register_CEIL(); -// TODO(b/160234179): Change custom OPs to also return by value. -TfLiteRegistration* Register_CIRCULAR_BUFFER(); -TfLiteRegistration Register_CONV_2D(); -TfLiteRegistration Register_CONCATENATION(); -TfLiteRegistration Register_COS(); -TfLiteRegistration Register_DEPTHWISE_CONV_2D(); -TfLiteRegistration Register_DEQUANTIZE(); -TfLiteRegistration Register_EQUAL(); -TfLiteRegistration Register_FLOOR(); -TfLiteRegistration Register_FULLY_CONNECTED(); -TfLiteRegistration Register_GREATER(); -TfLiteRegistration Register_GREATER_EQUAL(); -TfLiteRegistration Register_HARD_SWISH(); -TfLiteRegistration Register_LESS(); -TfLiteRegistration Register_LESS_EQUAL(); -TfLiteRegistration Register_LOG(); -TfLiteRegistration Register_LOGICAL_AND(); -TfLiteRegistration Register_LOGICAL_NOT(); -TfLiteRegistration Register_LOGICAL_OR(); -TfLiteRegistration Register_LOGISTIC(); -TfLiteRegistration Register_MAXIMUM(); -TfLiteRegistration Register_MAX_POOL_2D(); -TfLiteRegistration Register_MEAN(); -TfLiteRegistration Register_MINIMUM(); -TfLiteRegistration Register_MUL(); -TfLiteRegistration Register_NEG(); -TfLiteRegistration Register_NOT_EQUAL(); -TfLiteRegistration Register_PACK(); -TfLiteRegistration Register_PAD(); -TfLiteRegistration Register_PADV2(); -TfLiteRegistration Register_PRELU(); -TfLiteRegistration Register_QUANTIZE(); -TfLiteRegistration Register_RELU(); -TfLiteRegistration Register_RELU6(); -TfLiteRegistration Register_RESHAPE(); -TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR(); -TfLiteRegistration Register_ROUND(); -TfLiteRegistration Register_RSQRT(); -TfLiteRegistration Register_SIN(); -TfLiteRegistration Register_SOFTMAX(); -TfLiteRegistration Register_SPLIT(); -TfLiteRegistration Register_SQRT(); -TfLiteRegistration Register_SQUARE(); -TfLiteRegistration Register_STRIDED_SLICE(); -TfLiteRegistration Register_SUB(); -TfLiteRegistration Register_SVDF(); -TfLiteRegistration Register_UNPACK(); -TfLiteRegistration Register_L2_NORMALIZATION(); -TfLiteRegistration Register_TANH(); - -} // namespace micro -} // namespace ops -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_utils.h deleted file mode 100644 index 85db263e..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/kernels/micro_utils.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_ -namespace tflite { -namespace ops { -namespace micro { - -// Same as gtl::Greater but defined here to reduce dependencies and -// binary size for micro environment. -struct Greater { - template - bool operator()(const T& x, const T& y) const { - return x > y; - } -}; - -struct Less { - template - bool operator()(const T& x, const T& y) const { - return x < y; - } -}; - -} // namespace micro -} // namespace ops -} // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_helpers.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_helpers.h deleted file mode 100644 index 8f5526ce..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_helpers.h +++ /dev/null @@ -1,59 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -// Returns the next pointer address aligned to the given alignment. -uint8_t* AlignPointerUp(uint8_t* data, size_t alignment); - -// Returns the previous pointer address aligned to the given alignment. -uint8_t* AlignPointerDown(uint8_t* data, size_t alignment); - -// Returns an increased size that's a multiple of alignment. -size_t AlignSizeUp(size_t size, size_t alignment); - -// Returns size in bytes for a given TfLiteType. -TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size); - -// How many bytes are needed to hold a tensor's contents. -TfLiteStatus BytesRequiredForTensor(const tflite::Tensor& flatbuffer_tensor, - size_t* bytes, size_t* type_size, - ErrorReporter* error_reporter); - -// How many bytes are used in a TfLiteEvalTensor instance. The byte length is -// returned in out_bytes. -TfLiteStatus TfLiteEvalTensorByteLength(const TfLiteEvalTensor* eval_tensor, - size_t* out_bytes); - -// Deduce output dimensions from input and allocate given size. -// Useful for operators with two inputs where the largest input should equal the -// output dimension. -TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteTensor* output); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h deleted file mode 100644 index f5f26a8b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h +++ /dev/null @@ -1,163 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_planner/memory_planner.h" - -namespace tflite { - -constexpr int kOnlinePlannedBuffer = -1; - -// A memory planner that uses a greedy algorithm to arrange buffers in memory -// to minimize the overall arena size needed. -// -// The algorithm works like this: -// - The client enters the buffer information through AddBuffer(). -// - When a function like GetOffsetForBuffer() is called, the -// CalculateOffsetsIfNeeded() method is invoked. -// - If an up to date plan is not already present, one will be calculated. -// - The buffers are sorted in descending order of size. -// - The largest buffer is placed at offset zero. -// - The rest of the buffers are looped through in descending size order. -// - The other buffers that need to be in memory at the same time are found. -// - The first gap between simultaneously active buffers that the current -// buffer fits into will be used. -// - If no large-enough gap is found, the current buffer is placed after the -// last buffer that's simultaneously active. -// - This continues until all buffers are placed, and the offsets stored. -// -// This is not guaranteed to produce the best placement, since that's an -// NP-Complete problem, but in practice it should produce one that's decent. -class GreedyMemoryPlanner : public MemoryPlanner { - public: - // You need to pass in an area of memory to be used for planning. This memory - // needs to have a lifetime as long as the planner, but isn't owned by this - // object, so management should be handled by the client. This is so it can be - // stack or globally allocated if necessary on devices without dynamic memory - // allocation. How many buffers can be planned for will depend on the size of - // this scratch memory, so you should enlarge it if you see an error when - // calling AddBuffer(). The memory can be reused once you're done with the - // planner, as long as you copy the calculated offsets to another location. - // Each buffer requires about 36 bytes of scratch. - GreedyMemoryPlanner(unsigned char* scratch_buffer, int scratch_buffer_size); - ~GreedyMemoryPlanner() override; - - // Record details of a buffer we want to place. - TfLiteStatus AddBuffer(ErrorReporter* error_reporter, int size, - int first_time_used, int last_time_used) override; - - // Record details of an offline planned buffer offset we want to place. - // offline_offset is the buffer offset from the start of the arena. - TfLiteStatus AddBuffer(ErrorReporter* error_reporter, int size, - int first_time_used, int last_time_used, - int offline_offset); - - // Returns the high-water mark of used memory. This is the minimum size of a - // memory arena you'd need to allocate to hold these buffers. - size_t GetMaximumMemorySize() override; - - // How many buffers have been recorded. - int GetBufferCount() override; - - // Where a given buffer should be placed in the memory arena. - // This information is stored in the memory arena itself, so once the arena - // is used for inference, it will be overwritten. - TfLiteStatus GetOffsetForBuffer(ErrorReporter* error_reporter, - int buffer_index, int* offset) override; - - // Prints an ascii-art diagram of the buffer layout plan. - void PrintMemoryPlan(ErrorReporter* error_reporter); - - // Debug method to check whether any buffer allocations are overlapping. This - // is an O(N^2) complexity operation, so only use for testing. - bool DoAnyBuffersOverlap(ErrorReporter* error_reporter); - - // Used to store a list of buffers ordered by their offset. - struct ListEntry { - int offset; - int requirements_index; - int next_entry_index; - }; - - // Number of bytes required in order to plan a buffer. - static size_t per_buffer_size() { - const int per_buffer_size = - sizeof(BufferRequirements) + // requirements_ - sizeof(int) + // buffer_sizes_sorted_ - sizeof(int) + // buffer_ids_sorted_ - sizeof(ListEntry) + // buffers_sorted_by_offset_ - sizeof(int); // buffer_offsets_; - return per_buffer_size; - } - - private: - // Whether a buffer is active in a given time range. - bool DoesEntryOverlapInTime(const ListEntry* entry, const int first_time_used, - const int last_time_used) const; - - // Walks the list to return the next buffer that is active in a given time - // range, or a null pointer if there are none. - ListEntry* NextSimultaneouslyActiveBuffer(const ListEntry* start, - const int first_time_used, - const int last_time_used); - - // If there isn't an up to date plan, calculate a new one. - void CalculateOffsetsIfNeeded(); - - // How many buffers we can plan for, based on the arena size we're given in - // the constructor. - int max_buffer_count_; - - // The number of buffers added so far. - int buffer_count_; - - // Records the client-provided information about each buffer. - struct BufferRequirements { - int size; - int offline_offset; - int first_time_used; - int last_time_used; - }; - - // Working arrays used during the layout algorithm. - BufferRequirements* requirements_; - // buffer_sizes_sorted_ and buffer_ids_sorted_ are sorted according to: - // { - // offline planned buffers, - // online planned buffers sorted by size - // } - int* buffer_sizes_sorted_; - int* buffer_ids_sorted_; - ListEntry* buffers_sorted_by_offset_; - int next_free_entry_; // Index of the next free entry of - // buffers_sorted_by_offset_ - int first_entry_index_; // Index of the first entry (smallest offset) of - // buffers_sorted_by_offset_ - - // Stores the outcome of the plan, the location of each buffer in the arena. - int* buffer_offsets_; - - // Whether buffers have been added since the last plan was calculated. - bool need_to_calculate_offsets_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/linear_memory_planner.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/linear_memory_planner.h deleted file mode 100644 index 4d77e778..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/linear_memory_planner.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_planner/memory_planner.h" - -namespace tflite { - -// The simplest possible memory planner that just lays out all buffers at -// increasing offsets without trying to reuse memory. -class LinearMemoryPlanner : public MemoryPlanner { - public: - LinearMemoryPlanner(); - ~LinearMemoryPlanner() override; - - TfLiteStatus AddBuffer(tflite::ErrorReporter* error_reporter, int size, - int first_time_used, int last_time_used) override; - - size_t GetMaximumMemorySize() override; - int GetBufferCount() override; - TfLiteStatus GetOffsetForBuffer(tflite::ErrorReporter* error_reporter, - int buffer_index, int* offset) override; - - private: - static constexpr int kMaxBufferCount = 1024; - size_t buffer_offsets_[kMaxBufferCount]; - int current_buffer_count_; - size_t next_free_offset_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/memory_planner.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/memory_planner.h deleted file mode 100644 index 2c39fbe0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/memory_planner/memory_planner.h +++ /dev/null @@ -1,71 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" - -namespace tflite { - -// Interface class for planning the layout of memory buffers during the -// execution of a graph. -// It's designed to be used by a client that iterates in any order through the -// buffers it wants to lay out, and then calls the getter functions for -// information about the calculated layout. For example: -// -// SomeMemoryPlanner planner; -// planner.AddBuffer(reporter, 100, 0, 1); // Buffer 0 -// planner.AddBuffer(reporter, 50, 2, 3); // Buffer 1 -// planner.AddBuffer(reporter, 50, 2, 3); // Buffer 2 -// -// int offset0; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 0, &offset0)); -// int offset1; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 1, &offset1)); -// int offset2; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 2, &offset2)); -// const int arena_size_needed = planner.GetMaximumMemorySize(); -// -// The goal is for applications to be able to experiment with different layout -// strategies without changing their client code, by swapping out classes that -// implement this interface.= -class MemoryPlanner { - public: - MemoryPlanner() {} - virtual ~MemoryPlanner() {} - - // Pass information about a buffer's size and lifetime to the layout - // algorithm. The order this is called implicitly assigns an index to the - // result, so the buffer information that's passed into the N-th call of - // this method will be used as the buffer_index argument to - // GetOffsetForBuffer(). - virtual TfLiteStatus AddBuffer(tflite::ErrorReporter* error_reporter, - int size, int first_time_used, - int last_time_used) = 0; - - // The largest contiguous block of memory that's needed to hold the layout. - virtual size_t GetMaximumMemorySize() = 0; - // How many buffers have been added to the planner. - virtual int GetBufferCount() = 0; - // Calculated layout offset for the N-th buffer added to the planner. - virtual TfLiteStatus GetOffsetForBuffer(tflite::ErrorReporter* error_reporter, - int buffer_index, int* offset) = 0; -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_allocator.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_allocator.h deleted file mode 100644 index efd11b8b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_allocator.h +++ /dev/null @@ -1,250 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. -b/160894903 -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/micro/simple_memory_allocator.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -// Namespace used for unittests. -namespace internal { - -// Sets up all of the data structure members for a TfLiteTensor based on the -// contents of a serialized tensor in the flatbuffer. -// TODO(b/160894903): Once all kernels have been updated to the new -// TfLiteEvalTensor API - drop the allocate_temp flag. This enables internal -// flatbuffer quantization or dimension allocations to take place in either the -// temp or tail section of the arena. -TfLiteStatus InitializeTfLiteTensorFromFlatbuffer( - SimpleMemoryAllocator* allocator, bool allocate_temp, - const tflite::Tensor& flatbuffer_tensor, - const flatbuffers::Vector>* buffers, - ErrorReporter* error_reporter, TfLiteTensor* result); - -// A handle tracking scratch buffer allocation. This handle is created by -// `RequestScratchBufferInArena`. `data` field is populated in -// `FinishModelAllocation` after static memory planning. -// TODO(b/150257460) As a future optimization, this struct could be replaced by -// a union, since once `data` is populated, `bytes` and `node_idx` is not -// needed. -typedef struct { - // Pointer to the scratch buffer. - uint8_t* data; - // Number of bytes required by the buffer. The actual allocated size might be - // greater than `bytes` due to buffer alignment. - size_t bytes; - // Node where the buffer is allocated for. This provides useful information to - // determine the lifetime of the buffer. In AllocationInfo, this buffer will - // have `before` = node_idx and `after` = node_idx. - int node_idx; -} ScratchBufferHandle; -} // namespace internal - -typedef struct { - TfLiteNode node; - const TfLiteRegistration* registration; -} NodeAndRegistration; - -// Allocator responsible for allocating memory for all intermediate tensors -// necessary to invoke a model. -// -// The lifetime of the model, tensor arena and error reporter must be at -// least as long as that of the allocator object, since the allocator needs -// them to be accessible during its entire lifetime. -// -// The MicroAllocator simply plans out additional allocations that are required -// to standup a model for inference in TF Micro. This class currently relies on -// an additional allocator - SimpleMemoryAllocator - for all allocations from an -// arena. These allocations are divided into head (non-persistent) and tail -// (persistent) regions: -// -// Memory layout to help understand how it works -// This information could change in the future version. -// ************** .memory_allocator->GetBuffer() -// Tensors/Scratch buffers (head) -// ************** .head_watermark -// unused memory -// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() -// - ->GetDataSize() -// persistent area (tail) -// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() -class MicroAllocator { - public: - // Creates a MicroAllocator instance from a given tensor arena. This arena - // will be managed by the created instance. - // Note: Please use __declspec(align(16)) to make sure tensor_arena is 16 - // bytes aligned, otherwise some head room will be wasted. - // TODO(b/157615197): Cleanup constructor + factory usage. - static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size, - ErrorReporter* error_reporter); - - // Creates a MicroAllocator instance using the provided SimpleMemoryAllocator - // intance. This allocator instance will use the SimpleMemoryAllocator - // instance to manage allocations internally. - static MicroAllocator* Create(SimpleMemoryAllocator* memory_allocator, - ErrorReporter* error_reporter); - - // Begin allocating internal resources required for model inference. - // This method will run through the flatbuffer data supplied in the model to - // properly allocate tensor, node, and op registration data. This method is - // expected to be followed with a call to FinishModelAllocation() before - // resuming allocation with another model. All persistent tensor buffers are - // stored in the out-param eval_tensors. This value is allocated from the - // persistent memory arena and will be used to host runtime tensor buffers. - TfLiteStatus StartModelAllocation( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration** node_and_registrations, - TfLiteEvalTensor** eval_tensors); - - // Finish allocating internal resources required for model inference. - // This method will plan non-persistent buffers and commit a memory plan to - // the 'head' section of the memory arena. All variable tensor data will also - // be allocated. This method should be called after assigning model resources - // in StartModelAllocation(). The eval_tensors pointer should be the value - // passed into this class during StartModelAllocation(). - TfLiteStatus FinishModelAllocation(const Model* model, - TfLiteEvalTensor* eval_tensors); - - // Allocates a TfLiteTensor struct and populates the returned value with - // properties from the model flatbuffer. This struct is allocated from - // persistent arena memory is only guaranteed for the lifetime of the - // application. The eval_tensors pointer should be the value passed into this - // class during StartModelAllocation() and contains the source-of-truth for - // buffers. - virtual TfLiteTensor* AllocatePersistentTfLiteTensor( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index); - - // Allocates a TfLiteTensor struct and populates the returned value with - // properties from the model flatbuffer. This struct is allocated from - // temporary arena memory is only guaranteed until a call is made to - // ResetTempAllocations(). The eval_tensors pointer should be the value passed - // into this class during StartModelAllocation() and contains the - // source-of-truth for buffers. - virtual TfLiteTensor* AllocateTempTfLiteTensor(const Model* model, - TfLiteEvalTensor* eval_tensors, - int tensor_index); - - // Resets all temporary allocations. This method should be called after a - // chain of temp allocations (e.g. chain of TfLiteTensor objects via - // AllocateTfLiteTensor()). - virtual void ResetTempAllocations(); - - // Allocates persistent buffer which has the same life time as the allocator. - // The memory is immediately available and is allocated from the tail of the - // arena. - void* AllocatePersistentBuffer(size_t bytes); - - // Register a scratch buffer of size `bytes` for Node with `node_id`. - // This method only allocates a BufferHandle holding information for memory - // planning. The buffer ptr is ready after `FinishModelAllocation` and can - // be retrieved by `GetScratchBuffer` method using the returned buffer_idx. - // Note that there should be no tail allocation between two consecutive - // `RequestScratchBufferInArena` calls. - TfLiteStatus RequestScratchBufferInArena(int node_id, size_t bytes, - int* buffer_idx); - // Returns the pointer to the planned scratch buffer. - void* GetScratchBuffer(int buffer_idx) const; - - // Returns the arena usage in bytes, only available after - // `FinishModelAllocation`. Otherwise, it will return 0. - size_t used_bytes() const; - - protected: - MicroAllocator(SimpleMemoryAllocator* memory_allocator, - ErrorReporter* error_reporter); - virtual ~MicroAllocator(); - - // Allocates an array in the arena to hold pointers to the node and - // registration pointers required to represent the inference graph of the - // model. - virtual TfLiteStatus AllocateNodeAndRegistrations( - const Model* model, NodeAndRegistration** node_and_registrations); - - // Populates node and registration pointers representing the inference graph - // of the model from values inside the flatbuffer (loaded from the TfLiteModel - // instance). Persistent data (e.g. operator data) is allocated from the - // arena. - virtual TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration* node_and_registrations); - - // Allocates the list of persistent TfLiteEvalTensors that are used for the - // "eval" phase of model inference. These structs will be the source of truth - // for all tensor buffers. Allocation results are stored in the out-param - // eval_tensors. - virtual TfLiteStatus AllocateTfLiteEvalTensors( - const Model* model, TfLiteEvalTensor** eval_tensors); - - // Allocates persistent tensor buffers for variable tensors in the subgraph. - virtual TfLiteStatus AllocateVariables(const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors); - - // TODO(b/160894903): Once all kernels have been updated to the new API drop - // this method. It is only used to record TfLiteTensor persistent allocations. - virtual TfLiteTensor* AllocatePersistentTfLiteTensorInternal( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index); - - // Populates a TfLiteTensor struct with data from the model flatbuffer. Any - // quantization data is allocated from either the tail (persistent) or temp - // sections of the arena based on the allocation flag. - // TODO(b/160894903): Once all kernels have been updated to the new API drop - // this function since all allocations for quantized data will take place in - // the temp section. - virtual TfLiteStatus PopulateTfLiteTensorFromFlatbuffer( - const Model* model, const SubGraph* subgraph, TfLiteTensor* tensor, - int tensor_index, bool allocate_temp); - - ErrorReporter* error_reporter() const; - - // Returns the first subgraph from the model. - const SubGraph* GetSubGraphFromModel(const Model* model); - - private: - // Commits a memory plan for all non-persistent buffer allocations in the - // 'head' section of the memory arena. The eval_tensors pointer is the list of - // pre-allocated TfLiteEvalTensor structs that will point to the buffers that - // will be allocated into the head section in this function call. - virtual TfLiteStatus CommitStaticMemoryPlan(const Model* model, - const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors); - - // A simple memory allocator that always allocate from the arena tail or head. - SimpleMemoryAllocator* memory_allocator_; - - ErrorReporter* error_reporter_; - bool model_is_allocating_; - - // In reverse order for efficiency. - // i.e. scratch_buffer_handles_[0] is the handle for the last buffer, - // corresponding to the last RequestScratchBufferInArena call. - internal::ScratchBufferHandle* scratch_buffer_handles_ = nullptr; - // How many scratch buffers have been allocated. - size_t scratch_buffer_count_ = 0; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_error_reporter.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_error_reporter.h deleted file mode 100644 index e2c073a4..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_error_reporter.h +++ /dev/null @@ -1,36 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_ - -#include - -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite { - -class MicroErrorReporter : public ErrorReporter { - public: - ~MicroErrorReporter() override {} - int Report(const char* format, va_list args) override; - - private: - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_interpreter.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_interpreter.h deleted file mode 100644 index 67d74574..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_interpreter.h +++ /dev/null @@ -1,208 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/profiler.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/schema/schema_generated.h" -#include "tensorflow/lite/type_to_tflitetype.h" - -namespace tflite { - -namespace internal { - -// A helper class to encapsulate the implementation of APIs in Context. -// context->impl_ points to an instance of this class. -// Check tensorflow/lite/c/common.h for detailed descriptions. -// TODO(b/16157777): Consider rolling this class into MicroInterpreter. -class ContextHelper { - public: - explicit ContextHelper(ErrorReporter* error_reporter, - MicroAllocator* allocator, const Model* model); - - // Functions that will be assigned to function pointers on TfLiteContext: - static void* AllocatePersistentBuffer(TfLiteContext* ctx, size_t bytes); - static TfLiteStatus RequestScratchBufferInArena(TfLiteContext* ctx, - size_t bytes, - int* buffer_idx); - static void* GetScratchBuffer(TfLiteContext* ctx, int buffer_idx); - static void ReportOpError(struct TfLiteContext* context, const char* format, - ...); - static TfLiteTensor* GetTensor(const struct TfLiteContext* context, - int tensor_idx); - static TfLiteEvalTensor* GetEvalTensor(const struct TfLiteContext* context, - int tensor_idx); - - // Sets the current node index to assist with scratch buffer allocations: - void SetNodeIndex(int idx); - - // Sets the pointer to a list of TfLiteEvalTensor instances. - void SetTfLiteEvalTensors(TfLiteEvalTensor* eval_tensors); - - private: - MicroAllocator* allocator_; - ErrorReporter* error_reporter_; - const Model* model_; - TfLiteEvalTensor* eval_tensors_; - int current_node_idx_ = -1; -}; - -} // namespace internal - -class MicroInterpreter { - public: - // The lifetime of the model, op resolver, tensor arena, error reporter and - // profiler must be at least as long as that of the interpreter object, since - // the interpreter may need to access them at any time. This means that you - // should usually create them with the same scope as each other, for example - // having them all allocated on the stack as local variables through a - // top-level function. The interpreter doesn't do any deallocation of any of - // the pointed-to objects, ownership remains with the caller. - MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, - uint8_t* tensor_arena, size_t tensor_arena_size, - ErrorReporter* error_reporter, - tflite::Profiler* profiler = nullptr); - - // Create an interpreter instance using an existing MicroAllocator instance. - // This constructor should be used when creating an allocator that needs to - // have allocation handled in more than one interpreter or for recording - // allocations inside the interpreter. The lifetime of the allocator must be - // as long as that of the interpreter object. - MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, - MicroAllocator* allocator, ErrorReporter* error_reporter, - tflite::Profiler* profiler = nullptr); - - ~MicroInterpreter(); - - // Runs through the model and allocates all necessary input, output and - // intermediate tensors. - TfLiteStatus AllocateTensors(); - - // In order to support partial graph runs for strided models, this can return - // values other than kTfLiteOk and kTfLiteError. - // TODO(b/149795762): Add this to the TfLiteStatus enum. - TfLiteStatus Invoke(); - - size_t tensors_size() const { return context_.tensors_size; } - TfLiteTensor* tensor(size_t tensor_index); - template - T* typed_tensor(int tensor_index) { - if (TfLiteTensor* tensor_ptr = tensor(tensor_index)) { - if (tensor_ptr->type == typeToTfLiteType()) { - return GetTensorData(tensor_ptr); - } - } - return nullptr; - } - - TfLiteTensor* input(size_t index); - size_t inputs_size() const { return subgraph_->inputs()->Length(); } - const flatbuffers::Vector& inputs() const { - return *subgraph_->inputs(); - } - TfLiteTensor* input_tensor(size_t index) { return input(index); } - template - T* typed_input_tensor(int tensor_index) { - if (TfLiteTensor* tensor_ptr = input_tensor(tensor_index)) { - if (tensor_ptr->type == typeToTfLiteType()) { - return GetTensorData(tensor_ptr); - } - } - return nullptr; - } - - TfLiteTensor* output(size_t index); - size_t outputs_size() const { return subgraph_->outputs()->Length(); } - const flatbuffers::Vector& outputs() const { - return *subgraph_->outputs(); - } - TfLiteTensor* output_tensor(size_t index) { return output(index); } - template - T* typed_output_tensor(int tensor_index) { - if (TfLiteTensor* tensor_ptr = output_tensor(tensor_index)) { - if (tensor_ptr->type == typeToTfLiteType()) { - return GetTensorData(tensor_ptr); - } - } - return nullptr; - } - - // Reset all variable tensors to the default value. - TfLiteStatus ResetVariableTensors(); - - TfLiteStatus initialization_status() const { return initialization_status_; } - - size_t operators_size() const { return subgraph_->operators()->size(); } - - // For debugging only. - const NodeAndRegistration node_and_registration(int node_index) const { - return node_and_registrations_[node_index]; - } - - // For debugging only. - // Returns the actual used arena in bytes. This method gives the optimal arena - // size. It's only available after `AllocateTensors` has been called. - // Note that normally `tensor_arena` requires 16 bytes alignment to fully - // utilize the space. If it's not the case, the optimial arena size would be - // arena_used_bytes() + 16. - size_t arena_used_bytes() const { return allocator_.used_bytes(); } - - protected: - const MicroAllocator& allocator() const { return allocator_; } - const TfLiteContext& context() const { return context_; } - - private: - // TODO(b/158263161): Consider switching to Create() function to enable better - // error reporting during initialization. - void Init(tflite::Profiler* profiler); - - void CorrectTensorEndianness(TfLiteEvalTensor* tensorCorr); - - template - void CorrectTensorDataEndianness(T* data, int32_t size); - - NodeAndRegistration* node_and_registrations_ = nullptr; - - const Model* model_; - const MicroOpResolver& op_resolver_; - ErrorReporter* error_reporter_; - TfLiteContext context_ = {}; - MicroAllocator& allocator_; - bool tensors_allocated_; - - TfLiteStatus initialization_status_; - - const SubGraph* subgraph_; - TfLiteEvalTensor* eval_tensors_; - internal::ContextHelper context_helper_; - - // TODO(b/160894903): Clean these pointers up when all APIs are updated to new - // TfLiteEvalTensor buffers. - TfLiteTensor* input_tensor_; - TfLiteTensor* output_tensor_; -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_mutable_op_resolver.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_mutable_op_resolver.h deleted file mode 100644 index 27a66a91..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_mutable_op_resolver.h +++ /dev/null @@ -1,458 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/kernels/micro_ops.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -template -class MicroMutableOpResolver : public MicroOpResolver { - public: - explicit MicroMutableOpResolver(ErrorReporter* error_reporter = nullptr) - : error_reporter_(error_reporter) {} - - const TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override { - if (op == BuiltinOperator_CUSTOM) return nullptr; - - for (unsigned int i = 0; i < registrations_len_; ++i) { - const TfLiteRegistration& registration = registrations_[i]; - if (registration.builtin_code == op) { - return ®istration; - } - } - return nullptr; - } - - const TfLiteRegistration* FindOp(const char* op) const override { - for (unsigned int i = 0; i < registrations_len_; ++i) { - const TfLiteRegistration& registration = registrations_[i]; - if ((registration.builtin_code == BuiltinOperator_CUSTOM) && - (strcmp(registration.custom_name, op) == 0)) { - return ®istration; - } - } - return nullptr; - } - - MicroOpResolver::BuiltinParseFunction GetOpDataParser( - BuiltinOperator op) const override { - TFLITE_DCHECK(num_buitin_ops_ <= tOpCount); - for (unsigned int i = 0; i < num_buitin_ops_; ++i) { - if (builtin_codes_[i] == op) return builtin_parsers_[i]; - } - return nullptr; - } - - // Registers a Custom Operator with the MicroOpResolver. - // - // Only the first call for a given name will be successful. i.e. if this - // function is called again for a previously added Custom Operator, the - // MicroOpResolver will be unchanged and this function will return - // kTfLiteError. - TfLiteStatus AddCustom(const char* name, TfLiteRegistration* registration) { - if (registrations_len_ >= tOpCount) { - if (error_reporter_) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Couldn't register custom op '%s', resolver size is too small (%d)", - name, tOpCount); - } - return kTfLiteError; - } - - if (FindOp(name) != nullptr) { - if (error_reporter_ != nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Calling AddCustom for the same op more than once " - "is not supported (Op: %s).", - name); - } - return kTfLiteError; - } - - TfLiteRegistration* new_registration = ®istrations_[registrations_len_]; - registrations_len_ += 1; - - *new_registration = *registration; - new_registration->builtin_code = BuiltinOperator_CUSTOM; - new_registration->custom_name = name; - return kTfLiteOk; - } - - // The Add* functions below add the various Builtin operators to the - // MicroMutableOpResolver object. - - TfLiteStatus AddAbs() { - return AddBuiltin(BuiltinOperator_ABS, tflite::ops::micro::Register_ABS(), - ParseAbs); - } - - TfLiteStatus AddAdd() { - return AddBuiltin(BuiltinOperator_ADD, tflite::ops::micro::Register_ADD(), - ParseAdd); - } - - TfLiteStatus AddArgMax() { - return AddBuiltin(BuiltinOperator_ARG_MAX, - tflite::ops::micro::Register_ARG_MAX(), ParseArgMax); - } - - TfLiteStatus AddArgMin() { - return AddBuiltin(BuiltinOperator_ARG_MIN, - tflite::ops::micro::Register_ARG_MIN(), ParseArgMin); - } - - TfLiteStatus AddAveragePool2D() { - return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, - tflite::ops::micro::Register_AVERAGE_POOL_2D(), - ParsePool); - } - - TfLiteStatus AddCeil() { - return AddBuiltin(BuiltinOperator_CEIL, tflite::ops::micro::Register_CEIL(), - ParseCeil); - } - - TfLiteStatus AddCircularBuffer() { - return AddCustom("CIRCULAR_BUFFER", - tflite::ops::micro::Register_CIRCULAR_BUFFER()); - } - - TfLiteStatus AddConcatenation() { - return AddBuiltin(BuiltinOperator_CONCATENATION, - tflite::ops::micro::Register_CONCATENATION(), - ParseConcatenation); - } - - TfLiteStatus AddConv2D() { - return AddBuiltin(BuiltinOperator_CONV_2D, - tflite::ops::micro::Register_CONV_2D(), ParseConv2D); - } - - TfLiteStatus AddCos() { - return AddBuiltin(BuiltinOperator_COS, tflite::ops::micro::Register_COS(), - ParseCos); - } - - TfLiteStatus AddDepthwiseConv2D() { - return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, - tflite::ops::micro::Register_DEPTHWISE_CONV_2D(), - ParseDepthwiseConv2D); - } - - TfLiteStatus AddDequantize() { - return AddBuiltin(BuiltinOperator_DEQUANTIZE, - tflite::ops::micro::Register_DEQUANTIZE(), - ParseDequantize); - } - - TfLiteStatus AddEqual() { - return AddBuiltin(BuiltinOperator_EQUAL, - tflite::ops::micro::Register_EQUAL(), ParseEqual); - } - - TfLiteStatus AddFloor() { - return AddBuiltin(BuiltinOperator_FLOOR, - tflite::ops::micro::Register_FLOOR(), ParseFloor); - } - - TfLiteStatus AddFullyConnected() { - return AddBuiltin(BuiltinOperator_FULLY_CONNECTED, - tflite::ops::micro::Register_FULLY_CONNECTED(), - ParseFullyConnected); - } - - TfLiteStatus AddGreater() { - return AddBuiltin(BuiltinOperator_GREATER, - tflite::ops::micro::Register_GREATER(), ParseGreater); - } - - TfLiteStatus AddGreaterEqual() { - return AddBuiltin(BuiltinOperator_GREATER_EQUAL, - tflite::ops::micro::Register_GREATER_EQUAL(), - ParseGreaterEqual); - } - - TfLiteStatus AddHardSwish() { - return AddBuiltin(BuiltinOperator_HARD_SWISH, - tflite::ops::micro::Register_HARD_SWISH(), - ParseHardSwish); - } - - TfLiteStatus AddL2Normalization() { - return AddBuiltin(BuiltinOperator_L2_NORMALIZATION, - tflite::ops::micro::Register_L2_NORMALIZATION(), - ParseL2Normalization); - } - - TfLiteStatus AddLess() { - return AddBuiltin(BuiltinOperator_LESS, tflite::ops::micro::Register_LESS(), - ParseLess); - } - - TfLiteStatus AddLessEqual() { - return AddBuiltin(BuiltinOperator_LESS_EQUAL, - tflite::ops::micro::Register_LESS_EQUAL(), - ParseLessEqual); - } - - TfLiteStatus AddLog() { - return AddBuiltin(BuiltinOperator_LOG, tflite::ops::micro::Register_LOG(), - ParseLog); - } - - TfLiteStatus AddLogicalAnd() { - return AddBuiltin(BuiltinOperator_LOGICAL_AND, - tflite::ops::micro::Register_LOGICAL_AND(), - ParseLogicalAnd); - } - - TfLiteStatus AddLogicalNot() { - return AddBuiltin(BuiltinOperator_LOGICAL_NOT, - tflite::ops::micro::Register_LOGICAL_NOT(), - ParseLogicalNot); - } - - TfLiteStatus AddLogicalOr() { - return AddBuiltin(BuiltinOperator_LOGICAL_OR, - tflite::ops::micro::Register_LOGICAL_OR(), - ParseLogicalOr); - } - - TfLiteStatus AddLogistic() { - return AddBuiltin(BuiltinOperator_LOGISTIC, - tflite::ops::micro::Register_LOGISTIC(), ParseLogistic); - } - - TfLiteStatus AddMaximum() { - return AddBuiltin(BuiltinOperator_MAXIMUM, - tflite::ops::micro::Register_MAXIMUM(), ParseMaximum); - } - - TfLiteStatus AddMaxPool2D() { - return AddBuiltin(BuiltinOperator_MAX_POOL_2D, - tflite::ops::micro::Register_MAX_POOL_2D(), ParsePool); - } - - TfLiteStatus AddMean() { - return AddBuiltin(BuiltinOperator_MEAN, tflite::ops::micro::Register_MEAN(), - ParseReducer); - } - - TfLiteStatus AddMinimum() { - return AddBuiltin(BuiltinOperator_MINIMUM, - tflite::ops::micro::Register_MINIMUM(), ParseMinimum); - } - - TfLiteStatus AddMul() { - return AddBuiltin(BuiltinOperator_MUL, tflite::ops::micro::Register_MUL(), - ParseMul); - } - - TfLiteStatus AddNeg() { - return AddBuiltin(BuiltinOperator_NEG, tflite::ops::micro::Register_NEG(), - ParseNeg); - } - - TfLiteStatus AddNotEqual() { - return AddBuiltin(BuiltinOperator_NOT_EQUAL, - tflite::ops::micro::Register_NOT_EQUAL(), ParseNotEqual); - } - - TfLiteStatus AddPack() { - return AddBuiltin(BuiltinOperator_PACK, tflite::ops::micro::Register_PACK(), - ParsePack); - } - - TfLiteStatus AddPad() { - return AddBuiltin(BuiltinOperator_PAD, tflite::ops::micro::Register_PAD(), - ParsePad); - } - - TfLiteStatus AddPadV2() { - return AddBuiltin(BuiltinOperator_PADV2, - tflite::ops::micro::Register_PADV2(), ParsePadV2); - } - - TfLiteStatus AddPrelu() { - return AddBuiltin(BuiltinOperator_PRELU, - tflite::ops::micro::Register_PRELU(), ParsePrelu); - } - - TfLiteStatus AddQuantize() { - return AddBuiltin(BuiltinOperator_QUANTIZE, - tflite::ops::micro::Register_QUANTIZE(), ParseQuantize); - } - - TfLiteStatus AddRelu() { - return AddBuiltin(BuiltinOperator_RELU, tflite::ops::micro::Register_RELU(), - ParseRelu); - } - - TfLiteStatus AddRelu6() { - return AddBuiltin(BuiltinOperator_RELU6, - tflite::ops::micro::Register_RELU6(), ParseRelu6); - } - - TfLiteStatus AddReshape() { - return AddBuiltin(BuiltinOperator_RESHAPE, - tflite::ops::micro::Register_RESHAPE(), ParseReshape); - } - - TfLiteStatus AddResizeNearestNeighbor() { - return AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - tflite::ops::micro::Register_RESIZE_NEAREST_NEIGHBOR(), - ParseResizeNearestNeighbor); - } - - TfLiteStatus AddRound() { - return AddBuiltin(BuiltinOperator_ROUND, - tflite::ops::micro::Register_ROUND(), ParseRound); - } - - TfLiteStatus AddRsqrt() { - return AddBuiltin(BuiltinOperator_RSQRT, - tflite::ops::micro::Register_RSQRT(), ParseRsqrt); - } - - TfLiteStatus AddSin() { - return AddBuiltin(BuiltinOperator_SIN, tflite::ops::micro::Register_SIN(), - ParseSin); - } - - TfLiteStatus AddSoftmax() { - return AddBuiltin(BuiltinOperator_SOFTMAX, - tflite::ops::micro::Register_SOFTMAX(), ParseSoftmax); - } - - TfLiteStatus AddSplit() { - return AddBuiltin(BuiltinOperator_SPLIT, - tflite::ops::micro::Register_SPLIT(), ParseSplit); - } - - TfLiteStatus AddSqrt() { - return AddBuiltin(BuiltinOperator_SQRT, tflite::ops::micro::Register_SQRT(), - ParseSqrt); - } - - TfLiteStatus AddSquare() { - return AddBuiltin(BuiltinOperator_SQUARE, - tflite::ops::micro::Register_SQUARE(), ParseSquare); - } - - TfLiteStatus AddStridedSlice() { - return AddBuiltin(BuiltinOperator_STRIDED_SLICE, - tflite::ops::micro::Register_STRIDED_SLICE(), - ParseStridedSlice); - } - - TfLiteStatus AddSub() { - return AddBuiltin(BuiltinOperator_SUB, tflite::ops::micro::Register_SUB(), - ParseSub); - } - - TfLiteStatus AddSvdf() { - return AddBuiltin(BuiltinOperator_SVDF, tflite::ops::micro::Register_SVDF(), - ParseSvdf); - } - - TfLiteStatus AddTanh() { - return AddBuiltin(BuiltinOperator_TANH, tflite::ops::micro::Register_TANH(), - ParseTanh); - } - - TfLiteStatus AddUnpack() { - return AddBuiltin(BuiltinOperator_UNPACK, - tflite::ops::micro::Register_UNPACK(), ParseUnpack); - } - - unsigned int GetRegistrationLength() { return registrations_len_; } - - private: - TfLiteStatus AddBuiltin(tflite::BuiltinOperator op, - const TfLiteRegistration& registration, - MicroOpResolver::BuiltinParseFunction parser) { - if (op == BuiltinOperator_CUSTOM) { - if (error_reporter_ != nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Invalid parameter BuiltinOperator_CUSTOM to the " - "AddBuiltin function."); - } - return kTfLiteError; - } - - if (FindOp(op) != nullptr) { - if (error_reporter_ != nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Calling AddBuiltin with the same op more than " - "once is not supported (Op: #%d).", - op); - } - return kTfLiteError; - } - - if (registrations_len_ >= tOpCount) { - if (error_reporter_) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Couldn't register builtin op #%d, resolver size " - "is too small (%d).", - op, tOpCount); - } - return kTfLiteError; - } - - registrations_[registrations_len_] = registration; - // Strictly speaking, the builtin_code is not necessary for TFLM but filling - // it in regardless. - registrations_[registrations_len_].builtin_code = op; - registrations_len_++; - - builtin_codes_[num_buitin_ops_] = op; - builtin_parsers_[num_buitin_ops_] = parser; - num_buitin_ops_++; - - return kTfLiteOk; - } - - TfLiteRegistration registrations_[tOpCount]; - unsigned int registrations_len_ = 0; - - // Arrays (and counter) to store the builtin codes and their corresponding - // parse functions as these are registered with the Op Resolver. - BuiltinOperator builtin_codes_[tOpCount]; - MicroOpResolver::BuiltinParseFunction builtin_parsers_[tOpCount]; - unsigned int num_buitin_ops_ = 0; - - ErrorReporter* error_reporter_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -}; // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_op_resolver.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_op_resolver.h deleted file mode 100644 index 757b6b89..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_op_resolver.h +++ /dev/null @@ -1,73 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/core/api/op_resolver.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -// This is an interface for the OpResolver for TFLiteMicro. The differences from -// the TFLite OpResolver base class are to: -// * explicitly remove support for Op versions -// * allow for finer grained registration of the Builtin Ops to reduce code -// size for TFLiteMicro. -// -// We need an interface class instead of directly using MicroMutableOpResolver -// because MicroMutableOpResolver is a class template with the number of -// registered Ops as the template parameter. -class MicroOpResolver : public OpResolver { - public: - typedef TfLiteStatus (*BuiltinParseFunction)(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - - // Returns the Op registration struct corresponding to the enum code from the - // flatbuffer schema. Returns nullptr if the op is not found or if op == - // BuiltinOperator_CUSTOM. - virtual const TfLiteRegistration* FindOp(BuiltinOperator op) const = 0; - - // Returns the Op registration struct corresponding to the custom operator by - // name. - virtual const TfLiteRegistration* FindOp(const char* op) const = 0; - - // This implementation exists for compatibility with the OpResolver base class - // and disregards the version parameter. - const TfLiteRegistration* FindOp(BuiltinOperator op, - int version) const final { - return FindOp(op); - } - - // This implementation exists for compatibility with the OpResolver base class - // and disregards the version parameter. - const TfLiteRegistration* FindOp(const char* op, int version) const final { - return FindOp(op); - } - - // Returns the operator specific parsing function for the OpData for a - // BuiltinOperator (if registered), else nullptr. - virtual BuiltinParseFunction GetOpDataParser(BuiltinOperator op) const = 0; - - ~MicroOpResolver() override {} -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_optional_debug_tools.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_optional_debug_tools.h deleted file mode 100644 index cc9630e6..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_optional_debug_tools.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -// Optional debugging functionality. For small sized binaries, these are not -// needed. -#ifndef TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_ - -#include "tensorflow/lite/micro/micro_interpreter.h" - -namespace tflite { -// Helper function to print model flatbuffer data. This function is not called -// by default. Hence it's not linked in to the final binary code. -void PrintModelData(const Model* model, ErrorReporter* error_reporter); -// Prints a dump of what tensors and what nodes are in the interpreter. -void PrintInterpreterState(MicroInterpreter* interpreter); -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_profiler.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_profiler.h deleted file mode 100644 index a3144b3a..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_profiler.h +++ /dev/null @@ -1,71 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ - -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/profiler.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite { - -// MicroProfiler creates a common way to gain fine-grained insight into runtime -// performance. Bottleck operators can be identified along with slow code -// sections. This can be used in conjunction with running the relevant micro -// benchmark to evaluate end-to-end performance. -// -// Usage example: -// MicroProfiler profiler(error_reporter); -// { -// ScopedProfile scoped_profile(profiler, tag); -// work_to_profile(); -// } -// -// This will call the following methods in order: -// int event_handle = profiler->BeginEvent(op_name, EventType::DEFAULT, 0) -// work_to_profile(); -// profiler->EndEvent(event_handle) -class MicroProfiler : public tflite::Profiler { - public: - explicit MicroProfiler(tflite::ErrorReporter* reporter); - ~MicroProfiler() override = default; - - // AddEvent is unused for Tf Micro. - void AddEvent(const char* tag, EventType event_type, uint64_t start, - uint64_t end, int64_t event_metadata1, - int64_t event_metadata2) override{}; - - // BeginEvent followed by code followed by EndEvent will profile the code - // enclosed. Multiple concurrent events are unsupported, so the return value - // is always 0. Event_metadata1 and event_metadata2 are unused. The tag - // pointer must be valid until EndEvent is called. - uint32_t BeginEvent(const char* tag, EventType event_type, - int64_t event_metadata1, - int64_t event_metadata2) override; - - // Event_handle is ignored since TF Micro does not support concurrent events. - void EndEvent(uint32_t event_handle) override; - - private: - tflite::ErrorReporter* reporter_; - int32_t start_time_; - const char* event_tag_; - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_string.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_string.h deleted file mode 100644 index 59303e82..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_string.h +++ /dev/null @@ -1,33 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_STRING_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_STRING_H_ - -#include - -// Implements simple string formatting for numeric types. Returns the number of -// bytes written to output. -extern "C" { -// Functionally equivalent to vsnprintf, trimmed down for TFLite Micro. -// MicroSnprintf() is implemented using MicroVsnprintf(). -int MicroVsnprintf(char* output, int len, const char* format, va_list args); -// Functionally equavalent to snprintf, trimmed down for TFLite Micro. -// For example, MicroSnprintf(buffer, 10, "int %d", 10) will put the string -// "int 10" in the buffer. -// Floating point values are logged in exponent notation (1.XXX*2^N). -int MicroSnprintf(char* output, int len, const char* format, ...); -} - -#endif // TENSORFLOW_LITE_MICRO_MICRO_STRING_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_time.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_time.h deleted file mode 100644 index 465490a8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_time.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ - -#include - -namespace tflite { - -// These functions should be implemented by each target platform, and provide an -// accurate tick count along with how many ticks there are per second. -int32_t ticks_per_second(); - -// Return time in ticks. The meaning of a tick varies per platform. -int32_t GetCurrentTimeTicks(); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_utils.h deleted file mode 100644 index 24aebad8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/micro_utils.h +++ /dev/null @@ -1,110 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ - -#include - -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -// Returns number of elements in the shape array. - -int ElementCount(const TfLiteIntArray& dims); - -uint8_t FloatToAsymmetricQuantizedUInt8(const float value, const float scale, - const int zero_point); - -uint8_t FloatToSymmetricQuantizedUInt8(const float value, const float scale); - -int8_t FloatToAsymmetricQuantizedInt8(const float value, const float scale, - const int zero_point); - -int16_t FloatToAsymmetricQuantizedInt16(const float value, const float scale, - const int zero_point); - -int8_t FloatToSymmetricQuantizedInt8(const float value, const float scale); - -// Converts a float value into a signed thirty-two-bit quantized value. Note -// that values close to max int and min int may see significant error due to -// a lack of floating point granularity for large values. -int32_t FloatToSymmetricQuantizedInt32(const float value, const float scale); - -// Helper methods to quantize arrays of floats to the desired format. -// -// There are several key flavors of quantization in TfLite: -// asymmetric symmetric per channel -// int8_t | X | X | X | -// uint8_t | X | X | | -// int16_t | X | | | -// int32_t | | X | X | -// -// The per-op quantization spec can be found here: -// https://www.tensorflow.org/lite/performance/quantization_spec - -void AsymmetricQuantize(const float* input, int8_t* output, int num_elements, - float scale, int zero_point = 0); - -void AsymmetricQuantize(const float* input, uint8_t* output, int num_elements, - float scale, int zero_point = 128); - -void AsymmetricQuantize(const float* input, int16_t* output, int num_elements, - float scale, int zero_point = 0); - -void SymmetricQuantize(const float* input, int32_t* output, int num_elements, - float scale); - -void SymmetricPerChannelQuantize(const float* input, int32_t* output, - int num_elements, int num_channels, - float* scales); - -void SignedSymmetricPerChannelQuantize(const float* values, - TfLiteIntArray* dims, - int quantized_dimension, - int8_t* quantized_values, - float* scaling_factor); - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int8_t* quantized_values, float* scaling_factor); - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int16_t* quantized_values, float* scaling_factor); - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int32_t* quantized_values, float* scaling_factor); - -void SymmetricQuantize(const float* values, TfLiteIntArray* dims, - uint8_t* quantized_values, float* scaling_factor); - -void SymmetricDequantize(const int8_t* values, const int size, - const float dequantization_scale, - float* dequantized_values); - -template -void AsymmetricDequantize(const T* values, const int size, - const float dequantization_scale, - int dequantization_zero_point, - float* dequantized_values) { - for (int i = 0; i < size; ++i) { - dequantized_values[i] = - (values[i] - dequantization_zero_point) * dequantization_scale; - } -} - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_allocator.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_allocator.h deleted file mode 100644 index 9243fec1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_allocator.h +++ /dev/null @@ -1,120 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" - -namespace tflite { - -// List of buckets currently recorded by this class. Each type keeps a list of -// allocated information during model initialization. -enum class RecordedAllocationType { - kTfLiteEvalTensorData, - kPersistentTfLiteTensorData, - kPersistentTfLiteTensorQuantizationData, - kTfLiteTensorVariableBufferData, - kNodeAndRegistrationArray, - kOpData, -}; - -// Container for holding information about allocation recordings by a given -// type. Each recording contains the number of bytes requested, the actual bytes -// allocated (can defer from requested by alignment), and the number of items -// allocated. -struct RecordedAllocation { - size_t requested_bytes; - size_t used_bytes; - size_t count; -}; - -// Utility subclass of MicroAllocator that records all allocations -// inside the arena. A summary of allocations can be logged through the -// ErrorReporter by invoking LogAllocations(). This special allocator requires -// an instance of RecordingSimpleMemoryAllocator to capture allocations in the -// head and tail. Arena allocation recording can be retrieved by type through -// the GetRecordedAllocation() function. This class should only be used for -// auditing memory usage or integration testing. -class RecordingMicroAllocator : public MicroAllocator { - public: - static RecordingMicroAllocator* Create(uint8_t* tensor_arena, - size_t arena_size, - ErrorReporter* error_reporter); - - // Returns the recorded allocations information for a given allocation type. - RecordedAllocation GetRecordedAllocation( - RecordedAllocationType allocation_type) const; - - const RecordingSimpleMemoryAllocator* GetSimpleMemoryAllocator() const; - - // Logs out through the ErrorReporter all allocation recordings by type - // defined in RecordedAllocationType. - void PrintAllocations() const; - - protected: - TfLiteStatus AllocateNodeAndRegistrations( - const Model* model, - NodeAndRegistration** node_and_registrations) override; - TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration* node_and_registrations) override; - TfLiteStatus AllocateTfLiteEvalTensors( - const Model* model, TfLiteEvalTensor** eval_tensors) override; - TfLiteStatus AllocateVariables(const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors) override; - // TODO(b/160894903): Once all kernels have been updated to the new API drop - // this method. It is only used to record TfLiteTensor persistent allocations. - TfLiteTensor* AllocatePersistentTfLiteTensorInternal( - const Model* model, TfLiteEvalTensor* eval_tensors, - int tensor_index) override; - // TODO(b/160894903): Once all kernels have been updated to the new API drop - // this function since all allocations for quantized data will take place in - // the temp section. - TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model, - const SubGraph* subgraph, - TfLiteTensor* tensor, - int tensor_index, - bool allocate_temp) override; - - private: - RecordingMicroAllocator(RecordingSimpleMemoryAllocator* memory_allocator, - ErrorReporter* error_reporter); - - void PrintRecordedAllocation(RecordedAllocationType allocation_type, - const char* allocation_name, - const char* allocation_description) const; - - RecordedAllocation SnapshotAllocationUsage() const; - void RecordAllocationUsage(const RecordedAllocation& snapshotted_allocation, - RecordedAllocation& recorded_allocation); - - const RecordingSimpleMemoryAllocator* recording_memory_allocator_; - - RecordedAllocation recorded_tflite_eval_tensor_data_ = {}; - RecordedAllocation recorded_persistent_tflite_tensor_data_ = {}; - RecordedAllocation recorded_persistent_tflite_tensor_quantization_data_ = {}; - RecordedAllocation recorded_tflite_tensor_variable_buffer_data_ = {}; - RecordedAllocation recorded_node_and_registration_array_data_ = {}; - RecordedAllocation recorded_op_data_ = {}; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_interpreter.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_interpreter.h deleted file mode 100644 index 0a579b0b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_micro_interpreter.h +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ -#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ - -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "tensorflow/lite/micro/recording_micro_allocator.h" - -namespace tflite { - -// Utility subclass that enables internal recordings of the MicroInterpreter. -// This class should be used to audit and analyze memory arena usage for a given -// model and interpreter. -// -// After construction and the first Invoke() or AllocateTensors() call - the -// memory usage is recorded and available through the GetMicroAllocator() -// function. See RecordingMicroAlloctor for more details on what is currently -// recorded from arena allocations. -// -// It is recommended for users to increase the tensor arena size by at least 1kb -// to ensure enough additional memory is available for internal recordings. -class RecordingMicroInterpreter : public MicroInterpreter { - public: - RecordingMicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - uint8_t* tensor_arena, size_t tensor_arena_size, - ErrorReporter* error_reporter) - : MicroInterpreter(model, op_resolver, - RecordingMicroAllocator::Create( - tensor_arena, tensor_arena_size, error_reporter), - error_reporter), - recording_micro_allocator_( - static_cast(allocator())) {} - - RecordingMicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - RecordingMicroAllocator* allocator, - ErrorReporter* error_reporter) - : MicroInterpreter(model, op_resolver, allocator, error_reporter), - recording_micro_allocator_(*allocator) {} - - const RecordingMicroAllocator& GetMicroAllocator() const { - return recording_micro_allocator_; - } - - private: - const RecordingMicroAllocator& recording_micro_allocator_; -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_simple_memory_allocator.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_simple_memory_allocator.h deleted file mode 100644 index 8d3e9fb4..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/recording_simple_memory_allocator.h +++ /dev/null @@ -1,64 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/simple_memory_allocator.h" - -namespace tflite { - -// Utility class used to log allocations of a SimpleMemoryAllocator. Should only -// be used in debug/evaluation settings or unit tests to evaluate allocation -// usage. -class RecordingSimpleMemoryAllocator : public SimpleMemoryAllocator { - public: - RecordingSimpleMemoryAllocator(ErrorReporter* error_reporter, - uint8_t* buffer_head, size_t buffer_size); - // TODO(b/157615197): Cleanup constructors/destructor and use factory - // functions. - ~RecordingSimpleMemoryAllocator() override; - - static RecordingSimpleMemoryAllocator* Create(ErrorReporter* error_reporter, - uint8_t* buffer_head, - size_t buffer_size); - - // Returns the number of bytes requested from the head or tail. - size_t GetRequestedBytes() const; - - // Returns the number of bytes actually allocated from the head or tail. This - // value will be >= to the number of requested bytes due to padding and - // alignment. - size_t GetUsedBytes() const; - - // Returns the number of alloc calls from the head or tail. - size_t GetAllocatedCount() const; - - TfLiteStatus EnsureHeadSize(size_t size, size_t alignment) override; - uint8_t* AllocateFromTail(size_t size, size_t alignment) override; - - private: - size_t requested_head_bytes_; - size_t requested_tail_bytes_; - size_t used_bytes_; - size_t alloc_count_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/simple_memory_allocator.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/simple_memory_allocator.h deleted file mode 100644 index 8c216f47..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/simple_memory_allocator.h +++ /dev/null @@ -1,99 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite { - -// TODO(petewarden): This allocator never frees up or reuses any memory, even -// though we have enough information about lifetimes of the tensors to do so. -// This makes it pretty wasteful, so we should use a more intelligent method. -class SimpleMemoryAllocator { - public: - // TODO(b/157615197): Cleanup constructors/destructor and use factory - // functions. - SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer_head, - uint8_t* buffer_tail); - SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer, - size_t buffer_size); - virtual ~SimpleMemoryAllocator(); - - // Creates a new SimpleMemoryAllocator from a given buffer head and size. - static SimpleMemoryAllocator* Create(ErrorReporter* error_reporter, - uint8_t* buffer_head, - size_t buffer_size); - - // Ensure that the head (lowest address and moving upwards) memory allocation - // is at least a given size. This function will only increase the head size if - // the passed in value is larger than the current head size. Calls to this - // method will also invalidate all temporary allocation values. This call will - // fail if a chain of allocations through AllocateTemp() have not been cleaned - // up with a call to ResetTempAllocations(). - virtual TfLiteStatus EnsureHeadSize(size_t size, size_t alignment); - - // Allocates memory starting at the tail of the arena (highest address and - // moving downwards). - virtual uint8_t* AllocateFromTail(size_t size, size_t alignment); - - // Allocates a temporary buffer from the head of the arena (lowest address and - // moving upwards) but does not update the actual head allocation size or - // position. The returned buffer is guaranteed until either - // ResetTempAllocations() is called or another call to AllocateFromHead(). - // Repeat calls to this function will create a chain of temp allocations. All - // calls to AllocateTemp() must end with a call to ResetTempAllocations(). If - // AllocateFromHead() is called before a call to ResetTempAllocations(), it - // will fail with an error message. - virtual uint8_t* AllocateTemp(size_t size, size_t alignment); - - // Resets a chain of temporary allocations back to the current head of the - // arena (lowest address). - virtual void ResetTempAllocations(); - - uint8_t* GetHead() const; - uint8_t* GetBufferHead() const; - uint8_t* GetTail() const; - - size_t GetHeadUsedBytes() const; - size_t GetTailUsedBytes() const; - - // Returns the number of bytes available with a given alignment. - size_t GetAvailableMemory(size_t alignment) const; - - size_t GetUsedBytes() const; - - private: - size_t GetBufferSize() const; - - ErrorReporter* error_reporter_; - uint8_t* buffer_head_; - uint8_t* buffer_tail_; - uint8_t* head_; - uint8_t* tail_; - uint8_t* temp_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/test_helpers.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/test_helpers.h deleted file mode 100644 index a7897145..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/test_helpers.h +++ /dev/null @@ -1,186 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ -#define TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ - -// Useful functions for writing tests. - -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/all_ops_resolver.h" -#include "tensorflow/lite/micro/micro_utils.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { -namespace testing { - -constexpr int kOfflinePlannerHeaderSize = 3; - -struct NodeConnection_ { - std::initializer_list input; - std::initializer_list output; -}; -typedef struct NodeConnection_ NodeConnection; - -// A simple operator that returns the median of the input with the number of -// times the kernel was invoked. The implementation below is deliberately -// complicated, just to demonstrate how kernel memory planning works. -class SimpleStatefulOp { - static constexpr int kBufferNotAllocated = 0; - // Inputs: - static constexpr int kInputTensor = 0; - // Outputs: - static constexpr int kMedianTensor = 0; - static constexpr int kInvokeCount = 1; - struct OpData { - int invoke_count = 0; - int sorting_buffer = kBufferNotAllocated; - }; - - public: - static const TfLiteRegistration* getRegistration(); - static TfLiteRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); -}; - -class MockCustom { - public: - static const TfLiteRegistration* getRegistration(); - static TfLiteRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static void Free(TfLiteContext* context, void* buffer); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); - - static bool freed_; -}; - -// Returns an Op Resolver that can be used in the testing code. -AllOpsResolver GetOpResolver(); - -// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input, -// 1 layer of weights, 1 output Tensor, and 1 operator. -const Model* GetSimpleMockModel(); - -// Returns a flatbuffer TensorFlow Lite model with more inputs, variable -// tensors, and operators. -const Model* GetComplexMockModel(); - -// Returns a simple flatbuffer model with two branches. -const Model* GetSimpleModelWithBranch(); - -// Returns a simple flatbuffer model with offline planned tensors -const Model* GetModelWithOfflinePlanning(int num_tensors, - const int32_t* metadata_buffer, - NodeConnection* node_conn, - int num_conns); - -// Returns a flatbuffer model with `simple_stateful_op` -const Model* GetSimpleStatefulModel(); - -// Builds a one-dimensional flatbuffer tensor of the given size. -const Tensor* Create1dFlatbufferTensor(int size, bool is_variable = false); - -// Builds a one-dimensional flatbuffer tensor of the given size with -// quantization metadata. -const Tensor* CreateQuantizedFlatbufferTensor(int size); - -// Creates a one-dimensional tensor with no quantization metadata. -const Tensor* CreateMissingQuantizationFlatbufferTensor(int size); - -// Creates a vector of flatbuffer buffers. -const flatbuffers::Vector>* -CreateFlatbufferBuffers(); - -// Performs a simple string comparison without requiring standard C library. -int TestStrcmp(const char* a, const char* b); - -// Wrapper to forward kernel errors to the interpreter's error reporter. -void ReportOpError(struct TfLiteContext* context, const char* format, ...); - -void PopulateContext(TfLiteTensor* tensors, int tensors_size, - TfLiteContext* context); - -// Create a TfLiteIntArray from an array of ints. The first element in the -// supplied array must be the size of the array expressed as an int. -TfLiteIntArray* IntArrayFromInts(const int* int_array); - -// Create a TfLiteFloatArray from an array of floats. The first element in the -// supplied array must be the size of the array expressed as a float. -TfLiteFloatArray* FloatArrayFromFloats(const float* floats); - -TfLiteTensor CreateFloatTensor(const float* data, TfLiteIntArray* dims, - bool is_variable = false); - -void PopulateFloatTensor(TfLiteTensor* tensor, float* begin, float* end); - -TfLiteTensor CreateBoolTensor(const bool* data, TfLiteIntArray* dims, - bool is_variable = false); - -TfLiteTensor CreateInt32Tensor(const int32_t*, TfLiteIntArray* dims, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(const uint8_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(const int8_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(const int16_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable = false); - -template -TfLiteTensor CreateQuantizedTensor(const float* input, T* quantized, - TfLiteIntArray* dims, float scale, - int zero_point, bool is_variable = false) { - int input_size = ElementCount(*dims); - tflite::AsymmetricQuantize(input, quantized, input_size, scale, zero_point); - return CreateQuantizedTensor(quantized, dims, scale, zero_point, is_variable); -} - -TfLiteTensor CreateQuantizedBiasTensor(const float* data, int32_t* quantized, - TfLiteIntArray* dims, float input_scale, - float weights_scale, - bool is_variable = false); - -// Quantizes int32_t bias tensor with per-channel weights determined by input -// scale multiplied by weight scale for each channel. -TfLiteTensor CreatePerChannelQuantizedBiasTensor( - const float* input, int32_t* quantized, TfLiteIntArray* dims, - float input_scale, float* weight_scales, float* scales, int* zero_points, - TfLiteAffineQuantization* affine_quant, int quantized_dimension, - bool is_variable = false); - -TfLiteTensor CreateSymmetricPerChannelQuantizedTensor( - const float* input, int8_t* quantized, TfLiteIntArray* dims, float* scales, - int* zero_points, TfLiteAffineQuantization* affine_quant, - int quantized_dimension, bool is_variable = false); - -// Returns the number of tensors in the default subgraph for a tflite::Model. -size_t GetModelTensorCount(const Model* model); - -} // namespace testing -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/micro_test.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/micro_test.h deleted file mode 100644 index 0b523f34..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/micro_test.h +++ /dev/null @@ -1,241 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// An ultra-lightweight testing framework designed for use with microcontroller -// applications. Its only dependency is on TensorFlow Lite's ErrorReporter -// interface, where log messages are output. This is designed to be usable even -// when no standard C or C++ libraries are available, and without any dynamic -// memory allocation or reliance on global constructors. -// -// To build a test, you use syntax similar to gunit, but with some extra -// decoration to create a hidden 'main' function containing each of the tests to -// be run. Your code should look something like: -// ---------------------------------------------------------------------------- -// #include "path/to/this/header" -// -// TF_LITE_MICRO_TESTS_BEGIN -// -// TF_LITE_MICRO_TEST(SomeTest) { -// TF_LITE_LOG_EXPECT_EQ(true, true); -// } -// -// TF_LITE_MICRO_TESTS_END -// ---------------------------------------------------------------------------- -// If you compile this for your platform, you'll get a normal binary that you -// should be able to run. Executing it will output logging information like this -// to stderr (or whatever equivalent is available and written to by -// ErrorReporter): -// ---------------------------------------------------------------------------- -// Testing SomeTest -// 1/1 tests passed -// ~~~ALL TESTS PASSED~~~ -// ---------------------------------------------------------------------------- -// This is designed to be human-readable, so you can just run tests manually, -// but the string "~~~ALL TESTS PASSED~~~" should only appear if all of the -// tests do pass. This makes it possible to integrate with automated test -// systems by scanning the output logs and looking for that magic value. -// -// This framework is intended to be a rudimentary alternative to no testing at -// all on systems that struggle to run more conventional approaches, so use with -// caution! - -#ifndef TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ -#define TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -namespace micro_test { -extern int tests_passed; -extern int tests_failed; -extern bool is_test_complete; -extern bool did_test_fail; -extern tflite::ErrorReporter* reporter; -} // namespace micro_test - -#define TF_LITE_MICRO_TESTS_BEGIN \ - namespace micro_test { \ - int tests_passed; \ - int tests_failed; \ - bool is_test_complete; \ - bool did_test_fail; \ - tflite::ErrorReporter* reporter; \ - } \ - \ - int main(void) { \ - micro_test::tests_passed = 0; \ - micro_test::tests_failed = 0; \ - tflite::MicroErrorReporter error_reporter; \ - micro_test::reporter = &error_reporter; \ - HAL_Init(); \ - SystemClock_Config(); \ - board_init(); \ - printf("Init Successful"); - -#define TF_LITE_MICRO_TESTS_END \ - micro_test::reporter->Report( \ - "%d/%d tests passed", micro_test::tests_passed, \ - (micro_test::tests_failed + micro_test::tests_passed)); \ - if (micro_test::tests_failed == 0) { \ - micro_test::reporter->Report("~~~ALL TESTS PASSED~~~\n"); \ - } else { \ - micro_test::reporter->Report("~~~SOME TESTS FAILED~~~\n"); \ - } \ - while(1); \ - } - -// TODO(petewarden): I'm going to hell for what I'm doing to this poor for loop. -#define TF_LITE_MICRO_TEST(name) \ - micro_test::reporter->Report("Testing " #name); \ - for (micro_test::is_test_complete = false, \ - micro_test::did_test_fail = false; \ - !micro_test::is_test_complete; micro_test::is_test_complete = true, \ - micro_test::tests_passed += (micro_test::did_test_fail) ? 0 : 1, \ - micro_test::tests_failed += (micro_test::did_test_fail) ? 1 : 0) - -#define TF_LITE_MICRO_EXPECT(x) \ - do { \ - if (!(x)) { \ - micro_test::reporter->Report(#x " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -// TODO(b/139142772): this macro is used with types other than ints even though -// the printf specifier is %d. -#define TF_LITE_MICRO_EXPECT_EQ(x, y) \ - do { \ - auto vx = x; \ - auto vy = y; \ - if ((vx) != (vy)) { \ - micro_test::reporter->Report(#x " == " #y " failed at %s:%d (%d vs %d)", \ - __FILE__, __LINE__, static_cast(vx), \ - static_cast(vy)); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_NE(x, y) \ - do { \ - if ((x) == (y)) { \ - micro_test::reporter->Report(#x " != " #y " failed at %s:%d", __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -// TODO(wangtz): Making it more generic once needed. -#define TF_LITE_MICRO_ARRAY_ELEMENT_EXPECT_NEAR(arr1, idx1, arr2, idx2, \ - epsilon) \ - do { \ - auto delta = ((arr1)[(idx1)] > (arr2)[(idx2)]) \ - ? ((arr1)[(idx1)] - (arr2)[(idx2)]) \ - : ((arr2)[(idx2)] - (arr1)[(idx1)]); \ - if (delta > epsilon) { \ - micro_test::reporter->Report( \ - #arr1 "[%d] (%f) near " #arr2 "[%d] (%f) failed at %s:%d", \ - static_cast(idx1), static_cast((arr1)[(idx1)]), \ - static_cast(idx2), static_cast((arr2)[(idx2)]), \ - __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_NEAR(x, y, epsilon) \ - do { \ - auto vx = (x); \ - auto vy = (y); \ - auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ - if (delta > epsilon) { \ - micro_test::reporter->Report( \ - #x " (%f) near " #y " (%f) failed at %s:%d", \ - static_cast(vx), static_cast(vy), __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_GT(x, y) \ - do { \ - if ((x) <= (y)) { \ - micro_test::reporter->Report(#x " > " #y " failed at %s:%d", __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_LT(x, y) \ - do { \ - if ((x) >= (y)) { \ - micro_test::reporter->Report(#x " < " #y " failed at %s:%d", __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_GE(x, y) \ - do { \ - if ((x) < (y)) { \ - micro_test::reporter->Report(#x " >= " #y " failed at %s:%d", __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_LE(x, y) \ - do { \ - if ((x) > (y)) { \ - micro_test::reporter->Report(#x " <= " #y " failed at %s:%d", __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_TRUE(x) \ - do { \ - if (!(x)) { \ - micro_test::reporter->Report(#x " was not true failed at %s:%d", \ - __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_FALSE(x) \ - do { \ - if (x) { \ - micro_test::reporter->Report(#x " was not false failed at %s:%d", \ - __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_FAIL(msg) \ - do { \ - micro_test::reporter->Report("FAIL: %s", msg, __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_STRING_EQ(string1, string2) \ - do { \ - for (int i = 0; string1[i] != '\0' && string2[i] != '\0'; i++) { \ - if (string1[i] != string2[i]) { \ - micro_test::reporter->Report("FAIL: %s did not match %s", string1, \ - string2, __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } \ - } while (false) - -#endif // TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_conv_model.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_conv_model.h deleted file mode 100644 index 2103196e..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_conv_model.h +++ /dev/null @@ -1,23 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ -#define TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ - -// See generate_test_models.py for updating the contents of this model: -extern const unsigned char kTestConvModelData[]; -extern const unsigned int kTestConvModelDataSize; - -#endif // TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_utils.h deleted file mode 100644 index e83ac806..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/testing/test_utils.h +++ /dev/null @@ -1,116 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_TESTING_TEST_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_TESTING_TEST_UTILS_H_ - -#include -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/tensor_utils.h" -#include "tensorflow/lite/micro/micro_utils.h" -#include "tensorflow/lite/micro/test_helpers.h" -#include "tensorflow/lite/micro/testing/micro_test.h" - -namespace tflite { -namespace testing { - -// Note: These methods are deprecated, do not use. See b/141332970. - - -// Derives the quantization range max from scaling factor and zero point. -template -inline float MaxFromZeroPointScale(const int zero_point, const float scale) { - return (std::numeric_limits::max() - zero_point) * scale; -} - -// Derives the quantization range min from scaling factor and zero point. -template -inline float MinFromZeroPointScale(const int zero_point, const float scale) { - return (std::numeric_limits::min() - zero_point) * scale; -} - -// Derives the quantization scaling factor from a min and max range. -template -inline float ScaleFromMinMax(const float min, const float max) { - return (max - min) / - static_cast((std::numeric_limits::max() * 1.0) - - std::numeric_limits::min()); -} - -// Derives the quantization zero point from a min and max range. -template -inline int ZeroPointFromMinMax(const float min, const float max) { - return static_cast(std::numeric_limits::min()) + - static_cast(-min / ScaleFromMinMax(min, max) + 0.5f); -} - -// Converts a float value into an unsigned eight-bit quantized value. -uint8_t F2Q(float value, float min, float max); - -// Converts a float value into a signed eight-bit quantized value. -int8_t F2QS(const float value, const float min, const float max); - -// Converts a float value into a signed thirty-two-bit quantized value. Note -// that values close to max int and min int may see significant error due to -// a lack of floating point granularity for large values. -int32_t F2Q32(const float value, const float scale); - -// TODO(b/141330728): Move this method elsewhere as part clean up. -void PopulateContext(TfLiteTensor* tensors, int tensors_size, - ErrorReporter* error_reporter, TfLiteContext* context); - -TfLiteTensor CreateQuantizedTensor(const uint8_t* data, TfLiteIntArray* dims, - float min, float max, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(const int8_t* data, TfLiteIntArray* dims, - float min, float max, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(float* data, uint8_t* quantized_data, - TfLiteIntArray* dims, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(float* data, int8_t* quantized_data, - TfLiteIntArray* dims, - bool is_variable = false); - -TfLiteTensor CreateQuantizedTensor(float* data, int16_t* quantized_data, - TfLiteIntArray* dims, - bool is_variable = false); - -TfLiteTensor CreateQuantized32Tensor(const int32_t* data, TfLiteIntArray* dims, - float scale, bool is_variable = false); - -template -inline TfLiteTensor CreateTensor(const input_type* data, TfLiteIntArray* dims, - bool is_variable = false) { - TfLiteTensor result; - result.type = tensor_input_type; - result.data.raw = reinterpret_cast(const_cast(data)); - result.dims = dims; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(input_type); - result.is_variable = is_variable; - return result; -} - -} // namespace testing -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_TESTING_TEST_UTILS_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_common_tables.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_common_tables.h deleted file mode 100644 index e82e62d2..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_common_tables.h +++ /dev/null @@ -1,528 +0,0 @@ -/* ---------------------------------------------------------------------- - * Project: CMSIS DSP Library - * Title: arm_common_tables.h - * Description: Extern declaration for common tables - * - * $Date: 27. January 2017 - * $Revision: V.1.5.1 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ -/* - * Copyright (C) 2010-2017 ARM Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef _ARM_COMMON_TABLES_H -#define _ARM_COMMON_TABLES_H - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - -#ifdef __cplusplus -extern "C" -{ -#endif - -#if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_FFT_ALLOW_TABLES) - /* Double Precision Float CFFT twiddles */ - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREV_1024) - extern const uint16_t armBitRevTable[1024]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_16) - extern const uint64_t twiddleCoefF64_16[32]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_32) - extern const uint64_t twiddleCoefF64_32[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_64) - extern const uint64_t twiddleCoefF64_64[128]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_128) - extern const uint64_t twiddleCoefF64_128[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_256) - extern const uint64_t twiddleCoefF64_256[512]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_512) - extern const uint64_t twiddleCoefF64_512[1024]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_1024) - extern const uint64_t twiddleCoefF64_1024[2048]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_2048) - extern const uint64_t twiddleCoefF64_2048[4096]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F64_4096) - extern const uint64_t twiddleCoefF64_4096[8192]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_16) - extern const float32_t twiddleCoef_16[32]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_32) - extern const float32_t twiddleCoef_32[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_64) - extern const float32_t twiddleCoef_64[128]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_128) - extern const float32_t twiddleCoef_128[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_256) - extern const float32_t twiddleCoef_256[512]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_512) - extern const float32_t twiddleCoef_512[1024]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_1024) - extern const float32_t twiddleCoef_1024[2048]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_2048) - extern const float32_t twiddleCoef_2048[4096]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_F32_4096) - extern const float32_t twiddleCoef_4096[8192]; - #define twiddleCoef twiddleCoef_4096 - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - /* Q31 */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_16) - extern const q31_t twiddleCoef_16_q31[24]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_32) - extern const q31_t twiddleCoef_32_q31[48]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_64) - extern const q31_t twiddleCoef_64_q31[96]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_128) - extern const q31_t twiddleCoef_128_q31[192]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_256) - extern const q31_t twiddleCoef_256_q31[384]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_512) - extern const q31_t twiddleCoef_512_q31[768]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_1024) - extern const q31_t twiddleCoef_1024_q31[1536]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_2048) - extern const q31_t twiddleCoef_2048_q31[3072]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q31_4096) - extern const q31_t twiddleCoef_4096_q31[6144]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_16) - extern const q15_t twiddleCoef_16_q15[24]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_32) - extern const q15_t twiddleCoef_32_q15[48]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_64) - extern const q15_t twiddleCoef_64_q15[96]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_128) - extern const q15_t twiddleCoef_128_q15[192]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_256) - extern const q15_t twiddleCoef_256_q15[384]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_512) - extern const q15_t twiddleCoef_512_q15[768]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_1024) - extern const q15_t twiddleCoef_1024_q15[1536]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_2048) - extern const q15_t twiddleCoef_2048_q15[3072]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_Q15_4096) - extern const q15_t twiddleCoef_4096_q15[6144]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - /* Double Precision Float RFFT twiddles */ - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_32) - extern const uint64_t twiddleCoefF64_rfft_32[32]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_64) - extern const uint64_t twiddleCoefF64_rfft_64[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_128) - extern const uint64_t twiddleCoefF64_rfft_128[128]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_256) - extern const uint64_t twiddleCoefF64_rfft_256[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_512) - extern const uint64_t twiddleCoefF64_rfft_512[512]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_1024) - extern const uint64_t twiddleCoefF64_rfft_1024[1024]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_2048) - extern const uint64_t twiddleCoefF64_rfft_2048[2048]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F64_4096) - extern const uint64_t twiddleCoefF64_rfft_4096[4096]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_32) - extern const float32_t twiddleCoef_rfft_32[32]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_64) - extern const float32_t twiddleCoef_rfft_64[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_128) - extern const float32_t twiddleCoef_rfft_128[128]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_256) - extern const float32_t twiddleCoef_rfft_256[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_512) - extern const float32_t twiddleCoef_rfft_512[512]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_1024) - extern const float32_t twiddleCoef_rfft_1024[1024]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_2048) - extern const float32_t twiddleCoef_rfft_2048[2048]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_TWIDDLECOEF_RFFT_F32_4096) - extern const float32_t twiddleCoef_rfft_4096[4096]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - - /* Double precision floating-point bit reversal tables */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_16) - #define ARMBITREVINDEXTABLEF64_16_TABLE_LENGTH ((uint16_t)12) - extern const uint16_t armBitRevIndexTableF64_16[ARMBITREVINDEXTABLEF64_16_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_32) - #define ARMBITREVINDEXTABLEF64_32_TABLE_LENGTH ((uint16_t)24) - extern const uint16_t armBitRevIndexTableF64_32[ARMBITREVINDEXTABLEF64_32_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_64) - #define ARMBITREVINDEXTABLEF64_64_TABLE_LENGTH ((uint16_t)56) - extern const uint16_t armBitRevIndexTableF64_64[ARMBITREVINDEXTABLEF64_64_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_128) - #define ARMBITREVINDEXTABLEF64_128_TABLE_LENGTH ((uint16_t)112) - extern const uint16_t armBitRevIndexTableF64_128[ARMBITREVINDEXTABLEF64_128_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_256) - #define ARMBITREVINDEXTABLEF64_256_TABLE_LENGTH ((uint16_t)240) - extern const uint16_t armBitRevIndexTableF64_256[ARMBITREVINDEXTABLEF64_256_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_512) - #define ARMBITREVINDEXTABLEF64_512_TABLE_LENGTH ((uint16_t)480) - extern const uint16_t armBitRevIndexTableF64_512[ARMBITREVINDEXTABLEF64_512_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_1024) - #define ARMBITREVINDEXTABLEF64_1024_TABLE_LENGTH ((uint16_t)992) - extern const uint16_t armBitRevIndexTableF64_1024[ARMBITREVINDEXTABLEF64_1024_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_2048) - #define ARMBITREVINDEXTABLEF64_2048_TABLE_LENGTH ((uint16_t)1984) - extern const uint16_t armBitRevIndexTableF64_2048[ARMBITREVINDEXTABLEF64_2048_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT64_4096) - #define ARMBITREVINDEXTABLEF64_4096_TABLE_LENGTH ((uint16_t)4032) - extern const uint16_t armBitRevIndexTableF64_4096[ARMBITREVINDEXTABLEF64_4096_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - /* floating-point bit reversal tables */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_16) - #define ARMBITREVINDEXTABLE_16_TABLE_LENGTH ((uint16_t)20) - extern const uint16_t armBitRevIndexTable16[ARMBITREVINDEXTABLE_16_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_32) - #define ARMBITREVINDEXTABLE_32_TABLE_LENGTH ((uint16_t)48) - extern const uint16_t armBitRevIndexTable32[ARMBITREVINDEXTABLE_32_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_64) - #define ARMBITREVINDEXTABLE_64_TABLE_LENGTH ((uint16_t)56) - extern const uint16_t armBitRevIndexTable64[ARMBITREVINDEXTABLE_64_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_128) - #define ARMBITREVINDEXTABLE_128_TABLE_LENGTH ((uint16_t)208) - extern const uint16_t armBitRevIndexTable128[ARMBITREVINDEXTABLE_128_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_256) - #define ARMBITREVINDEXTABLE_256_TABLE_LENGTH ((uint16_t)440) - extern const uint16_t armBitRevIndexTable256[ARMBITREVINDEXTABLE_256_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_512) - #define ARMBITREVINDEXTABLE_512_TABLE_LENGTH ((uint16_t)448) - extern const uint16_t armBitRevIndexTable512[ARMBITREVINDEXTABLE_512_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_1024) - #define ARMBITREVINDEXTABLE_1024_TABLE_LENGTH ((uint16_t)1800) - extern const uint16_t armBitRevIndexTable1024[ARMBITREVINDEXTABLE_1024_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_2048) - #define ARMBITREVINDEXTABLE_2048_TABLE_LENGTH ((uint16_t)3808) - extern const uint16_t armBitRevIndexTable2048[ARMBITREVINDEXTABLE_2048_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FLT_4096) - #define ARMBITREVINDEXTABLE_4096_TABLE_LENGTH ((uint16_t)4032) - extern const uint16_t armBitRevIndexTable4096[ARMBITREVINDEXTABLE_4096_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - - /* fixed-point bit reversal tables */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_16) - #define ARMBITREVINDEXTABLE_FIXED_16_TABLE_LENGTH ((uint16_t)12) - extern const uint16_t armBitRevIndexTable_fixed_16[ARMBITREVINDEXTABLE_FIXED_16_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_32) - #define ARMBITREVINDEXTABLE_FIXED_32_TABLE_LENGTH ((uint16_t)24) - extern const uint16_t armBitRevIndexTable_fixed_32[ARMBITREVINDEXTABLE_FIXED_32_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_64) - #define ARMBITREVINDEXTABLE_FIXED_64_TABLE_LENGTH ((uint16_t)56) - extern const uint16_t armBitRevIndexTable_fixed_64[ARMBITREVINDEXTABLE_FIXED_64_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_128) - #define ARMBITREVINDEXTABLE_FIXED_128_TABLE_LENGTH ((uint16_t)112) - extern const uint16_t armBitRevIndexTable_fixed_128[ARMBITREVINDEXTABLE_FIXED_128_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_256) - #define ARMBITREVINDEXTABLE_FIXED_256_TABLE_LENGTH ((uint16_t)240) - extern const uint16_t armBitRevIndexTable_fixed_256[ARMBITREVINDEXTABLE_FIXED_256_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_512) - #define ARMBITREVINDEXTABLE_FIXED_512_TABLE_LENGTH ((uint16_t)480) - extern const uint16_t armBitRevIndexTable_fixed_512[ARMBITREVINDEXTABLE_FIXED_512_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_1024) - #define ARMBITREVINDEXTABLE_FIXED_1024_TABLE_LENGTH ((uint16_t)992) - extern const uint16_t armBitRevIndexTable_fixed_1024[ARMBITREVINDEXTABLE_FIXED_1024_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_2048) - #define ARMBITREVINDEXTABLE_FIXED_2048_TABLE_LENGTH ((uint16_t)1984) - extern const uint16_t armBitRevIndexTable_fixed_2048[ARMBITREVINDEXTABLE_FIXED_2048_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_BITREVIDX_FXT_4096) - #define ARMBITREVINDEXTABLE_FIXED_4096_TABLE_LENGTH ((uint16_t)4032) - extern const uint16_t armBitRevIndexTable_fixed_4096[ARMBITREVINDEXTABLE_FIXED_4096_TABLE_LENGTH]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_REALCOEF_F32) - extern const float32_t realCoefA[8192]; - extern const float32_t realCoefB[8192]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_REALCOEF_Q31) - extern const q31_t realCoefAQ31[8192]; - extern const q31_t realCoefBQ31[8192]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_REALCOEF_Q15) - extern const q15_t realCoefAQ15[8192]; - extern const q15_t realCoefBQ15[8192]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_F32_128) - extern const float32_t Weights_128[256]; - extern const float32_t cos_factors_128[128]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_F32_512) - extern const float32_t Weights_512[1024]; - extern const float32_t cos_factors_512[512]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_F32_2048) - extern const float32_t Weights_2048[4096]; - extern const float32_t cos_factors_2048[2048]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_F32_8192) - extern const float32_t Weights_8192[16384]; - extern const float32_t cos_factors_8192[8192]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q15_128) - extern const q15_t WeightsQ15_128[256]; - extern const q15_t cos_factorsQ15_128[128]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q15_512) - extern const q15_t WeightsQ15_512[1024]; - extern const q15_t cos_factorsQ15_512[512]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q15_2048) - extern const q15_t WeightsQ15_2048[4096]; - extern const q15_t cos_factorsQ15_2048[2048]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q15_8192) - extern const q15_t WeightsQ15_8192[16384]; - extern const q15_t cos_factorsQ15_8192[8192]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q31_128) - extern const q31_t WeightsQ31_128[256]; - extern const q31_t cos_factorsQ31_128[128]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q31_512) - extern const q31_t WeightsQ31_512[1024]; - extern const q31_t cos_factorsQ31_512[512]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q31_2048) - extern const q31_t WeightsQ31_2048[4096]; - extern const q31_t cos_factorsQ31_2048[2048]; - #endif - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FFT_TABLES) || defined(ARM_TABLE_DCT4_Q31_8192) - extern const q31_t WeightsQ31_8192[16384]; - extern const q31_t cos_factorsQ31_8192[8192]; - #endif - -#endif /* if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_FFT_TABLES) */ - -#if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_FAST_ALLOW_TABLES) - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_RECIP_Q15) - extern const q15_t armRecipTableQ15[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_RECIP_Q31) - extern const q31_t armRecipTableQ31[64]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - - /* Tables for Fast Math Sine and Cosine */ - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_SIN_F32) - extern const float32_t sinTable_f32[FAST_MATH_TABLE_SIZE + 1]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_SIN_Q31) - extern const q31_t sinTable_q31[FAST_MATH_TABLE_SIZE + 1]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_SIN_Q15) - extern const q15_t sinTable_q15[FAST_MATH_TABLE_SIZE + 1]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - - #if defined(ARM_MATH_MVEI) - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_FAST_SQRT_Q31_MVE) - extern const q31_t sqrtTable_Q31[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - #endif - - #if defined(ARM_MATH_MVEI) - #if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_FAST_SQRT_Q15_MVE) - extern const q15_t sqrtTable_Q15[256]; - #endif /* !defined(ARM_DSP_CONFIG_TABLES) defined(ARM_ALL_FAST_TABLES) */ - #endif - -#endif /* if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_FAST_TABLES) */ - -#if (defined(ARM_MATH_MVEF) || defined(ARM_MATH_HELIUM)) && !defined(ARM_MATH_AUTOVECTORIZE) - extern const float32_t exp_tab[8]; - extern const float32_t __logf_lut_f32[8]; -#endif /* (defined(ARM_MATH_MVEF) || defined(ARM_MATH_HELIUM)) && !defined(ARM_MATH_AUTOVECTORIZE) */ - -#if (defined(ARM_MATH_MVEI) || defined(ARM_MATH_HELIUM)) -extern const unsigned char hwLUT[256]; -#endif /* (defined(ARM_MATH_MVEI) || defined(ARM_MATH_HELIUM)) */ - -#ifdef __cplusplus -} -#endif - -#endif /* ARM_COMMON_TABLES_H */ - diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_helium_utils.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_helium_utils.h deleted file mode 100644 index 53a5c338..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_helium_utils.h +++ /dev/null @@ -1,371 +0,0 @@ -/* ---------------------------------------------------------------------- - * Project: CMSIS DSP Library - * Title: arm_helium_utils.h - * Description: Utility functions for Helium development - * - * $Date: 09. September 2019 - * $Revision: V.1.5.1 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ -/* - * Copyright (C) 2010-2019 ARM Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef _ARM_UTILS_HELIUM_H_ -#define _ARM_UTILS_HELIUM_H_ - - -#ifdef __cplusplus -extern "C" -{ -#endif -/*************************************** - -Definitions available for MVEF and MVEI - -***************************************/ -#if defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEF) || defined(ARM_MATH_MVEI) - -#define INACTIVELANE 0 /* inactive lane content */ - - -#endif /* defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEF) || defined(ARM_MATH_MVEI) */ - -/*************************************** - -Definitions available for MVEF only - -***************************************/ -#if defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEF) - -__STATIC_FORCEINLINE float32_t vecAddAcrossF32Mve(float32x4_t in) -{ - float32_t acc; - - acc = vgetq_lane(in, 0) + vgetq_lane(in, 1) + - vgetq_lane(in, 2) + vgetq_lane(in, 3); - - return acc; -} - -__STATIC_FORCEINLINE float16_t vecAddAcrossF16Mve(float16x8_t in) -{ - float16x8_t tmpVec; - float16_t acc; - - tmpVec = (float16x8_t) vrev32q_s16((int16x8_t) in); - in = vaddq_f16(tmpVec, in); - tmpVec = (float16x8_t) vrev64q_s32((int32x4_t) in); - in = vaddq_f16(tmpVec, in); - acc = vgetq_lane_f16(in, 0) + vgetq_lane_f16(in, 4); - - return acc; -} - - -/* newton initial guess */ -#define INVSQRT_MAGIC_F32 0x5f3759df - -#define INVSQRT_NEWTON_MVE_F32(invSqrt, xHalf, xStart)\ -{ \ - float32x4_t tmp; \ - \ - /* tmp = xhalf * x * x */ \ - tmp = vmulq(xStart, xStart); \ - tmp = vmulq(tmp, xHalf); \ - /* (1.5f - xhalf * x * x) */ \ - tmp = vsubq(vdupq_n_f32(1.5f), tmp); \ - /* x = x*(1.5f-xhalf*x*x); */ \ - invSqrt = vmulq(tmp, xStart); \ -} -#endif /* defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEF) */ - -/*************************************** - -Definitions available for MVEI only - -***************************************/ -#if defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEI) - -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" - -/* Following functions are used to transpose matrix in f32 and q31 cases */ -__STATIC_INLINE arm_status arm_mat_trans_32bit_2x2_mve( - uint32_t * pDataSrc, - uint32_t * pDataDest) -{ - static const uint32x4_t vecOffs = { 0, 2, 1, 3 }; - /* - * - * | 0 1 | => | 0 2 | - * | 2 3 | | 1 3 | - * - */ - uint32x4_t vecIn = vldrwq_u32((uint32_t const *)pDataSrc); - vstrwq_scatter_shifted_offset_u32(pDataDest, vecOffs, vecIn); - - return (ARM_MATH_SUCCESS); -} - -__STATIC_INLINE arm_status arm_mat_trans_32bit_3x3_mve( - uint32_t * pDataSrc, - uint32_t * pDataDest) -{ - const uint32x4_t vecOffs1 = { 0, 3, 6, 1}; - const uint32x4_t vecOffs2 = { 4, 7, 2, 5}; - /* - * - * | 0 1 2 | | 0 3 6 | 4 x 32 flattened version | 0 3 6 1 | - * | 3 4 5 | => | 1 4 7 | => | 4 7 2 5 | - * | 6 7 8 | | 2 5 8 | (row major) | 8 . . . | - * - */ - uint32x4_t vecIn1 = vldrwq_u32((uint32_t const *) pDataSrc); - uint32x4_t vecIn2 = vldrwq_u32((uint32_t const *) &pDataSrc[4]); - - vstrwq_scatter_shifted_offset_u32(pDataDest, vecOffs1, vecIn1); - vstrwq_scatter_shifted_offset_u32(pDataDest, vecOffs2, vecIn2); - - pDataDest[8] = pDataSrc[8]; - - return (ARM_MATH_SUCCESS); -} - -__STATIC_INLINE arm_status arm_mat_trans_32bit_4x4_mve(uint32_t * pDataSrc, uint32_t * pDataDest) -{ - /* - * 4x4 Matrix transposition - * is 4 x de-interleave operation - * - * 0 1 2 3 0 4 8 12 - * 4 5 6 7 1 5 9 13 - * 8 9 10 11 2 6 10 14 - * 12 13 14 15 3 7 11 15 - */ - - uint32x4x4_t vecIn; - - vecIn = vld4q((uint32_t const *) pDataSrc); - vstrwq(pDataDest, vecIn.val[0]); - pDataDest += 4; - vstrwq(pDataDest, vecIn.val[1]); - pDataDest += 4; - vstrwq(pDataDest, vecIn.val[2]); - pDataDest += 4; - vstrwq(pDataDest, vecIn.val[3]); - - return (ARM_MATH_SUCCESS); -} - - -__STATIC_INLINE arm_status arm_mat_trans_32bit_generic_mve( - uint16_t srcRows, - uint16_t srcCols, - uint32_t * pDataSrc, - uint32_t * pDataDest) -{ - uint32x4_t vecOffs; - uint32_t i; - uint32_t blkCnt; - uint32_t const *pDataC; - uint32_t *pDataDestR; - uint32x4_t vecIn; - - vecOffs = vidupq_u32((uint32_t)0, 1); - vecOffs = vecOffs * srcCols; - - i = srcCols; - do - { - pDataC = (uint32_t const *) pDataSrc; - pDataDestR = pDataDest; - - blkCnt = srcRows >> 2; - while (blkCnt > 0U) - { - vecIn = vldrwq_gather_shifted_offset_u32(pDataC, vecOffs); - vstrwq(pDataDestR, vecIn); - pDataDestR += 4; - pDataC = pDataC + srcCols * 4; - /* - * Decrement the blockSize loop counter - */ - blkCnt--; - } - - /* - * tail - */ - blkCnt = srcRows & 3; - if (blkCnt > 0U) - { - mve_pred16_t p0 = vctp32q(blkCnt); - vecIn = vldrwq_gather_shifted_offset_u32(pDataC, vecOffs); - vstrwq_p(pDataDestR, vecIn, p0); - } - - pDataSrc += 1; - pDataDest += srcRows; - } - while (--i); - - return (ARM_MATH_SUCCESS); -} - -#if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_FAST_SQRT_Q31_MVE) -__STATIC_INLINE q31x4_t FAST_VSQRT_Q31(q31x4_t vecIn) -{ - q63x2_t vecTmpLL; - q31x4_t vecTmp0, vecTmp1; - q31_t scale; - q63_t tmp64; - q31x4_t vecNrm, vecDst, vecIdx, vecSignBits; - - - vecSignBits = vclsq(vecIn); - vecSignBits = vbicq(vecSignBits, 1); - /* - * in = in << no_of_sign_bits; - */ - vecNrm = vshlq(vecIn, vecSignBits); - /* - * index = in >> 24; - */ - vecIdx = vecNrm >> 24; - vecIdx = vecIdx << 1; - - vecTmp0 = vldrwq_gather_shifted_offset_s32(sqrtTable_Q31, (uint32x4_t)vecIdx); - - vecIdx = vecIdx + 1; - - vecTmp1 = vldrwq_gather_shifted_offset_s32(sqrtTable_Q31, (uint32x4_t)vecIdx); - - vecTmp1 = vqrdmulhq(vecTmp1, vecNrm); - vecTmp0 = vecTmp0 - vecTmp1; - vecTmp1 = vqrdmulhq(vecTmp0, vecTmp0); - vecTmp1 = vqrdmulhq(vecNrm, vecTmp1); - vecTmp1 = vdupq_n_s32(0x18000000) - vecTmp1; - vecTmp0 = vqrdmulhq(vecTmp0, vecTmp1); - vecTmpLL = vmullbq_int(vecNrm, vecTmp0); - - /* - * scale elements 0, 2 - */ - scale = 26 + (vecSignBits[0] >> 1); - tmp64 = asrl(vecTmpLL[0], scale); - vecDst[0] = (q31_t) tmp64; - - scale = 26 + (vecSignBits[2] >> 1); - tmp64 = asrl(vecTmpLL[1], scale); - vecDst[2] = (q31_t) tmp64; - - vecTmpLL = vmulltq_int(vecNrm, vecTmp0); - - /* - * scale elements 1, 3 - */ - scale = 26 + (vecSignBits[1] >> 1); - tmp64 = asrl(vecTmpLL[0], scale); - vecDst[1] = (q31_t) tmp64; - - scale = 26 + (vecSignBits[3] >> 1); - tmp64 = asrl(vecTmpLL[1], scale); - vecDst[3] = (q31_t) tmp64; - /* - * set negative values to 0 - */ - vecDst = vdupq_m(vecDst, 0, vcmpltq_n_s32(vecIn, 0)); - - return vecDst; -} -#endif - -#if !defined(ARM_DSP_CONFIG_TABLES) || defined(ARM_ALL_FAST_TABLES) || defined(ARM_TABLE_FAST_SQRT_Q15_MVE) -__STATIC_INLINE q15x8_t FAST_VSQRT_Q15(q15x8_t vecIn) -{ - q31x4_t vecTmpLev, vecTmpLodd, vecSignL; - q15x8_t vecTmp0, vecTmp1; - q15x8_t vecNrm, vecDst, vecIdx, vecSignBits; - - vecDst = vuninitializedq_s16(); - - vecSignBits = vclsq(vecIn); - vecSignBits = vbicq(vecSignBits, 1); - /* - * in = in << no_of_sign_bits; - */ - vecNrm = vshlq(vecIn, vecSignBits); - - vecIdx = vecNrm >> 8; - vecIdx = vecIdx << 1; - - vecTmp0 = vldrhq_gather_shifted_offset_s16(sqrtTable_Q15, (uint16x8_t)vecIdx); - - vecIdx = vecIdx + 1; - - vecTmp1 = vldrhq_gather_shifted_offset_s16(sqrtTable_Q15, (uint16x8_t)vecIdx); - - vecTmp1 = vqrdmulhq(vecTmp1, vecNrm); - vecTmp0 = vecTmp0 - vecTmp1; - vecTmp1 = vqrdmulhq(vecTmp0, vecTmp0); - vecTmp1 = vqrdmulhq(vecNrm, vecTmp1); - vecTmp1 = vdupq_n_s16(0x1800) - vecTmp1; - vecTmp0 = vqrdmulhq(vecTmp0, vecTmp1); - - vecSignBits = vecSignBits >> 1; - - vecTmpLev = vmullbq_int(vecNrm, vecTmp0); - vecTmpLodd = vmulltq_int(vecNrm, vecTmp0); - - vecTmp0 = vecSignBits + 10; - /* - * negate sign to apply register based vshl - */ - vecTmp0 = -vecTmp0; - - /* - * shift even elements - */ - vecSignL = vmovlbq(vecTmp0); - vecTmpLev = vshlq(vecTmpLev, vecSignL); - /* - * shift odd elements - */ - vecSignL = vmovltq(vecTmp0); - vecTmpLodd = vshlq(vecTmpLodd, vecSignL); - /* - * merge and narrow odd and even parts - */ - vecDst = vmovnbq_s32(vecDst, vecTmpLev); - vecDst = vmovntq_s32(vecDst, vecTmpLodd); - /* - * set negative values to 0 - */ - vecDst = vdupq_m(vecDst, 0, vcmpltq_n_s16(vecIn, 0)); - - return vecDst; -} -#endif - -#endif /* defined (ARM_MATH_HELIUM) || defined(ARM_MATH_MVEI) */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_math.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_math.h deleted file mode 100644 index a5eb4ff1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/arm_math.h +++ /dev/null @@ -1,8955 +0,0 @@ -/****************************************************************************** - * @file arm_math.h - * @brief Public header file for CMSIS DSP Library - * @version V1.7.0 - * @date 18. March 2019 - ******************************************************************************/ -/* - * Copyright (c) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - \mainpage CMSIS DSP Software Library - * - * \section intro Introduction - * - * This user manual describes the CMSIS DSP software library, - * a suite of common signal processing functions for use on Cortex-M and Cortex-A processor - * based devices. - * - * The library is divided into a number of functions each covering a specific category: - * - Basic math functions - * - Fast math functions - * - Complex math functions - * - Filtering functions - * - Matrix functions - * - Transform functions - * - Motor control functions - * - Statistical functions - * - Support functions - * - Interpolation functions - * - Support Vector Machine functions (SVM) - * - Bayes classifier functions - * - Distance functions - * - * The library has generally separate functions for operating on 8-bit integers, 16-bit integers, - * 32-bit integer and 32-bit floating-point values. - * - * \section using Using the Library - * - * The library installer contains prebuilt versions of the libraries in the Lib folder. - * - * Here is the list of pre-built libraries : - * - arm_cortexM7lfdp_math.lib (Cortex-M7, Little endian, Double Precision Floating Point Unit) - * - arm_cortexM7bfdp_math.lib (Cortex-M7, Big endian, Double Precision Floating Point Unit) - * - arm_cortexM7lfsp_math.lib (Cortex-M7, Little endian, Single Precision Floating Point Unit) - * - arm_cortexM7bfsp_math.lib (Cortex-M7, Big endian and Single Precision Floating Point Unit on) - * - arm_cortexM7l_math.lib (Cortex-M7, Little endian) - * - arm_cortexM7b_math.lib (Cortex-M7, Big endian) - * - arm_cortexM4lf_math.lib (Cortex-M4, Little endian, Floating Point Unit) - * - arm_cortexM4bf_math.lib (Cortex-M4, Big endian, Floating Point Unit) - * - arm_cortexM4l_math.lib (Cortex-M4, Little endian) - * - arm_cortexM4b_math.lib (Cortex-M4, Big endian) - * - arm_cortexM3l_math.lib (Cortex-M3, Little endian) - * - arm_cortexM3b_math.lib (Cortex-M3, Big endian) - * - arm_cortexM0l_math.lib (Cortex-M0 / Cortex-M0+, Little endian) - * - arm_cortexM0b_math.lib (Cortex-M0 / Cortex-M0+, Big endian) - * - arm_ARMv8MBLl_math.lib (Armv8-M Baseline, Little endian) - * - arm_ARMv8MMLl_math.lib (Armv8-M Mainline, Little endian) - * - arm_ARMv8MMLlfsp_math.lib (Armv8-M Mainline, Little endian, Single Precision Floating Point Unit) - * - arm_ARMv8MMLld_math.lib (Armv8-M Mainline, Little endian, DSP instructions) - * - arm_ARMv8MMLldfsp_math.lib (Armv8-M Mainline, Little endian, DSP instructions, Single Precision Floating Point Unit) - * - * The library functions are declared in the public file arm_math.h which is placed in the Include folder. - * Simply include this file and link the appropriate library in the application and begin calling the library functions. The Library supports single - * public header file arm_math.h for Cortex-M cores with little endian and big endian. Same header file will be used for floating point unit(FPU) variants. - * - * - * \section example Examples - * - * The library ships with a number of examples which demonstrate how to use the library functions. - * - * \section toolchain Toolchain Support - * - * The library is now tested on Fast Models building with cmake. - * Core M0, M7, A5 are tested. - * - * - * - * \section building Building the Library - * - * The library installer contains a project file to rebuild libraries on MDK toolchain in the CMSIS\\DSP\\Projects\\ARM folder. - * - arm_cortexM_math.uvprojx - * - * - * The libraries can be built by opening the arm_cortexM_math.uvprojx project in MDK-ARM, selecting a specific target, and defining the optional preprocessor macros detailed above. - * - * There is also a work in progress cmake build. The README file is giving more details. - * - * \section preprocessor Preprocessor Macros - * - * Each library project have different preprocessor macros. - * - * - ARM_MATH_BIG_ENDIAN: - * - * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets. - * - * - ARM_MATH_MATRIX_CHECK: - * - * Define macro ARM_MATH_MATRIX_CHECK for checking on the input and output sizes of matrices - * - * - ARM_MATH_ROUNDING: - * - * Define macro ARM_MATH_ROUNDING for rounding on support functions - * - * - ARM_MATH_LOOPUNROLL: - * - * Define macro ARM_MATH_LOOPUNROLL to enable manual loop unrolling in DSP functions - * - * - ARM_MATH_NEON: - * - * Define macro ARM_MATH_NEON to enable Neon versions of the DSP functions. - * It is not enabled by default when Neon is available because performances are - * dependent on the compiler and target architecture. - * - * - ARM_MATH_NEON_EXPERIMENTAL: - * - * Define macro ARM_MATH_NEON_EXPERIMENTAL to enable experimental Neon versions of - * of some DSP functions. Experimental Neon versions currently do not have better - * performances than the scalar versions. - * - * - ARM_MATH_HELIUM: - * - * It implies the flags ARM_MATH_MVEF and ARM_MATH_MVEI and ARM_MATH_FLOAT16. - * - * - ARM_MATH_MVEF: - * - * Select Helium versions of the f32 algorithms. - * It implies ARM_MATH_FLOAT16 and ARM_MATH_MVEI. - * - * - ARM_MATH_MVEI: - * - * Select Helium versions of the int and fixed point algorithms. - * - * - ARM_MATH_MVE_FLOAT16: - * - * MVE Float16 implementations of some algorithms (Requires MVE extension). - * - *
- * \section pack CMSIS-DSP in ARM::CMSIS Pack - * - * The following files relevant to CMSIS-DSP are present in the ARM::CMSIS Pack directories: - * |File/Folder |Content | - * |---------------------------------|------------------------------------------------------------------------| - * |\b CMSIS\\Documentation\\DSP | This documentation | - * |\b CMSIS\\DSP\\DSP_Lib_TestSuite | DSP_Lib test suite | - * |\b CMSIS\\DSP\\Examples | Example projects demonstrating the usage of the library functions | - * |\b CMSIS\\DSP\\Include | DSP_Lib include files | - * |\b CMSIS\\DSP\\Lib | DSP_Lib binaries | - * |\b CMSIS\\DSP\\Projects | Projects to rebuild DSP_Lib binaries | - * |\b CMSIS\\DSP\\Source | DSP_Lib source files | - * - *
- * \section rev Revision History of CMSIS-DSP - * Please refer to \ref ChangeLog_pg. - */ - - -/** - * @defgroup groupMath Basic Math Functions - */ - -/** - * @defgroup groupFastMath Fast Math Functions - * This set of functions provides a fast approximation to sine, cosine, and square root. - * As compared to most of the other functions in the CMSIS math library, the fast math functions - * operate on individual values and not arrays. - * There are separate functions for Q15, Q31, and floating-point data. - * - */ - -/** - * @defgroup groupCmplxMath Complex Math Functions - * This set of functions operates on complex data vectors. - * The data in the complex arrays is stored in an interleaved fashion - * (real, imag, real, imag, ...). - * In the API functions, the number of samples in a complex array refers - * to the number of complex values; the array contains twice this number of - * real values. - */ - -/** - * @defgroup groupFilters Filtering Functions - */ - -/** - * @defgroup groupMatrix Matrix Functions - * - * This set of functions provides basic matrix math operations. - * The functions operate on matrix data structures. For example, - * the type - * definition for the floating-point matrix structure is shown - * below: - * 
- *     typedef struct
- *     {
- *       uint16_t numRows;     // number of rows of the matrix.
- *       uint16_t numCols;     // number of columns of the matrix.
- *       float32_t *pData;     // points to the data of the matrix.
- *     } arm_matrix_instance_f32;
- *
- * There are similar definitions for Q15 and Q31 data types. - * - * The structure specifies the size of the matrix and then points to - * an array of data. The array is of size numRows X numCols - * and the values are arranged in row order. That is, the - * matrix element (i, j) is stored at: - * 
- *     pData[i*numCols + j]
- *
- * - * \par Init Functions - * There is an associated initialization function for each type of matrix - * data structure. - * The initialization function sets the values of the internal structure fields. - * Refer to \ref arm_mat_init_f32(), \ref arm_mat_init_q31() and \ref arm_mat_init_q15() - * for floating-point, Q31 and Q15 types, respectively. - * - * \par - * Use of the initialization function is optional. However, if initialization function is used - * then the instance structure cannot be placed into a const data section. - * To place the instance structure in a const data - * section, manually initialize the data structure. For example: - * 
- * arm_matrix_instance_f32 S = {nRows, nColumns, pData};
- * arm_matrix_instance_q31 S = {nRows, nColumns, pData};
- * arm_matrix_instance_q15 S = {nRows, nColumns, pData};
- *
- * where nRows specifies the number of rows, nColumns - * specifies the number of columns, and pData points to the - * data array. - * - * \par Size Checking - * By default all of the matrix functions perform size checking on the input and - * output matrices. For example, the matrix addition function verifies that the - * two input matrices and the output matrix all have the same number of rows and - * columns. If the size check fails the functions return: - * 
- *     ARM_MATH_SIZE_MISMATCH
- *
- * Otherwise the functions return - * 
- *     ARM_MATH_SUCCESS
- *
- * There is some overhead associated with this matrix size checking. - * The matrix size checking is enabled via the \#define - * 
- *     ARM_MATH_MATRIX_CHECK
- *
- * within the library project settings. By default this macro is defined - * and size checking is enabled. By changing the project settings and - * undefining this macro size checking is eliminated and the functions - * run a bit faster. With size checking disabled the functions always - * return ARM_MATH_SUCCESS. - */ - -/** - * @defgroup groupTransforms Transform Functions - */ - -/** - * @defgroup groupController Controller Functions - */ - -/** - * @defgroup groupStats Statistics Functions - */ - -/** - * @defgroup groupSupport Support Functions - */ - -/** - * @defgroup groupInterpolation Interpolation Functions - * These functions perform 1- and 2-dimensional interpolation of data. - * Linear interpolation is used for 1-dimensional data and - * bilinear interpolation is used for 2-dimensional data. - */ - -/** - * @defgroup groupExamples Examples - */ - -/** - * @defgroup groupSVM SVM Functions - * This set of functions is implementing SVM classification on 2 classes. - * The training must be done from scikit-learn. The parameters can be easily - * generated from the scikit-learn object. Some examples are given in - * DSP/Testing/PatternGeneration/SVM.py - * - * If more than 2 classes are needed, the functions in this folder - * will have to be used, as building blocks, to do multi-class classification. - * - * No multi-class classification is provided in this SVM folder. - * - */ - - -/** - * @defgroup groupBayes Bayesian estimators - * - * Implement the naive gaussian Bayes estimator. - * The training must be done from scikit-learn. - * - * The parameters can be easily - * generated from the scikit-learn object. Some examples are given in - * DSP/Testing/PatternGeneration/Bayes.py - */ - -/** - * @defgroup groupDistance Distance functions - * - * Distance functions for use with clustering algorithms. - * There are distance functions for float vectors and boolean vectors. - * - */ - - -#ifndef _ARM_MATH_H -#define _ARM_MATH_H - -#ifdef __cplusplus -extern "C" -{ -#endif - -/* Compiler specific diagnostic adjustment */ -#if defined ( __CC_ARM ) - -#elif defined ( __ARMCC_VERSION ) && ( __ARMCC_VERSION >= 6010050 ) - -#elif defined ( __GNUC__ ) - #pragma GCC diagnostic push - #pragma GCC diagnostic ignored "-Wsign-conversion" - #pragma GCC diagnostic ignored "-Wconversion" - #pragma GCC diagnostic ignored "-Wunused-parameter" - -#elif defined ( __ICCARM__ ) - -#elif defined ( __TI_ARM__ ) - -#elif defined ( __CSMC__ ) - -#elif defined ( __TASKING__ ) - -#elif defined ( _MSC_VER ) - -#else - #error Unknown compiler -#endif - - -/* Included for instrinsics definitions */ -#if defined (_MSC_VER ) -#include -#define __STATIC_FORCEINLINE static __forceinline -#define __STATIC_INLINE static __inline -#define __ALIGNED(x) __declspec(align(x)) - -#elif defined (__GNUC_PYTHON__) -#include -#define __ALIGNED(x) __attribute__((aligned(x))) -#define __STATIC_FORCEINLINE static __attribute__((inline)) -#define __STATIC_INLINE static __attribute__((inline)) -#pragma GCC diagnostic ignored "-Wunused-function" -#pragma GCC diagnostic ignored "-Wattributes" - -#else -#include "cmsis_compiler.h" -#endif - - - -#include -#include -#include -#include - -/* evaluate ARM DSP feature */ -#if (defined (__ARM_FEATURE_DSP) && (__ARM_FEATURE_DSP == 1)) - #define ARM_MATH_DSP 1 -#endif - -#if defined(ARM_MATH_NEON) -#include -#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - #if !defined(ARM_MATH_NEON_FLOAT16) - #define ARM_MATH_NEON_FLOAT16 - #endif -#endif -#endif - -#if !defined(ARM_MATH_AUTOVECTORIZE) - -#if __ARM_FEATURE_MVE - #if !defined(ARM_MATH_MVEI) - #define ARM_MATH_MVEI - #endif -#endif - -#if (__ARM_FEATURE_MVE & 2) - #if !defined(ARM_MATH_MVEF) - #define ARM_MATH_MVEF - #endif - #if !defined(ARM_MATH_MVE_FLOAT16) - /* HW Float16 not yet well supported on gcc for M55 */ - #if !defined(__CMSIS_GCC_H) - #define ARM_MATH_MVE_FLOAT16 - #endif - #endif -#endif - -#endif /*!defined(ARM_MATH_AUTOVECTORIZE)*/ - - -#if defined (ARM_MATH_HELIUM) - #if !defined(ARM_MATH_MVEF) - #define ARM_MATH_MVEF - #endif - - #if !defined(ARM_MATH_MVEI) - #define ARM_MATH_MVEI - #endif - - #if !defined(ARM_MATH_MVE_FLOAT16) - /* HW Float16 not yet well supported on gcc for M55 */ - #if !defined(__CMSIS_GCC_H) - #define ARM_MATH_MVE_FLOAT16 - #endif - #endif -#endif - -#ifdef __cplusplus -} -#endif - -#if __ARM_FEATURE_MVE -#include -#endif - -#ifdef __cplusplus -extern "C" -{ -#endif - /** - * @brief 8-bit fractional data type in 1.7 format. - */ - typedef int8_t q7_t; - - /** - * @brief 16-bit fractional data type in 1.15 format. - */ - typedef int16_t q15_t; - - /** - * @brief 32-bit fractional data type in 1.31 format. - */ - typedef int32_t q31_t; - - /** - * @brief 64-bit fractional data type in 1.63 format. - */ - typedef int64_t q63_t; - - /** - * @brief 32-bit floating-point type definition. - */ - typedef float float32_t; - - /** - * @brief 64-bit floating-point type definition. - */ - typedef double float64_t; - - /** - * @brief vector types - */ -#if defined(ARM_MATH_NEON) || defined (ARM_MATH_MVEI) - /** - * @brief 64-bit fractional 128-bit vector data type in 1.63 format - */ - typedef int64x2_t q63x2_t; - - /** - * @brief 32-bit fractional 128-bit vector data type in 1.31 format. - */ - typedef int32x4_t q31x4_t; - - /** - * @brief 16-bit fractional 128-bit vector data type with 16-bit alignement in 1.15 format. - */ - typedef __ALIGNED(2) int16x8_t q15x8_t; - - /** - * @brief 8-bit fractional 128-bit vector data type with 8-bit alignement in 1.7 format. - */ - typedef __ALIGNED(1) int8x16_t q7x16_t; - - /** - * @brief 32-bit fractional 128-bit vector pair data type in 1.31 format. - */ - typedef int32x4x2_t q31x4x2_t; - - /** - * @brief 32-bit fractional 128-bit vector quadruplet data type in 1.31 format. - */ - typedef int32x4x4_t q31x4x4_t; - - /** - * @brief 16-bit fractional 128-bit vector pair data type in 1.15 format. - */ - typedef int16x8x2_t q15x8x2_t; - - /** - * @brief 16-bit fractional 128-bit vector quadruplet data type in 1.15 format. - */ - typedef int16x8x4_t q15x8x4_t; - - /** - * @brief 8-bit fractional 128-bit vector pair data type in 1.7 format. - */ - typedef int8x16x2_t q7x16x2_t; - - /** - * @brief 8-bit fractional 128-bit vector quadruplet data type in 1.7 format. - */ - typedef int8x16x4_t q7x16x4_t; - - /** - * @brief 32-bit fractional data type in 9.23 format. - */ - typedef int32_t q23_t; - - /** - * @brief 32-bit fractional 128-bit vector data type in 9.23 format. - */ - typedef int32x4_t q23x4_t; - - /** - * @brief 64-bit status 128-bit vector data type. - */ - typedef int64x2_t status64x2_t; - - /** - * @brief 32-bit status 128-bit vector data type. - */ - typedef int32x4_t status32x4_t; - - /** - * @brief 16-bit status 128-bit vector data type. - */ - typedef int16x8_t status16x8_t; - - /** - * @brief 8-bit status 128-bit vector data type. - */ - typedef int8x16_t status8x16_t; - - -#endif - -#if defined(ARM_MATH_NEON) || defined(ARM_MATH_MVEF) /* floating point vector*/ - /** - * @brief 32-bit floating-point 128-bit vector type - */ - typedef float32x4_t f32x4_t; - - /** - * @brief 32-bit floating-point 128-bit vector pair data type - */ - typedef float32x4x2_t f32x4x2_t; - - /** - * @brief 32-bit floating-point 128-bit vector quadruplet data type - */ - typedef float32x4x4_t f32x4x4_t; - - /** - * @brief 32-bit ubiquitous 128-bit vector data type - */ - typedef union _any32x4_t - { - float32x4_t f; - int32x4_t i; - } any32x4_t; - -#endif - -#if defined(ARM_MATH_NEON) - /** - * @brief 32-bit fractional 64-bit vector data type in 1.31 format. - */ - typedef int32x2_t q31x2_t; - - /** - * @brief 16-bit fractional 64-bit vector data type in 1.15 format. - */ - typedef __ALIGNED(2) int16x4_t q15x4_t; - - /** - * @brief 8-bit fractional 64-bit vector data type in 1.7 format. - */ - typedef __ALIGNED(1) int8x8_t q7x8_t; - - /** - * @brief 32-bit float 64-bit vector data type. - */ - typedef float32x2_t f32x2_t; - - /** - * @brief 32-bit floating-point 128-bit vector triplet data type - */ - typedef float32x4x3_t f32x4x3_t; - - - /** - * @brief 32-bit fractional 128-bit vector triplet data type in 1.31 format - */ - typedef int32x4x3_t q31x4x3_t; - - /** - * @brief 16-bit fractional 128-bit vector triplet data type in 1.15 format - */ - typedef int16x8x3_t q15x8x3_t; - - /** - * @brief 8-bit fractional 128-bit vector triplet data type in 1.7 format - */ - typedef int8x16x3_t q7x16x3_t; - - /** - * @brief 32-bit floating-point 64-bit vector pair data type - */ - typedef float32x2x2_t f32x2x2_t; - - /** - * @brief 32-bit floating-point 64-bit vector triplet data type - */ - typedef float32x2x3_t f32x2x3_t; - - /** - * @brief 32-bit floating-point 64-bit vector quadruplet data type - */ - typedef float32x2x4_t f32x2x4_t; - - - /** - * @brief 32-bit fractional 64-bit vector pair data type in 1.31 format - */ - typedef int32x2x2_t q31x2x2_t; - - /** - * @brief 32-bit fractional 64-bit vector triplet data type in 1.31 format - */ - typedef int32x2x3_t q31x2x3_t; - - /** - * @brief 32-bit fractional 64-bit vector quadruplet data type in 1.31 format - */ - typedef int32x4x3_t q31x2x4_t; - - /** - * @brief 16-bit fractional 64-bit vector pair data type in 1.15 format - */ - typedef int16x4x2_t q15x4x2_t; - - /** - * @brief 16-bit fractional 64-bit vector triplet data type in 1.15 format - */ - typedef int16x4x2_t q15x4x3_t; - - /** - * @brief 16-bit fractional 64-bit vector quadruplet data type in 1.15 format - */ - typedef int16x4x3_t q15x4x4_t; - - /** - * @brief 8-bit fractional 64-bit vector pair data type in 1.7 format - */ - typedef int8x8x2_t q7x8x2_t; - - /** - * @brief 8-bit fractional 64-bit vector triplet data type in 1.7 format - */ - typedef int8x8x3_t q7x8x3_t; - - /** - * @brief 8-bit fractional 64-bit vector quadruplet data type in 1.7 format - */ - typedef int8x8x4_t q7x8x4_t; - - /** - * @brief 32-bit ubiquitous 64-bit vector data type - */ - typedef union _any32x2_t - { - float32x2_t f; - int32x2_t i; - } any32x2_t; - - - /** - * @brief 32-bit status 64-bit vector data type. - */ - typedef int32x4_t status32x2_t; - - /** - * @brief 16-bit status 64-bit vector data type. - */ - typedef int16x8_t status16x4_t; - - /** - * @brief 8-bit status 64-bit vector data type. - */ - typedef int8x16_t status8x8_t; - -#endif - - - - - -#define F64_MAX ((float64_t)DBL_MAX) -#define F32_MAX ((float32_t)FLT_MAX) - - - -#define F64_MIN (-DBL_MAX) -#define F32_MIN (-FLT_MAX) - - - -#define F64_ABSMAX ((float64_t)DBL_MAX) -#define F32_ABSMAX ((float32_t)FLT_MAX) - - - -#define F64_ABSMIN ((float64_t)0.0) -#define F32_ABSMIN ((float32_t)0.0) - - -#define Q31_MAX ((q31_t)(0x7FFFFFFFL)) -#define Q15_MAX ((q15_t)(0x7FFF)) -#define Q7_MAX ((q7_t)(0x7F)) -#define Q31_MIN ((q31_t)(0x80000000L)) -#define Q15_MIN ((q15_t)(0x8000)) -#define Q7_MIN ((q7_t)(0x80)) - -#define Q31_ABSMAX ((q31_t)(0x7FFFFFFFL)) -#define Q15_ABSMAX ((q15_t)(0x7FFF)) -#define Q7_ABSMAX ((q7_t)(0x7F)) -#define Q31_ABSMIN ((q31_t)0) -#define Q15_ABSMIN ((q15_t)0) -#define Q7_ABSMIN ((q7_t)0) - - - - /** - * @brief Macros required for reciprocal calculation in Normalized LMS - */ - -#define DELTA_Q31 ((q31_t)(0x100)) -#define DELTA_Q15 ((q15_t)0x5) -#define INDEX_MASK 0x0000003F -#ifndef PI - #define PI 3.14159265358979f -#endif - - /** - * @brief Macros required for SINE and COSINE Fast math approximations - */ - -#define FAST_MATH_TABLE_SIZE 512 -#define FAST_MATH_Q31_SHIFT (32 - 10) -#define FAST_MATH_Q15_SHIFT (16 - 10) -#define CONTROLLER_Q31_SHIFT (32 - 9) -#define TABLE_SPACING_Q31 0x400000 -#define TABLE_SPACING_Q15 0x80 - - /** - * @brief Macros required for SINE and COSINE Controller functions - */ - /* 1.31(q31) Fixed value of 2/360 */ - /* -1 to +1 is divided into 360 values so total spacing is (2/360) */ -#define INPUT_SPACING 0xB60B61 - - /** - * @brief Macros for complex numbers - */ - - /* Dimension C vector space */ - #define CMPLX_DIM 2 - - /** - * @brief Error status returned by some functions in the library. - */ - - typedef enum - { - ARM_MATH_SUCCESS = 0, /**< No error */ - ARM_MATH_ARGUMENT_ERROR = -1, /**< One or more arguments are incorrect */ - ARM_MATH_LENGTH_ERROR = -2, /**< Length of data buffer is incorrect */ - ARM_MATH_SIZE_MISMATCH = -3, /**< Size of matrices is not compatible with the operation */ - ARM_MATH_NANINF = -4, /**< Not-a-number (NaN) or infinity is generated */ - ARM_MATH_SINGULAR = -5, /**< Input matrix is singular and cannot be inverted */ - ARM_MATH_TEST_FAILURE = -6 /**< Test Failed */ - } arm_status; - - -/** - @brief definition to read/write two 16 bit values. - @deprecated - */ -#if defined ( __CC_ARM ) - #define __SIMD32_TYPE int32_t __packed -#elif defined ( __ARMCC_VERSION ) && ( __ARMCC_VERSION >= 6010050 ) - #define __SIMD32_TYPE int32_t -#elif defined ( __GNUC__ ) - #define __SIMD32_TYPE int32_t -#elif defined ( __ICCARM__ ) - #define __SIMD32_TYPE int32_t __packed -#elif defined ( __TI_ARM__ ) - #define __SIMD32_TYPE int32_t -#elif defined ( __CSMC__ ) - #define __SIMD32_TYPE int32_t -#elif defined ( __TASKING__ ) - #define __SIMD32_TYPE __un(aligned) int32_t -#elif defined(_MSC_VER ) - #define __SIMD32_TYPE int32_t -#else - #error Unknown compiler -#endif - -#define __SIMD32(addr) (*(__SIMD32_TYPE **) & (addr)) -#define __SIMD32_CONST(addr) ( (__SIMD32_TYPE * ) (addr)) -#define _SIMD32_OFFSET(addr) (*(__SIMD32_TYPE * ) (addr)) -#define __SIMD64(addr) (*( int64_t **) & (addr)) - -#define STEP(x) (x) <= 0 ? 0 : 1 -#define SQ(x) ((x) * (x)) - -/* SIMD replacement */ - - -/** - @brief Read 2 Q15 from Q15 pointer. - @param[in] pQ15 points to input value - @return Q31 value - */ -__STATIC_FORCEINLINE q31_t read_q15x2 ( - q15_t * pQ15) -{ - q31_t val; - -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (&val, pQ15, 4); -#else - val = (pQ15[1] << 16) | (pQ15[0] & 0x0FFFF) ; -#endif - - return (val); -} - -/** - @brief Read 2 Q15 from Q15 pointer and increment pointer afterwards. - @param[in] pQ15 points to input value - @return Q31 value - */ -__STATIC_FORCEINLINE q31_t read_q15x2_ia ( - q15_t ** pQ15) -{ - q31_t val; - -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (&val, *pQ15, 4); -#else - val = ((*pQ15)[1] << 16) | ((*pQ15)[0] & 0x0FFFF); -#endif - - *pQ15 += 2; - return (val); -} - -/** - @brief Read 2 Q15 from Q15 pointer and decrement pointer afterwards. - @param[in] pQ15 points to input value - @return Q31 value - */ -__STATIC_FORCEINLINE q31_t read_q15x2_da ( - q15_t ** pQ15) -{ - q31_t val; - -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (&val, *pQ15, 4); -#else - val = ((*pQ15)[1] << 16) | ((*pQ15)[0] & 0x0FFFF); -#endif - - *pQ15 -= 2; - return (val); -} - -/** - @brief Write 2 Q15 to Q15 pointer and increment pointer afterwards. - @param[in] pQ15 points to input value - @param[in] value Q31 value - @return none - */ -__STATIC_FORCEINLINE void write_q15x2_ia ( - q15_t ** pQ15, - q31_t value) -{ - q31_t val = value; -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (*pQ15, &val, 4); -#else - (*pQ15)[0] = (val & 0x0FFFF); - (*pQ15)[1] = (val >> 16) & 0x0FFFF; -#endif - - *pQ15 += 2; -} - -/** - @brief Write 2 Q15 to Q15 pointer. - @param[in] pQ15 points to input value - @param[in] value Q31 value - @return none - */ -__STATIC_FORCEINLINE void write_q15x2 ( - q15_t * pQ15, - q31_t value) -{ - q31_t val = value; - -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (pQ15, &val, 4); -#else - pQ15[0] = val & 0x0FFFF; - pQ15[1] = val >> 16; -#endif -} - - -/** - @brief Read 4 Q7 from Q7 pointer and increment pointer afterwards. - @param[in] pQ7 points to input value - @return Q31 value - */ -__STATIC_FORCEINLINE q31_t read_q7x4_ia ( - q7_t ** pQ7) -{ - q31_t val; - - -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (&val, *pQ7, 4); -#else - val =(((*pQ7)[3] & 0x0FF) << 24) | (((*pQ7)[2] & 0x0FF) << 16) | (((*pQ7)[1] & 0x0FF) << 8) | ((*pQ7)[0] & 0x0FF); -#endif - - *pQ7 += 4; - - return (val); -} - -/** - @brief Read 4 Q7 from Q7 pointer and decrement pointer afterwards. - @param[in] pQ7 points to input value - @return Q31 value - */ -__STATIC_FORCEINLINE q31_t read_q7x4_da ( - q7_t ** pQ7) -{ - q31_t val; -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (&val, *pQ7, 4); -#else - val = ((((*pQ7)[3]) & 0x0FF) << 24) | ((((*pQ7)[2]) & 0x0FF) << 16) | ((((*pQ7)[1]) & 0x0FF) << 8) | ((*pQ7)[0] & 0x0FF); -#endif - *pQ7 -= 4; - - return (val); -} - -/** - @brief Write 4 Q7 to Q7 pointer and increment pointer afterwards. - @param[in] pQ7 points to input value - @param[in] value Q31 value - @return none - */ -__STATIC_FORCEINLINE void write_q7x4_ia ( - q7_t ** pQ7, - q31_t value) -{ - q31_t val = value; -#ifdef __ARM_FEATURE_UNALIGNED - memcpy (*pQ7, &val, 4); -#else - (*pQ7)[0] = val & 0x0FF; - (*pQ7)[1] = (val >> 8) & 0x0FF; - (*pQ7)[2] = (val >> 16) & 0x0FF; - (*pQ7)[3] = (val >> 24) & 0x0FF; - -#endif - *pQ7 += 4; -} - -/* - -Normally those kind of definitions are in a compiler file -in Core or Core_A. - -But for MSVC compiler it is a bit special. The goal is very specific -to CMSIS-DSP and only to allow the use of this library from other -systems like Python or Matlab. - -MSVC is not going to be used to cross-compile to ARM. So, having a MSVC -compiler file in Core or Core_A would not make sense. - -*/ -#if defined ( _MSC_VER ) || defined(__GNUC_PYTHON__) - __STATIC_FORCEINLINE uint8_t __CLZ(uint32_t data) - { - if (data == 0U) { return 32U; } - - uint32_t count = 0U; - uint32_t mask = 0x80000000U; - - while ((data & mask) == 0U) - { - count += 1U; - mask = mask >> 1U; - } - return count; - } - - __STATIC_FORCEINLINE int32_t __SSAT(int32_t val, uint32_t sat) - { - if ((sat >= 1U) && (sat <= 32U)) - { - const int32_t max = (int32_t)((1U << (sat - 1U)) - 1U); - const int32_t min = -1 - max ; - if (val > max) - { - return max; - } - else if (val < min) - { - return min; - } - } - return val; - } - - __STATIC_FORCEINLINE uint32_t __USAT(int32_t val, uint32_t sat) - { - if (sat <= 31U) - { - const uint32_t max = ((1U << sat) - 1U); - if (val > (int32_t)max) - { - return max; - } - else if (val < 0) - { - return 0U; - } - } - return (uint32_t)val; - } -#endif - -#ifndef ARM_MATH_DSP - /** - * @brief definition to pack two 16 bit values. - */ - #define __PKHBT(ARG1, ARG2, ARG3) ( (((int32_t)(ARG1) << 0) & (int32_t)0x0000FFFF) | \ - (((int32_t)(ARG2) << ARG3) & (int32_t)0xFFFF0000) ) - #define __PKHTB(ARG1, ARG2, ARG3) ( (((int32_t)(ARG1) << 0) & (int32_t)0xFFFF0000) | \ - (((int32_t)(ARG2) >> ARG3) & (int32_t)0x0000FFFF) ) -#endif - - /** - * @brief definition to pack four 8 bit values. - */ -#ifndef ARM_MATH_BIG_ENDIAN - #define __PACKq7(v0,v1,v2,v3) ( (((int32_t)(v0) << 0) & (int32_t)0x000000FF) | \ - (((int32_t)(v1) << 8) & (int32_t)0x0000FF00) | \ - (((int32_t)(v2) << 16) & (int32_t)0x00FF0000) | \ - (((int32_t)(v3) << 24) & (int32_t)0xFF000000) ) -#else - #define __PACKq7(v0,v1,v2,v3) ( (((int32_t)(v3) << 0) & (int32_t)0x000000FF) | \ - (((int32_t)(v2) << 8) & (int32_t)0x0000FF00) | \ - (((int32_t)(v1) << 16) & (int32_t)0x00FF0000) | \ - (((int32_t)(v0) << 24) & (int32_t)0xFF000000) ) -#endif - - - /** - * @brief Clips Q63 to Q31 values. - */ - __STATIC_FORCEINLINE q31_t clip_q63_to_q31( - q63_t x) - { - return ((q31_t) (x >> 32) != ((q31_t) x >> 31)) ? - ((0x7FFFFFFF ^ ((q31_t) (x >> 63)))) : (q31_t) x; - } - - /** - * @brief Clips Q63 to Q15 values. - */ - __STATIC_FORCEINLINE q15_t clip_q63_to_q15( - q63_t x) - { - return ((q31_t) (x >> 32) != ((q31_t) x >> 31)) ? - ((0x7FFF ^ ((q15_t) (x >> 63)))) : (q15_t) (x >> 15); - } - - /** - * @brief Clips Q31 to Q7 values. - */ - __STATIC_FORCEINLINE q7_t clip_q31_to_q7( - q31_t x) - { - return ((q31_t) (x >> 24) != ((q31_t) x >> 23)) ? - ((0x7F ^ ((q7_t) (x >> 31)))) : (q7_t) x; - } - - /** - * @brief Clips Q31 to Q15 values. - */ - __STATIC_FORCEINLINE q15_t clip_q31_to_q15( - q31_t x) - { - return ((q31_t) (x >> 16) != ((q31_t) x >> 15)) ? - ((0x7FFF ^ ((q15_t) (x >> 31)))) : (q15_t) x; - } - - /** - * @brief Multiplies 32 X 64 and returns 32 bit result in 2.30 format. - */ - __STATIC_FORCEINLINE q63_t mult32x64( - q63_t x, - q31_t y) - { - return ((((q63_t) (x & 0x00000000FFFFFFFF) * y) >> 32) + - (((q63_t) (x >> 32) * y) ) ); - } - - /** - * @brief Function to Calculates 1/in (reciprocal) value of Q31 Data type. - */ - __STATIC_FORCEINLINE uint32_t arm_recip_q31( - q31_t in, - q31_t * dst, - const q31_t * pRecipTable) - { - q31_t out; - uint32_t tempVal; - uint32_t index, i; - uint32_t signBits; - - if (in > 0) - { - signBits = ((uint32_t) (__CLZ( in) - 1)); - } - else - { - signBits = ((uint32_t) (__CLZ(-in) - 1)); - } - - /* Convert input sample to 1.31 format */ - in = (in << signBits); - - /* calculation of index for initial approximated Val */ - index = (uint32_t)(in >> 24); - index = (index & INDEX_MASK); - - /* 1.31 with exp 1 */ - out = pRecipTable[index]; - - /* calculation of reciprocal value */ - /* running approximation for two iterations */ - for (i = 0U; i < 2U; i++) - { - tempVal = (uint32_t) (((q63_t) in * out) >> 31); - tempVal = 0x7FFFFFFFu - tempVal; - /* 1.31 with exp 1 */ - /* out = (q31_t) (((q63_t) out * tempVal) >> 30); */ - out = clip_q63_to_q31(((q63_t) out * tempVal) >> 30); - } - - /* write output */ - *dst = out; - - /* return num of signbits of out = 1/in value */ - return (signBits + 1U); - } - - - /** - * @brief Function to Calculates 1/in (reciprocal) value of Q15 Data type. - */ - __STATIC_FORCEINLINE uint32_t arm_recip_q15( - q15_t in, - q15_t * dst, - const q15_t * pRecipTable) - { - q15_t out = 0; - uint32_t tempVal = 0; - uint32_t index = 0, i = 0; - uint32_t signBits = 0; - - if (in > 0) - { - signBits = ((uint32_t)(__CLZ( in) - 17)); - } - else - { - signBits = ((uint32_t)(__CLZ(-in) - 17)); - } - - /* Convert input sample to 1.15 format */ - in = (in << signBits); - - /* calculation of index for initial approximated Val */ - index = (uint32_t)(in >> 8); - index = (index & INDEX_MASK); - - /* 1.15 with exp 1 */ - out = pRecipTable[index]; - - /* calculation of reciprocal value */ - /* running approximation for two iterations */ - for (i = 0U; i < 2U; i++) - { - tempVal = (uint32_t) (((q31_t) in * out) >> 15); - tempVal = 0x7FFFu - tempVal; - /* 1.15 with exp 1 */ - out = (q15_t) (((q31_t) out * tempVal) >> 14); - /* out = clip_q31_to_q15(((q31_t) out * tempVal) >> 14); */ - } - - /* write output */ - *dst = out; - - /* return num of signbits of out = 1/in value */ - return (signBits + 1); - } - -/** - * @brief Integer exponentiation - * @param[in] x value - * @param[in] nb integer exponent >= 1 - * @return x^nb - * - */ -__STATIC_INLINE float32_t arm_exponent_f32(float32_t x, int32_t nb) -{ - float32_t r = x; - nb --; - while(nb > 0) - { - r = r * x; - nb--; - } - return(r); -} - -/** - * @brief 64-bit to 32-bit unsigned normalization - * @param[in] in is input unsigned long long value - * @param[out] normalized is the 32-bit normalized value - * @param[out] norm is norm scale - */ -__STATIC_INLINE void arm_norm_64_to_32u(uint64_t in, int32_t * normalized, int32_t *norm) -{ - int32_t n1; - int32_t hi = (int32_t) (in >> 32); - int32_t lo = (int32_t) ((in << 32) >> 32); - - n1 = __CLZ(hi) - 32; - if (!n1) - { - /* - * input fits in 32-bit - */ - n1 = __CLZ(lo); - if (!n1) - { - /* - * MSB set, need to scale down by 1 - */ - *norm = -1; - *normalized = (((uint32_t) lo) >> 1); - } else - { - if (n1 == 32) - { - /* - * input is zero - */ - *norm = 0; - *normalized = 0; - } else - { - /* - * 32-bit normalization - */ - *norm = n1 - 1; - *normalized = lo << *norm; - } - } - } else - { - /* - * input fits in 64-bit - */ - n1 = 1 - n1; - *norm = -n1; - /* - * 64 bit normalization - */ - *normalized = (((uint32_t) lo) >> n1) | (hi << (32 - n1)); - } -} - -__STATIC_INLINE q31_t arm_div_q63_to_q31(q63_t num, q31_t den) -{ - q31_t result; - uint64_t absNum; - int32_t normalized; - int32_t norm; - - /* - * if sum fits in 32bits - * avoid costly 64-bit division - */ - absNum = num > 0 ? num : -num; - arm_norm_64_to_32u(absNum, &normalized, &norm); - if (norm > 0) - /* - * 32-bit division - */ - result = (q31_t) num / den; - else - /* - * 64-bit division - */ - result = (q31_t) (num / den); - - return result; -} - - -/* - * @brief C custom defined intrinsic functions - */ -#if !defined (ARM_MATH_DSP) - - /* - * @brief C custom defined QADD8 - */ - __STATIC_FORCEINLINE uint32_t __QADD8( - uint32_t x, - uint32_t y) - { - q31_t r, s, t, u; - - r = __SSAT(((((q31_t)x << 24) >> 24) + (((q31_t)y << 24) >> 24)), 8) & (int32_t)0x000000FF; - s = __SSAT(((((q31_t)x << 16) >> 24) + (((q31_t)y << 16) >> 24)), 8) & (int32_t)0x000000FF; - t = __SSAT(((((q31_t)x << 8) >> 24) + (((q31_t)y << 8) >> 24)), 8) & (int32_t)0x000000FF; - u = __SSAT(((((q31_t)x ) >> 24) + (((q31_t)y ) >> 24)), 8) & (int32_t)0x000000FF; - - return ((uint32_t)((u << 24) | (t << 16) | (s << 8) | (r ))); - } - - - /* - * @brief C custom defined QSUB8 - */ - __STATIC_FORCEINLINE uint32_t __QSUB8( - uint32_t x, - uint32_t y) - { - q31_t r, s, t, u; - - r = __SSAT(((((q31_t)x << 24) >> 24) - (((q31_t)y << 24) >> 24)), 8) & (int32_t)0x000000FF; - s = __SSAT(((((q31_t)x << 16) >> 24) - (((q31_t)y << 16) >> 24)), 8) & (int32_t)0x000000FF; - t = __SSAT(((((q31_t)x << 8) >> 24) - (((q31_t)y << 8) >> 24)), 8) & (int32_t)0x000000FF; - u = __SSAT(((((q31_t)x ) >> 24) - (((q31_t)y ) >> 24)), 8) & (int32_t)0x000000FF; - - return ((uint32_t)((u << 24) | (t << 16) | (s << 8) | (r ))); - } - - - /* - * @brief C custom defined QADD16 - */ - __STATIC_FORCEINLINE uint32_t __QADD16( - uint32_t x, - uint32_t y) - { -/* q31_t r, s; without initialisation 'arm_offset_q15 test' fails but 'intrinsic' tests pass! for armCC */ - q31_t r = 0, s = 0; - - r = __SSAT(((((q31_t)x << 16) >> 16) + (((q31_t)y << 16) >> 16)), 16) & (int32_t)0x0000FFFF; - s = __SSAT(((((q31_t)x ) >> 16) + (((q31_t)y ) >> 16)), 16) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined SHADD16 - */ - __STATIC_FORCEINLINE uint32_t __SHADD16( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = (((((q31_t)x << 16) >> 16) + (((q31_t)y << 16) >> 16)) >> 1) & (int32_t)0x0000FFFF; - s = (((((q31_t)x ) >> 16) + (((q31_t)y ) >> 16)) >> 1) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined QSUB16 - */ - __STATIC_FORCEINLINE uint32_t __QSUB16( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = __SSAT(((((q31_t)x << 16) >> 16) - (((q31_t)y << 16) >> 16)), 16) & (int32_t)0x0000FFFF; - s = __SSAT(((((q31_t)x ) >> 16) - (((q31_t)y ) >> 16)), 16) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined SHSUB16 - */ - __STATIC_FORCEINLINE uint32_t __SHSUB16( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = (((((q31_t)x << 16) >> 16) - (((q31_t)y << 16) >> 16)) >> 1) & (int32_t)0x0000FFFF; - s = (((((q31_t)x ) >> 16) - (((q31_t)y ) >> 16)) >> 1) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined QASX - */ - __STATIC_FORCEINLINE uint32_t __QASX( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = __SSAT(((((q31_t)x << 16) >> 16) - (((q31_t)y ) >> 16)), 16) & (int32_t)0x0000FFFF; - s = __SSAT(((((q31_t)x ) >> 16) + (((q31_t)y << 16) >> 16)), 16) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined SHASX - */ - __STATIC_FORCEINLINE uint32_t __SHASX( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = (((((q31_t)x << 16) >> 16) - (((q31_t)y ) >> 16)) >> 1) & (int32_t)0x0000FFFF; - s = (((((q31_t)x ) >> 16) + (((q31_t)y << 16) >> 16)) >> 1) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined QSAX - */ - __STATIC_FORCEINLINE uint32_t __QSAX( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = __SSAT(((((q31_t)x << 16) >> 16) + (((q31_t)y ) >> 16)), 16) & (int32_t)0x0000FFFF; - s = __SSAT(((((q31_t)x ) >> 16) - (((q31_t)y << 16) >> 16)), 16) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined SHSAX - */ - __STATIC_FORCEINLINE uint32_t __SHSAX( - uint32_t x, - uint32_t y) - { - q31_t r, s; - - r = (((((q31_t)x << 16) >> 16) + (((q31_t)y ) >> 16)) >> 1) & (int32_t)0x0000FFFF; - s = (((((q31_t)x ) >> 16) - (((q31_t)y << 16) >> 16)) >> 1) & (int32_t)0x0000FFFF; - - return ((uint32_t)((s << 16) | (r ))); - } - - - /* - * @brief C custom defined SMUSDX - */ - __STATIC_FORCEINLINE uint32_t __SMUSDX( - uint32_t x, - uint32_t y) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y ) >> 16)) - - ((((q31_t)x ) >> 16) * (((q31_t)y << 16) >> 16)) )); - } - - /* - * @brief C custom defined SMUADX - */ - __STATIC_FORCEINLINE uint32_t __SMUADX( - uint32_t x, - uint32_t y) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y ) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y << 16) >> 16)) )); - } - - - /* - * @brief C custom defined QADD - */ - __STATIC_FORCEINLINE int32_t __QADD( - int32_t x, - int32_t y) - { - return ((int32_t)(clip_q63_to_q31((q63_t)x + (q31_t)y))); - } - - - /* - * @brief C custom defined QSUB - */ - __STATIC_FORCEINLINE int32_t __QSUB( - int32_t x, - int32_t y) - { - return ((int32_t)(clip_q63_to_q31((q63_t)x - (q31_t)y))); - } - - - /* - * @brief C custom defined SMLAD - */ - __STATIC_FORCEINLINE uint32_t __SMLAD( - uint32_t x, - uint32_t y, - uint32_t sum) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y << 16) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y ) >> 16)) + - ( ((q31_t)sum ) ) )); - } - - - /* - * @brief C custom defined SMLADX - */ - __STATIC_FORCEINLINE uint32_t __SMLADX( - uint32_t x, - uint32_t y, - uint32_t sum) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y ) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y << 16) >> 16)) + - ( ((q31_t)sum ) ) )); - } - - - /* - * @brief C custom defined SMLSDX - */ - __STATIC_FORCEINLINE uint32_t __SMLSDX( - uint32_t x, - uint32_t y, - uint32_t sum) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y ) >> 16)) - - ((((q31_t)x ) >> 16) * (((q31_t)y << 16) >> 16)) + - ( ((q31_t)sum ) ) )); - } - - - /* - * @brief C custom defined SMLALD - */ - __STATIC_FORCEINLINE uint64_t __SMLALD( - uint32_t x, - uint32_t y, - uint64_t sum) - { -/* return (sum + ((q15_t) (x >> 16) * (q15_t) (y >> 16)) + ((q15_t) x * (q15_t) y)); */ - return ((uint64_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y << 16) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y ) >> 16)) + - ( ((q63_t)sum ) ) )); - } - - - /* - * @brief C custom defined SMLALDX - */ - __STATIC_FORCEINLINE uint64_t __SMLALDX( - uint32_t x, - uint32_t y, - uint64_t sum) - { -/* return (sum + ((q15_t) (x >> 16) * (q15_t) y)) + ((q15_t) x * (q15_t) (y >> 16)); */ - return ((uint64_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y ) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y << 16) >> 16)) + - ( ((q63_t)sum ) ) )); - } - - - /* - * @brief C custom defined SMUAD - */ - __STATIC_FORCEINLINE uint32_t __SMUAD( - uint32_t x, - uint32_t y) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y << 16) >> 16)) + - ((((q31_t)x ) >> 16) * (((q31_t)y ) >> 16)) )); - } - - - /* - * @brief C custom defined SMUSD - */ - __STATIC_FORCEINLINE uint32_t __SMUSD( - uint32_t x, - uint32_t y) - { - return ((uint32_t)(((((q31_t)x << 16) >> 16) * (((q31_t)y << 16) >> 16)) - - ((((q31_t)x ) >> 16) * (((q31_t)y ) >> 16)) )); - } - - - /* - * @brief C custom defined SXTB16 - */ - __STATIC_FORCEINLINE uint32_t __SXTB16( - uint32_t x) - { - return ((uint32_t)(((((q31_t)x << 24) >> 24) & (q31_t)0x0000FFFF) | - ((((q31_t)x << 8) >> 8) & (q31_t)0xFFFF0000) )); - } - - /* - * @brief C custom defined SMMLA - */ - __STATIC_FORCEINLINE int32_t __SMMLA( - int32_t x, - int32_t y, - int32_t sum) - { - return (sum + (int32_t) (((int64_t) x * y) >> 32)); - } - -#endif /* !defined (ARM_MATH_DSP) */ - - - /** - * @brief Instance structure for the Q7 FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of filter coefficients in the filter. */ - q7_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - const q7_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - } arm_fir_instance_q7; - - /** - * @brief Instance structure for the Q15 FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of filter coefficients in the filter. */ - q15_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - const q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - } arm_fir_instance_q15; - - /** - * @brief Instance structure for the Q31 FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of filter coefficients in the filter. */ - q31_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - const q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - } arm_fir_instance_q31; - - /** - * @brief Instance structure for the floating-point FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of filter coefficients in the filter. */ - float32_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - const float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - } arm_fir_instance_f32; - - /** - * @brief Processing function for the Q7 FIR filter. - * @param[in] S points to an instance of the Q7 FIR filter structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_q7( - const arm_fir_instance_q7 * S, - const q7_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the Q7 FIR filter. - * @param[in,out] S points to an instance of the Q7 FIR structure. - * @param[in] numTaps Number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of samples that are processed. - * - * For the MVE version, the coefficient length must be a multiple of 16. - * You can pad with zeros if you have less coefficients. - */ - void arm_fir_init_q7( - arm_fir_instance_q7 * S, - uint16_t numTaps, - const q7_t * pCoeffs, - q7_t * pState, - uint32_t blockSize); - - /** - * @brief Processing function for the Q15 FIR filter. - * @param[in] S points to an instance of the Q15 FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_q15( - const arm_fir_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - /** - * @brief Processing function for the fast Q15 FIR filter (fast version). - * @param[in] S points to an instance of the Q15 FIR filter structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_fast_q15( - const arm_fir_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the Q15 FIR filter. - * @param[in,out] S points to an instance of the Q15 FIR filter structure. - * @param[in] numTaps Number of filter coefficients in the filter. Must be even and greater than or equal to 4. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of samples that are processed at a time. - * @return The function returns either - * ARM_MATH_SUCCESS if initialization was successful or - * ARM_MATH_ARGUMENT_ERROR if numTaps is not a supported value. - * - * For the MVE version, the coefficient length must be a multiple of 8. - * You can pad with zeros if you have less coefficients. - * - */ - arm_status arm_fir_init_q15( - arm_fir_instance_q15 * S, - uint16_t numTaps, - const q15_t * pCoeffs, - q15_t * pState, - uint32_t blockSize); - - /** - * @brief Processing function for the Q31 FIR filter. - * @param[in] S points to an instance of the Q31 FIR filter structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_q31( - const arm_fir_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - /** - * @brief Processing function for the fast Q31 FIR filter (fast version). - * @param[in] S points to an instance of the Q31 FIR filter structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_fast_q31( - const arm_fir_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the Q31 FIR filter. - * @param[in,out] S points to an instance of the Q31 FIR structure. - * @param[in] numTaps Number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of samples that are processed at a time. - * - * For the MVE version, the coefficient length must be a multiple of 4. - * You can pad with zeros if you have less coefficients. - */ - void arm_fir_init_q31( - arm_fir_instance_q31 * S, - uint16_t numTaps, - const q31_t * pCoeffs, - q31_t * pState, - uint32_t blockSize); - - /** - * @brief Processing function for the floating-point FIR filter. - * @param[in] S points to an instance of the floating-point FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_f32( - const arm_fir_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the floating-point FIR filter. - * @param[in,out] S points to an instance of the floating-point FIR filter structure. - * @param[in] numTaps Number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of samples that are processed at a time. - */ - void arm_fir_init_f32( - arm_fir_instance_f32 * S, - uint16_t numTaps, - const float32_t * pCoeffs, - float32_t * pState, - uint32_t blockSize); - - /** - * @brief Instance structure for the Q15 Biquad cascade filter. - */ - typedef struct - { - int8_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - q15_t *pState; /**< Points to the array of state coefficients. The array is of length 4*numStages. */ - const q15_t *pCoeffs; /**< Points to the array of coefficients. The array is of length 5*numStages. */ - int8_t postShift; /**< Additional shift, in bits, applied to each output sample. */ - } arm_biquad_casd_df1_inst_q15; - - /** - * @brief Instance structure for the Q31 Biquad cascade filter. - */ - typedef struct - { - uint32_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - q31_t *pState; /**< Points to the array of state coefficients. The array is of length 4*numStages. */ - const q31_t *pCoeffs; /**< Points to the array of coefficients. The array is of length 5*numStages. */ - uint8_t postShift; /**< Additional shift, in bits, applied to each output sample. */ - } arm_biquad_casd_df1_inst_q31; - - /** - * @brief Instance structure for the floating-point Biquad cascade filter. - */ - typedef struct - { - uint32_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - float32_t *pState; /**< Points to the array of state coefficients. The array is of length 4*numStages. */ - const float32_t *pCoeffs; /**< Points to the array of coefficients. The array is of length 5*numStages. */ - } arm_biquad_casd_df1_inst_f32; - -#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) - /** - * @brief Instance structure for the modified Biquad coefs required by vectorized code. - */ - typedef struct - { - float32_t coeffs[8][4]; /**< Points to the array of modified coefficients. The array is of length 32. There is one per stage */ - } arm_biquad_mod_coef_f32; -#endif - - /** - * @brief Processing function for the Q15 Biquad cascade filter. - * @param[in] S points to an instance of the Q15 Biquad cascade structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df1_q15( - const arm_biquad_casd_df1_inst_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the Q15 Biquad cascade filter. - * @param[in,out] S points to an instance of the Q15 Biquad cascade structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] postShift Shift to be applied to the output. Varies according to the coefficients format - */ - void arm_biquad_cascade_df1_init_q15( - arm_biquad_casd_df1_inst_q15 * S, - uint8_t numStages, - const q15_t * pCoeffs, - q15_t * pState, - int8_t postShift); - - /** - * @brief Fast but less precise processing function for the Q15 Biquad cascade filter for Cortex-M3 and Cortex-M4. - * @param[in] S points to an instance of the Q15 Biquad cascade structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df1_fast_q15( - const arm_biquad_casd_df1_inst_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - /** - * @brief Processing function for the Q31 Biquad cascade filter - * @param[in] S points to an instance of the Q31 Biquad cascade structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df1_q31( - const arm_biquad_casd_df1_inst_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - /** - * @brief Fast but less precise processing function for the Q31 Biquad cascade filter for Cortex-M3 and Cortex-M4. - * @param[in] S points to an instance of the Q31 Biquad cascade structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df1_fast_q31( - const arm_biquad_casd_df1_inst_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the Q31 Biquad cascade filter. - * @param[in,out] S points to an instance of the Q31 Biquad cascade structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] postShift Shift to be applied to the output. Varies according to the coefficients format - */ - void arm_biquad_cascade_df1_init_q31( - arm_biquad_casd_df1_inst_q31 * S, - uint8_t numStages, - const q31_t * pCoeffs, - q31_t * pState, - int8_t postShift); - - /** - * @brief Processing function for the floating-point Biquad cascade filter. - * @param[in] S points to an instance of the floating-point Biquad cascade structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df1_f32( - const arm_biquad_casd_df1_inst_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the floating-point Biquad cascade filter. - * @param[in,out] S points to an instance of the floating-point Biquad cascade structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pCoeffsMod points to the modified filter coefficients (only MVE version). - * @param[in] pState points to the state buffer. - */ -#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) - void arm_biquad_cascade_df1_mve_init_f32( - arm_biquad_casd_df1_inst_f32 * S, - uint8_t numStages, - const float32_t * pCoeffs, - arm_biquad_mod_coef_f32 * pCoeffsMod, - float32_t * pState); -#endif - - void arm_biquad_cascade_df1_init_f32( - arm_biquad_casd_df1_inst_f32 * S, - uint8_t numStages, - const float32_t * pCoeffs, - float32_t * pState); - - - /** - * @brief Compute the logical bitwise AND of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_and_u16( - const uint16_t * pSrcA, - const uint16_t * pSrcB, - uint16_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise AND of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_and_u32( - const uint32_t * pSrcA, - const uint32_t * pSrcB, - uint32_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise AND of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_and_u8( - const uint8_t * pSrcA, - const uint8_t * pSrcB, - uint8_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise OR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_or_u16( - const uint16_t * pSrcA, - const uint16_t * pSrcB, - uint16_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise OR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_or_u32( - const uint32_t * pSrcA, - const uint32_t * pSrcB, - uint32_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise OR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_or_u8( - const uint8_t * pSrcA, - const uint8_t * pSrcB, - uint8_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise NOT of a fixed-point vector. - * @param[in] pSrc points to input vector - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_not_u16( - const uint16_t * pSrc, - uint16_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise NOT of a fixed-point vector. - * @param[in] pSrc points to input vector - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_not_u32( - const uint32_t * pSrc, - uint32_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise NOT of a fixed-point vector. - * @param[in] pSrc points to input vector - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_not_u8( - const uint8_t * pSrc, - uint8_t * pDst, - uint32_t blockSize); - -/** - * @brief Compute the logical bitwise XOR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_xor_u16( - const uint16_t * pSrcA, - const uint16_t * pSrcB, - uint16_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise XOR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_xor_u32( - const uint32_t * pSrcA, - const uint32_t * pSrcB, - uint32_t * pDst, - uint32_t blockSize); - - /** - * @brief Compute the logical bitwise XOR of two fixed-point vectors. - * @param[in] pSrcA points to input vector A - * @param[in] pSrcB points to input vector B - * @param[out] pDst points to output vector - * @param[in] blockSize number of samples in each vector - * @return none - */ - void arm_xor_u8( - const uint8_t * pSrcA, - const uint8_t * pSrcB, - uint8_t * pDst, - uint32_t blockSize); - - /** - * @brief Struct for specifying sorting algorithm - */ - typedef enum - { - ARM_SORT_BITONIC = 0, - /**< Bitonic sort */ - ARM_SORT_BUBBLE = 1, - /**< Bubble sort */ - ARM_SORT_HEAP = 2, - /**< Heap sort */ - ARM_SORT_INSERTION = 3, - /**< Insertion sort */ - ARM_SORT_QUICK = 4, - /**< Quick sort */ - ARM_SORT_SELECTION = 5 - /**< Selection sort */ - } arm_sort_alg; - - /** - * @brief Struct for specifying sorting algorithm - */ - typedef enum - { - ARM_SORT_DESCENDING = 0, - /**< Descending order (9 to 0) */ - ARM_SORT_ASCENDING = 1 - /**< Ascending order (0 to 9) */ - } arm_sort_dir; - - /** - * @brief Instance structure for the sorting algorithms. - */ - typedef struct - { - arm_sort_alg alg; /**< Sorting algorithm selected */ - arm_sort_dir dir; /**< Sorting order (direction) */ - } arm_sort_instance_f32; - - /** - * @param[in] S points to an instance of the sorting structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_sort_f32( - const arm_sort_instance_f32 * S, - float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - /** - * @param[in,out] S points to an instance of the sorting structure. - * @param[in] alg Selected algorithm. - * @param[in] dir Sorting order. - */ - void arm_sort_init_f32( - arm_sort_instance_f32 * S, - arm_sort_alg alg, - arm_sort_dir dir); - - /** - * @brief Instance structure for the sorting algorithms. - */ - typedef struct - { - arm_sort_dir dir; /**< Sorting order (direction) */ - float32_t * buffer; /**< Working buffer */ - } arm_merge_sort_instance_f32; - - /** - * @param[in] S points to an instance of the sorting structure. - * @param[in,out] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_merge_sort_f32( - const arm_merge_sort_instance_f32 * S, - float32_t *pSrc, - float32_t *pDst, - uint32_t blockSize); - - /** - * @param[in,out] S points to an instance of the sorting structure. - * @param[in] dir Sorting order. - * @param[in] buffer Working buffer. - */ - void arm_merge_sort_init_f32( - arm_merge_sort_instance_f32 * S, - arm_sort_dir dir, - float32_t * buffer); - - /** - * @brief Struct for specifying cubic spline type - */ - typedef enum - { - ARM_SPLINE_NATURAL = 0, /**< Natural spline */ - ARM_SPLINE_PARABOLIC_RUNOUT = 1 /**< Parabolic runout spline */ - } arm_spline_type; - - /** - * @brief Instance structure for the floating-point cubic spline interpolation. - */ - typedef struct - { - arm_spline_type type; /**< Type (boundary conditions) */ - const float32_t * x; /**< x values */ - const float32_t * y; /**< y values */ - uint32_t n_x; /**< Number of known data points */ - float32_t * coeffs; /**< Coefficients buffer (b,c, and d) */ - } arm_spline_instance_f32; - - /** - * @brief Processing function for the floating-point cubic spline interpolation. - * @param[in] S points to an instance of the floating-point spline structure. - * @param[in] xq points to the x values ot the interpolated data points. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples of output data. - */ - void arm_spline_f32( - arm_spline_instance_f32 * S, - const float32_t * xq, - float32_t * pDst, - uint32_t blockSize); - - /** - * @brief Initialization function for the floating-point cubic spline interpolation. - * @param[in,out] S points to an instance of the floating-point spline structure. - * @param[in] type type of cubic spline interpolation (boundary conditions) - * @param[in] x points to the x values of the known data points. - * @param[in] y points to the y values of the known data points. - * @param[in] n number of known data points. - * @param[in] coeffs coefficients array for b, c, and d - * @param[in] tempBuffer buffer array for internal computations - */ - void arm_spline_init_f32( - arm_spline_instance_f32 * S, - arm_spline_type type, - const float32_t * x, - const float32_t * y, - uint32_t n, - float32_t * coeffs, - float32_t * tempBuffer); - - /** - * @brief Instance structure for the floating-point matrix structure. - */ - typedef struct - { - uint16_t numRows; /**< number of rows of the matrix. */ - uint16_t numCols; /**< number of columns of the matrix. */ - float32_t *pData; /**< points to the data of the matrix. */ - } arm_matrix_instance_f32; - - /** - * @brief Instance structure for the floating-point matrix structure. - */ - typedef struct - { - uint16_t numRows; /**< number of rows of the matrix. */ - uint16_t numCols; /**< number of columns of the matrix. */ - float64_t *pData; /**< points to the data of the matrix. */ - } arm_matrix_instance_f64; - - /** - * @brief Instance structure for the Q15 matrix structure. - */ - typedef struct - { - uint16_t numRows; /**< number of rows of the matrix. */ - uint16_t numCols; /**< number of columns of the matrix. */ - q15_t *pData; /**< points to the data of the matrix. */ - } arm_matrix_instance_q15; - - /** - * @brief Instance structure for the Q31 matrix structure. - */ - typedef struct - { - uint16_t numRows; /**< number of rows of the matrix. */ - uint16_t numCols; /**< number of columns of the matrix. */ - q31_t *pData; /**< points to the data of the matrix. */ - } arm_matrix_instance_q31; - - /** - * @brief Floating-point matrix addition. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_add_f32( - const arm_matrix_instance_f32 * pSrcA, - const arm_matrix_instance_f32 * pSrcB, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15 matrix addition. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_add_q15( - const arm_matrix_instance_q15 * pSrcA, - const arm_matrix_instance_q15 * pSrcB, - arm_matrix_instance_q15 * pDst); - - /** - * @brief Q31 matrix addition. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_add_q31( - const arm_matrix_instance_q31 * pSrcA, - const arm_matrix_instance_q31 * pSrcB, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Floating-point, complex, matrix multiplication. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_cmplx_mult_f32( - const arm_matrix_instance_f32 * pSrcA, - const arm_matrix_instance_f32 * pSrcB, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15, complex, matrix multiplication. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_cmplx_mult_q15( - const arm_matrix_instance_q15 * pSrcA, - const arm_matrix_instance_q15 * pSrcB, - arm_matrix_instance_q15 * pDst, - q15_t * pScratch); - - /** - * @brief Q31, complex, matrix multiplication. - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_cmplx_mult_q31( - const arm_matrix_instance_q31 * pSrcA, - const arm_matrix_instance_q31 * pSrcB, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Floating-point matrix transpose. - * @param[in] pSrc points to the input matrix - * @param[out] pDst points to the output matrix - * @return The function returns either ARM_MATH_SIZE_MISMATCH - * or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_trans_f32( - const arm_matrix_instance_f32 * pSrc, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15 matrix transpose. - * @param[in] pSrc points to the input matrix - * @param[out] pDst points to the output matrix - * @return The function returns either ARM_MATH_SIZE_MISMATCH - * or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_trans_q15( - const arm_matrix_instance_q15 * pSrc, - arm_matrix_instance_q15 * pDst); - - /** - * @brief Q31 matrix transpose. - * @param[in] pSrc points to the input matrix - * @param[out] pDst points to the output matrix - * @return The function returns either ARM_MATH_SIZE_MISMATCH - * or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_trans_q31( - const arm_matrix_instance_q31 * pSrc, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Floating-point matrix multiplication - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_mult_f32( - const arm_matrix_instance_f32 * pSrcA, - const arm_matrix_instance_f32 * pSrcB, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15 matrix multiplication - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @param[in] pState points to the array for storing intermediate results - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_mult_q15( - const arm_matrix_instance_q15 * pSrcA, - const arm_matrix_instance_q15 * pSrcB, - arm_matrix_instance_q15 * pDst, - q15_t * pState); - - /** - * @brief Q15 matrix multiplication (fast variant) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @param[in] pState points to the array for storing intermediate results - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_mult_fast_q15( - const arm_matrix_instance_q15 * pSrcA, - const arm_matrix_instance_q15 * pSrcB, - arm_matrix_instance_q15 * pDst, - q15_t * pState); - - /** - * @brief Q31 matrix multiplication - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_mult_q31( - const arm_matrix_instance_q31 * pSrcA, - const arm_matrix_instance_q31 * pSrcB, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Q31 matrix multiplication (fast variant) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_mult_fast_q31( - const arm_matrix_instance_q31 * pSrcA, - const arm_matrix_instance_q31 * pSrcB, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Floating-point matrix subtraction - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_sub_f32( - const arm_matrix_instance_f32 * pSrcA, - const arm_matrix_instance_f32 * pSrcB, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15 matrix subtraction - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_sub_q15( - const arm_matrix_instance_q15 * pSrcA, - const arm_matrix_instance_q15 * pSrcB, - arm_matrix_instance_q15 * pDst); - - /** - * @brief Q31 matrix subtraction - * @param[in] pSrcA points to the first input matrix structure - * @param[in] pSrcB points to the second input matrix structure - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_sub_q31( - const arm_matrix_instance_q31 * pSrcA, - const arm_matrix_instance_q31 * pSrcB, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Floating-point matrix scaling. - * @param[in] pSrc points to the input matrix - * @param[in] scale scale factor - * @param[out] pDst points to the output matrix - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_scale_f32( - const arm_matrix_instance_f32 * pSrc, - float32_t scale, - arm_matrix_instance_f32 * pDst); - - /** - * @brief Q15 matrix scaling. - * @param[in] pSrc points to input matrix - * @param[in] scaleFract fractional portion of the scale factor - * @param[in] shift number of bits to shift the result by - * @param[out] pDst points to output matrix - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_scale_q15( - const arm_matrix_instance_q15 * pSrc, - q15_t scaleFract, - int32_t shift, - arm_matrix_instance_q15 * pDst); - - /** - * @brief Q31 matrix scaling. - * @param[in] pSrc points to input matrix - * @param[in] scaleFract fractional portion of the scale factor - * @param[in] shift number of bits to shift the result by - * @param[out] pDst points to output matrix structure - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - */ -arm_status arm_mat_scale_q31( - const arm_matrix_instance_q31 * pSrc, - q31_t scaleFract, - int32_t shift, - arm_matrix_instance_q31 * pDst); - - /** - * @brief Q31 matrix initialization. - * @param[in,out] S points to an instance of the floating-point matrix structure. - * @param[in] nRows number of rows in the matrix. - * @param[in] nColumns number of columns in the matrix. - * @param[in] pData points to the matrix data array. - */ -void arm_mat_init_q31( - arm_matrix_instance_q31 * S, - uint16_t nRows, - uint16_t nColumns, - q31_t * pData); - - /** - * @brief Q15 matrix initialization. - * @param[in,out] S points to an instance of the floating-point matrix structure. - * @param[in] nRows number of rows in the matrix. - * @param[in] nColumns number of columns in the matrix. - * @param[in] pData points to the matrix data array. - */ -void arm_mat_init_q15( - arm_matrix_instance_q15 * S, - uint16_t nRows, - uint16_t nColumns, - q15_t * pData); - - /** - * @brief Floating-point matrix initialization. - * @param[in,out] S points to an instance of the floating-point matrix structure. - * @param[in] nRows number of rows in the matrix. - * @param[in] nColumns number of columns in the matrix. - * @param[in] pData points to the matrix data array. - */ -void arm_mat_init_f32( - arm_matrix_instance_f32 * S, - uint16_t nRows, - uint16_t nColumns, - float32_t * pData); - - - /** - * @brief Instance structure for the Q15 PID Control. - */ - typedef struct - { - q15_t A0; /**< The derived gain, A0 = Kp + Ki + Kd . */ -#if !defined (ARM_MATH_DSP) - q15_t A1; - q15_t A2; -#else - q31_t A1; /**< The derived gain A1 = -Kp - 2Kd | Kd.*/ -#endif - q15_t state[3]; /**< The state array of length 3. */ - q15_t Kp; /**< The proportional gain. */ - q15_t Ki; /**< The integral gain. */ - q15_t Kd; /**< The derivative gain. */ - } arm_pid_instance_q15; - - /** - * @brief Instance structure for the Q31 PID Control. - */ - typedef struct - { - q31_t A0; /**< The derived gain, A0 = Kp + Ki + Kd . */ - q31_t A1; /**< The derived gain, A1 = -Kp - 2Kd. */ - q31_t A2; /**< The derived gain, A2 = Kd . */ - q31_t state[3]; /**< The state array of length 3. */ - q31_t Kp; /**< The proportional gain. */ - q31_t Ki; /**< The integral gain. */ - q31_t Kd; /**< The derivative gain. */ - } arm_pid_instance_q31; - - /** - * @brief Instance structure for the floating-point PID Control. - */ - typedef struct - { - float32_t A0; /**< The derived gain, A0 = Kp + Ki + Kd . */ - float32_t A1; /**< The derived gain, A1 = -Kp - 2Kd. */ - float32_t A2; /**< The derived gain, A2 = Kd . */ - float32_t state[3]; /**< The state array of length 3. */ - float32_t Kp; /**< The proportional gain. */ - float32_t Ki; /**< The integral gain. */ - float32_t Kd; /**< The derivative gain. */ - } arm_pid_instance_f32; - - - - /** - * @brief Initialization function for the floating-point PID Control. - * @param[in,out] S points to an instance of the PID structure. - * @param[in] resetStateFlag flag to reset the state. 0 = no change in state 1 = reset the state. - */ - void arm_pid_init_f32( - arm_pid_instance_f32 * S, - int32_t resetStateFlag); - - - /** - * @brief Reset function for the floating-point PID Control. - * @param[in,out] S is an instance of the floating-point PID Control structure - */ - void arm_pid_reset_f32( - arm_pid_instance_f32 * S); - - - /** - * @brief Initialization function for the Q31 PID Control. - * @param[in,out] S points to an instance of the Q15 PID structure. - * @param[in] resetStateFlag flag to reset the state. 0 = no change in state 1 = reset the state. - */ - void arm_pid_init_q31( - arm_pid_instance_q31 * S, - int32_t resetStateFlag); - - - /** - * @brief Reset function for the Q31 PID Control. - * @param[in,out] S points to an instance of the Q31 PID Control structure - */ - - void arm_pid_reset_q31( - arm_pid_instance_q31 * S); - - - /** - * @brief Initialization function for the Q15 PID Control. - * @param[in,out] S points to an instance of the Q15 PID structure. - * @param[in] resetStateFlag flag to reset the state. 0 = no change in state 1 = reset the state. - */ - void arm_pid_init_q15( - arm_pid_instance_q15 * S, - int32_t resetStateFlag); - - - /** - * @brief Reset function for the Q15 PID Control. - * @param[in,out] S points to an instance of the q15 PID Control structure - */ - void arm_pid_reset_q15( - arm_pid_instance_q15 * S); - - - /** - * @brief Instance structure for the floating-point Linear Interpolate function. - */ - typedef struct - { - uint32_t nValues; /**< nValues */ - float32_t x1; /**< x1 */ - float32_t xSpacing; /**< xSpacing */ - float32_t *pYData; /**< pointer to the table of Y values */ - } arm_linear_interp_instance_f32; - - /** - * @brief Instance structure for the floating-point bilinear interpolation function. - */ - typedef struct - { - uint16_t numRows; /**< number of rows in the data table. */ - uint16_t numCols; /**< number of columns in the data table. */ - float32_t *pData; /**< points to the data table. */ - } arm_bilinear_interp_instance_f32; - - /** - * @brief Instance structure for the Q31 bilinear interpolation function. - */ - typedef struct - { - uint16_t numRows; /**< number of rows in the data table. */ - uint16_t numCols; /**< number of columns in the data table. */ - q31_t *pData; /**< points to the data table. */ - } arm_bilinear_interp_instance_q31; - - /** - * @brief Instance structure for the Q15 bilinear interpolation function. - */ - typedef struct - { - uint16_t numRows; /**< number of rows in the data table. */ - uint16_t numCols; /**< number of columns in the data table. */ - q15_t *pData; /**< points to the data table. */ - } arm_bilinear_interp_instance_q15; - - /** - * @brief Instance structure for the Q15 bilinear interpolation function. - */ - typedef struct - { - uint16_t numRows; /**< number of rows in the data table. */ - uint16_t numCols; /**< number of columns in the data table. */ - q7_t *pData; /**< points to the data table. */ - } arm_bilinear_interp_instance_q7; - - - /** - * @brief Q7 vector multiplication. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_mult_q7( - const q7_t * pSrcA, - const q7_t * pSrcB, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q15 vector multiplication. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_mult_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q31 vector multiplication. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_mult_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Floating-point vector multiplication. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_mult_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q15 CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const q15_t *pTwiddle; /**< points to the Sin twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - } arm_cfft_radix2_instance_q15; - -/* Deprecated */ - arm_status arm_cfft_radix2_init_q15( - arm_cfft_radix2_instance_q15 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - -/* Deprecated */ - void arm_cfft_radix2_q15( - const arm_cfft_radix2_instance_q15 * S, - q15_t * pSrc); - - - /** - * @brief Instance structure for the Q15 CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const q15_t *pTwiddle; /**< points to the twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - } arm_cfft_radix4_instance_q15; - -/* Deprecated */ - arm_status arm_cfft_radix4_init_q15( - arm_cfft_radix4_instance_q15 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - -/* Deprecated */ - void arm_cfft_radix4_q15( - const arm_cfft_radix4_instance_q15 * S, - q15_t * pSrc); - - /** - * @brief Instance structure for the Radix-2 Q31 CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const q31_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - } arm_cfft_radix2_instance_q31; - -/* Deprecated */ - arm_status arm_cfft_radix2_init_q31( - arm_cfft_radix2_instance_q31 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - -/* Deprecated */ - void arm_cfft_radix2_q31( - const arm_cfft_radix2_instance_q31 * S, - q31_t * pSrc); - - /** - * @brief Instance structure for the Q31 CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const q31_t *pTwiddle; /**< points to the twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - } arm_cfft_radix4_instance_q31; - -/* Deprecated */ - void arm_cfft_radix4_q31( - const arm_cfft_radix4_instance_q31 * S, - q31_t * pSrc); - -/* Deprecated */ - arm_status arm_cfft_radix4_init_q31( - arm_cfft_radix4_instance_q31 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - - /** - * @brief Instance structure for the floating-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const float32_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - float32_t onebyfftLen; /**< value of 1/fftLen. */ - } arm_cfft_radix2_instance_f32; - - -/* Deprecated */ - arm_status arm_cfft_radix2_init_f32( - arm_cfft_radix2_instance_f32 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - -/* Deprecated */ - void arm_cfft_radix2_f32( - const arm_cfft_radix2_instance_f32 * S, - float32_t * pSrc); - - /** - * @brief Instance structure for the floating-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - uint8_t ifftFlag; /**< flag that selects forward (ifftFlag=0) or inverse (ifftFlag=1) transform. */ - uint8_t bitReverseFlag; /**< flag that enables (bitReverseFlag=1) or disables (bitReverseFlag=0) bit reversal of output. */ - const float32_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t twidCoefModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - uint16_t bitRevFactor; /**< bit reversal modifier that supports different size FFTs with the same bit reversal table. */ - float32_t onebyfftLen; /**< value of 1/fftLen. */ - } arm_cfft_radix4_instance_f32; - - - -/* Deprecated */ - arm_status arm_cfft_radix4_init_f32( - arm_cfft_radix4_instance_f32 * S, - uint16_t fftLen, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - -/* Deprecated */ - void arm_cfft_radix4_f32( - const arm_cfft_radix4_instance_f32 * S, - float32_t * pSrc); - - /** - * @brief Instance structure for the fixed-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - const q15_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t bitRevLength; /**< bit reversal table length. */ -#if defined(ARM_MATH_MVEI) - const uint32_t *rearranged_twiddle_tab_stride1_arr; /**< Per stage reordered twiddle pointer (offset 1) */ \ - const uint32_t *rearranged_twiddle_tab_stride2_arr; /**< Per stage reordered twiddle pointer (offset 2) */ \ - const uint32_t *rearranged_twiddle_tab_stride3_arr; /**< Per stage reordered twiddle pointer (offset 3) */ \ - const q15_t *rearranged_twiddle_stride1; /**< reordered twiddle offset 1 storage */ \ - const q15_t *rearranged_twiddle_stride2; /**< reordered twiddle offset 2 storage */ \ - const q15_t *rearranged_twiddle_stride3; -#endif - } arm_cfft_instance_q15; - -arm_status arm_cfft_init_q15( - arm_cfft_instance_q15 * S, - uint16_t fftLen); - -void arm_cfft_q15( - const arm_cfft_instance_q15 * S, - q15_t * p1, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - - /** - * @brief Instance structure for the fixed-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - const q31_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t bitRevLength; /**< bit reversal table length. */ -#if defined(ARM_MATH_MVEI) - const uint32_t *rearranged_twiddle_tab_stride1_arr; /**< Per stage reordered twiddle pointer (offset 1) */ \ - const uint32_t *rearranged_twiddle_tab_stride2_arr; /**< Per stage reordered twiddle pointer (offset 2) */ \ - const uint32_t *rearranged_twiddle_tab_stride3_arr; /**< Per stage reordered twiddle pointer (offset 3) */ \ - const q31_t *rearranged_twiddle_stride1; /**< reordered twiddle offset 1 storage */ \ - const q31_t *rearranged_twiddle_stride2; /**< reordered twiddle offset 2 storage */ \ - const q31_t *rearranged_twiddle_stride3; -#endif - } arm_cfft_instance_q31; - -arm_status arm_cfft_init_q31( - arm_cfft_instance_q31 * S, - uint16_t fftLen); - -void arm_cfft_q31( - const arm_cfft_instance_q31 * S, - q31_t * p1, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - - /** - * @brief Instance structure for the floating-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - const float32_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t bitRevLength; /**< bit reversal table length. */ -#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) - const uint32_t *rearranged_twiddle_tab_stride1_arr; /**< Per stage reordered twiddle pointer (offset 1) */ \ - const uint32_t *rearranged_twiddle_tab_stride2_arr; /**< Per stage reordered twiddle pointer (offset 2) */ \ - const uint32_t *rearranged_twiddle_tab_stride3_arr; /**< Per stage reordered twiddle pointer (offset 3) */ \ - const float32_t *rearranged_twiddle_stride1; /**< reordered twiddle offset 1 storage */ \ - const float32_t *rearranged_twiddle_stride2; /**< reordered twiddle offset 2 storage */ \ - const float32_t *rearranged_twiddle_stride3; -#endif - } arm_cfft_instance_f32; - - - - arm_status arm_cfft_init_f32( - arm_cfft_instance_f32 * S, - uint16_t fftLen); - - void arm_cfft_f32( - const arm_cfft_instance_f32 * S, - float32_t * p1, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - - - /** - * @brief Instance structure for the Double Precision Floating-point CFFT/CIFFT function. - */ - typedef struct - { - uint16_t fftLen; /**< length of the FFT. */ - const float64_t *pTwiddle; /**< points to the Twiddle factor table. */ - const uint16_t *pBitRevTable; /**< points to the bit reversal table. */ - uint16_t bitRevLength; /**< bit reversal table length. */ - } arm_cfft_instance_f64; - - arm_status arm_cfft_init_f64( - arm_cfft_instance_f64 * S, - uint16_t fftLen); - - void arm_cfft_f64( - const arm_cfft_instance_f64 * S, - float64_t * p1, - uint8_t ifftFlag, - uint8_t bitReverseFlag); - - /** - * @brief Instance structure for the Q15 RFFT/RIFFT function. - */ - typedef struct - { - uint32_t fftLenReal; /**< length of the real FFT. */ - uint8_t ifftFlagR; /**< flag that selects forward (ifftFlagR=0) or inverse (ifftFlagR=1) transform. */ - uint8_t bitReverseFlagR; /**< flag that enables (bitReverseFlagR=1) or disables (bitReverseFlagR=0) bit reversal of output. */ - uint32_t twidCoefRModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - const q15_t *pTwiddleAReal; /**< points to the real twiddle factor table. */ - const q15_t *pTwiddleBReal; /**< points to the imag twiddle factor table. */ -#if defined(ARM_MATH_MVEI) - arm_cfft_instance_q15 cfftInst; -#else - const arm_cfft_instance_q15 *pCfft; /**< points to the complex FFT instance. */ -#endif - } arm_rfft_instance_q15; - - arm_status arm_rfft_init_q15( - arm_rfft_instance_q15 * S, - uint32_t fftLenReal, - uint32_t ifftFlagR, - uint32_t bitReverseFlag); - - void arm_rfft_q15( - const arm_rfft_instance_q15 * S, - q15_t * pSrc, - q15_t * pDst); - - /** - * @brief Instance structure for the Q31 RFFT/RIFFT function. - */ - typedef struct - { - uint32_t fftLenReal; /**< length of the real FFT. */ - uint8_t ifftFlagR; /**< flag that selects forward (ifftFlagR=0) or inverse (ifftFlagR=1) transform. */ - uint8_t bitReverseFlagR; /**< flag that enables (bitReverseFlagR=1) or disables (bitReverseFlagR=0) bit reversal of output. */ - uint32_t twidCoefRModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - const q31_t *pTwiddleAReal; /**< points to the real twiddle factor table. */ - const q31_t *pTwiddleBReal; /**< points to the imag twiddle factor table. */ -#if defined(ARM_MATH_MVEI) - arm_cfft_instance_q31 cfftInst; -#else - const arm_cfft_instance_q31 *pCfft; /**< points to the complex FFT instance. */ -#endif - } arm_rfft_instance_q31; - - arm_status arm_rfft_init_q31( - arm_rfft_instance_q31 * S, - uint32_t fftLenReal, - uint32_t ifftFlagR, - uint32_t bitReverseFlag); - - void arm_rfft_q31( - const arm_rfft_instance_q31 * S, - q31_t * pSrc, - q31_t * pDst); - - /** - * @brief Instance structure for the floating-point RFFT/RIFFT function. - */ - typedef struct - { - uint32_t fftLenReal; /**< length of the real FFT. */ - uint16_t fftLenBy2; /**< length of the complex FFT. */ - uint8_t ifftFlagR; /**< flag that selects forward (ifftFlagR=0) or inverse (ifftFlagR=1) transform. */ - uint8_t bitReverseFlagR; /**< flag that enables (bitReverseFlagR=1) or disables (bitReverseFlagR=0) bit reversal of output. */ - uint32_t twidCoefRModifier; /**< twiddle coefficient modifier that supports different size FFTs with the same twiddle factor table. */ - const float32_t *pTwiddleAReal; /**< points to the real twiddle factor table. */ - const float32_t *pTwiddleBReal; /**< points to the imag twiddle factor table. */ - arm_cfft_radix4_instance_f32 *pCfft; /**< points to the complex FFT instance. */ - } arm_rfft_instance_f32; - - arm_status arm_rfft_init_f32( - arm_rfft_instance_f32 * S, - arm_cfft_radix4_instance_f32 * S_CFFT, - uint32_t fftLenReal, - uint32_t ifftFlagR, - uint32_t bitReverseFlag); - - void arm_rfft_f32( - const arm_rfft_instance_f32 * S, - float32_t * pSrc, - float32_t * pDst); - - /** - * @brief Instance structure for the Double Precision Floating-point RFFT/RIFFT function. - */ -typedef struct - { - arm_cfft_instance_f64 Sint; /**< Internal CFFT structure. */ - uint16_t fftLenRFFT; /**< length of the real sequence */ - const float64_t * pTwiddleRFFT; /**< Twiddle factors real stage */ - } arm_rfft_fast_instance_f64 ; - -arm_status arm_rfft_fast_init_f64 ( - arm_rfft_fast_instance_f64 * S, - uint16_t fftLen); - - -void arm_rfft_fast_f64( - arm_rfft_fast_instance_f64 * S, - float64_t * p, float64_t * pOut, - uint8_t ifftFlag); - - - /** - * @brief Instance structure for the floating-point RFFT/RIFFT function. - */ -typedef struct - { - arm_cfft_instance_f32 Sint; /**< Internal CFFT structure. */ - uint16_t fftLenRFFT; /**< length of the real sequence */ - const float32_t * pTwiddleRFFT; /**< Twiddle factors real stage */ - } arm_rfft_fast_instance_f32 ; - -arm_status arm_rfft_fast_init_f32 ( - arm_rfft_fast_instance_f32 * S, - uint16_t fftLen); - - - void arm_rfft_fast_f32( - const arm_rfft_fast_instance_f32 * S, - float32_t * p, float32_t * pOut, - uint8_t ifftFlag); - - /** - * @brief Instance structure for the floating-point DCT4/IDCT4 function. - */ - typedef struct - { - uint16_t N; /**< length of the DCT4. */ - uint16_t Nby2; /**< half of the length of the DCT4. */ - float32_t normalize; /**< normalizing factor. */ - const float32_t *pTwiddle; /**< points to the twiddle factor table. */ - const float32_t *pCosFactor; /**< points to the cosFactor table. */ - arm_rfft_instance_f32 *pRfft; /**< points to the real FFT instance. */ - arm_cfft_radix4_instance_f32 *pCfft; /**< points to the complex FFT instance. */ - } arm_dct4_instance_f32; - - - /** - * @brief Initialization function for the floating-point DCT4/IDCT4. - * @param[in,out] S points to an instance of floating-point DCT4/IDCT4 structure. - * @param[in] S_RFFT points to an instance of floating-point RFFT/RIFFT structure. - * @param[in] S_CFFT points to an instance of floating-point CFFT/CIFFT structure. - * @param[in] N length of the DCT4. - * @param[in] Nby2 half of the length of the DCT4. - * @param[in] normalize normalizing factor. - * @return arm_status function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_ARGUMENT_ERROR if fftLenReal is not a supported transform length. - */ - arm_status arm_dct4_init_f32( - arm_dct4_instance_f32 * S, - arm_rfft_instance_f32 * S_RFFT, - arm_cfft_radix4_instance_f32 * S_CFFT, - uint16_t N, - uint16_t Nby2, - float32_t normalize); - - - /** - * @brief Processing function for the floating-point DCT4/IDCT4. - * @param[in] S points to an instance of the floating-point DCT4/IDCT4 structure. - * @param[in] pState points to state buffer. - * @param[in,out] pInlineBuffer points to the in-place input and output buffer. - */ - void arm_dct4_f32( - const arm_dct4_instance_f32 * S, - float32_t * pState, - float32_t * pInlineBuffer); - - - /** - * @brief Instance structure for the Q31 DCT4/IDCT4 function. - */ - typedef struct - { - uint16_t N; /**< length of the DCT4. */ - uint16_t Nby2; /**< half of the length of the DCT4. */ - q31_t normalize; /**< normalizing factor. */ - const q31_t *pTwiddle; /**< points to the twiddle factor table. */ - const q31_t *pCosFactor; /**< points to the cosFactor table. */ - arm_rfft_instance_q31 *pRfft; /**< points to the real FFT instance. */ - arm_cfft_radix4_instance_q31 *pCfft; /**< points to the complex FFT instance. */ - } arm_dct4_instance_q31; - - - /** - * @brief Initialization function for the Q31 DCT4/IDCT4. - * @param[in,out] S points to an instance of Q31 DCT4/IDCT4 structure. - * @param[in] S_RFFT points to an instance of Q31 RFFT/RIFFT structure - * @param[in] S_CFFT points to an instance of Q31 CFFT/CIFFT structure - * @param[in] N length of the DCT4. - * @param[in] Nby2 half of the length of the DCT4. - * @param[in] normalize normalizing factor. - * @return arm_status function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_ARGUMENT_ERROR if N is not a supported transform length. - */ - arm_status arm_dct4_init_q31( - arm_dct4_instance_q31 * S, - arm_rfft_instance_q31 * S_RFFT, - arm_cfft_radix4_instance_q31 * S_CFFT, - uint16_t N, - uint16_t Nby2, - q31_t normalize); - - - /** - * @brief Processing function for the Q31 DCT4/IDCT4. - * @param[in] S points to an instance of the Q31 DCT4 structure. - * @param[in] pState points to state buffer. - * @param[in,out] pInlineBuffer points to the in-place input and output buffer. - */ - void arm_dct4_q31( - const arm_dct4_instance_q31 * S, - q31_t * pState, - q31_t * pInlineBuffer); - - - /** - * @brief Instance structure for the Q15 DCT4/IDCT4 function. - */ - typedef struct - { - uint16_t N; /**< length of the DCT4. */ - uint16_t Nby2; /**< half of the length of the DCT4. */ - q15_t normalize; /**< normalizing factor. */ - const q15_t *pTwiddle; /**< points to the twiddle factor table. */ - const q15_t *pCosFactor; /**< points to the cosFactor table. */ - arm_rfft_instance_q15 *pRfft; /**< points to the real FFT instance. */ - arm_cfft_radix4_instance_q15 *pCfft; /**< points to the complex FFT instance. */ - } arm_dct4_instance_q15; - - - /** - * @brief Initialization function for the Q15 DCT4/IDCT4. - * @param[in,out] S points to an instance of Q15 DCT4/IDCT4 structure. - * @param[in] S_RFFT points to an instance of Q15 RFFT/RIFFT structure. - * @param[in] S_CFFT points to an instance of Q15 CFFT/CIFFT structure. - * @param[in] N length of the DCT4. - * @param[in] Nby2 half of the length of the DCT4. - * @param[in] normalize normalizing factor. - * @return arm_status function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_ARGUMENT_ERROR if N is not a supported transform length. - */ - arm_status arm_dct4_init_q15( - arm_dct4_instance_q15 * S, - arm_rfft_instance_q15 * S_RFFT, - arm_cfft_radix4_instance_q15 * S_CFFT, - uint16_t N, - uint16_t Nby2, - q15_t normalize); - - - /** - * @brief Processing function for the Q15 DCT4/IDCT4. - * @param[in] S points to an instance of the Q15 DCT4 structure. - * @param[in] pState points to state buffer. - * @param[in,out] pInlineBuffer points to the in-place input and output buffer. - */ - void arm_dct4_q15( - const arm_dct4_instance_q15 * S, - q15_t * pState, - q15_t * pInlineBuffer); - - - /** - * @brief Floating-point vector addition. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_add_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - float32_t * pDst, - uint32_t blockSize); - - - - /** - * @brief Q7 vector addition. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_add_q7( - const q7_t * pSrcA, - const q7_t * pSrcB, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q15 vector addition. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_add_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q31 vector addition. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_add_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Floating-point vector subtraction. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_sub_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - float32_t * pDst, - uint32_t blockSize); - - - - /** - * @brief Q7 vector subtraction. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_sub_q7( - const q7_t * pSrcA, - const q7_t * pSrcB, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q15 vector subtraction. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_sub_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q31 vector subtraction. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in each vector - */ - void arm_sub_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Multiplies a floating-point vector by a scalar. - * @param[in] pSrc points to the input vector - * @param[in] scale scale factor to be applied - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_scale_f32( - const float32_t * pSrc, - float32_t scale, - float32_t * pDst, - uint32_t blockSize); - - - - /** - * @brief Multiplies a Q7 vector by a scalar. - * @param[in] pSrc points to the input vector - * @param[in] scaleFract fractional portion of the scale value - * @param[in] shift number of bits to shift the result by - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_scale_q7( - const q7_t * pSrc, - q7_t scaleFract, - int8_t shift, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Multiplies a Q15 vector by a scalar. - * @param[in] pSrc points to the input vector - * @param[in] scaleFract fractional portion of the scale value - * @param[in] shift number of bits to shift the result by - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_scale_q15( - const q15_t * pSrc, - q15_t scaleFract, - int8_t shift, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Multiplies a Q31 vector by a scalar. - * @param[in] pSrc points to the input vector - * @param[in] scaleFract fractional portion of the scale value - * @param[in] shift number of bits to shift the result by - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_scale_q31( - const q31_t * pSrc, - q31_t scaleFract, - int8_t shift, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q7 vector absolute value. - * @param[in] pSrc points to the input buffer - * @param[out] pDst points to the output buffer - * @param[in] blockSize number of samples in each vector - */ - void arm_abs_q7( - const q7_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Floating-point vector absolute value. - * @param[in] pSrc points to the input buffer - * @param[out] pDst points to the output buffer - * @param[in] blockSize number of samples in each vector - */ - void arm_abs_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - - - /** - * @brief Q15 vector absolute value. - * @param[in] pSrc points to the input buffer - * @param[out] pDst points to the output buffer - * @param[in] blockSize number of samples in each vector - */ - void arm_abs_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Q31 vector absolute value. - * @param[in] pSrc points to the input buffer - * @param[out] pDst points to the output buffer - * @param[in] blockSize number of samples in each vector - */ - void arm_abs_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Dot product of floating-point vectors. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] blockSize number of samples in each vector - * @param[out] result output result returned here - */ - void arm_dot_prod_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - uint32_t blockSize, - float32_t * result); - - - - /** - * @brief Dot product of Q7 vectors. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] blockSize number of samples in each vector - * @param[out] result output result returned here - */ - void arm_dot_prod_q7( - const q7_t * pSrcA, - const q7_t * pSrcB, - uint32_t blockSize, - q31_t * result); - - - /** - * @brief Dot product of Q15 vectors. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] blockSize number of samples in each vector - * @param[out] result output result returned here - */ - void arm_dot_prod_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - uint32_t blockSize, - q63_t * result); - - - /** - * @brief Dot product of Q31 vectors. - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] blockSize number of samples in each vector - * @param[out] result output result returned here - */ - void arm_dot_prod_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - uint32_t blockSize, - q63_t * result); - - - /** - * @brief Shifts the elements of a Q7 vector a specified number of bits. - * @param[in] pSrc points to the input vector - * @param[in] shiftBits number of bits to shift. A positive value shifts left; a negative value shifts right. - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_shift_q7( - const q7_t * pSrc, - int8_t shiftBits, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Shifts the elements of a Q15 vector a specified number of bits. - * @param[in] pSrc points to the input vector - * @param[in] shiftBits number of bits to shift. A positive value shifts left; a negative value shifts right. - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_shift_q15( - const q15_t * pSrc, - int8_t shiftBits, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Shifts the elements of a Q31 vector a specified number of bits. - * @param[in] pSrc points to the input vector - * @param[in] shiftBits number of bits to shift. A positive value shifts left; a negative value shifts right. - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_shift_q31( - const q31_t * pSrc, - int8_t shiftBits, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Adds a constant offset to a floating-point vector. - * @param[in] pSrc points to the input vector - * @param[in] offset is the offset to be added - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_offset_f32( - const float32_t * pSrc, - float32_t offset, - float32_t * pDst, - uint32_t blockSize); - - - - /** - * @brief Adds a constant offset to a Q7 vector. - * @param[in] pSrc points to the input vector - * @param[in] offset is the offset to be added - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_offset_q7( - const q7_t * pSrc, - q7_t offset, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Adds a constant offset to a Q15 vector. - * @param[in] pSrc points to the input vector - * @param[in] offset is the offset to be added - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_offset_q15( - const q15_t * pSrc, - q15_t offset, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Adds a constant offset to a Q31 vector. - * @param[in] pSrc points to the input vector - * @param[in] offset is the offset to be added - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_offset_q31( - const q31_t * pSrc, - q31_t offset, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Negates the elements of a floating-point vector. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_negate_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Negates the elements of a Q7 vector. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_negate_q7( - const q7_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Negates the elements of a Q15 vector. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_negate_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Negates the elements of a Q31 vector. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] blockSize number of samples in the vector - */ - void arm_negate_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Copies the elements of a floating-point vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_copy_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Copies the elements of a Q7 vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_copy_q7( - const q7_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Copies the elements of a Q15 vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_copy_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Copies the elements of a Q31 vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_copy_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Fills a constant value into a floating-point vector. - * @param[in] value input value to be filled - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_fill_f32( - float32_t value, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Fills a constant value into a Q7 vector. - * @param[in] value input value to be filled - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_fill_q7( - q7_t value, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Fills a constant value into a Q15 vector. - * @param[in] value input value to be filled - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_fill_q15( - q15_t value, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Fills a constant value into a Q31 vector. - * @param[in] value input value to be filled - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_fill_q31( - q31_t value, - q31_t * pDst, - uint32_t blockSize); - - -/** - * @brief Convolution of floating-point sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the location where the output result is written. Length srcALen+srcBLen-1. - */ - void arm_conv_f32( - const float32_t * pSrcA, - uint32_t srcALen, - const float32_t * pSrcB, - uint32_t srcBLen, - float32_t * pDst); - - - /** - * @brief Convolution of Q15 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - * @param[in] pScratch1 points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer of size min(srcALen, srcBLen). - */ - void arm_conv_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - q15_t * pScratch1, - q15_t * pScratch2); - - -/** - * @brief Convolution of Q15 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the location where the output result is written. Length srcALen+srcBLen-1. - */ - void arm_conv_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst); - - - /** - * @brief Convolution of Q15 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - */ - void arm_conv_fast_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst); - - - /** - * @brief Convolution of Q15 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - * @param[in] pScratch1 points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer of size min(srcALen, srcBLen). - */ - void arm_conv_fast_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - q15_t * pScratch1, - q15_t * pScratch2); - - - /** - * @brief Convolution of Q31 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - */ - void arm_conv_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst); - - - /** - * @brief Convolution of Q31 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - */ - void arm_conv_fast_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst); - - - /** - * @brief Convolution of Q7 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - * @param[in] pScratch1 points to scratch buffer(of type q15_t) of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer (of type q15_t) of size min(srcALen, srcBLen). - */ - void arm_conv_opt_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst, - q15_t * pScratch1, - q15_t * pScratch2); - - - /** - * @brief Convolution of Q7 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length srcALen+srcBLen-1. - */ - void arm_conv_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst); - - - /** - * @brief Partial convolution of floating-point sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_f32( - const float32_t * pSrcA, - uint32_t srcALen, - const float32_t * pSrcB, - uint32_t srcBLen, - float32_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Partial convolution of Q15 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @param[in] pScratch1 points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer of size min(srcALen, srcBLen). - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - uint32_t firstIndex, - uint32_t numPoints, - q15_t * pScratch1, - q15_t * pScratch2); - - - /** - * @brief Partial convolution of Q15 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Partial convolution of Q15 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_fast_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Partial convolution of Q15 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @param[in] pScratch1 points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer of size min(srcALen, srcBLen). - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_fast_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - uint32_t firstIndex, - uint32_t numPoints, - q15_t * pScratch1, - q15_t * pScratch2); - - - /** - * @brief Partial convolution of Q31 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Partial convolution of Q31 sequences (fast version) for Cortex-M3 and Cortex-M4 - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_fast_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Partial convolution of Q7 sequences - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @param[in] pScratch1 points to scratch buffer(of type q15_t) of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer (of type q15_t) of size min(srcALen, srcBLen). - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_opt_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst, - uint32_t firstIndex, - uint32_t numPoints, - q15_t * pScratch1, - q15_t * pScratch2); - - -/** - * @brief Partial convolution of Q7 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data - * @param[in] firstIndex is the first output sample to start with. - * @param[in] numPoints is the number of output points to be computed. - * @return Returns either ARM_MATH_SUCCESS if the function completed correctly or ARM_MATH_ARGUMENT_ERROR if the requested subset is not in the range [0 srcALen+srcBLen-2]. - */ - arm_status arm_conv_partial_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst, - uint32_t firstIndex, - uint32_t numPoints); - - - /** - * @brief Instance structure for the Q15 FIR decimator. - */ - typedef struct - { - uint8_t M; /**< decimation factor. */ - uint16_t numTaps; /**< number of coefficients in the filter. */ - const q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - q15_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - } arm_fir_decimate_instance_q15; - - /** - * @brief Instance structure for the Q31 FIR decimator. - */ - typedef struct - { - uint8_t M; /**< decimation factor. */ - uint16_t numTaps; /**< number of coefficients in the filter. */ - const q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - q31_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - } arm_fir_decimate_instance_q31; - -/** - @brief Instance structure for floating-point FIR decimator. - */ -typedef struct - { - uint8_t M; /**< decimation factor. */ - uint16_t numTaps; /**< number of coefficients in the filter. */ - const float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - float32_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - } arm_fir_decimate_instance_f32; - - -/** - @brief Processing function for floating-point FIR decimator. - @param[in] S points to an instance of the floating-point FIR decimator structure - @param[in] pSrc points to the block of input data - @param[out] pDst points to the block of output data - @param[in] blockSize number of samples to process - */ -void arm_fir_decimate_f32( - const arm_fir_decimate_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - -/** - @brief Initialization function for the floating-point FIR decimator. - @param[in,out] S points to an instance of the floating-point FIR decimator structure - @param[in] numTaps number of coefficients in the filter - @param[in] M decimation factor - @param[in] pCoeffs points to the filter coefficients - @param[in] pState points to the state buffer - @param[in] blockSize number of input samples to process per call - @return execution status - - \ref ARM_MATH_SUCCESS : Operation successful - - \ref ARM_MATH_LENGTH_ERROR : blockSize is not a multiple of M - */ -arm_status arm_fir_decimate_init_f32( - arm_fir_decimate_instance_f32 * S, - uint16_t numTaps, - uint8_t M, - const float32_t * pCoeffs, - float32_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q15 FIR decimator. - * @param[in] S points to an instance of the Q15 FIR decimator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_decimate_q15( - const arm_fir_decimate_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q15 FIR decimator (fast variant) for Cortex-M3 and Cortex-M4. - * @param[in] S points to an instance of the Q15 FIR decimator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_decimate_fast_q15( - const arm_fir_decimate_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q15 FIR decimator. - * @param[in,out] S points to an instance of the Q15 FIR decimator structure. - * @param[in] numTaps number of coefficients in the filter. - * @param[in] M decimation factor. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of input samples to process per call. - * @return The function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_LENGTH_ERROR if - * blockSize is not a multiple of M. - */ - arm_status arm_fir_decimate_init_q15( - arm_fir_decimate_instance_q15 * S, - uint16_t numTaps, - uint8_t M, - const q15_t * pCoeffs, - q15_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q31 FIR decimator. - * @param[in] S points to an instance of the Q31 FIR decimator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_decimate_q31( - const arm_fir_decimate_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - /** - * @brief Processing function for the Q31 FIR decimator (fast variant) for Cortex-M3 and Cortex-M4. - * @param[in] S points to an instance of the Q31 FIR decimator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_decimate_fast_q31( - const arm_fir_decimate_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q31 FIR decimator. - * @param[in,out] S points to an instance of the Q31 FIR decimator structure. - * @param[in] numTaps number of coefficients in the filter. - * @param[in] M decimation factor. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of input samples to process per call. - * @return The function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_LENGTH_ERROR if - * blockSize is not a multiple of M. - */ - arm_status arm_fir_decimate_init_q31( - arm_fir_decimate_instance_q31 * S, - uint16_t numTaps, - uint8_t M, - const q31_t * pCoeffs, - q31_t * pState, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q15 FIR interpolator. - */ - typedef struct - { - uint8_t L; /**< upsample factor. */ - uint16_t phaseLength; /**< length of each polyphase filter component. */ - const q15_t *pCoeffs; /**< points to the coefficient array. The array is of length L*phaseLength. */ - q15_t *pState; /**< points to the state variable array. The array is of length blockSize+phaseLength-1. */ - } arm_fir_interpolate_instance_q15; - - /** - * @brief Instance structure for the Q31 FIR interpolator. - */ - typedef struct - { - uint8_t L; /**< upsample factor. */ - uint16_t phaseLength; /**< length of each polyphase filter component. */ - const q31_t *pCoeffs; /**< points to the coefficient array. The array is of length L*phaseLength. */ - q31_t *pState; /**< points to the state variable array. The array is of length blockSize+phaseLength-1. */ - } arm_fir_interpolate_instance_q31; - - /** - * @brief Instance structure for the floating-point FIR interpolator. - */ - typedef struct - { - uint8_t L; /**< upsample factor. */ - uint16_t phaseLength; /**< length of each polyphase filter component. */ - const float32_t *pCoeffs; /**< points to the coefficient array. The array is of length L*phaseLength. */ - float32_t *pState; /**< points to the state variable array. The array is of length phaseLength+numTaps-1. */ - } arm_fir_interpolate_instance_f32; - - - /** - * @brief Processing function for the Q15 FIR interpolator. - * @param[in] S points to an instance of the Q15 FIR interpolator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_interpolate_q15( - const arm_fir_interpolate_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q15 FIR interpolator. - * @param[in,out] S points to an instance of the Q15 FIR interpolator structure. - * @param[in] L upsample factor. - * @param[in] numTaps number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficient buffer. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of input samples to process per call. - * @return The function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_LENGTH_ERROR if - * the filter length numTaps is not a multiple of the interpolation factor L. - */ - arm_status arm_fir_interpolate_init_q15( - arm_fir_interpolate_instance_q15 * S, - uint8_t L, - uint16_t numTaps, - const q15_t * pCoeffs, - q15_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q31 FIR interpolator. - * @param[in] S points to an instance of the Q15 FIR interpolator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_interpolate_q31( - const arm_fir_interpolate_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q31 FIR interpolator. - * @param[in,out] S points to an instance of the Q31 FIR interpolator structure. - * @param[in] L upsample factor. - * @param[in] numTaps number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficient buffer. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of input samples to process per call. - * @return The function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_LENGTH_ERROR if - * the filter length numTaps is not a multiple of the interpolation factor L. - */ - arm_status arm_fir_interpolate_init_q31( - arm_fir_interpolate_instance_q31 * S, - uint8_t L, - uint16_t numTaps, - const q31_t * pCoeffs, - q31_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the floating-point FIR interpolator. - * @param[in] S points to an instance of the floating-point FIR interpolator structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_interpolate_f32( - const arm_fir_interpolate_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the floating-point FIR interpolator. - * @param[in,out] S points to an instance of the floating-point FIR interpolator structure. - * @param[in] L upsample factor. - * @param[in] numTaps number of filter coefficients in the filter. - * @param[in] pCoeffs points to the filter coefficient buffer. - * @param[in] pState points to the state buffer. - * @param[in] blockSize number of input samples to process per call. - * @return The function returns ARM_MATH_SUCCESS if initialization is successful or ARM_MATH_LENGTH_ERROR if - * the filter length numTaps is not a multiple of the interpolation factor L. - */ - arm_status arm_fir_interpolate_init_f32( - arm_fir_interpolate_instance_f32 * S, - uint8_t L, - uint16_t numTaps, - const float32_t * pCoeffs, - float32_t * pState, - uint32_t blockSize); - - - /** - * @brief Instance structure for the high precision Q31 Biquad cascade filter. - */ - typedef struct - { - uint8_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - q63_t *pState; /**< points to the array of state coefficients. The array is of length 4*numStages. */ - const q31_t *pCoeffs; /**< points to the array of coefficients. The array is of length 5*numStages. */ - uint8_t postShift; /**< additional shift, in bits, applied to each output sample. */ - } arm_biquad_cas_df1_32x64_ins_q31; - - - /** - * @param[in] S points to an instance of the high precision Q31 Biquad cascade filter structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cas_df1_32x64_q31( - const arm_biquad_cas_df1_32x64_ins_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @param[in,out] S points to an instance of the high precision Q31 Biquad cascade filter structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] postShift shift to be applied to the output. Varies according to the coefficients format - */ - void arm_biquad_cas_df1_32x64_init_q31( - arm_biquad_cas_df1_32x64_ins_q31 * S, - uint8_t numStages, - const q31_t * pCoeffs, - q63_t * pState, - uint8_t postShift); - - - /** - * @brief Instance structure for the floating-point transposed direct form II Biquad cascade filter. - */ - typedef struct - { - uint8_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - float32_t *pState; /**< points to the array of state coefficients. The array is of length 2*numStages. */ - const float32_t *pCoeffs; /**< points to the array of coefficients. The array is of length 5*numStages. */ - } arm_biquad_cascade_df2T_instance_f32; - - /** - * @brief Instance structure for the floating-point transposed direct form II Biquad cascade filter. - */ - typedef struct - { - uint8_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - float32_t *pState; /**< points to the array of state coefficients. The array is of length 4*numStages. */ - const float32_t *pCoeffs; /**< points to the array of coefficients. The array is of length 5*numStages. */ - } arm_biquad_cascade_stereo_df2T_instance_f32; - - /** - * @brief Instance structure for the floating-point transposed direct form II Biquad cascade filter. - */ - typedef struct - { - uint8_t numStages; /**< number of 2nd order stages in the filter. Overall order is 2*numStages. */ - float64_t *pState; /**< points to the array of state coefficients. The array is of length 2*numStages. */ - const float64_t *pCoeffs; /**< points to the array of coefficients. The array is of length 5*numStages. */ - } arm_biquad_cascade_df2T_instance_f64; - - - /** - * @brief Processing function for the floating-point transposed direct form II Biquad cascade filter. - * @param[in] S points to an instance of the filter data structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df2T_f32( - const arm_biquad_cascade_df2T_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Processing function for the floating-point transposed direct form II Biquad cascade filter. 2 channels - * @param[in] S points to an instance of the filter data structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_stereo_df2T_f32( - const arm_biquad_cascade_stereo_df2T_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Processing function for the floating-point transposed direct form II Biquad cascade filter. - * @param[in] S points to an instance of the filter data structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_biquad_cascade_df2T_f64( - const arm_biquad_cascade_df2T_instance_f64 * S, - const float64_t * pSrc, - float64_t * pDst, - uint32_t blockSize); - - -#if defined(ARM_MATH_NEON) -void arm_biquad_cascade_df2T_compute_coefs_f32( - arm_biquad_cascade_df2T_instance_f32 * S, - uint8_t numStages, - float32_t * pCoeffs); -#endif - /** - * @brief Initialization function for the floating-point transposed direct form II Biquad cascade filter. - * @param[in,out] S points to an instance of the filter data structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - */ - void arm_biquad_cascade_df2T_init_f32( - arm_biquad_cascade_df2T_instance_f32 * S, - uint8_t numStages, - const float32_t * pCoeffs, - float32_t * pState); - - - /** - * @brief Initialization function for the floating-point transposed direct form II Biquad cascade filter. - * @param[in,out] S points to an instance of the filter data structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - */ - void arm_biquad_cascade_stereo_df2T_init_f32( - arm_biquad_cascade_stereo_df2T_instance_f32 * S, - uint8_t numStages, - const float32_t * pCoeffs, - float32_t * pState); - - - /** - * @brief Initialization function for the floating-point transposed direct form II Biquad cascade filter. - * @param[in,out] S points to an instance of the filter data structure. - * @param[in] numStages number of 2nd order stages in the filter. - * @param[in] pCoeffs points to the filter coefficients. - * @param[in] pState points to the state buffer. - */ - void arm_biquad_cascade_df2T_init_f64( - arm_biquad_cascade_df2T_instance_f64 * S, - uint8_t numStages, - const float64_t * pCoeffs, - float64_t * pState); - - - /** - * @brief Instance structure for the Q15 FIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of filter stages. */ - q15_t *pState; /**< points to the state variable array. The array is of length numStages. */ - const q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numStages. */ - } arm_fir_lattice_instance_q15; - - /** - * @brief Instance structure for the Q31 FIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of filter stages. */ - q31_t *pState; /**< points to the state variable array. The array is of length numStages. */ - const q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numStages. */ - } arm_fir_lattice_instance_q31; - - /** - * @brief Instance structure for the floating-point FIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of filter stages. */ - float32_t *pState; /**< points to the state variable array. The array is of length numStages. */ - const float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numStages. */ - } arm_fir_lattice_instance_f32; - - - /** - * @brief Initialization function for the Q15 FIR lattice filter. - * @param[in] S points to an instance of the Q15 FIR lattice structure. - * @param[in] numStages number of filter stages. - * @param[in] pCoeffs points to the coefficient buffer. The array is of length numStages. - * @param[in] pState points to the state buffer. The array is of length numStages. - */ - void arm_fir_lattice_init_q15( - arm_fir_lattice_instance_q15 * S, - uint16_t numStages, - const q15_t * pCoeffs, - q15_t * pState); - - - /** - * @brief Processing function for the Q15 FIR lattice filter. - * @param[in] S points to an instance of the Q15 FIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_fir_lattice_q15( - const arm_fir_lattice_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q31 FIR lattice filter. - * @param[in] S points to an instance of the Q31 FIR lattice structure. - * @param[in] numStages number of filter stages. - * @param[in] pCoeffs points to the coefficient buffer. The array is of length numStages. - * @param[in] pState points to the state buffer. The array is of length numStages. - */ - void arm_fir_lattice_init_q31( - arm_fir_lattice_instance_q31 * S, - uint16_t numStages, - const q31_t * pCoeffs, - q31_t * pState); - - - /** - * @brief Processing function for the Q31 FIR lattice filter. - * @param[in] S points to an instance of the Q31 FIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_fir_lattice_q31( - const arm_fir_lattice_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - -/** - * @brief Initialization function for the floating-point FIR lattice filter. - * @param[in] S points to an instance of the floating-point FIR lattice structure. - * @param[in] numStages number of filter stages. - * @param[in] pCoeffs points to the coefficient buffer. The array is of length numStages. - * @param[in] pState points to the state buffer. The array is of length numStages. - */ - void arm_fir_lattice_init_f32( - arm_fir_lattice_instance_f32 * S, - uint16_t numStages, - const float32_t * pCoeffs, - float32_t * pState); - - - /** - * @brief Processing function for the floating-point FIR lattice filter. - * @param[in] S points to an instance of the floating-point FIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] blockSize number of samples to process. - */ - void arm_fir_lattice_f32( - const arm_fir_lattice_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q15 IIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of stages in the filter. */ - q15_t *pState; /**< points to the state variable array. The array is of length numStages+blockSize. */ - q15_t *pkCoeffs; /**< points to the reflection coefficient array. The array is of length numStages. */ - q15_t *pvCoeffs; /**< points to the ladder coefficient array. The array is of length numStages+1. */ - } arm_iir_lattice_instance_q15; - - /** - * @brief Instance structure for the Q31 IIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of stages in the filter. */ - q31_t *pState; /**< points to the state variable array. The array is of length numStages+blockSize. */ - q31_t *pkCoeffs; /**< points to the reflection coefficient array. The array is of length numStages. */ - q31_t *pvCoeffs; /**< points to the ladder coefficient array. The array is of length numStages+1. */ - } arm_iir_lattice_instance_q31; - - /** - * @brief Instance structure for the floating-point IIR lattice filter. - */ - typedef struct - { - uint16_t numStages; /**< number of stages in the filter. */ - float32_t *pState; /**< points to the state variable array. The array is of length numStages+blockSize. */ - float32_t *pkCoeffs; /**< points to the reflection coefficient array. The array is of length numStages. */ - float32_t *pvCoeffs; /**< points to the ladder coefficient array. The array is of length numStages+1. */ - } arm_iir_lattice_instance_f32; - - - /** - * @brief Processing function for the floating-point IIR lattice filter. - * @param[in] S points to an instance of the floating-point IIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_iir_lattice_f32( - const arm_iir_lattice_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the floating-point IIR lattice filter. - * @param[in] S points to an instance of the floating-point IIR lattice structure. - * @param[in] numStages number of stages in the filter. - * @param[in] pkCoeffs points to the reflection coefficient buffer. The array is of length numStages. - * @param[in] pvCoeffs points to the ladder coefficient buffer. The array is of length numStages+1. - * @param[in] pState points to the state buffer. The array is of length numStages+blockSize-1. - * @param[in] blockSize number of samples to process. - */ - void arm_iir_lattice_init_f32( - arm_iir_lattice_instance_f32 * S, - uint16_t numStages, - float32_t * pkCoeffs, - float32_t * pvCoeffs, - float32_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q31 IIR lattice filter. - * @param[in] S points to an instance of the Q31 IIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_iir_lattice_q31( - const arm_iir_lattice_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q31 IIR lattice filter. - * @param[in] S points to an instance of the Q31 IIR lattice structure. - * @param[in] numStages number of stages in the filter. - * @param[in] pkCoeffs points to the reflection coefficient buffer. The array is of length numStages. - * @param[in] pvCoeffs points to the ladder coefficient buffer. The array is of length numStages+1. - * @param[in] pState points to the state buffer. The array is of length numStages+blockSize. - * @param[in] blockSize number of samples to process. - */ - void arm_iir_lattice_init_q31( - arm_iir_lattice_instance_q31 * S, - uint16_t numStages, - q31_t * pkCoeffs, - q31_t * pvCoeffs, - q31_t * pState, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q15 IIR lattice filter. - * @param[in] S points to an instance of the Q15 IIR lattice structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data. - * @param[in] blockSize number of samples to process. - */ - void arm_iir_lattice_q15( - const arm_iir_lattice_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - -/** - * @brief Initialization function for the Q15 IIR lattice filter. - * @param[in] S points to an instance of the fixed-point Q15 IIR lattice structure. - * @param[in] numStages number of stages in the filter. - * @param[in] pkCoeffs points to reflection coefficient buffer. The array is of length numStages. - * @param[in] pvCoeffs points to ladder coefficient buffer. The array is of length numStages+1. - * @param[in] pState points to state buffer. The array is of length numStages+blockSize. - * @param[in] blockSize number of samples to process per call. - */ - void arm_iir_lattice_init_q15( - arm_iir_lattice_instance_q15 * S, - uint16_t numStages, - q15_t * pkCoeffs, - q15_t * pvCoeffs, - q15_t * pState, - uint32_t blockSize); - - - /** - * @brief Instance structure for the floating-point LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - float32_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - float32_t mu; /**< step size that controls filter coefficient updates. */ - } arm_lms_instance_f32; - - - /** - * @brief Processing function for floating-point LMS filter. - * @param[in] S points to an instance of the floating-point LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_f32( - const arm_lms_instance_f32 * S, - const float32_t * pSrc, - float32_t * pRef, - float32_t * pOut, - float32_t * pErr, - uint32_t blockSize); - - - /** - * @brief Initialization function for floating-point LMS filter. - * @param[in] S points to an instance of the floating-point LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to the coefficient buffer. - * @param[in] pState points to state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_init_f32( - arm_lms_instance_f32 * S, - uint16_t numTaps, - float32_t * pCoeffs, - float32_t * pState, - float32_t mu, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q15 LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - q15_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - q15_t mu; /**< step size that controls filter coefficient updates. */ - uint32_t postShift; /**< bit shift applied to coefficients. */ - } arm_lms_instance_q15; - - - /** - * @brief Initialization function for the Q15 LMS filter. - * @param[in] S points to an instance of the Q15 LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to the coefficient buffer. - * @param[in] pState points to the state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - * @param[in] postShift bit shift applied to coefficients. - */ - void arm_lms_init_q15( - arm_lms_instance_q15 * S, - uint16_t numTaps, - q15_t * pCoeffs, - q15_t * pState, - q15_t mu, - uint32_t blockSize, - uint32_t postShift); - - - /** - * @brief Processing function for Q15 LMS filter. - * @param[in] S points to an instance of the Q15 LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_q15( - const arm_lms_instance_q15 * S, - const q15_t * pSrc, - q15_t * pRef, - q15_t * pOut, - q15_t * pErr, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q31 LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - q31_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - q31_t mu; /**< step size that controls filter coefficient updates. */ - uint32_t postShift; /**< bit shift applied to coefficients. */ - } arm_lms_instance_q31; - - - /** - * @brief Processing function for Q31 LMS filter. - * @param[in] S points to an instance of the Q15 LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_q31( - const arm_lms_instance_q31 * S, - const q31_t * pSrc, - q31_t * pRef, - q31_t * pOut, - q31_t * pErr, - uint32_t blockSize); - - - /** - * @brief Initialization function for Q31 LMS filter. - * @param[in] S points to an instance of the Q31 LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to coefficient buffer. - * @param[in] pState points to state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - * @param[in] postShift bit shift applied to coefficients. - */ - void arm_lms_init_q31( - arm_lms_instance_q31 * S, - uint16_t numTaps, - q31_t * pCoeffs, - q31_t * pState, - q31_t mu, - uint32_t blockSize, - uint32_t postShift); - - - /** - * @brief Instance structure for the floating-point normalized LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - float32_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - float32_t mu; /**< step size that control filter coefficient updates. */ - float32_t energy; /**< saves previous frame energy. */ - float32_t x0; /**< saves previous input sample. */ - } arm_lms_norm_instance_f32; - - - /** - * @brief Processing function for floating-point normalized LMS filter. - * @param[in] S points to an instance of the floating-point normalized LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_norm_f32( - arm_lms_norm_instance_f32 * S, - const float32_t * pSrc, - float32_t * pRef, - float32_t * pOut, - float32_t * pErr, - uint32_t blockSize); - - - /** - * @brief Initialization function for floating-point normalized LMS filter. - * @param[in] S points to an instance of the floating-point LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to coefficient buffer. - * @param[in] pState points to state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_norm_init_f32( - arm_lms_norm_instance_f32 * S, - uint16_t numTaps, - float32_t * pCoeffs, - float32_t * pState, - float32_t mu, - uint32_t blockSize); - - - /** - * @brief Instance structure for the Q31 normalized LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - q31_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - q31_t mu; /**< step size that controls filter coefficient updates. */ - uint8_t postShift; /**< bit shift applied to coefficients. */ - const q31_t *recipTable; /**< points to the reciprocal initial value table. */ - q31_t energy; /**< saves previous frame energy. */ - q31_t x0; /**< saves previous input sample. */ - } arm_lms_norm_instance_q31; - - - /** - * @brief Processing function for Q31 normalized LMS filter. - * @param[in] S points to an instance of the Q31 normalized LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_norm_q31( - arm_lms_norm_instance_q31 * S, - const q31_t * pSrc, - q31_t * pRef, - q31_t * pOut, - q31_t * pErr, - uint32_t blockSize); - - - /** - * @brief Initialization function for Q31 normalized LMS filter. - * @param[in] S points to an instance of the Q31 normalized LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to coefficient buffer. - * @param[in] pState points to state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - * @param[in] postShift bit shift applied to coefficients. - */ - void arm_lms_norm_init_q31( - arm_lms_norm_instance_q31 * S, - uint16_t numTaps, - q31_t * pCoeffs, - q31_t * pState, - q31_t mu, - uint32_t blockSize, - uint8_t postShift); - - - /** - * @brief Instance structure for the Q15 normalized LMS filter. - */ - typedef struct - { - uint16_t numTaps; /**< Number of coefficients in the filter. */ - q15_t *pState; /**< points to the state variable array. The array is of length numTaps+blockSize-1. */ - q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps. */ - q15_t mu; /**< step size that controls filter coefficient updates. */ - uint8_t postShift; /**< bit shift applied to coefficients. */ - const q15_t *recipTable; /**< Points to the reciprocal initial value table. */ - q15_t energy; /**< saves previous frame energy. */ - q15_t x0; /**< saves previous input sample. */ - } arm_lms_norm_instance_q15; - - - /** - * @brief Processing function for Q15 normalized LMS filter. - * @param[in] S points to an instance of the Q15 normalized LMS filter structure. - * @param[in] pSrc points to the block of input data. - * @param[in] pRef points to the block of reference data. - * @param[out] pOut points to the block of output data. - * @param[out] pErr points to the block of error data. - * @param[in] blockSize number of samples to process. - */ - void arm_lms_norm_q15( - arm_lms_norm_instance_q15 * S, - const q15_t * pSrc, - q15_t * pRef, - q15_t * pOut, - q15_t * pErr, - uint32_t blockSize); - - - /** - * @brief Initialization function for Q15 normalized LMS filter. - * @param[in] S points to an instance of the Q15 normalized LMS filter structure. - * @param[in] numTaps number of filter coefficients. - * @param[in] pCoeffs points to coefficient buffer. - * @param[in] pState points to state buffer. - * @param[in] mu step size that controls filter coefficient updates. - * @param[in] blockSize number of samples to process. - * @param[in] postShift bit shift applied to coefficients. - */ - void arm_lms_norm_init_q15( - arm_lms_norm_instance_q15 * S, - uint16_t numTaps, - q15_t * pCoeffs, - q15_t * pState, - q15_t mu, - uint32_t blockSize, - uint8_t postShift); - - - /** - * @brief Correlation of floating-point sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - */ - void arm_correlate_f32( - const float32_t * pSrcA, - uint32_t srcALen, - const float32_t * pSrcB, - uint32_t srcBLen, - float32_t * pDst); - - -/** - @brief Correlation of Q15 sequences - @param[in] pSrcA points to the first input sequence - @param[in] srcALen length of the first input sequence - @param[in] pSrcB points to the second input sequence - @param[in] srcBLen length of the second input sequence - @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - @param[in] pScratch points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. -*/ -void arm_correlate_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - q15_t * pScratch); - - -/** - @brief Correlation of Q15 sequences. - @param[in] pSrcA points to the first input sequence - @param[in] srcALen length of the first input sequence - @param[in] pSrcB points to the second input sequence - @param[in] srcBLen length of the second input sequence - @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - */ - void arm_correlate_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst); - - -/** - @brief Correlation of Q15 sequences (fast version). - @param[in] pSrcA points to the first input sequence - @param[in] srcALen length of the first input sequence - @param[in] pSrcB points to the second input sequence - @param[in] srcBLen length of the second input sequence - @param[out] pDst points to the location where the output result is written. Length 2 * max(srcALen, srcBLen) - 1. - @return none - */ -void arm_correlate_fast_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst); - - -/** - @brief Correlation of Q15 sequences (fast version). - @param[in] pSrcA points to the first input sequence. - @param[in] srcALen length of the first input sequence. - @param[in] pSrcB points to the second input sequence. - @param[in] srcBLen length of the second input sequence. - @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - @param[in] pScratch points to scratch buffer of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - */ -void arm_correlate_fast_opt_q15( - const q15_t * pSrcA, - uint32_t srcALen, - const q15_t * pSrcB, - uint32_t srcBLen, - q15_t * pDst, - q15_t * pScratch); - - - /** - * @brief Correlation of Q31 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - */ - void arm_correlate_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst); - - -/** - @brief Correlation of Q31 sequences (fast version). - @param[in] pSrcA points to the first input sequence - @param[in] srcALen length of the first input sequence - @param[in] pSrcB points to the second input sequence - @param[in] srcBLen length of the second input sequence - @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - */ -void arm_correlate_fast_q31( - const q31_t * pSrcA, - uint32_t srcALen, - const q31_t * pSrcB, - uint32_t srcBLen, - q31_t * pDst); - - - /** - * @brief Correlation of Q7 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - * @param[in] pScratch1 points to scratch buffer(of type q15_t) of size max(srcALen, srcBLen) + 2*min(srcALen, srcBLen) - 2. - * @param[in] pScratch2 points to scratch buffer (of type q15_t) of size min(srcALen, srcBLen). - */ - void arm_correlate_opt_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst, - q15_t * pScratch1, - q15_t * pScratch2); - - - /** - * @brief Correlation of Q7 sequences. - * @param[in] pSrcA points to the first input sequence. - * @param[in] srcALen length of the first input sequence. - * @param[in] pSrcB points to the second input sequence. - * @param[in] srcBLen length of the second input sequence. - * @param[out] pDst points to the block of output data Length 2 * max(srcALen, srcBLen) - 1. - */ - void arm_correlate_q7( - const q7_t * pSrcA, - uint32_t srcALen, - const q7_t * pSrcB, - uint32_t srcBLen, - q7_t * pDst); - - - /** - * @brief Instance structure for the floating-point sparse FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - uint16_t stateIndex; /**< state buffer index. Points to the oldest sample in the state buffer. */ - float32_t *pState; /**< points to the state buffer array. The array is of length maxDelay+blockSize-1. */ - const float32_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - uint16_t maxDelay; /**< maximum offset specified by the pTapDelay array. */ - int32_t *pTapDelay; /**< points to the array of delay values. The array is of length numTaps. */ - } arm_fir_sparse_instance_f32; - - /** - * @brief Instance structure for the Q31 sparse FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - uint16_t stateIndex; /**< state buffer index. Points to the oldest sample in the state buffer. */ - q31_t *pState; /**< points to the state buffer array. The array is of length maxDelay+blockSize-1. */ - const q31_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - uint16_t maxDelay; /**< maximum offset specified by the pTapDelay array. */ - int32_t *pTapDelay; /**< points to the array of delay values. The array is of length numTaps. */ - } arm_fir_sparse_instance_q31; - - /** - * @brief Instance structure for the Q15 sparse FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - uint16_t stateIndex; /**< state buffer index. Points to the oldest sample in the state buffer. */ - q15_t *pState; /**< points to the state buffer array. The array is of length maxDelay+blockSize-1. */ - const q15_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - uint16_t maxDelay; /**< maximum offset specified by the pTapDelay array. */ - int32_t *pTapDelay; /**< points to the array of delay values. The array is of length numTaps. */ - } arm_fir_sparse_instance_q15; - - /** - * @brief Instance structure for the Q7 sparse FIR filter. - */ - typedef struct - { - uint16_t numTaps; /**< number of coefficients in the filter. */ - uint16_t stateIndex; /**< state buffer index. Points to the oldest sample in the state buffer. */ - q7_t *pState; /**< points to the state buffer array. The array is of length maxDelay+blockSize-1. */ - const q7_t *pCoeffs; /**< points to the coefficient array. The array is of length numTaps.*/ - uint16_t maxDelay; /**< maximum offset specified by the pTapDelay array. */ - int32_t *pTapDelay; /**< points to the array of delay values. The array is of length numTaps. */ - } arm_fir_sparse_instance_q7; - - - /** - * @brief Processing function for the floating-point sparse FIR filter. - * @param[in] S points to an instance of the floating-point sparse FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] pScratchIn points to a temporary buffer of size blockSize. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_sparse_f32( - arm_fir_sparse_instance_f32 * S, - const float32_t * pSrc, - float32_t * pDst, - float32_t * pScratchIn, - uint32_t blockSize); - - - /** - * @brief Initialization function for the floating-point sparse FIR filter. - * @param[in,out] S points to an instance of the floating-point sparse FIR structure. - * @param[in] numTaps number of nonzero coefficients in the filter. - * @param[in] pCoeffs points to the array of filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] pTapDelay points to the array of offset times. - * @param[in] maxDelay maximum offset time supported. - * @param[in] blockSize number of samples that will be processed per block. - */ - void arm_fir_sparse_init_f32( - arm_fir_sparse_instance_f32 * S, - uint16_t numTaps, - const float32_t * pCoeffs, - float32_t * pState, - int32_t * pTapDelay, - uint16_t maxDelay, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q31 sparse FIR filter. - * @param[in] S points to an instance of the Q31 sparse FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] pScratchIn points to a temporary buffer of size blockSize. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_sparse_q31( - arm_fir_sparse_instance_q31 * S, - const q31_t * pSrc, - q31_t * pDst, - q31_t * pScratchIn, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q31 sparse FIR filter. - * @param[in,out] S points to an instance of the Q31 sparse FIR structure. - * @param[in] numTaps number of nonzero coefficients in the filter. - * @param[in] pCoeffs points to the array of filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] pTapDelay points to the array of offset times. - * @param[in] maxDelay maximum offset time supported. - * @param[in] blockSize number of samples that will be processed per block. - */ - void arm_fir_sparse_init_q31( - arm_fir_sparse_instance_q31 * S, - uint16_t numTaps, - const q31_t * pCoeffs, - q31_t * pState, - int32_t * pTapDelay, - uint16_t maxDelay, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q15 sparse FIR filter. - * @param[in] S points to an instance of the Q15 sparse FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] pScratchIn points to a temporary buffer of size blockSize. - * @param[in] pScratchOut points to a temporary buffer of size blockSize. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_sparse_q15( - arm_fir_sparse_instance_q15 * S, - const q15_t * pSrc, - q15_t * pDst, - q15_t * pScratchIn, - q31_t * pScratchOut, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q15 sparse FIR filter. - * @param[in,out] S points to an instance of the Q15 sparse FIR structure. - * @param[in] numTaps number of nonzero coefficients in the filter. - * @param[in] pCoeffs points to the array of filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] pTapDelay points to the array of offset times. - * @param[in] maxDelay maximum offset time supported. - * @param[in] blockSize number of samples that will be processed per block. - */ - void arm_fir_sparse_init_q15( - arm_fir_sparse_instance_q15 * S, - uint16_t numTaps, - const q15_t * pCoeffs, - q15_t * pState, - int32_t * pTapDelay, - uint16_t maxDelay, - uint32_t blockSize); - - - /** - * @brief Processing function for the Q7 sparse FIR filter. - * @param[in] S points to an instance of the Q7 sparse FIR structure. - * @param[in] pSrc points to the block of input data. - * @param[out] pDst points to the block of output data - * @param[in] pScratchIn points to a temporary buffer of size blockSize. - * @param[in] pScratchOut points to a temporary buffer of size blockSize. - * @param[in] blockSize number of input samples to process per call. - */ - void arm_fir_sparse_q7( - arm_fir_sparse_instance_q7 * S, - const q7_t * pSrc, - q7_t * pDst, - q7_t * pScratchIn, - q31_t * pScratchOut, - uint32_t blockSize); - - - /** - * @brief Initialization function for the Q7 sparse FIR filter. - * @param[in,out] S points to an instance of the Q7 sparse FIR structure. - * @param[in] numTaps number of nonzero coefficients in the filter. - * @param[in] pCoeffs points to the array of filter coefficients. - * @param[in] pState points to the state buffer. - * @param[in] pTapDelay points to the array of offset times. - * @param[in] maxDelay maximum offset time supported. - * @param[in] blockSize number of samples that will be processed per block. - */ - void arm_fir_sparse_init_q7( - arm_fir_sparse_instance_q7 * S, - uint16_t numTaps, - const q7_t * pCoeffs, - q7_t * pState, - int32_t * pTapDelay, - uint16_t maxDelay, - uint32_t blockSize); - - - /** - * @brief Floating-point sin_cos function. - * @param[in] theta input value in degrees - * @param[out] pSinVal points to the processed sine output. - * @param[out] pCosVal points to the processed cos output. - */ - void arm_sin_cos_f32( - float32_t theta, - float32_t * pSinVal, - float32_t * pCosVal); - - - /** - * @brief Q31 sin_cos function. - * @param[in] theta scaled input value in degrees - * @param[out] pSinVal points to the processed sine output. - * @param[out] pCosVal points to the processed cosine output. - */ - void arm_sin_cos_q31( - q31_t theta, - q31_t * pSinVal, - q31_t * pCosVal); - - - /** - * @brief Floating-point complex conjugate. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_conj_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t numSamples); - - /** - * @brief Q31 complex conjugate. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_conj_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q15 complex conjugate. - * @param[in] pSrc points to the input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_conj_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t numSamples); - - - /** - * @brief Floating-point complex magnitude squared - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_squared_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q31 complex magnitude squared - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_squared_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q15 complex magnitude squared - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_squared_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t numSamples); - - - /** - * @ingroup groupController - */ - - /** - * @defgroup PID PID Motor Control - * - * A Proportional Integral Derivative (PID) controller is a generic feedback control - * loop mechanism widely used in industrial control systems. - * A PID controller is the most commonly used type of feedback controller. - * - * This set of functions implements (PID) controllers - * for Q15, Q31, and floating-point data types. The functions operate on a single sample - * of data and each call to the function returns a single processed value. - * S points to an instance of the PID control data structure. in - * is the input sample value. The functions return the output value. - * - * \par Algorithm: - * 
-   *    y[n] = y[n-1] + A0 * x[n] + A1 * x[n-1] + A2 * x[n-2]
-   *    A0 = Kp + Ki + Kd
-   *    A1 = (-Kp ) - (2 * Kd )
-   *    A2 = Kd
-   *
- * - * \par - * where \c Kp is proportional constant, \c Ki is Integral constant and \c Kd is Derivative constant - * - * \par - * \image html PID.gif "Proportional Integral Derivative Controller" - * - * \par - * The PID controller calculates an "error" value as the difference between - * the measured output and the reference input. - * The controller attempts to minimize the error by adjusting the process control inputs. - * The proportional value determines the reaction to the current error, - * the integral value determines the reaction based on the sum of recent errors, - * and the derivative value determines the reaction based on the rate at which the error has been changing. - * - * \par Instance Structure - * The Gains A0, A1, A2 and state variables for a PID controller are stored together in an instance data structure. - * A separate instance structure must be defined for each PID Controller. - * There are separate instance structure declarations for each of the 3 supported data types. - * - * \par Reset Functions - * There is also an associated reset function for each data type which clears the state array. - * - * \par Initialization Functions - * There is also an associated initialization function for each data type. - * The initialization function performs the following operations: - * - Initializes the Gains A0, A1, A2 from Kp,Ki, Kd gains. - * - Zeros out the values in the state buffer. - * - * \par - * Instance structure cannot be placed into a const data section and it is recommended to use the initialization function. - * - * \par Fixed-Point Behavior - * Care must be taken when using the fixed-point versions of the PID Controller functions. - * In particular, the overflow and saturation behavior of the accumulator used in each function must be considered. - * Refer to the function specific documentation below for usage guidelines. - */ - - /** - * @addtogroup PID - * @{ - */ - - /** - * @brief Process function for the floating-point PID Control. - * @param[in,out] S is an instance of the floating-point PID Control structure - * @param[in] in input sample to process - * @return processed output sample. - */ - __STATIC_FORCEINLINE float32_t arm_pid_f32( - arm_pid_instance_f32 * S, - float32_t in) - { - float32_t out; - - /* y[n] = y[n-1] + A0 * x[n] + A1 * x[n-1] + A2 * x[n-2] */ - out = (S->A0 * in) + - (S->A1 * S->state[0]) + (S->A2 * S->state[1]) + (S->state[2]); - - /* Update state */ - S->state[1] = S->state[0]; - S->state[0] = in; - S->state[2] = out; - - /* return to application */ - return (out); - - } - -/** - @brief Process function for the Q31 PID Control. - @param[in,out] S points to an instance of the Q31 PID Control structure - @param[in] in input sample to process - @return processed output sample. - - \par Scaling and Overflow Behavior - The function is implemented using an internal 64-bit accumulator. - The accumulator has a 2.62 format and maintains full precision of the intermediate multiplication results but provides only a single guard bit. - Thus, if the accumulator result overflows it wraps around rather than clip. - In order to avoid overflows completely the input signal must be scaled down by 2 bits as there are four additions. - After all multiply-accumulates are performed, the 2.62 accumulator is truncated to 1.32 format and then saturated to 1.31 format. - */ -__STATIC_FORCEINLINE q31_t arm_pid_q31( - arm_pid_instance_q31 * S, - q31_t in) - { - q63_t acc; - q31_t out; - - /* acc = A0 * x[n] */ - acc = (q63_t) S->A0 * in; - - /* acc += A1 * x[n-1] */ - acc += (q63_t) S->A1 * S->state[0]; - - /* acc += A2 * x[n-2] */ - acc += (q63_t) S->A2 * S->state[1]; - - /* convert output to 1.31 format to add y[n-1] */ - out = (q31_t) (acc >> 31U); - - /* out += y[n-1] */ - out += S->state[2]; - - /* Update state */ - S->state[1] = S->state[0]; - S->state[0] = in; - S->state[2] = out; - - /* return to application */ - return (out); - } - - -/** - @brief Process function for the Q15 PID Control. - @param[in,out] S points to an instance of the Q15 PID Control structure - @param[in] in input sample to process - @return processed output sample. - - \par Scaling and Overflow Behavior - The function is implemented using a 64-bit internal accumulator. - Both Gains and state variables are represented in 1.15 format and multiplications yield a 2.30 result. - The 2.30 intermediate results are accumulated in a 64-bit accumulator in 34.30 format. - There is no risk of internal overflow with this approach and the full precision of intermediate multiplications is preserved. - After all additions have been performed, the accumulator is truncated to 34.15 format by discarding low 15 bits. - Lastly, the accumulator is saturated to yield a result in 1.15 format. - */ -__STATIC_FORCEINLINE q15_t arm_pid_q15( - arm_pid_instance_q15 * S, - q15_t in) - { - q63_t acc; - q15_t out; - -#if defined (ARM_MATH_DSP) - /* Implementation of PID controller */ - - /* acc = A0 * x[n] */ - acc = (q31_t) __SMUAD((uint32_t)S->A0, (uint32_t)in); - - /* acc += A1 * x[n-1] + A2 * x[n-2] */ - acc = (q63_t)__SMLALD((uint32_t)S->A1, (uint32_t)read_q15x2 (S->state), (uint64_t)acc); -#else - /* acc = A0 * x[n] */ - acc = ((q31_t) S->A0) * in; - - /* acc += A1 * x[n-1] + A2 * x[n-2] */ - acc += (q31_t) S->A1 * S->state[0]; - acc += (q31_t) S->A2 * S->state[1]; -#endif - - /* acc += y[n-1] */ - acc += (q31_t) S->state[2] << 15; - - /* saturate the output */ - out = (q15_t) (__SSAT((q31_t)(acc >> 15), 16)); - - /* Update state */ - S->state[1] = S->state[0]; - S->state[0] = in; - S->state[2] = out; - - /* return to application */ - return (out); - } - - /** - * @} end of PID group - */ - - - /** - * @brief Floating-point matrix inverse. - * @param[in] src points to the instance of the input floating-point matrix structure. - * @param[out] dst points to the instance of the output floating-point matrix structure. - * @return The function returns ARM_MATH_SIZE_MISMATCH, if the dimensions do not match. - * If the input matrix is singular (does not have an inverse), then the algorithm terminates and returns error status ARM_MATH_SINGULAR. - */ - arm_status arm_mat_inverse_f32( - const arm_matrix_instance_f32 * src, - arm_matrix_instance_f32 * dst); - - - /** - * @brief Floating-point matrix inverse. - * @param[in] src points to the instance of the input floating-point matrix structure. - * @param[out] dst points to the instance of the output floating-point matrix structure. - * @return The function returns ARM_MATH_SIZE_MISMATCH, if the dimensions do not match. - * If the input matrix is singular (does not have an inverse), then the algorithm terminates and returns error status ARM_MATH_SINGULAR. - */ - arm_status arm_mat_inverse_f64( - const arm_matrix_instance_f64 * src, - arm_matrix_instance_f64 * dst); - - - - /** - * @ingroup groupController - */ - - /** - * @defgroup clarke Vector Clarke Transform - * Forward Clarke transform converts the instantaneous stator phases into a two-coordinate time invariant vector. - * Generally the Clarke transform uses three-phase currents Ia, Ib and Ic to calculate currents - * in the two-phase orthogonal stator axis Ialpha and Ibeta. - * When Ialpha is superposed with Ia as shown in the figure below - * \image html clarke.gif Stator current space vector and its components in (a,b). - * and Ia + Ib + Ic = 0, in this condition Ialpha and Ibeta - * can be calculated using only Ia and Ib. - * - * The function operates on a single sample of data and each call to the function returns the processed output. - * The library provides separate functions for Q31 and floating-point data types. - * \par Algorithm - * \image html clarkeFormula.gif - * where Ia and Ib are the instantaneous stator phases and - * pIalpha and pIbeta are the two coordinates of time invariant vector. - * \par Fixed-Point Behavior - * Care must be taken when using the Q31 version of the Clarke transform. - * In particular, the overflow and saturation behavior of the accumulator used must be considered. - * Refer to the function specific documentation below for usage guidelines. - */ - - /** - * @addtogroup clarke - * @{ - */ - - /** - * - * @brief Floating-point Clarke transform - * @param[in] Ia input three-phase coordinate a - * @param[in] Ib input three-phase coordinate b - * @param[out] pIalpha points to output two-phase orthogonal vector axis alpha - * @param[out] pIbeta points to output two-phase orthogonal vector axis beta - * @return none - */ - __STATIC_FORCEINLINE void arm_clarke_f32( - float32_t Ia, - float32_t Ib, - float32_t * pIalpha, - float32_t * pIbeta) - { - /* Calculate pIalpha using the equation, pIalpha = Ia */ - *pIalpha = Ia; - - /* Calculate pIbeta using the equation, pIbeta = (1/sqrt(3)) * Ia + (2/sqrt(3)) * Ib */ - *pIbeta = (0.57735026919f * Ia + 1.15470053838f * Ib); - } - - -/** - @brief Clarke transform for Q31 version - @param[in] Ia input three-phase coordinate a - @param[in] Ib input three-phase coordinate b - @param[out] pIalpha points to output two-phase orthogonal vector axis alpha - @param[out] pIbeta points to output two-phase orthogonal vector axis beta - @return none - - \par Scaling and Overflow Behavior - The function is implemented using an internal 32-bit accumulator. - The accumulator maintains 1.31 format by truncating lower 31 bits of the intermediate multiplication in 2.62 format. - There is saturation on the addition, hence there is no risk of overflow. - */ -__STATIC_FORCEINLINE void arm_clarke_q31( - q31_t Ia, - q31_t Ib, - q31_t * pIalpha, - q31_t * pIbeta) - { - q31_t product1, product2; /* Temporary variables used to store intermediate results */ - - /* Calculating pIalpha from Ia by equation pIalpha = Ia */ - *pIalpha = Ia; - - /* Intermediate product is calculated by (1/(sqrt(3)) * Ia) */ - product1 = (q31_t) (((q63_t) Ia * 0x24F34E8B) >> 30); - - /* Intermediate product is calculated by (2/sqrt(3) * Ib) */ - product2 = (q31_t) (((q63_t) Ib * 0x49E69D16) >> 30); - - /* pIbeta is calculated by adding the intermediate products */ - *pIbeta = __QADD(product1, product2); - } - - /** - * @} end of clarke group - */ - - - /** - * @ingroup groupController - */ - - /** - * @defgroup inv_clarke Vector Inverse Clarke Transform - * Inverse Clarke transform converts the two-coordinate time invariant vector into instantaneous stator phases. - * - * The function operates on a single sample of data and each call to the function returns the processed output. - * The library provides separate functions for Q31 and floating-point data types. - * \par Algorithm - * \image html clarkeInvFormula.gif - * where pIa and pIb are the instantaneous stator phases and - * Ialpha and Ibeta are the two coordinates of time invariant vector. - * \par Fixed-Point Behavior - * Care must be taken when using the Q31 version of the Clarke transform. - * In particular, the overflow and saturation behavior of the accumulator used must be considered. - * Refer to the function specific documentation below for usage guidelines. - */ - - /** - * @addtogroup inv_clarke - * @{ - */ - - /** - * @brief Floating-point Inverse Clarke transform - * @param[in] Ialpha input two-phase orthogonal vector axis alpha - * @param[in] Ibeta input two-phase orthogonal vector axis beta - * @param[out] pIa points to output three-phase coordinate a - * @param[out] pIb points to output three-phase coordinate b - * @return none - */ - __STATIC_FORCEINLINE void arm_inv_clarke_f32( - float32_t Ialpha, - float32_t Ibeta, - float32_t * pIa, - float32_t * pIb) - { - /* Calculating pIa from Ialpha by equation pIa = Ialpha */ - *pIa = Ialpha; - - /* Calculating pIb from Ialpha and Ibeta by equation pIb = -(1/2) * Ialpha + (sqrt(3)/2) * Ibeta */ - *pIb = -0.5f * Ialpha + 0.8660254039f * Ibeta; - } - - -/** - @brief Inverse Clarke transform for Q31 version - @param[in] Ialpha input two-phase orthogonal vector axis alpha - @param[in] Ibeta input two-phase orthogonal vector axis beta - @param[out] pIa points to output three-phase coordinate a - @param[out] pIb points to output three-phase coordinate b - @return none - - \par Scaling and Overflow Behavior - The function is implemented using an internal 32-bit accumulator. - The accumulator maintains 1.31 format by truncating lower 31 bits of the intermediate multiplication in 2.62 format. - There is saturation on the subtraction, hence there is no risk of overflow. - */ -__STATIC_FORCEINLINE void arm_inv_clarke_q31( - q31_t Ialpha, - q31_t Ibeta, - q31_t * pIa, - q31_t * pIb) - { - q31_t product1, product2; /* Temporary variables used to store intermediate results */ - - /* Calculating pIa from Ialpha by equation pIa = Ialpha */ - *pIa = Ialpha; - - /* Intermediate product is calculated by (1/(2*sqrt(3)) * Ia) */ - product1 = (q31_t) (((q63_t) (Ialpha) * (0x40000000)) >> 31); - - /* Intermediate product is calculated by (1/sqrt(3) * pIb) */ - product2 = (q31_t) (((q63_t) (Ibeta) * (0x6ED9EBA1)) >> 31); - - /* pIb is calculated by subtracting the products */ - *pIb = __QSUB(product2, product1); - } - - /** - * @} end of inv_clarke group - */ - - - - /** - * @ingroup groupController - */ - - /** - * @defgroup park Vector Park Transform - * - * Forward Park transform converts the input two-coordinate vector to flux and torque components. - * The Park transform can be used to realize the transformation of the Ialpha and the Ibeta currents - * from the stationary to the moving reference frame and control the spatial relationship between - * the stator vector current and rotor flux vector. - * If we consider the d axis aligned with the rotor flux, the diagram below shows the - * current vector and the relationship from the two reference frames: - * \image html park.gif "Stator current space vector and its component in (a,b) and in the d,q rotating reference frame" - * - * The function operates on a single sample of data and each call to the function returns the processed output. - * The library provides separate functions for Q31 and floating-point data types. - * \par Algorithm - * \image html parkFormula.gif - * where Ialpha and Ibeta are the stator vector components, - * pId and pIq are rotor vector components and cosVal and sinVal are the - * cosine and sine values of theta (rotor flux position). - * \par Fixed-Point Behavior - * Care must be taken when using the Q31 version of the Park transform. - * In particular, the overflow and saturation behavior of the accumulator used must be considered. - * Refer to the function specific documentation below for usage guidelines. - */ - - /** - * @addtogroup park - * @{ - */ - - /** - * @brief Floating-point Park transform - * @param[in] Ialpha input two-phase vector coordinate alpha - * @param[in] Ibeta input two-phase vector coordinate beta - * @param[out] pId points to output rotor reference frame d - * @param[out] pIq points to output rotor reference frame q - * @param[in] sinVal sine value of rotation angle theta - * @param[in] cosVal cosine value of rotation angle theta - * @return none - * - * The function implements the forward Park transform. - * - */ - __STATIC_FORCEINLINE void arm_park_f32( - float32_t Ialpha, - float32_t Ibeta, - float32_t * pId, - float32_t * pIq, - float32_t sinVal, - float32_t cosVal) - { - /* Calculate pId using the equation, pId = Ialpha * cosVal + Ibeta * sinVal */ - *pId = Ialpha * cosVal + Ibeta * sinVal; - - /* Calculate pIq using the equation, pIq = - Ialpha * sinVal + Ibeta * cosVal */ - *pIq = -Ialpha * sinVal + Ibeta * cosVal; - } - - -/** - @brief Park transform for Q31 version - @param[in] Ialpha input two-phase vector coordinate alpha - @param[in] Ibeta input two-phase vector coordinate beta - @param[out] pId points to output rotor reference frame d - @param[out] pIq points to output rotor reference frame q - @param[in] sinVal sine value of rotation angle theta - @param[in] cosVal cosine value of rotation angle theta - @return none - - \par Scaling and Overflow Behavior - The function is implemented using an internal 32-bit accumulator. - The accumulator maintains 1.31 format by truncating lower 31 bits of the intermediate multiplication in 2.62 format. - There is saturation on the addition and subtraction, hence there is no risk of overflow. - */ -__STATIC_FORCEINLINE void arm_park_q31( - q31_t Ialpha, - q31_t Ibeta, - q31_t * pId, - q31_t * pIq, - q31_t sinVal, - q31_t cosVal) - { - q31_t product1, product2; /* Temporary variables used to store intermediate results */ - q31_t product3, product4; /* Temporary variables used to store intermediate results */ - - /* Intermediate product is calculated by (Ialpha * cosVal) */ - product1 = (q31_t) (((q63_t) (Ialpha) * (cosVal)) >> 31); - - /* Intermediate product is calculated by (Ibeta * sinVal) */ - product2 = (q31_t) (((q63_t) (Ibeta) * (sinVal)) >> 31); - - - /* Intermediate product is calculated by (Ialpha * sinVal) */ - product3 = (q31_t) (((q63_t) (Ialpha) * (sinVal)) >> 31); - - /* Intermediate product is calculated by (Ibeta * cosVal) */ - product4 = (q31_t) (((q63_t) (Ibeta) * (cosVal)) >> 31); - - /* Calculate pId by adding the two intermediate products 1 and 2 */ - *pId = __QADD(product1, product2); - - /* Calculate pIq by subtracting the two intermediate products 3 from 4 */ - *pIq = __QSUB(product4, product3); - } - - /** - * @} end of park group - */ - - - /** - * @ingroup groupController - */ - - /** - * @defgroup inv_park Vector Inverse Park transform - * Inverse Park transform converts the input flux and torque components to two-coordinate vector. - * - * The function operates on a single sample of data and each call to the function returns the processed output. - * The library provides separate functions for Q31 and floating-point data types. - * \par Algorithm - * \image html parkInvFormula.gif - * where pIalpha and pIbeta are the stator vector components, - * Id and Iq are rotor vector components and cosVal and sinVal are the - * cosine and sine values of theta (rotor flux position). - * \par Fixed-Point Behavior - * Care must be taken when using the Q31 version of the Park transform. - * In particular, the overflow and saturation behavior of the accumulator used must be considered. - * Refer to the function specific documentation below for usage guidelines. - */ - - /** - * @addtogroup inv_park - * @{ - */ - - /** - * @brief Floating-point Inverse Park transform - * @param[in] Id input coordinate of rotor reference frame d - * @param[in] Iq input coordinate of rotor reference frame q - * @param[out] pIalpha points to output two-phase orthogonal vector axis alpha - * @param[out] pIbeta points to output two-phase orthogonal vector axis beta - * @param[in] sinVal sine value of rotation angle theta - * @param[in] cosVal cosine value of rotation angle theta - * @return none - */ - __STATIC_FORCEINLINE void arm_inv_park_f32( - float32_t Id, - float32_t Iq, - float32_t * pIalpha, - float32_t * pIbeta, - float32_t sinVal, - float32_t cosVal) - { - /* Calculate pIalpha using the equation, pIalpha = Id * cosVal - Iq * sinVal */ - *pIalpha = Id * cosVal - Iq * sinVal; - - /* Calculate pIbeta using the equation, pIbeta = Id * sinVal + Iq * cosVal */ - *pIbeta = Id * sinVal + Iq * cosVal; - } - - -/** - @brief Inverse Park transform for Q31 version - @param[in] Id input coordinate of rotor reference frame d - @param[in] Iq input coordinate of rotor reference frame q - @param[out] pIalpha points to output two-phase orthogonal vector axis alpha - @param[out] pIbeta points to output two-phase orthogonal vector axis beta - @param[in] sinVal sine value of rotation angle theta - @param[in] cosVal cosine value of rotation angle theta - @return none - - @par Scaling and Overflow Behavior - The function is implemented using an internal 32-bit accumulator. - The accumulator maintains 1.31 format by truncating lower 31 bits of the intermediate multiplication in 2.62 format. - There is saturation on the addition, hence there is no risk of overflow. - */ -__STATIC_FORCEINLINE void arm_inv_park_q31( - q31_t Id, - q31_t Iq, - q31_t * pIalpha, - q31_t * pIbeta, - q31_t sinVal, - q31_t cosVal) - { - q31_t product1, product2; /* Temporary variables used to store intermediate results */ - q31_t product3, product4; /* Temporary variables used to store intermediate results */ - - /* Intermediate product is calculated by (Id * cosVal) */ - product1 = (q31_t) (((q63_t) (Id) * (cosVal)) >> 31); - - /* Intermediate product is calculated by (Iq * sinVal) */ - product2 = (q31_t) (((q63_t) (Iq) * (sinVal)) >> 31); - - - /* Intermediate product is calculated by (Id * sinVal) */ - product3 = (q31_t) (((q63_t) (Id) * (sinVal)) >> 31); - - /* Intermediate product is calculated by (Iq * cosVal) */ - product4 = (q31_t) (((q63_t) (Iq) * (cosVal)) >> 31); - - /* Calculate pIalpha by using the two intermediate products 1 and 2 */ - *pIalpha = __QSUB(product1, product2); - - /* Calculate pIbeta by using the two intermediate products 3 and 4 */ - *pIbeta = __QADD(product4, product3); - } - - /** - * @} end of Inverse park group - */ - - - /** - * @ingroup groupInterpolation - */ - - /** - * @defgroup LinearInterpolate Linear Interpolation - * - * Linear interpolation is a method of curve fitting using linear polynomials. - * Linear interpolation works by effectively drawing a straight line between two neighboring samples and returning the appropriate point along that line - * - * \par - * \image html LinearInterp.gif "Linear interpolation" - * - * \par - * A Linear Interpolate function calculates an output value(y), for the input(x) - * using linear interpolation of the input values x0, x1( nearest input values) and the output values y0 and y1(nearest output values) - * - * \par Algorithm: - * 
-   *       y = y0 + (x - x0) * ((y1 - y0)/(x1-x0))
-   *       where x0, x1 are nearest values of input x
-   *             y0, y1 are nearest values to output y
-   *
- * - * \par - * This set of functions implements Linear interpolation process - * for Q7, Q15, Q31, and floating-point data types. The functions operate on a single - * sample of data and each call to the function returns a single processed value. - * S points to an instance of the Linear Interpolate function data structure. - * x is the input sample value. The functions returns the output value. - * - * \par - * if x is outside of the table boundary, Linear interpolation returns first value of the table - * if x is below input range and returns last value of table if x is above range. - */ - - /** - * @addtogroup LinearInterpolate - * @{ - */ - - /** - * @brief Process function for the floating-point Linear Interpolation Function. - * @param[in,out] S is an instance of the floating-point Linear Interpolation structure - * @param[in] x input sample to process - * @return y processed output sample. - * - */ - __STATIC_FORCEINLINE float32_t arm_linear_interp_f32( - arm_linear_interp_instance_f32 * S, - float32_t x) - { - float32_t y; - float32_t x0, x1; /* Nearest input values */ - float32_t y0, y1; /* Nearest output values */ - float32_t xSpacing = S->xSpacing; /* spacing between input values */ - int32_t i; /* Index variable */ - float32_t *pYData = S->pYData; /* pointer to output table */ - - /* Calculation of index */ - i = (int32_t) ((x - S->x1) / xSpacing); - - if (i < 0) - { - /* Iniatilize output for below specified range as least output value of table */ - y = pYData[0]; - } - else if ((uint32_t)i >= (S->nValues - 1)) - { - /* Iniatilize output for above specified range as last output value of table */ - y = pYData[S->nValues - 1]; - } - else - { - /* Calculation of nearest input values */ - x0 = S->x1 + i * xSpacing; - x1 = S->x1 + (i + 1) * xSpacing; - - /* Read of nearest output values */ - y0 = pYData[i]; - y1 = pYData[i + 1]; - - /* Calculation of output */ - y = y0 + (x - x0) * ((y1 - y0) / (x1 - x0)); - - } - - /* returns output value */ - return (y); - } - - - /** - * - * @brief Process function for the Q31 Linear Interpolation Function. - * @param[in] pYData pointer to Q31 Linear Interpolation table - * @param[in] x input sample to process - * @param[in] nValues number of table values - * @return y processed output sample. - * - * \par - * Input sample x is in 12.20 format which contains 12 bits for table index and 20 bits for fractional part. - * This function can support maximum of table size 2^12. - * - */ - __STATIC_FORCEINLINE q31_t arm_linear_interp_q31( - q31_t * pYData, - q31_t x, - uint32_t nValues) - { - q31_t y; /* output */ - q31_t y0, y1; /* Nearest output values */ - q31_t fract; /* fractional part */ - int32_t index; /* Index to read nearest output values */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - index = ((x & (q31_t)0xFFF00000) >> 20); - - if (index >= (int32_t)(nValues - 1)) - { - return (pYData[nValues - 1]); - } - else if (index < 0) - { - return (pYData[0]); - } - else - { - /* 20 bits for the fractional part */ - /* shift left by 11 to keep fract in 1.31 format */ - fract = (x & 0x000FFFFF) << 11; - - /* Read two nearest output values from the index in 1.31(q31) format */ - y0 = pYData[index]; - y1 = pYData[index + 1]; - - /* Calculation of y0 * (1-fract) and y is in 2.30 format */ - y = ((q31_t) ((q63_t) y0 * (0x7FFFFFFF - fract) >> 32)); - - /* Calculation of y0 * (1-fract) + y1 *fract and y is in 2.30 format */ - y += ((q31_t) (((q63_t) y1 * fract) >> 32)); - - /* Convert y to 1.31 format */ - return (y << 1U); - } - } - - - /** - * - * @brief Process function for the Q15 Linear Interpolation Function. - * @param[in] pYData pointer to Q15 Linear Interpolation table - * @param[in] x input sample to process - * @param[in] nValues number of table values - * @return y processed output sample. - * - * \par - * Input sample x is in 12.20 format which contains 12 bits for table index and 20 bits for fractional part. - * This function can support maximum of table size 2^12. - * - */ - __STATIC_FORCEINLINE q15_t arm_linear_interp_q15( - q15_t * pYData, - q31_t x, - uint32_t nValues) - { - q63_t y; /* output */ - q15_t y0, y1; /* Nearest output values */ - q31_t fract; /* fractional part */ - int32_t index; /* Index to read nearest output values */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - index = ((x & (int32_t)0xFFF00000) >> 20); - - if (index >= (int32_t)(nValues - 1)) - { - return (pYData[nValues - 1]); - } - else if (index < 0) - { - return (pYData[0]); - } - else - { - /* 20 bits for the fractional part */ - /* fract is in 12.20 format */ - fract = (x & 0x000FFFFF); - - /* Read two nearest output values from the index */ - y0 = pYData[index]; - y1 = pYData[index + 1]; - - /* Calculation of y0 * (1-fract) and y is in 13.35 format */ - y = ((q63_t) y0 * (0xFFFFF - fract)); - - /* Calculation of (y0 * (1-fract) + y1 * fract) and y is in 13.35 format */ - y += ((q63_t) y1 * (fract)); - - /* convert y to 1.15 format */ - return (q15_t) (y >> 20); - } - } - - - /** - * - * @brief Process function for the Q7 Linear Interpolation Function. - * @param[in] pYData pointer to Q7 Linear Interpolation table - * @param[in] x input sample to process - * @param[in] nValues number of table values - * @return y processed output sample. - * - * \par - * Input sample x is in 12.20 format which contains 12 bits for table index and 20 bits for fractional part. - * This function can support maximum of table size 2^12. - */ - __STATIC_FORCEINLINE q7_t arm_linear_interp_q7( - q7_t * pYData, - q31_t x, - uint32_t nValues) - { - q31_t y; /* output */ - q7_t y0, y1; /* Nearest output values */ - q31_t fract; /* fractional part */ - uint32_t index; /* Index to read nearest output values */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - if (x < 0) - { - return (pYData[0]); - } - index = (x >> 20) & 0xfff; - - if (index >= (nValues - 1)) - { - return (pYData[nValues - 1]); - } - else - { - /* 20 bits for the fractional part */ - /* fract is in 12.20 format */ - fract = (x & 0x000FFFFF); - - /* Read two nearest output values from the index and are in 1.7(q7) format */ - y0 = pYData[index]; - y1 = pYData[index + 1]; - - /* Calculation of y0 * (1-fract ) and y is in 13.27(q27) format */ - y = ((y0 * (0xFFFFF - fract))); - - /* Calculation of y1 * fract + y0 * (1-fract) and y is in 13.27(q27) format */ - y += (y1 * fract); - - /* convert y to 1.7(q7) format */ - return (q7_t) (y >> 20); - } - } - - /** - * @} end of LinearInterpolate group - */ - - /** - * @brief Fast approximation to the trigonometric sine function for floating-point data. - * @param[in] x input value in radians. - * @return sin(x). - */ - float32_t arm_sin_f32( - float32_t x); - - - /** - * @brief Fast approximation to the trigonometric sine function for Q31 data. - * @param[in] x Scaled input value in radians. - * @return sin(x). - */ - q31_t arm_sin_q31( - q31_t x); - - - /** - * @brief Fast approximation to the trigonometric sine function for Q15 data. - * @param[in] x Scaled input value in radians. - * @return sin(x). - */ - q15_t arm_sin_q15( - q15_t x); - - - /** - * @brief Fast approximation to the trigonometric cosine function for floating-point data. - * @param[in] x input value in radians. - * @return cos(x). - */ - float32_t arm_cos_f32( - float32_t x); - - - /** - * @brief Fast approximation to the trigonometric cosine function for Q31 data. - * @param[in] x Scaled input value in radians. - * @return cos(x). - */ - q31_t arm_cos_q31( - q31_t x); - - - /** - * @brief Fast approximation to the trigonometric cosine function for Q15 data. - * @param[in] x Scaled input value in radians. - * @return cos(x). - */ - q15_t arm_cos_q15( - q15_t x); - - -/** - @brief Floating-point vector of log values. - @param[in] pSrc points to the input vector - @param[out] pDst points to the output vector - @param[in] blockSize number of samples in each vector - @return none - */ - void arm_vlog_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - -/** - @brief Floating-point vector of exp values. - @param[in] pSrc points to the input vector - @param[out] pDst points to the output vector - @param[in] blockSize number of samples in each vector - @return none - */ - void arm_vexp_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - /** - * @ingroup groupFastMath - */ - - - /** - * @defgroup SQRT Square Root - * - * Computes the square root of a number. - * There are separate functions for Q15, Q31, and floating-point data types. - * The square root function is computed using the Newton-Raphson algorithm. - * This is an iterative algorithm of the form: - * 
-   *      x1 = x0 - f(x0)/f'(x0)
-   *
- * where x1 is the current estimate, - * x0 is the previous estimate, and - * f'(x0) is the derivative of f() evaluated at x0. - * For the square root function, the algorithm reduces to: - * 
-   *     x0 = in/2                         [initial guess]
-   *     x1 = 1/2 * ( x0 + in / x0)        [each iteration]
-   *
- */ - - - /** - * @addtogroup SQRT - * @{ - */ - -/** - @brief Floating-point square root function. - @param[in] in input value - @param[out] pOut square root of input value - @return execution status - - \ref ARM_MATH_SUCCESS : input value is positive - - \ref ARM_MATH_ARGUMENT_ERROR : input value is negative; *pOut is set to 0 - */ -__STATIC_FORCEINLINE arm_status arm_sqrt_f32( - float32_t in, - float32_t * pOut) - { - if (in >= 0.0f) - { -#if defined ( __CC_ARM ) - #if defined __TARGET_FPU_VFP - *pOut = __sqrtf(in); - #else - *pOut = sqrtf(in); - #endif - -#elif defined ( __ICCARM__ ) - #if defined __ARMVFP__ - __ASM("VSQRT.F32 %0,%1" : "=t"(*pOut) : "t"(in)); - #else - *pOut = sqrtf(in); - #endif - -#else - *pOut = sqrtf(in); -#endif - - return (ARM_MATH_SUCCESS); - } - else - { - *pOut = 0.0f; - return (ARM_MATH_ARGUMENT_ERROR); - } - } - - -/** - @brief Q31 square root function. - @param[in] in input value. The range of the input value is [0 +1) or 0x00000000 to 0x7FFFFFFF - @param[out] pOut points to square root of input value - @return execution status - - \ref ARM_MATH_SUCCESS : input value is positive - - \ref ARM_MATH_ARGUMENT_ERROR : input value is negative; *pOut is set to 0 - */ -arm_status arm_sqrt_q31( - q31_t in, - q31_t * pOut); - - -/** - @brief Q15 square root function. - @param[in] in input value. The range of the input value is [0 +1) or 0x0000 to 0x7FFF - @param[out] pOut points to square root of input value - @return execution status - - \ref ARM_MATH_SUCCESS : input value is positive - - \ref ARM_MATH_ARGUMENT_ERROR : input value is negative; *pOut is set to 0 - */ -arm_status arm_sqrt_q15( - q15_t in, - q15_t * pOut); - - /** - * @brief Vector Floating-point square root function. - * @param[in] pIn input vector. - * @param[out] pOut vector of square roots of input elements. - * @param[in] len length of input vector. - * @return The function returns ARM_MATH_SUCCESS if input value is positive value or ARM_MATH_ARGUMENT_ERROR if - * in is negative value and returns zero output for negative values. - */ - void arm_vsqrt_f32( - float32_t * pIn, - float32_t * pOut, - uint16_t len); - - void arm_vsqrt_q31( - q31_t * pIn, - q31_t * pOut, - uint16_t len); - - void arm_vsqrt_q15( - q15_t * pIn, - q15_t * pOut, - uint16_t len); - - /** - * @} end of SQRT group - */ - - - /** - * @brief floating-point Circular write function. - */ - __STATIC_FORCEINLINE void arm_circularWrite_f32( - int32_t * circBuffer, - int32_t L, - uint16_t * writeOffset, - int32_t bufferInc, - const int32_t * src, - int32_t srcInc, - uint32_t blockSize) - { - uint32_t i = 0U; - int32_t wOffset; - - /* Copy the value of Index pointer that points - * to the current location where the input samples to be copied */ - wOffset = *writeOffset; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the input sample to the circular buffer */ - circBuffer[wOffset] = *src; - - /* Update the input pointer */ - src += srcInc; - - /* Circularly update wOffset. Watch out for positive and negative value */ - wOffset += bufferInc; - if (wOffset >= L) - wOffset -= L; - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *writeOffset = (uint16_t)wOffset; - } - - - - /** - * @brief floating-point Circular Read function. - */ - __STATIC_FORCEINLINE void arm_circularRead_f32( - int32_t * circBuffer, - int32_t L, - int32_t * readOffset, - int32_t bufferInc, - int32_t * dst, - int32_t * dst_base, - int32_t dst_length, - int32_t dstInc, - uint32_t blockSize) - { - uint32_t i = 0U; - int32_t rOffset; - int32_t* dst_end; - - /* Copy the value of Index pointer that points - * to the current location from where the input samples to be read */ - rOffset = *readOffset; - dst_end = dst_base + dst_length; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the sample from the circular buffer to the destination buffer */ - *dst = circBuffer[rOffset]; - - /* Update the input pointer */ - dst += dstInc; - - if (dst == dst_end) - { - dst = dst_base; - } - - /* Circularly update rOffset. Watch out for positive and negative value */ - rOffset += bufferInc; - - if (rOffset >= L) - { - rOffset -= L; - } - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *readOffset = rOffset; - } - - - /** - * @brief Q15 Circular write function. - */ - __STATIC_FORCEINLINE void arm_circularWrite_q15( - q15_t * circBuffer, - int32_t L, - uint16_t * writeOffset, - int32_t bufferInc, - const q15_t * src, - int32_t srcInc, - uint32_t blockSize) - { - uint32_t i = 0U; - int32_t wOffset; - - /* Copy the value of Index pointer that points - * to the current location where the input samples to be copied */ - wOffset = *writeOffset; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the input sample to the circular buffer */ - circBuffer[wOffset] = *src; - - /* Update the input pointer */ - src += srcInc; - - /* Circularly update wOffset. Watch out for positive and negative value */ - wOffset += bufferInc; - if (wOffset >= L) - wOffset -= L; - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *writeOffset = (uint16_t)wOffset; - } - - - /** - * @brief Q15 Circular Read function. - */ - __STATIC_FORCEINLINE void arm_circularRead_q15( - q15_t * circBuffer, - int32_t L, - int32_t * readOffset, - int32_t bufferInc, - q15_t * dst, - q15_t * dst_base, - int32_t dst_length, - int32_t dstInc, - uint32_t blockSize) - { - uint32_t i = 0; - int32_t rOffset; - q15_t* dst_end; - - /* Copy the value of Index pointer that points - * to the current location from where the input samples to be read */ - rOffset = *readOffset; - - dst_end = dst_base + dst_length; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the sample from the circular buffer to the destination buffer */ - *dst = circBuffer[rOffset]; - - /* Update the input pointer */ - dst += dstInc; - - if (dst == dst_end) - { - dst = dst_base; - } - - /* Circularly update wOffset. Watch out for positive and negative value */ - rOffset += bufferInc; - - if (rOffset >= L) - { - rOffset -= L; - } - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *readOffset = rOffset; - } - - - /** - * @brief Q7 Circular write function. - */ - __STATIC_FORCEINLINE void arm_circularWrite_q7( - q7_t * circBuffer, - int32_t L, - uint16_t * writeOffset, - int32_t bufferInc, - const q7_t * src, - int32_t srcInc, - uint32_t blockSize) - { - uint32_t i = 0U; - int32_t wOffset; - - /* Copy the value of Index pointer that points - * to the current location where the input samples to be copied */ - wOffset = *writeOffset; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the input sample to the circular buffer */ - circBuffer[wOffset] = *src; - - /* Update the input pointer */ - src += srcInc; - - /* Circularly update wOffset. Watch out for positive and negative value */ - wOffset += bufferInc; - if (wOffset >= L) - wOffset -= L; - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *writeOffset = (uint16_t)wOffset; - } - - - /** - * @brief Q7 Circular Read function. - */ - __STATIC_FORCEINLINE void arm_circularRead_q7( - q7_t * circBuffer, - int32_t L, - int32_t * readOffset, - int32_t bufferInc, - q7_t * dst, - q7_t * dst_base, - int32_t dst_length, - int32_t dstInc, - uint32_t blockSize) - { - uint32_t i = 0; - int32_t rOffset; - q7_t* dst_end; - - /* Copy the value of Index pointer that points - * to the current location from where the input samples to be read */ - rOffset = *readOffset; - - dst_end = dst_base + dst_length; - - /* Loop over the blockSize */ - i = blockSize; - - while (i > 0U) - { - /* copy the sample from the circular buffer to the destination buffer */ - *dst = circBuffer[rOffset]; - - /* Update the input pointer */ - dst += dstInc; - - if (dst == dst_end) - { - dst = dst_base; - } - - /* Circularly update rOffset. Watch out for positive and negative value */ - rOffset += bufferInc; - - if (rOffset >= L) - { - rOffset -= L; - } - - /* Decrement the loop counter */ - i--; - } - - /* Update the index pointer */ - *readOffset = rOffset; - } - - - /** - * @brief Sum of the squares of the elements of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_power_q31( - const q31_t * pSrc, - uint32_t blockSize, - q63_t * pResult); - - - /** - * @brief Sum of the squares of the elements of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_power_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult); - - - /** - * @brief Sum of the squares of the elements of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_power_q15( - const q15_t * pSrc, - uint32_t blockSize, - q63_t * pResult); - - - /** - * @brief Sum of the squares of the elements of a Q7 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_power_q7( - const q7_t * pSrc, - uint32_t blockSize, - q31_t * pResult); - - - /** - * @brief Mean value of a Q7 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_mean_q7( - const q7_t * pSrc, - uint32_t blockSize, - q7_t * pResult); - - - /** - * @brief Mean value of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_mean_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult); - - - /** - * @brief Mean value of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_mean_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult); - - - /** - * @brief Mean value of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_mean_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult); - - - /** - * @brief Variance of the elements of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_var_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult); - - - /** - * @brief Variance of the elements of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_var_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult); - - - /** - * @brief Variance of the elements of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_var_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult); - - - /** - * @brief Root Mean Square of the elements of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_rms_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult); - - - /** - * @brief Root Mean Square of the elements of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_rms_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult); - - - /** - * @brief Root Mean Square of the elements of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_rms_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult); - - - /** - * @brief Standard deviation of the elements of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_std_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult); - - - /** - * @brief Standard deviation of the elements of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_std_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult); - - - /** - * @brief Standard deviation of the elements of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output value. - */ - void arm_std_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult); - - - /** - * @brief Floating-point complex magnitude - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_f32( - const float32_t * pSrc, - float32_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q31 complex magnitude - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_q31( - const q31_t * pSrc, - q31_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q15 complex magnitude - * @param[in] pSrc points to the complex input vector - * @param[out] pDst points to the real output vector - * @param[in] numSamples number of complex samples in the input vector - */ - void arm_cmplx_mag_q15( - const q15_t * pSrc, - q15_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q15 complex dot product - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] numSamples number of complex samples in each vector - * @param[out] realResult real part of the result returned here - * @param[out] imagResult imaginary part of the result returned here - */ - void arm_cmplx_dot_prod_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - uint32_t numSamples, - q31_t * realResult, - q31_t * imagResult); - - - /** - * @brief Q31 complex dot product - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] numSamples number of complex samples in each vector - * @param[out] realResult real part of the result returned here - * @param[out] imagResult imaginary part of the result returned here - */ - void arm_cmplx_dot_prod_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - uint32_t numSamples, - q63_t * realResult, - q63_t * imagResult); - - - /** - * @brief Floating-point complex dot product - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] numSamples number of complex samples in each vector - * @param[out] realResult real part of the result returned here - * @param[out] imagResult imaginary part of the result returned here - */ - void arm_cmplx_dot_prod_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - uint32_t numSamples, - float32_t * realResult, - float32_t * imagResult); - - - /** - * @brief Q15 complex-by-real multiplication - * @param[in] pSrcCmplx points to the complex input vector - * @param[in] pSrcReal points to the real input vector - * @param[out] pCmplxDst points to the complex output vector - * @param[in] numSamples number of samples in each vector - */ - void arm_cmplx_mult_real_q15( - const q15_t * pSrcCmplx, - const q15_t * pSrcReal, - q15_t * pCmplxDst, - uint32_t numSamples); - - - /** - * @brief Q31 complex-by-real multiplication - * @param[in] pSrcCmplx points to the complex input vector - * @param[in] pSrcReal points to the real input vector - * @param[out] pCmplxDst points to the complex output vector - * @param[in] numSamples number of samples in each vector - */ - void arm_cmplx_mult_real_q31( - const q31_t * pSrcCmplx, - const q31_t * pSrcReal, - q31_t * pCmplxDst, - uint32_t numSamples); - - - /** - * @brief Floating-point complex-by-real multiplication - * @param[in] pSrcCmplx points to the complex input vector - * @param[in] pSrcReal points to the real input vector - * @param[out] pCmplxDst points to the complex output vector - * @param[in] numSamples number of samples in each vector - */ - void arm_cmplx_mult_real_f32( - const float32_t * pSrcCmplx, - const float32_t * pSrcReal, - float32_t * pCmplxDst, - uint32_t numSamples); - - - /** - * @brief Minimum value of a Q7 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] result is output pointer - * @param[in] index is the array index of the minimum value in the input buffer. - */ - void arm_min_q7( - const q7_t * pSrc, - uint32_t blockSize, - q7_t * result, - uint32_t * index); - - - /** - * @brief Minimum value of a Q15 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output pointer - * @param[in] pIndex is the array index of the minimum value in the input buffer. - */ - void arm_min_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult, - uint32_t * pIndex); - - - /** - * @brief Minimum value of a Q31 vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output pointer - * @param[out] pIndex is the array index of the minimum value in the input buffer. - */ - void arm_min_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult, - uint32_t * pIndex); - - - /** - * @brief Minimum value of a floating-point vector. - * @param[in] pSrc is input pointer - * @param[in] blockSize is the number of samples to process - * @param[out] pResult is output pointer - * @param[out] pIndex is the array index of the minimum value in the input buffer. - */ - void arm_min_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult, - uint32_t * pIndex); - - -/** - * @brief Maximum value of a Q7 vector. - * @param[in] pSrc points to the input buffer - * @param[in] blockSize length of the input vector - * @param[out] pResult maximum value returned here - * @param[out] pIndex index of maximum value returned here - */ - void arm_max_q7( - const q7_t * pSrc, - uint32_t blockSize, - q7_t * pResult, - uint32_t * pIndex); - - -/** - * @brief Maximum value of a Q15 vector. - * @param[in] pSrc points to the input buffer - * @param[in] blockSize length of the input vector - * @param[out] pResult maximum value returned here - * @param[out] pIndex index of maximum value returned here - */ - void arm_max_q15( - const q15_t * pSrc, - uint32_t blockSize, - q15_t * pResult, - uint32_t * pIndex); - - -/** - * @brief Maximum value of a Q31 vector. - * @param[in] pSrc points to the input buffer - * @param[in] blockSize length of the input vector - * @param[out] pResult maximum value returned here - * @param[out] pIndex index of maximum value returned here - */ - void arm_max_q31( - const q31_t * pSrc, - uint32_t blockSize, - q31_t * pResult, - uint32_t * pIndex); - - -/** - * @brief Maximum value of a floating-point vector. - * @param[in] pSrc points to the input buffer - * @param[in] blockSize length of the input vector - * @param[out] pResult maximum value returned here - * @param[out] pIndex index of maximum value returned here - */ - void arm_max_f32( - const float32_t * pSrc, - uint32_t blockSize, - float32_t * pResult, - uint32_t * pIndex); - - /** - @brief Maximum value of a floating-point vector. - @param[in] pSrc points to the input vector - @param[in] blockSize number of samples in input vector - @param[out] pResult maximum value returned here - @return none - */ - void arm_max_no_idx_f32( - const float32_t *pSrc, - uint32_t blockSize, - float32_t *pResult); - - /** - * @brief Q15 complex-by-complex multiplication - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_mult_cmplx_q15( - const q15_t * pSrcA, - const q15_t * pSrcB, - q15_t * pDst, - uint32_t numSamples); - - - /** - * @brief Q31 complex-by-complex multiplication - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_mult_cmplx_q31( - const q31_t * pSrcA, - const q31_t * pSrcB, - q31_t * pDst, - uint32_t numSamples); - - - /** - * @brief Floating-point complex-by-complex multiplication - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[out] pDst points to the output vector - * @param[in] numSamples number of complex samples in each vector - */ - void arm_cmplx_mult_cmplx_f32( - const float32_t * pSrcA, - const float32_t * pSrcB, - float32_t * pDst, - uint32_t numSamples); - - - /** - * @brief Converts the elements of the floating-point vector to Q31 vector. - * @param[in] pSrc points to the floating-point input vector - * @param[out] pDst points to the Q31 output vector - * @param[in] blockSize length of the input vector - */ - void arm_float_to_q31( - const float32_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the floating-point vector to Q15 vector. - * @param[in] pSrc points to the floating-point input vector - * @param[out] pDst points to the Q15 output vector - * @param[in] blockSize length of the input vector - */ - void arm_float_to_q15( - const float32_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the floating-point vector to Q7 vector. - * @param[in] pSrc points to the floating-point input vector - * @param[out] pDst points to the Q7 output vector - * @param[in] blockSize length of the input vector - */ - void arm_float_to_q7( - const float32_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q31 vector to floating-point vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q31_to_float( - const q31_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q31 vector to Q15 vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q31_to_q15( - const q31_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q31 vector to Q7 vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q31_to_q7( - const q31_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q15 vector to floating-point vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q15_to_float( - const q15_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q15 vector to Q31 vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q15_to_q31( - const q15_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q15 vector to Q7 vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q15_to_q7( - const q15_t * pSrc, - q7_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q7 vector to floating-point vector. - * @param[in] pSrc is input pointer - * @param[out] pDst is output pointer - * @param[in] blockSize is the number of samples to process - */ - void arm_q7_to_float( - const q7_t * pSrc, - float32_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q7 vector to Q31 vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_q7_to_q31( - const q7_t * pSrc, - q31_t * pDst, - uint32_t blockSize); - - - /** - * @brief Converts the elements of the Q7 vector to Q15 vector. - * @param[in] pSrc input pointer - * @param[out] pDst output pointer - * @param[in] blockSize number of samples to process - */ - void arm_q7_to_q15( - const q7_t * pSrc, - q15_t * pDst, - uint32_t blockSize); - -/** - * @brief Struct for specifying SVM Kernel - */ -typedef enum -{ - ARM_ML_KERNEL_LINEAR = 0, - /**< Linear kernel */ - ARM_ML_KERNEL_POLYNOMIAL = 1, - /**< Polynomial kernel */ - ARM_ML_KERNEL_RBF = 2, - /**< Radial Basis Function kernel */ - ARM_ML_KERNEL_SIGMOID = 3 - /**< Sigmoid kernel */ -} arm_ml_kernel_type; - - -/** - * @brief Instance structure for linear SVM prediction function. - */ -typedef struct -{ - uint32_t nbOfSupportVectors; /**< Number of support vectors */ - uint32_t vectorDimension; /**< Dimension of vector space */ - float32_t intercept; /**< Intercept */ - const float32_t *dualCoefficients; /**< Dual coefficients */ - const float32_t *supportVectors; /**< Support vectors */ - const int32_t *classes; /**< The two SVM classes */ -} arm_svm_linear_instance_f32; - - -/** - * @brief Instance structure for polynomial SVM prediction function. - */ -typedef struct -{ - uint32_t nbOfSupportVectors; /**< Number of support vectors */ - uint32_t vectorDimension; /**< Dimension of vector space */ - float32_t intercept; /**< Intercept */ - const float32_t *dualCoefficients; /**< Dual coefficients */ - const float32_t *supportVectors; /**< Support vectors */ - const int32_t *classes; /**< The two SVM classes */ - int32_t degree; /**< Polynomial degree */ - float32_t coef0; /**< Polynomial constant */ - float32_t gamma; /**< Gamma factor */ -} arm_svm_polynomial_instance_f32; - -/** - * @brief Instance structure for rbf SVM prediction function. - */ -typedef struct -{ - uint32_t nbOfSupportVectors; /**< Number of support vectors */ - uint32_t vectorDimension; /**< Dimension of vector space */ - float32_t intercept; /**< Intercept */ - const float32_t *dualCoefficients; /**< Dual coefficients */ - const float32_t *supportVectors; /**< Support vectors */ - const int32_t *classes; /**< The two SVM classes */ - float32_t gamma; /**< Gamma factor */ -} arm_svm_rbf_instance_f32; - -/** - * @brief Instance structure for sigmoid SVM prediction function. - */ -typedef struct -{ - uint32_t nbOfSupportVectors; /**< Number of support vectors */ - uint32_t vectorDimension; /**< Dimension of vector space */ - float32_t intercept; /**< Intercept */ - const float32_t *dualCoefficients; /**< Dual coefficients */ - const float32_t *supportVectors; /**< Support vectors */ - const int32_t *classes; /**< The two SVM classes */ - float32_t coef0; /**< Independant constant */ - float32_t gamma; /**< Gamma factor */ -} arm_svm_sigmoid_instance_f32; - -/** - * @brief SVM linear instance init function - * @param[in] S Parameters for SVM functions - * @param[in] nbOfSupportVectors Number of support vectors - * @param[in] vectorDimension Dimension of vector space - * @param[in] intercept Intercept - * @param[in] dualCoefficients Array of dual coefficients - * @param[in] supportVectors Array of support vectors - * @param[in] classes Array of 2 classes ID - * @return none. - * - */ - - -void arm_svm_linear_init_f32(arm_svm_linear_instance_f32 *S, - uint32_t nbOfSupportVectors, - uint32_t vectorDimension, - float32_t intercept, - const float32_t *dualCoefficients, - const float32_t *supportVectors, - const int32_t *classes); - -/** - * @brief SVM linear prediction - * @param[in] S Pointer to an instance of the linear SVM structure. - * @param[in] in Pointer to input vector - * @param[out] pResult Decision value - * @return none. - * - */ - -void arm_svm_linear_predict_f32(const arm_svm_linear_instance_f32 *S, - const float32_t * in, - int32_t * pResult); - - -/** - * @brief SVM polynomial instance init function - * @param[in] S points to an instance of the polynomial SVM structure. - * @param[in] nbOfSupportVectors Number of support vectors - * @param[in] vectorDimension Dimension of vector space - * @param[in] intercept Intercept - * @param[in] dualCoefficients Array of dual coefficients - * @param[in] supportVectors Array of support vectors - * @param[in] classes Array of 2 classes ID - * @param[in] degree Polynomial degree - * @param[in] coef0 coeff0 (scikit-learn terminology) - * @param[in] gamma gamma (scikit-learn terminology) - * @return none. - * - */ - - -void arm_svm_polynomial_init_f32(arm_svm_polynomial_instance_f32 *S, - uint32_t nbOfSupportVectors, - uint32_t vectorDimension, - float32_t intercept, - const float32_t *dualCoefficients, - const float32_t *supportVectors, - const int32_t *classes, - int32_t degree, - float32_t coef0, - float32_t gamma - ); - -/** - * @brief SVM polynomial prediction - * @param[in] S Pointer to an instance of the polynomial SVM structure. - * @param[in] in Pointer to input vector - * @param[out] pResult Decision value - * @return none. - * - */ -void arm_svm_polynomial_predict_f32(const arm_svm_polynomial_instance_f32 *S, - const float32_t * in, - int32_t * pResult); - - -/** - * @brief SVM radial basis function instance init function - * @param[in] S points to an instance of the polynomial SVM structure. - * @param[in] nbOfSupportVectors Number of support vectors - * @param[in] vectorDimension Dimension of vector space - * @param[in] intercept Intercept - * @param[in] dualCoefficients Array of dual coefficients - * @param[in] supportVectors Array of support vectors - * @param[in] classes Array of 2 classes ID - * @param[in] gamma gamma (scikit-learn terminology) - * @return none. - * - */ - -void arm_svm_rbf_init_f32(arm_svm_rbf_instance_f32 *S, - uint32_t nbOfSupportVectors, - uint32_t vectorDimension, - float32_t intercept, - const float32_t *dualCoefficients, - const float32_t *supportVectors, - const int32_t *classes, - float32_t gamma - ); - -/** - * @brief SVM rbf prediction - * @param[in] S Pointer to an instance of the rbf SVM structure. - * @param[in] in Pointer to input vector - * @param[out] pResult decision value - * @return none. - * - */ -void arm_svm_rbf_predict_f32(const arm_svm_rbf_instance_f32 *S, - const float32_t * in, - int32_t * pResult); - -/** - * @brief SVM sigmoid instance init function - * @param[in] S points to an instance of the rbf SVM structure. - * @param[in] nbOfSupportVectors Number of support vectors - * @param[in] vectorDimension Dimension of vector space - * @param[in] intercept Intercept - * @param[in] dualCoefficients Array of dual coefficients - * @param[in] supportVectors Array of support vectors - * @param[in] classes Array of 2 classes ID - * @param[in] coef0 coeff0 (scikit-learn terminology) - * @param[in] gamma gamma (scikit-learn terminology) - * @return none. - * - */ - -void arm_svm_sigmoid_init_f32(arm_svm_sigmoid_instance_f32 *S, - uint32_t nbOfSupportVectors, - uint32_t vectorDimension, - float32_t intercept, - const float32_t *dualCoefficients, - const float32_t *supportVectors, - const int32_t *classes, - float32_t coef0, - float32_t gamma - ); - -/** - * @brief SVM sigmoid prediction - * @param[in] S Pointer to an instance of the rbf SVM structure. - * @param[in] in Pointer to input vector - * @param[out] pResult Decision value - * @return none. - * - */ -void arm_svm_sigmoid_predict_f32(const arm_svm_sigmoid_instance_f32 *S, - const float32_t * in, - int32_t * pResult); - - - -/** - * @brief Instance structure for Naive Gaussian Bayesian estimator. - */ -typedef struct -{ - uint32_t vectorDimension; /**< Dimension of vector space */ - uint32_t numberOfClasses; /**< Number of different classes */ - const float32_t *theta; /**< Mean values for the Gaussians */ - const float32_t *sigma; /**< Variances for the Gaussians */ - const float32_t *classPriors; /**< Class prior probabilities */ - float32_t epsilon; /**< Additive value to variances */ -} arm_gaussian_naive_bayes_instance_f32; - -/** - * @brief Naive Gaussian Bayesian Estimator - * - * @param[in] S points to a naive bayes instance structure - * @param[in] in points to the elements of the input vector. - * @param[in] pBuffer points to a buffer of length numberOfClasses - * @return The predicted class - * - */ - - -uint32_t arm_gaussian_naive_bayes_predict_f32(const arm_gaussian_naive_bayes_instance_f32 *S, - const float32_t * in, - float32_t *pBuffer); - -/** - * @brief Computation of the LogSumExp - * - * In probabilistic computations, the dynamic of the probability values can be very - * wide because they come from gaussian functions. - * To avoid underflow and overflow issues, the values are represented by their log. - * In this representation, multiplying the original exp values is easy : their logs are added. - * But adding the original exp values is requiring some special handling and it is the - * goal of the LogSumExp function. - * - * If the values are x1...xn, the function is computing: - * - * ln(exp(x1) + ... + exp(xn)) and the computation is done in such a way that - * rounding issues are minimised. - * - * The max xm of the values is extracted and the function is computing: - * xm + ln(exp(x1 - xm) + ... + exp(xn - xm)) - * - * @param[in] *in Pointer to an array of input values. - * @param[in] blockSize Number of samples in the input array. - * @return LogSumExp - * - */ - - -float32_t arm_logsumexp_f32(const float32_t *in, uint32_t blockSize); - -/** - * @brief Dot product with log arithmetic - * - * Vectors are containing the log of the samples - * - * @param[in] pSrcA points to the first input vector - * @param[in] pSrcB points to the second input vector - * @param[in] blockSize number of samples in each vector - * @param[in] pTmpBuffer temporary buffer of length blockSize - * @return The log of the dot product . - * - */ - - -float32_t arm_logsumexp_dot_prod_f32(const float32_t * pSrcA, - const float32_t * pSrcB, - uint32_t blockSize, - float32_t *pTmpBuffer); - -/** - * @brief Entropy - * - * @param[in] pSrcA Array of input values. - * @param[in] blockSize Number of samples in the input array. - * @return Entropy -Sum(p ln p) - * - */ - - -float32_t arm_entropy_f32(const float32_t * pSrcA,uint32_t blockSize); - - -/** - * @brief Entropy - * - * @param[in] pSrcA Array of input values. - * @param[in] blockSize Number of samples in the input array. - * @return Entropy -Sum(p ln p) - * - */ - - -float64_t arm_entropy_f64(const float64_t * pSrcA, uint32_t blockSize); - - -/** - * @brief Kullback-Leibler - * - * @param[in] pSrcA Pointer to an array of input values for probability distribution A. - * @param[in] pSrcB Pointer to an array of input values for probability distribution B. - * @param[in] blockSize Number of samples in the input array. - * @return Kullback-Leibler Divergence D(A || B) - * - */ -float32_t arm_kullback_leibler_f32(const float32_t * pSrcA - ,const float32_t * pSrcB - ,uint32_t blockSize); - - -/** - * @brief Kullback-Leibler - * - * @param[in] pSrcA Pointer to an array of input values for probability distribution A. - * @param[in] pSrcB Pointer to an array of input values for probability distribution B. - * @param[in] blockSize Number of samples in the input array. - * @return Kullback-Leibler Divergence D(A || B) - * - */ -float64_t arm_kullback_leibler_f64(const float64_t * pSrcA, - const float64_t * pSrcB, - uint32_t blockSize); - - -/** - * @brief Weighted sum - * - * - * @param[in] *in Array of input values. - * @param[in] *weigths Weights - * @param[in] blockSize Number of samples in the input array. - * @return Weighted sum - * - */ -float32_t arm_weighted_sum_f32(const float32_t *in - , const float32_t *weigths - , uint32_t blockSize); - - -/** - * @brief Barycenter - * - * - * @param[in] in List of vectors - * @param[in] weights Weights of the vectors - * @param[out] out Barycenter - * @param[in] nbVectors Number of vectors - * @param[in] vecDim Dimension of space (vector dimension) - * @return None - * - */ -void arm_barycenter_f32(const float32_t *in - , const float32_t *weights - , float32_t *out - , uint32_t nbVectors - , uint32_t vecDim); - -/** - * @brief Euclidean distance between two vectors - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ - -float32_t arm_euclidean_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - -/** - * @brief Bray-Curtis distance between two vectors - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ -float32_t arm_braycurtis_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - -/** - * @brief Canberra distance between two vectors - * - * This function may divide by zero when samples pA[i] and pB[i] are both zero. - * The result of the computation will be correct. So the division per zero may be - * ignored. - * - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ -float32_t arm_canberra_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - - -/** - * @brief Chebyshev distance between two vectors - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ -float32_t arm_chebyshev_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - - -/** - * @brief Cityblock (Manhattan) distance between two vectors - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ -float32_t arm_cityblock_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - -/** - * @brief Correlation distance between two vectors - * - * The input vectors are modified in place ! - * - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ -float32_t arm_correlation_distance_f32(float32_t *pA,float32_t *pB, uint32_t blockSize); - -/** - * @brief Cosine distance between two vectors - * - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ - -float32_t arm_cosine_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize); - -/** - * @brief Jensen-Shannon distance between two vectors - * - * This function is assuming that elements of second vector are > 0 - * and 0 only when the corresponding element of first vector is 0. - * Otherwise the result of the computation does not make sense - * and for speed reasons, the cases returning NaN or Infinity are not - * managed. - * - * When the function is computing x log (x / y) with x 0 and y 0, - * it will compute the right value (0) but a division per zero will occur - * and shoudl be ignored in client code. - * - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] blockSize vector length - * @return distance - * - */ - -float32_t arm_jensenshannon_distance_f32(const float32_t *pA,const float32_t *pB,uint32_t blockSize); - -/** - * @brief Minkowski distance between two vectors - * - * @param[in] pA First vector - * @param[in] pB Second vector - * @param[in] n Norm order (>= 2) - * @param[in] blockSize vector length - * @return distance - * - */ - - - -float32_t arm_minkowski_distance_f32(const float32_t *pA,const float32_t *pB, int32_t order, uint32_t blockSize); - -/** - * @brief Dice distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] order Distance order - * @param[in] blockSize Number of samples - * @return distance - * - */ - - -float32_t arm_dice_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Hamming distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_hamming_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Jaccard distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_jaccard_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Kulsinski distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_kulsinski_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Roger Stanimoto distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_rogerstanimoto_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Russell-Rao distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_russellrao_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Sokal-Michener distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_sokalmichener_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Sokal-Sneath distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_sokalsneath_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - -/** - * @brief Yule distance between two vectors - * - * @param[in] pA First vector of packed booleans - * @param[in] pB Second vector of packed booleans - * @param[in] numberOfBools Number of booleans - * @return distance - * - */ - -float32_t arm_yule_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools); - - - /** - * @ingroup groupInterpolation - */ - - /** - * @defgroup BilinearInterpolate Bilinear Interpolation - * - * Bilinear interpolation is an extension of linear interpolation applied to a two dimensional grid. - * The underlying function f(x, y) is sampled on a regular grid and the interpolation process - * determines values between the grid points. - * Bilinear interpolation is equivalent to two step linear interpolation, first in the x-dimension and then in the y-dimension. - * Bilinear interpolation is often used in image processing to rescale images. - * The CMSIS DSP library provides bilinear interpolation functions for Q7, Q15, Q31, and floating-point data types. - * - * Algorithm - * \par - * The instance structure used by the bilinear interpolation functions describes a two dimensional data table. - * For floating-point, the instance structure is defined as: - * 
-   *   typedef struct
-   *   {
-   *     uint16_t numRows;
-   *     uint16_t numCols;
-   *     float32_t *pData;
-   * } arm_bilinear_interp_instance_f32;
-   *
- * - * \par - * where numRows specifies the number of rows in the table; - * numCols specifies the number of columns in the table; - * and pData points to an array of size numRows*numCols values. - * The data table pTable is organized in row order and the supplied data values fall on integer indexes. - * That is, table element (x,y) is located at pTable[x + y*numCols] where x and y are integers. - * - * \par - * Let (x, y) specify the desired interpolation point. Then define: - * 
-   *     XF = floor(x)
-   *     YF = floor(y)
-   *
- * \par - * The interpolated output point is computed as: - * 
-   *  f(x, y) = f(XF, YF) * (1-(x-XF)) * (1-(y-YF))
-   *           + f(XF+1, YF) * (x-XF)*(1-(y-YF))
-   *           + f(XF, YF+1) * (1-(x-XF))*(y-YF)
-   *           + f(XF+1, YF+1) * (x-XF)*(y-YF)
-   *
- * Note that the coordinates (x, y) contain integer and fractional components. - * The integer components specify which portion of the table to use while the - * fractional components control the interpolation processor. - * - * \par - * if (x,y) are outside of the table boundary, Bilinear interpolation returns zero output. - */ - - - /** - * @addtogroup BilinearInterpolate - * @{ - */ - - /** - * @brief Floating-point bilinear interpolation. - * @param[in,out] S points to an instance of the interpolation structure. - * @param[in] X interpolation coordinate. - * @param[in] Y interpolation coordinate. - * @return out interpolated value. - */ - __STATIC_FORCEINLINE float32_t arm_bilinear_interp_f32( - const arm_bilinear_interp_instance_f32 * S, - float32_t X, - float32_t Y) - { - float32_t out; - float32_t f00, f01, f10, f11; - float32_t *pData = S->pData; - int32_t xIndex, yIndex, index; - float32_t xdiff, ydiff; - float32_t b1, b2, b3, b4; - - xIndex = (int32_t) X; - yIndex = (int32_t) Y; - - /* Care taken for table outside boundary */ - /* Returns zero output when values are outside table boundary */ - if (xIndex < 0 || xIndex > (S->numCols - 2) || yIndex < 0 || yIndex > (S->numRows - 2)) - { - return (0); - } - - /* Calculation of index for two nearest points in X-direction */ - index = (xIndex ) + (yIndex ) * S->numCols; - - - /* Read two nearest points in X-direction */ - f00 = pData[index]; - f01 = pData[index + 1]; - - /* Calculation of index for two nearest points in Y-direction */ - index = (xIndex ) + (yIndex+1) * S->numCols; - - - /* Read two nearest points in Y-direction */ - f10 = pData[index]; - f11 = pData[index + 1]; - - /* Calculation of intermediate values */ - b1 = f00; - b2 = f01 - f00; - b3 = f10 - f00; - b4 = f00 - f01 - f10 + f11; - - /* Calculation of fractional part in X */ - xdiff = X - xIndex; - - /* Calculation of fractional part in Y */ - ydiff = Y - yIndex; - - /* Calculation of bi-linear interpolated output */ - out = b1 + b2 * xdiff + b3 * ydiff + b4 * xdiff * ydiff; - - /* return to application */ - return (out); - } - - - /** - * @brief Q31 bilinear interpolation. - * @param[in,out] S points to an instance of the interpolation structure. - * @param[in] X interpolation coordinate in 12.20 format. - * @param[in] Y interpolation coordinate in 12.20 format. - * @return out interpolated value. - */ - __STATIC_FORCEINLINE q31_t arm_bilinear_interp_q31( - arm_bilinear_interp_instance_q31 * S, - q31_t X, - q31_t Y) - { - q31_t out; /* Temporary output */ - q31_t acc = 0; /* output */ - q31_t xfract, yfract; /* X, Y fractional parts */ - q31_t x1, x2, y1, y2; /* Nearest output values */ - int32_t rI, cI; /* Row and column indices */ - q31_t *pYData = S->pData; /* pointer to output table values */ - uint32_t nCols = S->numCols; /* num of rows */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - rI = ((X & (q31_t)0xFFF00000) >> 20); - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - cI = ((Y & (q31_t)0xFFF00000) >> 20); - - /* Care taken for table outside boundary */ - /* Returns zero output when values are outside table boundary */ - if (rI < 0 || rI > (S->numCols - 2) || cI < 0 || cI > (S->numRows - 2)) - { - return (0); - } - - /* 20 bits for the fractional part */ - /* shift left xfract by 11 to keep 1.31 format */ - xfract = (X & 0x000FFFFF) << 11U; - - /* Read two nearest output values from the index */ - x1 = pYData[(rI) + (int32_t)nCols * (cI) ]; - x2 = pYData[(rI) + (int32_t)nCols * (cI) + 1]; - - /* 20 bits for the fractional part */ - /* shift left yfract by 11 to keep 1.31 format */ - yfract = (Y & 0x000FFFFF) << 11U; - - /* Read two nearest output values from the index */ - y1 = pYData[(rI) + (int32_t)nCols * (cI + 1) ]; - y2 = pYData[(rI) + (int32_t)nCols * (cI + 1) + 1]; - - /* Calculation of x1 * (1-xfract ) * (1-yfract) and acc is in 3.29(q29) format */ - out = ((q31_t) (((q63_t) x1 * (0x7FFFFFFF - xfract)) >> 32)); - acc = ((q31_t) (((q63_t) out * (0x7FFFFFFF - yfract)) >> 32)); - - /* x2 * (xfract) * (1-yfract) in 3.29(q29) and adding to acc */ - out = ((q31_t) ((q63_t) x2 * (0x7FFFFFFF - yfract) >> 32)); - acc += ((q31_t) ((q63_t) out * (xfract) >> 32)); - - /* y1 * (1 - xfract) * (yfract) in 3.29(q29) and adding to acc */ - out = ((q31_t) ((q63_t) y1 * (0x7FFFFFFF - xfract) >> 32)); - acc += ((q31_t) ((q63_t) out * (yfract) >> 32)); - - /* y2 * (xfract) * (yfract) in 3.29(q29) and adding to acc */ - out = ((q31_t) ((q63_t) y2 * (xfract) >> 32)); - acc += ((q31_t) ((q63_t) out * (yfract) >> 32)); - - /* Convert acc to 1.31(q31) format */ - return ((q31_t)(acc << 2)); - } - - - /** - * @brief Q15 bilinear interpolation. - * @param[in,out] S points to an instance of the interpolation structure. - * @param[in] X interpolation coordinate in 12.20 format. - * @param[in] Y interpolation coordinate in 12.20 format. - * @return out interpolated value. - */ - __STATIC_FORCEINLINE q15_t arm_bilinear_interp_q15( - arm_bilinear_interp_instance_q15 * S, - q31_t X, - q31_t Y) - { - q63_t acc = 0; /* output */ - q31_t out; /* Temporary output */ - q15_t x1, x2, y1, y2; /* Nearest output values */ - q31_t xfract, yfract; /* X, Y fractional parts */ - int32_t rI, cI; /* Row and column indices */ - q15_t *pYData = S->pData; /* pointer to output table values */ - uint32_t nCols = S->numCols; /* num of rows */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - rI = ((X & (q31_t)0xFFF00000) >> 20); - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - cI = ((Y & (q31_t)0xFFF00000) >> 20); - - /* Care taken for table outside boundary */ - /* Returns zero output when values are outside table boundary */ - if (rI < 0 || rI > (S->numCols - 2) || cI < 0 || cI > (S->numRows - 2)) - { - return (0); - } - - /* 20 bits for the fractional part */ - /* xfract should be in 12.20 format */ - xfract = (X & 0x000FFFFF); - - /* Read two nearest output values from the index */ - x1 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI) ]; - x2 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI) + 1]; - - /* 20 bits for the fractional part */ - /* yfract should be in 12.20 format */ - yfract = (Y & 0x000FFFFF); - - /* Read two nearest output values from the index */ - y1 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI + 1) ]; - y2 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI + 1) + 1]; - - /* Calculation of x1 * (1-xfract ) * (1-yfract) and acc is in 13.51 format */ - - /* x1 is in 1.15(q15), xfract in 12.20 format and out is in 13.35 format */ - /* convert 13.35 to 13.31 by right shifting and out is in 1.31 */ - out = (q31_t) (((q63_t) x1 * (0x0FFFFF - xfract)) >> 4U); - acc = ((q63_t) out * (0x0FFFFF - yfract)); - - /* x2 * (xfract) * (1-yfract) in 1.51 and adding to acc */ - out = (q31_t) (((q63_t) x2 * (0x0FFFFF - yfract)) >> 4U); - acc += ((q63_t) out * (xfract)); - - /* y1 * (1 - xfract) * (yfract) in 1.51 and adding to acc */ - out = (q31_t) (((q63_t) y1 * (0x0FFFFF - xfract)) >> 4U); - acc += ((q63_t) out * (yfract)); - - /* y2 * (xfract) * (yfract) in 1.51 and adding to acc */ - out = (q31_t) (((q63_t) y2 * (xfract)) >> 4U); - acc += ((q63_t) out * (yfract)); - - /* acc is in 13.51 format and down shift acc by 36 times */ - /* Convert out to 1.15 format */ - return ((q15_t)(acc >> 36)); - } - - - /** - * @brief Q7 bilinear interpolation. - * @param[in,out] S points to an instance of the interpolation structure. - * @param[in] X interpolation coordinate in 12.20 format. - * @param[in] Y interpolation coordinate in 12.20 format. - * @return out interpolated value. - */ - __STATIC_FORCEINLINE q7_t arm_bilinear_interp_q7( - arm_bilinear_interp_instance_q7 * S, - q31_t X, - q31_t Y) - { - q63_t acc = 0; /* output */ - q31_t out; /* Temporary output */ - q31_t xfract, yfract; /* X, Y fractional parts */ - q7_t x1, x2, y1, y2; /* Nearest output values */ - int32_t rI, cI; /* Row and column indices */ - q7_t *pYData = S->pData; /* pointer to output table values */ - uint32_t nCols = S->numCols; /* num of rows */ - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - rI = ((X & (q31_t)0xFFF00000) >> 20); - - /* Input is in 12.20 format */ - /* 12 bits for the table index */ - /* Index value calculation */ - cI = ((Y & (q31_t)0xFFF00000) >> 20); - - /* Care taken for table outside boundary */ - /* Returns zero output when values are outside table boundary */ - if (rI < 0 || rI > (S->numCols - 2) || cI < 0 || cI > (S->numRows - 2)) - { - return (0); - } - - /* 20 bits for the fractional part */ - /* xfract should be in 12.20 format */ - xfract = (X & (q31_t)0x000FFFFF); - - /* Read two nearest output values from the index */ - x1 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI) ]; - x2 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI) + 1]; - - /* 20 bits for the fractional part */ - /* yfract should be in 12.20 format */ - yfract = (Y & (q31_t)0x000FFFFF); - - /* Read two nearest output values from the index */ - y1 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI + 1) ]; - y2 = pYData[((uint32_t)rI) + nCols * ((uint32_t)cI + 1) + 1]; - - /* Calculation of x1 * (1-xfract ) * (1-yfract) and acc is in 16.47 format */ - out = ((x1 * (0xFFFFF - xfract))); - acc = (((q63_t) out * (0xFFFFF - yfract))); - - /* x2 * (xfract) * (1-yfract) in 2.22 and adding to acc */ - out = ((x2 * (0xFFFFF - yfract))); - acc += (((q63_t) out * (xfract))); - - /* y1 * (1 - xfract) * (yfract) in 2.22 and adding to acc */ - out = ((y1 * (0xFFFFF - xfract))); - acc += (((q63_t) out * (yfract))); - - /* y2 * (xfract) * (yfract) in 2.22 and adding to acc */ - out = ((y2 * (yfract))); - acc += (((q63_t) out * (xfract))); - - /* acc in 16.47 format and down shift by 40 to convert to 1.7 format */ - return ((q7_t)(acc >> 40)); - } - - /** - * @} end of BilinearInterpolate group - */ - - -/* SMMLAR */ -#define multAcc_32x32_keep32_R(a, x, y) \ - a = (q31_t) (((((q63_t) a) << 32) + ((q63_t) x * y) + 0x80000000LL ) >> 32) - -/* SMMLSR */ -#define multSub_32x32_keep32_R(a, x, y) \ - a = (q31_t) (((((q63_t) a) << 32) - ((q63_t) x * y) + 0x80000000LL ) >> 32) - -/* SMMULR */ -#define mult_32x32_keep32_R(a, x, y) \ - a = (q31_t) (((q63_t) x * y + 0x80000000LL ) >> 32) - -/* SMMLA */ -#define multAcc_32x32_keep32(a, x, y) \ - a += (q31_t) (((q63_t) x * y) >> 32) - -/* SMMLS */ -#define multSub_32x32_keep32(a, x, y) \ - a -= (q31_t) (((q63_t) x * y) >> 32) - -/* SMMUL */ -#define mult_32x32_keep32(a, x, y) \ - a = (q31_t) (((q63_t) x * y ) >> 32) - - -#if defined ( __CC_ARM ) - /* Enter low optimization region - place directly above function definition */ - #if defined( __ARM_ARCH_7EM__ ) - #define LOW_OPTIMIZATION_ENTER \ - _Pragma ("push") \ - _Pragma ("O1") - #else - #define LOW_OPTIMIZATION_ENTER - #endif - - /* Exit low optimization region - place directly after end of function definition */ - #if defined ( __ARM_ARCH_7EM__ ) - #define LOW_OPTIMIZATION_EXIT \ - _Pragma ("pop") - #else - #define LOW_OPTIMIZATION_EXIT - #endif - - /* Enter low optimization region - place directly above function definition */ - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - - /* Exit low optimization region - place directly after end of function definition */ - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined (__ARMCC_VERSION ) && ( __ARMCC_VERSION >= 6010050 ) - #define LOW_OPTIMIZATION_ENTER - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( __GNUC__ ) - #define LOW_OPTIMIZATION_ENTER \ - __attribute__(( optimize("-O1") )) - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( __ICCARM__ ) - /* Enter low optimization region - place directly above function definition */ - #if defined ( __ARM_ARCH_7EM__ ) - #define LOW_OPTIMIZATION_ENTER \ - _Pragma ("optimize=low") - #else - #define LOW_OPTIMIZATION_ENTER - #endif - - /* Exit low optimization region - place directly after end of function definition */ - #define LOW_OPTIMIZATION_EXIT - - /* Enter low optimization region - place directly above function definition */ - #if defined ( __ARM_ARCH_7EM__ ) - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER \ - _Pragma ("optimize=low") - #else - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #endif - - /* Exit low optimization region - place directly after end of function definition */ - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( __TI_ARM__ ) - #define LOW_OPTIMIZATION_ENTER - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( __CSMC__ ) - #define LOW_OPTIMIZATION_ENTER - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( __TASKING__ ) - #define LOW_OPTIMIZATION_ENTER - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT - -#elif defined ( _MSC_VER ) || defined(__GNUC_PYTHON__) - #define LOW_OPTIMIZATION_ENTER - #define LOW_OPTIMIZATION_EXIT - #define IAR_ONLY_LOW_OPTIMIZATION_ENTER - #define IAR_ONLY_LOW_OPTIMIZATION_EXIT -#endif - - - -/* Compiler specific diagnostic adjustment */ -#if defined ( __CC_ARM ) - -#elif defined ( __ARMCC_VERSION ) && ( __ARMCC_VERSION >= 6010050 ) - -#elif defined ( __GNUC__ ) -#pragma GCC diagnostic pop - -#elif defined ( __ICCARM__ ) - -#elif defined ( __TI_ARM__ ) - -#elif defined ( __CSMC__ ) - -#elif defined ( __TASKING__ ) - -#elif defined ( _MSC_VER ) - -#else - #error Unknown compiler -#endif - -#ifdef __cplusplus -} -#endif - - -#endif /* _ARM_MATH_H */ - -/** - * - * End of file. - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_armclang.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_armclang.h deleted file mode 100644 index 90de9dbf..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_armclang.h +++ /dev/null @@ -1,1467 +0,0 @@ -/**************************************************************************//** - * @file cmsis_armclang.h - * @brief CMSIS compiler armclang (Arm Compiler 6) header file - * @version V5.3.1 - * @date 26. March 2020 - ******************************************************************************/ -/* - * Copyright (c) 2009-2020 Arm Limited. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/*lint -esym(9058, IRQn)*/ /* disable MISRA 2012 Rule 2.4 for IRQn */ - -#ifndef __CMSIS_ARMCLANG_H -#define __CMSIS_ARMCLANG_H - -#pragma clang system_header /* treat file as system include file */ - -#ifndef __ARM_COMPAT_H -#include /* Compatibility header for Arm Compiler 5 intrinsics */ -#endif - -/* CMSIS compiler specific defines */ -#ifndef __ASM - #define __ASM __asm -#endif -#ifndef __INLINE - #define __INLINE __inline -#endif -#ifndef __STATIC_INLINE - #define __STATIC_INLINE static __inline -#endif -#ifndef __STATIC_FORCEINLINE - #define __STATIC_FORCEINLINE __attribute__((always_inline)) static __inline -#endif -#ifndef __NO_RETURN - #define __NO_RETURN __attribute__((__noreturn__)) -#endif -#ifndef __USED - #define __USED __attribute__((used)) -#endif -#ifndef __WEAK - #define __WEAK __attribute__((weak)) -#endif -#ifndef __PACKED - #define __PACKED __attribute__((packed, aligned(1))) -#endif -#ifndef __PACKED_STRUCT - #define __PACKED_STRUCT struct __attribute__((packed, aligned(1))) -#endif -#ifndef __PACKED_UNION - #define __PACKED_UNION union __attribute__((packed, aligned(1))) -#endif -#ifndef __UNALIGNED_UINT32 /* deprecated */ - #pragma clang diagnostic push - #pragma clang diagnostic ignored "-Wpacked" -/*lint -esym(9058, T_UINT32)*/ /* disable MISRA 2012 Rule 2.4 for T_UINT32 */ - struct __attribute__((packed)) T_UINT32 { uint32_t v; }; - #pragma clang diagnostic pop - #define __UNALIGNED_UINT32(x) (((struct T_UINT32 *)(x))->v) -#endif -#ifndef __UNALIGNED_UINT16_WRITE - #pragma clang diagnostic push - #pragma clang diagnostic ignored "-Wpacked" -/*lint -esym(9058, T_UINT16_WRITE)*/ /* disable MISRA 2012 Rule 2.4 for T_UINT16_WRITE */ - __PACKED_STRUCT T_UINT16_WRITE { uint16_t v; }; - #pragma clang diagnostic pop - #define __UNALIGNED_UINT16_WRITE(addr, val) (void)((((struct T_UINT16_WRITE *)(void *)(addr))->v) = (val)) -#endif -#ifndef __UNALIGNED_UINT16_READ - #pragma clang diagnostic push - #pragma clang diagnostic ignored "-Wpacked" -/*lint -esym(9058, T_UINT16_READ)*/ /* disable MISRA 2012 Rule 2.4 for T_UINT16_READ */ - __PACKED_STRUCT T_UINT16_READ { uint16_t v; }; - #pragma clang diagnostic pop - #define __UNALIGNED_UINT16_READ(addr) (((const struct T_UINT16_READ *)(const void *)(addr))->v) -#endif -#ifndef __UNALIGNED_UINT32_WRITE - #pragma clang diagnostic push - #pragma clang diagnostic ignored "-Wpacked" -/*lint -esym(9058, T_UINT32_WRITE)*/ /* disable MISRA 2012 Rule 2.4 for T_UINT32_WRITE */ - __PACKED_STRUCT T_UINT32_WRITE { uint32_t v; }; - #pragma clang diagnostic pop - #define __UNALIGNED_UINT32_WRITE(addr, val) (void)((((struct T_UINT32_WRITE *)(void *)(addr))->v) = (val)) -#endif -#ifndef __UNALIGNED_UINT32_READ - #pragma clang diagnostic push - #pragma clang diagnostic ignored "-Wpacked" -/*lint -esym(9058, T_UINT32_READ)*/ /* disable MISRA 2012 Rule 2.4 for T_UINT32_READ */ - __PACKED_STRUCT T_UINT32_READ { uint32_t v; }; - #pragma clang diagnostic pop - #define __UNALIGNED_UINT32_READ(addr) (((const struct T_UINT32_READ *)(const void *)(addr))->v) -#endif -#ifndef __ALIGNED - #define __ALIGNED(x) __attribute__((aligned(x))) -#endif -#ifndef __RESTRICT - #define __RESTRICT __restrict -#endif -#ifndef __COMPILER_BARRIER - #define __COMPILER_BARRIER() __ASM volatile("":::"memory") -#endif - -/* ######################### Startup and Lowlevel Init ######################## */ - -#ifndef __PROGRAM_START -#define __PROGRAM_START __main -#endif - -#ifndef __INITIAL_SP -#define __INITIAL_SP Image$$ARM_LIB_STACK$$ZI$$Limit -#endif - -#ifndef __STACK_LIMIT -#define __STACK_LIMIT Image$$ARM_LIB_STACK$$ZI$$Base -#endif - -#ifndef __VECTOR_TABLE -#define __VECTOR_TABLE __Vectors -#endif - -#ifndef __VECTOR_TABLE_ATTRIBUTE -#define __VECTOR_TABLE_ATTRIBUTE __attribute__((used, section("RESET"))) -#endif - -/* ########################### Core Function Access ########################### */ -/** \ingroup CMSIS_Core_FunctionInterface - \defgroup CMSIS_Core_RegAccFunctions CMSIS Core Register Access Functions - @{ - */ - -/** - \brief Enable IRQ Interrupts - \details Enables IRQ interrupts by clearing the I-bit in the CPSR. - Can only be executed in Privileged modes. - */ -/* intrinsic void __enable_irq(); see arm_compat.h */ - - -/** - \brief Disable IRQ Interrupts - \details Disables IRQ interrupts by setting the I-bit in the CPSR. - Can only be executed in Privileged modes. - */ -/* intrinsic void __disable_irq(); see arm_compat.h */ - - -/** - \brief Get Control Register - \details Returns the content of the Control Register. - \return Control Register value - */ -__STATIC_FORCEINLINE uint32_t __get_CONTROL(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, control" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Control Register (non-secure) - \details Returns the content of the non-secure Control Register when in secure mode. - \return non-secure Control Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_CONTROL_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, control_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Control Register - \details Writes the given value to the Control Register. - \param [in] control Control Register value to set - */ -__STATIC_FORCEINLINE void __set_CONTROL(uint32_t control) -{ - __ASM volatile ("MSR control, %0" : : "r" (control) : "memory"); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Control Register (non-secure) - \details Writes the given value to the non-secure Control Register when in secure state. - \param [in] control Control Register value to set - */ -__STATIC_FORCEINLINE void __TZ_set_CONTROL_NS(uint32_t control) -{ - __ASM volatile ("MSR control_ns, %0" : : "r" (control) : "memory"); -} -#endif - - -/** - \brief Get IPSR Register - \details Returns the content of the IPSR Register. - \return IPSR Register value - */ -__STATIC_FORCEINLINE uint32_t __get_IPSR(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, ipsr" : "=r" (result) ); - return(result); -} - - -/** - \brief Get APSR Register - \details Returns the content of the APSR Register. - \return APSR Register value - */ -__STATIC_FORCEINLINE uint32_t __get_APSR(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, apsr" : "=r" (result) ); - return(result); -} - - -/** - \brief Get xPSR Register - \details Returns the content of the xPSR Register. - \return xPSR Register value - */ -__STATIC_FORCEINLINE uint32_t __get_xPSR(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, xpsr" : "=r" (result) ); - return(result); -} - - -/** - \brief Get Process Stack Pointer - \details Returns the current value of the Process Stack Pointer (PSP). - \return PSP Register value - */ -__STATIC_FORCEINLINE uint32_t __get_PSP(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, psp" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Process Stack Pointer (non-secure) - \details Returns the current value of the non-secure Process Stack Pointer (PSP) when in secure state. - \return PSP Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_PSP_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, psp_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Process Stack Pointer - \details Assigns the given value to the Process Stack Pointer (PSP). - \param [in] topOfProcStack Process Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __set_PSP(uint32_t topOfProcStack) -{ - __ASM volatile ("MSR psp, %0" : : "r" (topOfProcStack) : ); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Process Stack Pointer (non-secure) - \details Assigns the given value to the non-secure Process Stack Pointer (PSP) when in secure state. - \param [in] topOfProcStack Process Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __TZ_set_PSP_NS(uint32_t topOfProcStack) -{ - __ASM volatile ("MSR psp_ns, %0" : : "r" (topOfProcStack) : ); -} -#endif - - -/** - \brief Get Main Stack Pointer - \details Returns the current value of the Main Stack Pointer (MSP). - \return MSP Register value - */ -__STATIC_FORCEINLINE uint32_t __get_MSP(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, msp" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Main Stack Pointer (non-secure) - \details Returns the current value of the non-secure Main Stack Pointer (MSP) when in secure state. - \return MSP Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_MSP_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, msp_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Main Stack Pointer - \details Assigns the given value to the Main Stack Pointer (MSP). - \param [in] topOfMainStack Main Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __set_MSP(uint32_t topOfMainStack) -{ - __ASM volatile ("MSR msp, %0" : : "r" (topOfMainStack) : ); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Main Stack Pointer (non-secure) - \details Assigns the given value to the non-secure Main Stack Pointer (MSP) when in secure state. - \param [in] topOfMainStack Main Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __TZ_set_MSP_NS(uint32_t topOfMainStack) -{ - __ASM volatile ("MSR msp_ns, %0" : : "r" (topOfMainStack) : ); -} -#endif - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Stack Pointer (non-secure) - \details Returns the current value of the non-secure Stack Pointer (SP) when in secure state. - \return SP Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_SP_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, sp_ns" : "=r" (result) ); - return(result); -} - - -/** - \brief Set Stack Pointer (non-secure) - \details Assigns the given value to the non-secure Stack Pointer (SP) when in secure state. - \param [in] topOfStack Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __TZ_set_SP_NS(uint32_t topOfStack) -{ - __ASM volatile ("MSR sp_ns, %0" : : "r" (topOfStack) : ); -} -#endif - - -/** - \brief Get Priority Mask - \details Returns the current state of the priority mask bit from the Priority Mask Register. - \return Priority Mask value - */ -__STATIC_FORCEINLINE uint32_t __get_PRIMASK(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, primask" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Priority Mask (non-secure) - \details Returns the current state of the non-secure priority mask bit from the Priority Mask Register when in secure state. - \return Priority Mask value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_PRIMASK_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, primask_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Priority Mask - \details Assigns the given value to the Priority Mask Register. - \param [in] priMask Priority Mask - */ -__STATIC_FORCEINLINE void __set_PRIMASK(uint32_t priMask) -{ - __ASM volatile ("MSR primask, %0" : : "r" (priMask) : "memory"); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Priority Mask (non-secure) - \details Assigns the given value to the non-secure Priority Mask Register when in secure state. - \param [in] priMask Priority Mask - */ -__STATIC_FORCEINLINE void __TZ_set_PRIMASK_NS(uint32_t priMask) -{ - __ASM volatile ("MSR primask_ns, %0" : : "r" (priMask) : "memory"); -} -#endif - - -#if ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) -/** - \brief Enable FIQ - \details Enables FIQ interrupts by clearing the F-bit in the CPSR. - Can only be executed in Privileged modes. - */ -#define __enable_fault_irq __enable_fiq /* see arm_compat.h */ - - -/** - \brief Disable FIQ - \details Disables FIQ interrupts by setting the F-bit in the CPSR. - Can only be executed in Privileged modes. - */ -#define __disable_fault_irq __disable_fiq /* see arm_compat.h */ - - -/** - \brief Get Base Priority - \details Returns the current value of the Base Priority register. - \return Base Priority register value - */ -__STATIC_FORCEINLINE uint32_t __get_BASEPRI(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, basepri" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Base Priority (non-secure) - \details Returns the current value of the non-secure Base Priority register when in secure state. - \return Base Priority register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_BASEPRI_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, basepri_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Base Priority - \details Assigns the given value to the Base Priority register. - \param [in] basePri Base Priority value to set - */ -__STATIC_FORCEINLINE void __set_BASEPRI(uint32_t basePri) -{ - __ASM volatile ("MSR basepri, %0" : : "r" (basePri) : "memory"); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Base Priority (non-secure) - \details Assigns the given value to the non-secure Base Priority register when in secure state. - \param [in] basePri Base Priority value to set - */ -__STATIC_FORCEINLINE void __TZ_set_BASEPRI_NS(uint32_t basePri) -{ - __ASM volatile ("MSR basepri_ns, %0" : : "r" (basePri) : "memory"); -} -#endif - - -/** - \brief Set Base Priority with condition - \details Assigns the given value to the Base Priority register only if BASEPRI masking is disabled, - or the new value increases the BASEPRI priority level. - \param [in] basePri Base Priority value to set - */ -__STATIC_FORCEINLINE void __set_BASEPRI_MAX(uint32_t basePri) -{ - __ASM volatile ("MSR basepri_max, %0" : : "r" (basePri) : "memory"); -} - - -/** - \brief Get Fault Mask - \details Returns the current value of the Fault Mask register. - \return Fault Mask register value - */ -__STATIC_FORCEINLINE uint32_t __get_FAULTMASK(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, faultmask" : "=r" (result) ); - return(result); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Fault Mask (non-secure) - \details Returns the current value of the non-secure Fault Mask register when in secure state. - \return Fault Mask register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_FAULTMASK_NS(void) -{ - uint32_t result; - - __ASM volatile ("MRS %0, faultmask_ns" : "=r" (result) ); - return(result); -} -#endif - - -/** - \brief Set Fault Mask - \details Assigns the given value to the Fault Mask register. - \param [in] faultMask Fault Mask value to set - */ -__STATIC_FORCEINLINE void __set_FAULTMASK(uint32_t faultMask) -{ - __ASM volatile ("MSR faultmask, %0" : : "r" (faultMask) : "memory"); -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Fault Mask (non-secure) - \details Assigns the given value to the non-secure Fault Mask register when in secure state. - \param [in] faultMask Fault Mask value to set - */ -__STATIC_FORCEINLINE void __TZ_set_FAULTMASK_NS(uint32_t faultMask) -{ - __ASM volatile ("MSR faultmask_ns, %0" : : "r" (faultMask) : "memory"); -} -#endif - -#endif /* ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - - -#if ((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) - -/** - \brief Get Process Stack Pointer Limit - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence zero is returned always in non-secure - mode. - - \details Returns the current value of the Process Stack Pointer Limit (PSPLIM). - \return PSPLIM Register value - */ -__STATIC_FORCEINLINE uint32_t __get_PSPLIM(void) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) && \ - (!defined (__ARM_FEATURE_CMSE) || (__ARM_FEATURE_CMSE < 3))) - // without main extensions, the non-secure PSPLIM is RAZ/WI - return 0U; -#else - uint32_t result; - __ASM volatile ("MRS %0, psplim" : "=r" (result) ); - return result; -#endif -} - -#if (defined (__ARM_FEATURE_CMSE) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Process Stack Pointer Limit (non-secure) - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence zero is returned always in non-secure - mode. - - \details Returns the current value of the non-secure Process Stack Pointer Limit (PSPLIM) when in secure state. - \return PSPLIM Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_PSPLIM_NS(void) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) ) - // without main extensions, the non-secure PSPLIM is RAZ/WI - return 0U; -#else - uint32_t result; - __ASM volatile ("MRS %0, psplim_ns" : "=r" (result) ); - return result; -#endif -} -#endif - - -/** - \brief Set Process Stack Pointer Limit - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence the write is silently ignored in non-secure - mode. - - \details Assigns the given value to the Process Stack Pointer Limit (PSPLIM). - \param [in] ProcStackPtrLimit Process Stack Pointer Limit value to set - */ -__STATIC_FORCEINLINE void __set_PSPLIM(uint32_t ProcStackPtrLimit) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) && \ - (!defined (__ARM_FEATURE_CMSE) || (__ARM_FEATURE_CMSE < 3))) - // without main extensions, the non-secure PSPLIM is RAZ/WI - (void)ProcStackPtrLimit; -#else - __ASM volatile ("MSR psplim, %0" : : "r" (ProcStackPtrLimit)); -#endif -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Process Stack Pointer (non-secure) - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence the write is silently ignored in non-secure - mode. - - \details Assigns the given value to the non-secure Process Stack Pointer Limit (PSPLIM) when in secure state. - \param [in] ProcStackPtrLimit Process Stack Pointer Limit value to set - */ -__STATIC_FORCEINLINE void __TZ_set_PSPLIM_NS(uint32_t ProcStackPtrLimit) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) ) - // without main extensions, the non-secure PSPLIM is RAZ/WI - (void)ProcStackPtrLimit; -#else - __ASM volatile ("MSR psplim_ns, %0\n" : : "r" (ProcStackPtrLimit)); -#endif -} -#endif - - -/** - \brief Get Main Stack Pointer Limit - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence zero is returned always. - - \details Returns the current value of the Main Stack Pointer Limit (MSPLIM). - \return MSPLIM Register value - */ -__STATIC_FORCEINLINE uint32_t __get_MSPLIM(void) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) && \ - (!defined (__ARM_FEATURE_CMSE) || (__ARM_FEATURE_CMSE < 3))) - // without main extensions, the non-secure MSPLIM is RAZ/WI - return 0U; -#else - uint32_t result; - __ASM volatile ("MRS %0, msplim" : "=r" (result) ); - return result; -#endif -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Get Main Stack Pointer Limit (non-secure) - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence zero is returned always. - - \details Returns the current value of the non-secure Main Stack Pointer Limit(MSPLIM) when in secure state. - \return MSPLIM Register value - */ -__STATIC_FORCEINLINE uint32_t __TZ_get_MSPLIM_NS(void) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) ) - // without main extensions, the non-secure MSPLIM is RAZ/WI - return 0U; -#else - uint32_t result; - __ASM volatile ("MRS %0, msplim_ns" : "=r" (result) ); - return result; -#endif -} -#endif - - -/** - \brief Set Main Stack Pointer Limit - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence the write is silently ignored. - - \details Assigns the given value to the Main Stack Pointer Limit (MSPLIM). - \param [in] MainStackPtrLimit Main Stack Pointer Limit value to set - */ -__STATIC_FORCEINLINE void __set_MSPLIM(uint32_t MainStackPtrLimit) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) && \ - (!defined (__ARM_FEATURE_CMSE) || (__ARM_FEATURE_CMSE < 3))) - // without main extensions, the non-secure MSPLIM is RAZ/WI - (void)MainStackPtrLimit; -#else - __ASM volatile ("MSR msplim, %0" : : "r" (MainStackPtrLimit)); -#endif -} - - -#if (defined (__ARM_FEATURE_CMSE ) && (__ARM_FEATURE_CMSE == 3)) -/** - \brief Set Main Stack Pointer Limit (non-secure) - Devices without ARMv8-M Main Extensions (i.e. Cortex-M23) lack the non-secure - Stack Pointer Limit register hence the write is silently ignored. - - \details Assigns the given value to the non-secure Main Stack Pointer Limit (MSPLIM) when in secure state. - \param [in] MainStackPtrLimit Main Stack Pointer value to set - */ -__STATIC_FORCEINLINE void __TZ_set_MSPLIM_NS(uint32_t MainStackPtrLimit) -{ -#if (!((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__ ) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) ) - // without main extensions, the non-secure MSPLIM is RAZ/WI - (void)MainStackPtrLimit; -#else - __ASM volatile ("MSR msplim_ns, %0" : : "r" (MainStackPtrLimit)); -#endif -} -#endif - -#endif /* ((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - -/** - \brief Get FPSCR - \details Returns the current value of the Floating Point Status/Control register. - \return Floating Point Status/Control register value - */ -#if ((defined (__FPU_PRESENT) && (__FPU_PRESENT == 1U)) && \ - (defined (__FPU_USED ) && (__FPU_USED == 1U)) ) -#define __get_FPSCR (uint32_t)__builtin_arm_get_fpscr -#else -#define __get_FPSCR() ((uint32_t)0U) -#endif - -/** - \brief Set FPSCR - \details Assigns the given value to the Floating Point Status/Control register. - \param [in] fpscr Floating Point Status/Control value to set - */ -#if ((defined (__FPU_PRESENT) && (__FPU_PRESENT == 1U)) && \ - (defined (__FPU_USED ) && (__FPU_USED == 1U)) ) -#define __set_FPSCR __builtin_arm_set_fpscr -#else -#define __set_FPSCR(x) ((void)(x)) -#endif - - -/*@} end of CMSIS_Core_RegAccFunctions */ - - -/* ########################## Core Instruction Access ######################### */ -/** \defgroup CMSIS_Core_InstructionInterface CMSIS Core Instruction Interface - Access to dedicated instructions - @{ -*/ - -/* Define macros for porting to both thumb1 and thumb2. - * For thumb1, use low register (r0-r7), specified by constraint "l" - * Otherwise, use general registers, specified by constraint "r" */ -#if defined (__thumb__) && !defined (__thumb2__) -#define __CMSIS_GCC_OUT_REG(r) "=l" (r) -#define __CMSIS_GCC_RW_REG(r) "+l" (r) -#define __CMSIS_GCC_USE_REG(r) "l" (r) -#else -#define __CMSIS_GCC_OUT_REG(r) "=r" (r) -#define __CMSIS_GCC_RW_REG(r) "+r" (r) -#define __CMSIS_GCC_USE_REG(r) "r" (r) -#endif - -/** - \brief No Operation - \details No Operation does nothing. This instruction can be used for code alignment purposes. - */ -#define __NOP __builtin_arm_nop - -/** - \brief Wait For Interrupt - \details Wait For Interrupt is a hint instruction that suspends execution until one of a number of events occurs. - */ -#define __WFI __builtin_arm_wfi - - -/** - \brief Wait For Event - \details Wait For Event is a hint instruction that permits the processor to enter - a low-power state until one of a number of events occurs. - */ -#define __WFE __builtin_arm_wfe - - -/** - \brief Send Event - \details Send Event is a hint instruction. It causes an event to be signaled to the CPU. - */ -#define __SEV __builtin_arm_sev - - -/** - \brief Instruction Synchronization Barrier - \details Instruction Synchronization Barrier flushes the pipeline in the processor, - so that all instructions following the ISB are fetched from cache or memory, - after the instruction has been completed. - */ -#define __ISB() __builtin_arm_isb(0xF) - -/** - \brief Data Synchronization Barrier - \details Acts as a special kind of Data Memory Barrier. - It completes when all explicit memory accesses before this instruction complete. - */ -#define __DSB() __builtin_arm_dsb(0xF) - - -/** - \brief Data Memory Barrier - \details Ensures the apparent order of the explicit memory operations before - and after the instruction, without ensuring their completion. - */ -#define __DMB() __builtin_arm_dmb(0xF) - - -/** - \brief Reverse byte order (32 bit) - \details Reverses the byte order in unsigned integer value. For example, 0x12345678 becomes 0x78563412. - \param [in] value Value to reverse - \return Reversed value - */ -#define __REV(value) __builtin_bswap32(value) - - -/** - \brief Reverse byte order (16 bit) - \details Reverses the byte order within each halfword of a word. For example, 0x12345678 becomes 0x34127856. - \param [in] value Value to reverse - \return Reversed value - */ -#define __REV16(value) __ROR(__REV(value), 16) - - -/** - \brief Reverse byte order (16 bit) - \details Reverses the byte order in a 16-bit value and returns the signed 16-bit result. For example, 0x0080 becomes 0x8000. - \param [in] value Value to reverse - \return Reversed value - */ -#define __REVSH(value) (int16_t)__builtin_bswap16(value) - - -/** - \brief Rotate Right in unsigned value (32 bit) - \details Rotate Right (immediate) provides the value of the contents of a register rotated by a variable number of bits. - \param [in] op1 Value to rotate - \param [in] op2 Number of Bits to rotate - \return Rotated value - */ -__STATIC_FORCEINLINE uint32_t __ROR(uint32_t op1, uint32_t op2) -{ - op2 %= 32U; - if (op2 == 0U) - { - return op1; - } - return (op1 >> op2) | (op1 << (32U - op2)); -} - - -/** - \brief Breakpoint - \details Causes the processor to enter Debug state. - Debug tools can use this to investigate system state when the instruction at a particular address is reached. - \param [in] value is ignored by the processor. - If required, a debugger can use it to store additional information about the breakpoint. - */ -#define __BKPT(value) __ASM volatile ("bkpt "#value) - - -/** - \brief Reverse bit order of value - \details Reverses the bit order of the given value. - \param [in] value Value to reverse - \return Reversed value - */ -#define __RBIT __builtin_arm_rbit - -/** - \brief Count leading zeros - \details Counts the number of leading zeros of a data value. - \param [in] value Value to count the leading zeros - \return number of leading zeros in value - */ -__STATIC_FORCEINLINE uint8_t __CLZ(uint32_t value) -{ - /* Even though __builtin_clz produces a CLZ instruction on ARM, formally - __builtin_clz(0) is undefined behaviour, so handle this case specially. - This guarantees ARM-compatible results if happening to compile on a non-ARM - target, and ensures the compiler doesn't decide to activate any - optimisations using the logic "value was passed to __builtin_clz, so it - is non-zero". - ARM Compiler 6.10 and possibly earlier will optimise this test away, leaving a - single CLZ instruction. - */ - if (value == 0U) - { - return 32U; - } - return __builtin_clz(value); -} - - -#if ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) - -/** - \brief LDR Exclusive (8 bit) - \details Executes a exclusive LDR instruction for 8 bit value. - \param [in] ptr Pointer to data - \return value of type uint8_t at (*ptr) - */ -#define __LDREXB (uint8_t)__builtin_arm_ldrex - - -/** - \brief LDR Exclusive (16 bit) - \details Executes a exclusive LDR instruction for 16 bit values. - \param [in] ptr Pointer to data - \return value of type uint16_t at (*ptr) - */ -#define __LDREXH (uint16_t)__builtin_arm_ldrex - - -/** - \brief LDR Exclusive (32 bit) - \details Executes a exclusive LDR instruction for 32 bit values. - \param [in] ptr Pointer to data - \return value of type uint32_t at (*ptr) - */ -#define __LDREXW (uint32_t)__builtin_arm_ldrex - - -/** - \brief STR Exclusive (8 bit) - \details Executes a exclusive STR instruction for 8 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STREXB (uint32_t)__builtin_arm_strex - - -/** - \brief STR Exclusive (16 bit) - \details Executes a exclusive STR instruction for 16 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STREXH (uint32_t)__builtin_arm_strex - - -/** - \brief STR Exclusive (32 bit) - \details Executes a exclusive STR instruction for 32 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STREXW (uint32_t)__builtin_arm_strex - - -/** - \brief Remove the exclusive lock - \details Removes the exclusive lock which is created by LDREX. - */ -#define __CLREX __builtin_arm_clrex - -#endif /* ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - - -#if ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) - -/** - \brief Signed Saturate - \details Saturates a signed value. - \param [in] value Value to be saturated - \param [in] sat Bit position to saturate to (1..32) - \return Saturated value - */ -#define __SSAT __builtin_arm_ssat - - -/** - \brief Unsigned Saturate - \details Saturates an unsigned value. - \param [in] value Value to be saturated - \param [in] sat Bit position to saturate to (0..31) - \return Saturated value - */ -#define __USAT __builtin_arm_usat - - -/** - \brief Rotate Right with Extend (32 bit) - \details Moves each bit of a bitstring right by one bit. - The carry input is shifted in at the left end of the bitstring. - \param [in] value Value to rotate - \return Rotated value - */ -__STATIC_FORCEINLINE uint32_t __RRX(uint32_t value) -{ - uint32_t result; - - __ASM volatile ("rrx %0, %1" : __CMSIS_GCC_OUT_REG (result) : __CMSIS_GCC_USE_REG (value) ); - return(result); -} - - -/** - \brief LDRT Unprivileged (8 bit) - \details Executes a Unprivileged LDRT instruction for 8 bit value. - \param [in] ptr Pointer to data - \return value of type uint8_t at (*ptr) - */ -__STATIC_FORCEINLINE uint8_t __LDRBT(volatile uint8_t *ptr) -{ - uint32_t result; - - __ASM volatile ("ldrbt %0, %1" : "=r" (result) : "Q" (*ptr) ); - return ((uint8_t) result); /* Add explicit type cast here */ -} - - -/** - \brief LDRT Unprivileged (16 bit) - \details Executes a Unprivileged LDRT instruction for 16 bit values. - \param [in] ptr Pointer to data - \return value of type uint16_t at (*ptr) - */ -__STATIC_FORCEINLINE uint16_t __LDRHT(volatile uint16_t *ptr) -{ - uint32_t result; - - __ASM volatile ("ldrht %0, %1" : "=r" (result) : "Q" (*ptr) ); - return ((uint16_t) result); /* Add explicit type cast here */ -} - - -/** - \brief LDRT Unprivileged (32 bit) - \details Executes a Unprivileged LDRT instruction for 32 bit values. - \param [in] ptr Pointer to data - \return value of type uint32_t at (*ptr) - */ -__STATIC_FORCEINLINE uint32_t __LDRT(volatile uint32_t *ptr) -{ - uint32_t result; - - __ASM volatile ("ldrt %0, %1" : "=r" (result) : "Q" (*ptr) ); - return(result); -} - - -/** - \brief STRT Unprivileged (8 bit) - \details Executes a Unprivileged STRT instruction for 8 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STRBT(uint8_t value, volatile uint8_t *ptr) -{ - __ASM volatile ("strbt %1, %0" : "=Q" (*ptr) : "r" ((uint32_t)value) ); -} - - -/** - \brief STRT Unprivileged (16 bit) - \details Executes a Unprivileged STRT instruction for 16 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STRHT(uint16_t value, volatile uint16_t *ptr) -{ - __ASM volatile ("strht %1, %0" : "=Q" (*ptr) : "r" ((uint32_t)value) ); -} - - -/** - \brief STRT Unprivileged (32 bit) - \details Executes a Unprivileged STRT instruction for 32 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STRT(uint32_t value, volatile uint32_t *ptr) -{ - __ASM volatile ("strt %1, %0" : "=Q" (*ptr) : "r" (value) ); -} - -#else /* ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - -/** - \brief Signed Saturate - \details Saturates a signed value. - \param [in] value Value to be saturated - \param [in] sat Bit position to saturate to (1..32) - \return Saturated value - */ -__STATIC_FORCEINLINE int32_t __SSAT(int32_t val, uint32_t sat) -{ - if ((sat >= 1U) && (sat <= 32U)) - { - const int32_t max = (int32_t)((1U << (sat - 1U)) - 1U); - const int32_t min = -1 - max ; - if (val > max) - { - return max; - } - else if (val < min) - { - return min; - } - } - return val; -} - -/** - \brief Unsigned Saturate - \details Saturates an unsigned value. - \param [in] value Value to be saturated - \param [in] sat Bit position to saturate to (0..31) - \return Saturated value - */ -__STATIC_FORCEINLINE uint32_t __USAT(int32_t val, uint32_t sat) -{ - if (sat <= 31U) - { - const uint32_t max = ((1U << sat) - 1U); - if (val > (int32_t)max) - { - return max; - } - else if (val < 0) - { - return 0U; - } - } - return (uint32_t)val; -} - -#endif /* ((defined (__ARM_ARCH_7M__ ) && (__ARM_ARCH_7M__ == 1)) || \ - (defined (__ARM_ARCH_7EM__ ) && (__ARM_ARCH_7EM__ == 1)) || \ - (defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - - -#if ((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) - -/** - \brief Load-Acquire (8 bit) - \details Executes a LDAB instruction for 8 bit value. - \param [in] ptr Pointer to data - \return value of type uint8_t at (*ptr) - */ -__STATIC_FORCEINLINE uint8_t __LDAB(volatile uint8_t *ptr) -{ - uint32_t result; - - __ASM volatile ("ldab %0, %1" : "=r" (result) : "Q" (*ptr) : "memory" ); - return ((uint8_t) result); -} - - -/** - \brief Load-Acquire (16 bit) - \details Executes a LDAH instruction for 16 bit values. - \param [in] ptr Pointer to data - \return value of type uint16_t at (*ptr) - */ -__STATIC_FORCEINLINE uint16_t __LDAH(volatile uint16_t *ptr) -{ - uint32_t result; - - __ASM volatile ("ldah %0, %1" : "=r" (result) : "Q" (*ptr) : "memory" ); - return ((uint16_t) result); -} - - -/** - \brief Load-Acquire (32 bit) - \details Executes a LDA instruction for 32 bit values. - \param [in] ptr Pointer to data - \return value of type uint32_t at (*ptr) - */ -__STATIC_FORCEINLINE uint32_t __LDA(volatile uint32_t *ptr) -{ - uint32_t result; - - __ASM volatile ("lda %0, %1" : "=r" (result) : "Q" (*ptr) : "memory" ); - return(result); -} - - -/** - \brief Store-Release (8 bit) - \details Executes a STLB instruction for 8 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STLB(uint8_t value, volatile uint8_t *ptr) -{ - __ASM volatile ("stlb %1, %0" : "=Q" (*ptr) : "r" ((uint32_t)value) : "memory" ); -} - - -/** - \brief Store-Release (16 bit) - \details Executes a STLH instruction for 16 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STLH(uint16_t value, volatile uint16_t *ptr) -{ - __ASM volatile ("stlh %1, %0" : "=Q" (*ptr) : "r" ((uint32_t)value) : "memory" ); -} - - -/** - \brief Store-Release (32 bit) - \details Executes a STL instruction for 32 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - */ -__STATIC_FORCEINLINE void __STL(uint32_t value, volatile uint32_t *ptr) -{ - __ASM volatile ("stl %1, %0" : "=Q" (*ptr) : "r" ((uint32_t)value) : "memory" ); -} - - -/** - \brief Load-Acquire Exclusive (8 bit) - \details Executes a LDAB exclusive instruction for 8 bit value. - \param [in] ptr Pointer to data - \return value of type uint8_t at (*ptr) - */ -#define __LDAEXB (uint8_t)__builtin_arm_ldaex - - -/** - \brief Load-Acquire Exclusive (16 bit) - \details Executes a LDAH exclusive instruction for 16 bit values. - \param [in] ptr Pointer to data - \return value of type uint16_t at (*ptr) - */ -#define __LDAEXH (uint16_t)__builtin_arm_ldaex - - -/** - \brief Load-Acquire Exclusive (32 bit) - \details Executes a LDA exclusive instruction for 32 bit values. - \param [in] ptr Pointer to data - \return value of type uint32_t at (*ptr) - */ -#define __LDAEX (uint32_t)__builtin_arm_ldaex - - -/** - \brief Store-Release Exclusive (8 bit) - \details Executes a STLB exclusive instruction for 8 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STLEXB (uint32_t)__builtin_arm_stlex - - -/** - \brief Store-Release Exclusive (16 bit) - \details Executes a STLH exclusive instruction for 16 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STLEXH (uint32_t)__builtin_arm_stlex - - -/** - \brief Store-Release Exclusive (32 bit) - \details Executes a STL exclusive instruction for 32 bit values. - \param [in] value Value to store - \param [in] ptr Pointer to location - \return 0 Function succeeded - \return 1 Function failed - */ -#define __STLEX (uint32_t)__builtin_arm_stlex - -#endif /* ((defined (__ARM_ARCH_8M_MAIN__ ) && (__ARM_ARCH_8M_MAIN__ == 1)) || \ - (defined (__ARM_ARCH_8M_BASE__ ) && (__ARM_ARCH_8M_BASE__ == 1)) || \ - (defined (__ARM_ARCH_8_1M_MAIN__) && (__ARM_ARCH_8_1M_MAIN__ == 1)) ) */ - -/*@}*/ /* end of group CMSIS_Core_InstructionInterface */ - - -/* ################### Compiler specific Intrinsics ########################### */ -/** \defgroup CMSIS_SIMD_intrinsics CMSIS SIMD Intrinsics - Access to dedicated SIMD instructions - @{ -*/ - -#if (defined (__ARM_FEATURE_DSP) && (__ARM_FEATURE_DSP == 1)) - -#define __SADD8 __builtin_arm_sadd8 -#define __QADD8 __builtin_arm_qadd8 -#define __SHADD8 __builtin_arm_shadd8 -#define __UADD8 __builtin_arm_uadd8 -#define __UQADD8 __builtin_arm_uqadd8 -#define __UHADD8 __builtin_arm_uhadd8 -#define __SSUB8 __builtin_arm_ssub8 -#define __QSUB8 __builtin_arm_qsub8 -#define __SHSUB8 __builtin_arm_shsub8 -#define __USUB8 __builtin_arm_usub8 -#define __UQSUB8 __builtin_arm_uqsub8 -#define __UHSUB8 __builtin_arm_uhsub8 -#define __SADD16 __builtin_arm_sadd16 -#define __QADD16 __builtin_arm_qadd16 -#define __SHADD16 __builtin_arm_shadd16 -#define __UADD16 __builtin_arm_uadd16 -#define __UQADD16 __builtin_arm_uqadd16 -#define __UHADD16 __builtin_arm_uhadd16 -#define __SSUB16 __builtin_arm_ssub16 -#define __QSUB16 __builtin_arm_qsub16 -#define __SHSUB16 __builtin_arm_shsub16 -#define __USUB16 __builtin_arm_usub16 -#define __UQSUB16 __builtin_arm_uqsub16 -#define __UHSUB16 __builtin_arm_uhsub16 -#define __SASX __builtin_arm_sasx -#define __QASX __builtin_arm_qasx -#define __SHASX __builtin_arm_shasx -#define __UASX __builtin_arm_uasx -#define __UQASX __builtin_arm_uqasx -#define __UHASX __builtin_arm_uhasx -#define __SSAX __builtin_arm_ssax -#define __QSAX __builtin_arm_qsax -#define __SHSAX __builtin_arm_shsax -#define __USAX __builtin_arm_usax -#define __UQSAX __builtin_arm_uqsax -#define __UHSAX __builtin_arm_uhsax -#define __USAD8 __builtin_arm_usad8 -#define __USADA8 __builtin_arm_usada8 -#define __SSAT16 __builtin_arm_ssat16 -#define __USAT16 __builtin_arm_usat16 -#define __UXTB16 __builtin_arm_uxtb16 -#define __UXTAB16 __builtin_arm_uxtab16 -#define __SXTB16 __builtin_arm_sxtb16 -#define __SXTAB16 __builtin_arm_sxtab16 -#define __SMUAD __builtin_arm_smuad -#define __SMUADX __builtin_arm_smuadx -#define __SMLAD __builtin_arm_smlad -#define __SMLADX __builtin_arm_smladx -#define __SMLALD __builtin_arm_smlald -#define __SMLALDX __builtin_arm_smlaldx -#define __SMUSD __builtin_arm_smusd -#define __SMUSDX __builtin_arm_smusdx -#define __SMLSD __builtin_arm_smlsd -#define __SMLSDX __builtin_arm_smlsdx -#define __SMLSLD __builtin_arm_smlsld -#define __SMLSLDX __builtin_arm_smlsldx -#define __SEL __builtin_arm_sel -#define __QADD __builtin_arm_qadd -#define __QSUB __builtin_arm_qsub - -#define __PKHBT(ARG1,ARG2,ARG3) ( ((((uint32_t)(ARG1)) ) & 0x0000FFFFUL) | \ - ((((uint32_t)(ARG2)) << (ARG3)) & 0xFFFF0000UL) ) - -#define __PKHTB(ARG1,ARG2,ARG3) ( ((((uint32_t)(ARG1)) ) & 0xFFFF0000UL) | \ - ((((uint32_t)(ARG2)) >> (ARG3)) & 0x0000FFFFUL) ) - -#define __SXTB16_RORn(ARG1, ARG2) __SXTB16(__ROR(ARG1, ARG2)) - -__STATIC_FORCEINLINE int32_t __SMMLA (int32_t op1, int32_t op2, int32_t op3) -{ - int32_t result; - - __ASM volatile ("smmla %0, %1, %2, %3" : "=r" (result): "r" (op1), "r" (op2), "r" (op3) ); - return(result); -} - -#endif /* (__ARM_FEATURE_DSP == 1) */ -/*@} end of group CMSIS_SIMD_intrinsics */ - - -#endif /* __CMSIS_ARMCLANG_H */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_compiler.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_compiler.h deleted file mode 100644 index adbf296f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/cmsis_compiler.h +++ /dev/null @@ -1,283 +0,0 @@ -/**************************************************************************//** - * @file cmsis_compiler.h - * @brief CMSIS compiler generic header file - * @version V5.1.0 - * @date 09. October 2018 - ******************************************************************************/ -/* - * Copyright (c) 2009-2018 Arm Limited. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __CMSIS_COMPILER_H -#define __CMSIS_COMPILER_H - -#include - -/* - * Arm Compiler 4/5 - */ -#if defined ( __CC_ARM ) - #include "cmsis_armcc.h" - - -/* - * Arm Compiler 6.6 LTM (armclang) - */ -#elif defined (__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050) && (__ARMCC_VERSION < 6100100) - #include "cmsis_armclang_ltm.h" - - /* - * Arm Compiler above 6.10.1 (armclang) - */ -#elif defined (__ARMCC_VERSION) && (__ARMCC_VERSION >= 6100100) - #include "cmsis_armclang.h" - - -/* - * GNU Compiler - */ -#elif defined ( __GNUC__ ) - #include "cmsis_gcc.h" - - -/* - * IAR Compiler - */ -#elif defined ( __ICCARM__ ) - #include - - -/* - * TI Arm Compiler - */ -#elif defined ( __TI_ARM__ ) - #include - - #ifndef __ASM - #define __ASM __asm - #endif - #ifndef __INLINE - #define __INLINE inline - #endif - #ifndef __STATIC_INLINE - #define __STATIC_INLINE static inline - #endif - #ifndef __STATIC_FORCEINLINE - #define __STATIC_FORCEINLINE __STATIC_INLINE - #endif - #ifndef __NO_RETURN - #define __NO_RETURN __attribute__((noreturn)) - #endif - #ifndef __USED - #define __USED __attribute__((used)) - #endif - #ifndef __WEAK - #define __WEAK __attribute__((weak)) - #endif - #ifndef __PACKED - #define __PACKED __attribute__((packed)) - #endif - #ifndef __PACKED_STRUCT - #define __PACKED_STRUCT struct __attribute__((packed)) - #endif - #ifndef __PACKED_UNION - #define __PACKED_UNION union __attribute__((packed)) - #endif - #ifndef __UNALIGNED_UINT32 /* deprecated */ - struct __attribute__((packed)) T_UINT32 { uint32_t v; }; - #define __UNALIGNED_UINT32(x) (((struct T_UINT32 *)(x))->v) - #endif - #ifndef __UNALIGNED_UINT16_WRITE - __PACKED_STRUCT T_UINT16_WRITE { uint16_t v; }; - #define __UNALIGNED_UINT16_WRITE(addr, val) (void)((((struct T_UINT16_WRITE *)(void*)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT16_READ - __PACKED_STRUCT T_UINT16_READ { uint16_t v; }; - #define __UNALIGNED_UINT16_READ(addr) (((const struct T_UINT16_READ *)(const void *)(addr))->v) - #endif - #ifndef __UNALIGNED_UINT32_WRITE - __PACKED_STRUCT T_UINT32_WRITE { uint32_t v; }; - #define __UNALIGNED_UINT32_WRITE(addr, val) (void)((((struct T_UINT32_WRITE *)(void *)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT32_READ - __PACKED_STRUCT T_UINT32_READ { uint32_t v; }; - #define __UNALIGNED_UINT32_READ(addr) (((const struct T_UINT32_READ *)(const void *)(addr))->v) - #endif - #ifndef __ALIGNED - #define __ALIGNED(x) __attribute__((aligned(x))) - #endif - #ifndef __RESTRICT - #define __RESTRICT __restrict - #endif - #ifndef __COMPILER_BARRIER - #warning No compiler specific solution for __COMPILER_BARRIER. __COMPILER_BARRIER is ignored. - #define __COMPILER_BARRIER() (void)0 - #endif - - -/* - * TASKING Compiler - */ -#elif defined ( __TASKING__ ) - /* - * The CMSIS functions have been implemented as intrinsics in the compiler. - * Please use "carm -?i" to get an up to date list of all intrinsics, - * Including the CMSIS ones. - */ - - #ifndef __ASM - #define __ASM __asm - #endif - #ifndef __INLINE - #define __INLINE inline - #endif - #ifndef __STATIC_INLINE - #define __STATIC_INLINE static inline - #endif - #ifndef __STATIC_FORCEINLINE - #define __STATIC_FORCEINLINE __STATIC_INLINE - #endif - #ifndef __NO_RETURN - #define __NO_RETURN __attribute__((noreturn)) - #endif - #ifndef __USED - #define __USED __attribute__((used)) - #endif - #ifndef __WEAK - #define __WEAK __attribute__((weak)) - #endif - #ifndef __PACKED - #define __PACKED __packed__ - #endif - #ifndef __PACKED_STRUCT - #define __PACKED_STRUCT struct __packed__ - #endif - #ifndef __PACKED_UNION - #define __PACKED_UNION union __packed__ - #endif - #ifndef __UNALIGNED_UINT32 /* deprecated */ - struct __packed__ T_UINT32 { uint32_t v; }; - #define __UNALIGNED_UINT32(x) (((struct T_UINT32 *)(x))->v) - #endif - #ifndef __UNALIGNED_UINT16_WRITE - __PACKED_STRUCT T_UINT16_WRITE { uint16_t v; }; - #define __UNALIGNED_UINT16_WRITE(addr, val) (void)((((struct T_UINT16_WRITE *)(void *)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT16_READ - __PACKED_STRUCT T_UINT16_READ { uint16_t v; }; - #define __UNALIGNED_UINT16_READ(addr) (((const struct T_UINT16_READ *)(const void *)(addr))->v) - #endif - #ifndef __UNALIGNED_UINT32_WRITE - __PACKED_STRUCT T_UINT32_WRITE { uint32_t v; }; - #define __UNALIGNED_UINT32_WRITE(addr, val) (void)((((struct T_UINT32_WRITE *)(void *)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT32_READ - __PACKED_STRUCT T_UINT32_READ { uint32_t v; }; - #define __UNALIGNED_UINT32_READ(addr) (((const struct T_UINT32_READ *)(const void *)(addr))->v) - #endif - #ifndef __ALIGNED - #define __ALIGNED(x) __align(x) - #endif - #ifndef __RESTRICT - #warning No compiler specific solution for __RESTRICT. __RESTRICT is ignored. - #define __RESTRICT - #endif - #ifndef __COMPILER_BARRIER - #warning No compiler specific solution for __COMPILER_BARRIER. __COMPILER_BARRIER is ignored. - #define __COMPILER_BARRIER() (void)0 - #endif - - -/* - * COSMIC Compiler - */ -#elif defined ( __CSMC__ ) - #include - - #ifndef __ASM - #define __ASM _asm - #endif - #ifndef __INLINE - #define __INLINE inline - #endif - #ifndef __STATIC_INLINE - #define __STATIC_INLINE static inline - #endif - #ifndef __STATIC_FORCEINLINE - #define __STATIC_FORCEINLINE __STATIC_INLINE - #endif - #ifndef __NO_RETURN - // NO RETURN is automatically detected hence no warning here - #define __NO_RETURN - #endif - #ifndef __USED - #warning No compiler specific solution for __USED. __USED is ignored. - #define __USED - #endif - #ifndef __WEAK - #define __WEAK __weak - #endif - #ifndef __PACKED - #define __PACKED @packed - #endif - #ifndef __PACKED_STRUCT - #define __PACKED_STRUCT @packed struct - #endif - #ifndef __PACKED_UNION - #define __PACKED_UNION @packed union - #endif - #ifndef __UNALIGNED_UINT32 /* deprecated */ - @packed struct T_UINT32 { uint32_t v; }; - #define __UNALIGNED_UINT32(x) (((struct T_UINT32 *)(x))->v) - #endif - #ifndef __UNALIGNED_UINT16_WRITE - __PACKED_STRUCT T_UINT16_WRITE { uint16_t v; }; - #define __UNALIGNED_UINT16_WRITE(addr, val) (void)((((struct T_UINT16_WRITE *)(void *)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT16_READ - __PACKED_STRUCT T_UINT16_READ { uint16_t v; }; - #define __UNALIGNED_UINT16_READ(addr) (((const struct T_UINT16_READ *)(const void *)(addr))->v) - #endif - #ifndef __UNALIGNED_UINT32_WRITE - __PACKED_STRUCT T_UINT32_WRITE { uint32_t v; }; - #define __UNALIGNED_UINT32_WRITE(addr, val) (void)((((struct T_UINT32_WRITE *)(void *)(addr))->v) = (val)) - #endif - #ifndef __UNALIGNED_UINT32_READ - __PACKED_STRUCT T_UINT32_READ { uint32_t v; }; - #define __UNALIGNED_UINT32_READ(addr) (((const struct T_UINT32_READ *)(const void *)(addr))->v) - #endif - #ifndef __ALIGNED - #warning No compiler specific solution for __ALIGNED. __ALIGNED is ignored. - #define __ALIGNED(x) - #endif - #ifndef __RESTRICT - #warning No compiler specific solution for __RESTRICT. __RESTRICT is ignored. - #define __RESTRICT - #endif - #ifndef __COMPILER_BARRIER - #warning No compiler specific solution for __COMPILER_BARRIER. __COMPILER_BARRIER is ignored. - #define __COMPILER_BARRIER() (void)0 - #endif - - -#else - #error Unknown compiler. -#endif - - -#endif /* __CMSIS_COMPILER_H */ - diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_tables.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_tables.h deleted file mode 100644 index d6880699..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_tables.h +++ /dev/null @@ -1,56 +0,0 @@ -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_tables.h - * Description: Extern declaration for NN tables - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef _ARM_NN_TABLES_H -#define _ARM_NN_TABLES_H - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - -/** -* @brief tables for various activation functions -* -*/ - -extern const q15_t sigmoidTable_q15[256]; -extern const q7_t sigmoidTable_q7[256]; - -extern const q7_t tanhTable_q7[256]; -extern const q15_t tanhTable_q15[256]; - - /** - * @brief 2-way tables for various activation functions - * - * 2-way table, H table for value larger than 1/4 - * L table for value smaller than 1/4, H table for remaining - * We have this only for the q15_t version. It does not make - * sense to have it for q7_t type - */ -extern const q15_t sigmoidHTable_q15[192]; -extern const q15_t sigmoidLTable_q15[128]; - -#endif /* ARM_NN_TABLES_H */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_types.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_types.h deleted file mode 100644 index 4b216c93..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nn_types.h +++ /dev/null @@ -1,120 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_types.h - * Description: Public header file to contain the CMSIS-NN structs for the - * TensorFlowLite micro compliant functions - * - * $Date: April 23, 2020 - * $Revision: V.0.5.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - - -#ifndef _ARM_NN_TYPES_H -#define _ARM_NN_TYPES_H - -/** CMSIS-NN object to contain the width and height of a tile */ -typedef struct -{ - int32_t w; /**< Width */ - int32_t h; /**< Height */ -} cmsis_nn_tile; - -/** CMSIS-NN object used for the function context. */ -typedef struct -{ - void *buf; /**< Pointer to a buffer needed for the optimization */ - int32_t size; /**< Buffer size */ -} cmsis_nn_context; - -/** CMSIS-NN object to contain the dimensions of the tensors */ -typedef struct -{ - int32_t n; /**< Generic dimension to contain either the batch size or output channels. Please refer to the function documentation for more information */ - int32_t h; /**< Height */ - int32_t w; /**< Width */ - int32_t c; /**< Input channels */ -} cmsis_nn_dims; - -/** CMSIS-NN object for the per-channel quantization parameters */ -typedef struct -{ - int32_t *multiplier; /**< Multiplier values */ - int32_t *shift; /**< Shift values */ -} cmsis_nn_per_channel_quant_params; - -/** CMSIS-NN object for the per-tensor quantization parameters */ -typedef struct -{ - int32_t multiplier; /**< Multiplier value */ - int32_t shift; /**< Shift value */ -} cmsis_nn_per_tensor_quant_params; - -/** CMSIS-NN object for the quantized Relu activation */ -typedef struct -{ - int32_t min; /**< Min value used to clamp the result */ - int32_t max; /**< Max value used to clamp the result */ -} cmsis_nn_activation; - -/** CMSIS-NN object for the convolution layer parameters */ -typedef struct -{ - int32_t input_offset; /**< Zero value for the input tensor */ - int32_t output_offset; /**< Zero value for the output tensor */ - cmsis_nn_tile stride; - cmsis_nn_tile padding; - cmsis_nn_tile dilation; - cmsis_nn_activation activation; -} cmsis_nn_conv_params; - -/** CMSIS-NN object for Depthwise convolution layer parameters */ -typedef struct -{ - int32_t input_offset; /**< Zero value for the input tensor */ - int32_t output_offset; /**< Zero value for the output tensor */ - int32_t ch_mult; /**< Channel Multiplier. ch_mult * in_ch = out_ch */ - cmsis_nn_tile stride; - cmsis_nn_tile padding; - cmsis_nn_tile dilation; - cmsis_nn_activation activation; -} cmsis_nn_dw_conv_params; -/** CMSIS-NN object for pooling layer parameters */ -typedef struct -{ - cmsis_nn_tile stride; - cmsis_nn_tile padding; - cmsis_nn_activation activation; -} cmsis_nn_pool_params; - -/** CMSIS-NN object for Fully Connected layer parameters */ -typedef struct -{ - int32_t input_offset; /**< Zero value for the input tensor */ - int32_t filter_offset; /**< Zero value for the filter tensor */ - int32_t output_offset; /**< Zero value for the output tensor */ - cmsis_nn_activation activation; -} cmsis_nn_fc_params; - -#endif // _ARM_NN_TYPES_H - - diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnfunctions.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnfunctions.h deleted file mode 100644 index 1ddc46fc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnfunctions.h +++ /dev/null @@ -1,2068 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nnfunctions.h - * Description: Public header file for CMSIS NN Library - * - * $Date: June 11, 2020 - * $Revision: V.6.0.1 - * - * Target Processor: Cortex-M CPUs - * -------------------------------------------------------------------- */ - -/** - \mainpage CMSIS NN Software Library - * - * Introduction - * ------------ - * - * This user manual describes the CMSIS NN software library, - * a collection of efficient neural network kernels developed to maximize the - * performance and minimize the memory footprint of neural networks on Cortex-M processor cores. - * - * The library is divided into a number of functions each covering a specific category: - * - Convolution Functions - * - Activation Functions - * - Fully-connected Layer Functions - * - Pooling Functions - * - Softmax Functions - * - Basic math Functions - * - * The library has separate functions for operating on different weight and activation data - * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the - * kernels are included in the function description. The implementation details are also - * described in this paper [1]. - * - * Function Classification - * -------- - * The functions can be classified into two segments - * - Legacy functions supporting ARM's internal symmetric quantization(8 bits). - * - Functions that support TensorFlow Lite framework with symmetric quantization(8 bits). - * - * The legacy functions can be identified with their suffix of _q7 or _q15 and are no new development is done there. The article in [2] describes in detail - * how to run a network using the legacy functions. - * - * The functions supporting TensorFlow Lite framework is identified by the _s8 suffix and can be invoked from TFL micro. The functions are bit exact to - * TensorFlow Lite. Refer to the TensorFlow's documentation in [3] on how to run a TensorFlow Lite model using optimized CMSIS-NN kernels. - * - * Block Diagram - * -------- - * \image html CMSIS-NN-OVERVIEW.PNG - * - * Examples - * -------- - * - * The library ships with a number of examples which demonstrate how to use the library functions. - * - * Pre-processor Macros - * ------------ - * - * Each library project have different pre-processor macros. - * - * - ARM_MATH_DSP: - * - * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions(DSP extension). - * - * - ARM_MATH_MVEI: - * - * Define macro ARM_MATH_MVEI, If the silicon supports M-Profile Vector Extension. - - * - ARM_MATH_AUTOVECTORIZE - * Used in conjucture with ARM_MATH_MVEI to let the compiler auto vectorize for the functions that uses inline assembly. - * It does not affect functions that use C or intrinsics. - * - ARM_MATH_BIG_ENDIAN: - * - * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. This is supported only for the legacy functions i.e, functions targetted at - * TensorFlow Lite do not support big endianness. By default library builds for little endian targets. - * - * - ARM_NN_TRUNCATE: - * - * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation. - * - * Upcoming Interface Change - * -------- - * Starting from the 1.4.0 next release, CMSIS-NN will gradually switch to a new API interface to: - * - * -# have a stable API - * -# avoid passing many variables by value - * -# improve security - * -# improve validation - * -# improve code readability - * - * The upcoming API interface change will be based on "struct" and only affect the TensorFlowLite micro compliant APIs [4] (functions with _s8 suffix) - * - * Below you can find a snapshot of how the new API interface will look like (names can change) - * - * i.e. arm_convolve_1x1_s8_fast - * - * Current API interface | New API interface proposal - * ------------- | ------------- - * const q7_t *input | const cmsis_nn_context &ctx - * const uint16_t input_x | const cmsis_nn_conv_params ¶ms - * const uint16_t input_y | const cmsis_nn_dims &input_dims - * const uint16_t input_ch | const q7_t *input_data - * const uint16_t input_batches | const cmsis_nn_dims &filter_dims - * const q7_t *kernel | const q7_t *filter_data - * const uint16_t output_ch | const cmsis_nn_dims &bias_dims - * const uint16_t pad_x | const q31_t *bias_data - * const uint16_t pad_y | const cmsis_nn_dims &output_dims - * const uint16_t stride_x | q7_t *output_data - * const uint16_t stride_y |
- * const int32_t *bias |
- * q7_t *output |
- * const int32_t *output_shift |
- * const int32_t *output_mult |
- * const int32_t out_offset |
- * const int32_t input_offset |
- * const int32_t out_activation_min |
- * const int32_t out_activation_max |
- * const uint16_t output_x |
- * const uint16_t output_y |
- * q15_t *buffer_a |
- * - * Copyright Notice - * ------------ - * - * Copyright (C) 2010-2019 Arm Limited. All rights reserved. - * - * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601 - * - * [2] Converting a Neural Network for Arm Cortex-M with CMSIS-NN - * https://developer.arm.com/solutions/machine-learning-on-arm/developer-material/how-to-guides/converting-a-neural-network-for-arm-cortex-m-with-cmsis-nn/single-page - * [3] https://www.tensorflow.org/lite/microcontrollers/library - * - * [4] https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN#legacy-vs-tfl-micro-compliant-apis - */ - -/** - * @defgroup groupNN Neural Network Functions - * A collection of functions to perform basic operations for neural network layers. Functions with a _s8 suffix support - * TensorFlow Lite framework. - */ - -#ifndef _ARM_NNFUNCTIONS_H -#define _ARM_NNFUNCTIONS_H - -#include "arm_nnsupportfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_tables.h" -#include "arm_nn_types.h" - -#define USE_INTRINSIC - -//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */ - -#ifdef __cplusplus -extern "C" -{ -#endif - -/** - * @defgroup NNConv Convolution Functions - * - * Collection of convolution, depthwise convolution functions and their variants. - * - * The convolution is implemented in 2 steps: im2col and GEMM - * - * im2col is a process of converting each patch of image data into - * a column. After im2col, the convolution is computed as matrix-matrix - * multiplication. - * - * To reduce the memory footprint, the im2col is performed partially. - * Each iteration, only a few column (i.e., patches) are generated and - * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions. - * - */ - - /** - * @brief s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution. - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the implementation. - arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH if argument constraints fail. or, - * ARM_MATH_SUCCESS on successful completion. - * - */ - arm_status arm_convolve_wrapper_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for arm_convolve_wrapper_s8 - * - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * - * @return The function returns required buffer size(bytes) - * - */ - int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params* conv_params, - const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims, - const cmsis_nn_dims* output_dims); - - /** - * @brief Basic s8 convolution function - * @param[in, out] ctx Function context that contains the additional buffer if required by the implementation. - arm_convolve_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * 3. Additional memory is required for optimization. Refer to argument 'ctx' for details. - * - */ - arm_status arm_convolve_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for s8 convolution function - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions - * @return The function returns required buffer size(bytes) - * - */ - int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims); - - /** - * @brief Basic Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - */ - arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Basic Q7 convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - */ - arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Basic Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - */ - arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - - arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH if argument constraints fail. or, - * ARM_MATH_SUCCESS on successful completion. - * - * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1 - * and dim_kernel_y=1). It can be used for - * second half of MobileNets after depthwise separable convolution. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast s8 version for 1x1 convolution (non-square shape) - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the implementation. - arm_convolve_1x1_s8_fast_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH if argument constraints fail. or, - * ARM_MATH_SUCCESS on successful completion. - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# input_dims->c is a multiple of 4 - * -# conv_params->padding.w = conv_params->padding.h = 0 - * -# conv_params->stride.w = conv_params->stride.h = 1 - * - */ - arm_status arm_convolve_1x1_s8_fast(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for arm_convolve_1x1_s8_fast - * - * @param[in] input_dims Input (activation) dimensions - * @return The function returns the required buffer size in bytes - * - */ - int32_t arm_convolve_1x1_s8_fast_get_buffer_size(const cmsis_nn_dims* input_dims); - - /** - * @brief 1xn convolution - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the implementation. - arm_convolve_1_x_n_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal spatial filter dimension - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH if argument constraints fail. or, - * ARM_MATH_SUCCESS on successful completion. - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# input_dims->n equals 1 - * -# ouput_dims->w is a multiple of 4 - * -# Explicit constraints(since it is for 1xN convolution) - * -## input_dims->h equals 1 - * -## output_dims->h equals 1 - * -## filter_dims->h equals 1 - *@todo Remove constraint on output_dims->w to make the function generic. - * - */ - arm_status arm_convolve_1_x_n_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data); - - /** - * @brief Get the required additional buffer size for 1xn convolution - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal spatial filter dimension - * @return The function returns required buffer size(bytes) - * - */ - int32_t arm_convolve_1_x_n_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims); - - /** - * @brief Q7 version of convolution for RGB image - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This kernel is written exclusively for convolution with ch_im_in - * equals 3. This applies on the first layer of CNNs which has input - * image with RGB format. - */ - - arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - */ - - arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Fast Q15 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - - arm_status - arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Q7 depthwise separable convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - */ - - arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Q7 depthwise separable convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding sizes x - * @param[in] padding_y padding sizes y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - */ - arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB); - - /** - * @brief Wrapper function to pick the right optimized s8 depthwise convolution function - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if required. - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each - * output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Batch argument N is not used and assumed to be 1. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * ARM_MATH_SUCCESS - Successful completion. - * - * @details - * - Supported framework: TensorFlow Lite - * - Picks one of the the following functions - * -# arm_depthwise_conv_s8() - * -# arm_depthwise_conv_3x3_s8() - Cortex-M CPUs with DSP extension only - * -# arm_depthwise_conv_s8_opt() - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - Check details of arm_depthwise_conv_s8_opt() for potential data that can be accessed outside of the boundary. - */ - arm_status arm_depthwise_conv_wrapper_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Get size of additional buffer required by arm_depthwise_conv_wrapper_s8() - * - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->input_offset : [-128, 127] - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Batch argument N is not used and assumed to be 1. - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT] - * @return Size of additional memory required for optimizations in bytes. - * - */ - int32_t arm_depthwise_conv_wrapper_s8_get_buffer_size(const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims); - - /** - * @brief Basic s8 depthwise convolution function that doesn't have any constraints on the input dimensions. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * exists if additional memory is. - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->input_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each - * output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [1, H, W, C_IN] - * Batch argument N is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - Supported framework: TensorFlow Lite - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - */ - arm_status arm_depthwise_conv_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Optimized s8 depthwise convolution function for 3x3 kernel size with some constraints on - * the input arguments(documented below). Refer arm_depthwise_conv_s8() for function - * argument details. - * - * @return The function returns one of the following - * ARM_MATH_SIZE_MISMATCH - Unsupported dimension of tensors - * ARM_MATH_ARGUMENT_ERROR - Unsupported pad size along the x axis - * ARM_MATH_SUCCESS - Successful operation - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# Number of input channel equals number of output channels - * -# Filter height and width equals 3 - * -# Padding along x is either 0 or 1. - * - */ - arm_status arm_depthwise_conv_3x3_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel. - * Refer arm_depthwise_conv_s8() for function argument details. - * - * @return The function returns one of the following - * ARM_MATH_SIZE_MISMATCH - input channel != output channel or - * ch_mult != 1 - * ARM_MATH_SUCCESS - Successful operation - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read out - * for the following if MVE optimizations(Arm Helium Technology) are used. - * - Output shift - * - Output multiplier - * - Output bias - * - kernel - * @details - * - Supported framework: TensorFlow Lite - * - The following constrains on the arguments apply - * -# Number of input channel equals number of output channels or ch_mult equals 1 - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - Reccomended when number of channels is 4 or greater. - * - */ - arm_status arm_depthwise_conv_s8_opt(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for optimized s8 depthwise convolution - * function with constraint that in_channel equals out_channel. - * @param[in] input_dims Input (activation) tensor dimensions. Format: [1, H, W, C_IN] - * Batch argument N is not used. - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @return The function returns required buffer size in bytes - * - */ - int32_t arm_depthwise_conv_s8_opt_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims); - - /** - * @defgroup FC Fully-connected Layer Functions - * - * Collection of fully-connected and matrix multiplication functions. - * - * Fully-connected layer is basically a matrix-vector multiplication - * with bias. The matrix is the weights and the input/output vectors - * are the activation values. Supported {weight, activation} precisions - * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}. - * - * Here we have two types of kernel functions. The basic function - * implements the function using regular GEMV approach. The opt functions - * operates with weights in interleaved formats. - * - */ - - /** - * @brief Q7 basic fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_q7(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut, - q15_t * vec_buffer); - - /** - * @brief Basic s8 Fully Connected function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] fc_params Fully Connected layer parameters (e.g. strides, dilations, pads,...) - * Range of fc_params->input_offset : [-127, 128] - * Range of fc_params->filter_offset : [-127, 128] - * Range of fc_params->output_offset : [-128, 127] - * @param[in] quant_params Per-tensor quantization info. - * It contains the multiplier and shift values to be applied to the output tensor. - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * Input dimension is taken as Nx(H * W * C_IN) - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Two dimensional filter dimensions. Format: [N, C] - * N : accumulation depth and equals (H * W * C_IN) from input_dims - * C : output depth and equals C_OUT in output_dims - * H & W : Not used - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * N, H, W : Not used - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, C_OUT] - * N : Batches - * C_OUT : Output depth - * H & W : Not used. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - Supported framework: TensorFlow Lite - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - */ - arm_status - arm_fully_connected_s8(const cmsis_nn_context *ctx, - const cmsis_nn_fc_params *fc_params, - const cmsis_nn_per_tensor_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for S8 basic fully-connected and - * matrix multiplication layer function for TF Lite - * @param[in] filter_dims dimension of filter - * @return The function returns required buffer size in bytes - * - */ - int32_t arm_fully_connected_s8_get_buffer_size(const cmsis_nn_dims *filter_dims); - - /** - * @brief Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_q7_opt(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut, - q15_t * vec_buffer); - - /** - * @brief Q15 basic fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_q15(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer); - - /** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_q15_opt(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer); - - /** - * @brief Mixed Q15-Q7 fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q15_t * pOut, - q15_t * vec_buffer); - - /** - * @brief Mixed Q15-Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - */ - - arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q15_t * pOut, - q15_t * vec_buffer); - -/** - * @brief Matrix-Multiplication Kernels for Convolution - * - * These functions are used within convolution layer functions for - * matrix multiplication. - * - * The implementation is similar to CMSIS-DSP arm_mat_mult functions - * with one Q7 and one Q15 operands. The Q15 operand is the im2col - * output which is always with 2 columns. - * - */ - - /** - * @brief Matrix-multiplication function for convolution - * @param[in] pA pointer to operand A - * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors - * @param[in] ch_im_out numRow of A - * @param[in] numCol_A numCol of A - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias the bias - * @param[in,out] pOut pointer to output - * @return The function returns the incremented output pointer - */ - - q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut); - /** - * @brief Matrix-multiplication function for convolution with per-channel requantization. - * @param[in] input_a pointer to operand A - * @param[in] input_b pointer to operand B, always consists of 2 vectors. - * @param[in] output_ch number of rows of A - * @param[in] out_shift pointer to per output channel requantization shift parameter. - * @param[in] out_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] activation_min minimum value to clamp the output to. Range : int8 - * @param[in] activation_max maximum value to clamp the output to. Range : int8 - * @param[in] num_col_a number of columns of A - * @param[in] output_bias per output channel bias. Range : int32 - * @param[in,out] out_0 pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details This function does the matrix multiplication of weight matrix for all output channels - * with 2 columns from im2col and produces two elements/output_channel. The outputs are - * clamped in the range provided by activation min and max. - * Supported framework: TensorFlow Lite micro. - */ - q7_t *arm_nn_mat_mult_kernel_s8_s16(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0); - - /** - * @brief Matrix-multiplication of re-ordered input B with A. - * - * @details For arguments, refer arm_nn_mat_mult_kernel_s8_s16. The re-ordering is a consequence - * of sign extension done by the SXTB16 command on input_b. The outputs are clamped in the range - * provided by activation min and max. - * * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# num_col_a is a multiple of 4 - * -# output_ch is a multiple of 2 - * - */ - q7_t *arm_nn_mat_mult_kernel_s8_s16_reordered(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0); - - /** - * @brief Matrix-multiplication function for convolution with reordered columns - * @param[in] pA pointer to operand A - * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors - * @param[in] ch_im_out numRow of A - * @param[in] numCol_A numCol of A - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias the bias - * @param[in,out] pOut pointer to output - * @return The function returns the incremented output pointer - * - * @details This function assumes that data in pInBuffer are reordered - */ - q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut); - -#ifdef __cplusplus -} -#endif - -/* - * Other functions - * These layers are typically not timing critical - * Basic implementation is supported here - */ - -#ifdef __cplusplus -extern "C" -{ -#endif - -/** - * @defgroup BasicMath Basic math functions - * - * Element wise add and multiplication functions. - * - */ - -/** - * @brief s8 element wise add of two vectors - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Range: Range: -127 to 128 - * @param[in] input_1_mult multiplier for input 1 - * @param[in] input_1_shift shift for input 1 - * @param[in] input_2_offset offset for input 2. Range: Range: -127 to 128 - * @param[in] input_2_mult multiplier for input 2 - * @param[in] input_2_shift shift for input 2 - * @param[in] left_shift input left shift - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to - * @param[in] out_activation_max maximum value to clamp output to - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - */ - arm_status arm_elementwise_add_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_1_mult, - const int32_t input_1_shift, - const int32_t input_2_offset, - const int32_t input_2_mult, - const int32_t input_2_shift, - const int32_t left_shift, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size); - -/** - * @brief s8 element wise multiplication - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Range: Range: -127 to 128 - * @param[in] input_2_offset offset for input 2. Range: Range: -127 to 128 - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to - * @param[in] out_activation_max maximum value to clamp output to - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - * - * @details Supported framework: TensorFlow Lite micro - */ - arm_status arm_elementwise_mul_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_2_offset, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size); -/** - * @defgroup Acti Activation Functions - * - * Perform activation layers, including ReLU (Rectified Linear Unit), - * sigmoid and tanh - * - */ - - /** - * @brief Q7 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @return none. - */ - - void arm_relu_q7(q7_t *data, uint16_t size); - - /** - * @brief s8 ReLU6 function - * @param[in,out] data pointer to input - * @param[in] size number of elements - */ - - void arm_relu6_s8(q7_t *data, uint16_t size); - - /** - * @brief Q15 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @return none. - */ - - void arm_relu_q15(q15_t *data, uint16_t size); - - /** - * @brief Q7 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * @return none. - */ - - void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, - arm_nn_activation_type type); - - /** - * @brief Q15 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * @return none. - * - * @details - * - * This is the direct table look-up approach. - * - * Assume here the integer part of the fixed-point is <= 3. - * More than 3 just not making much sense, makes no difference with - * saturation followed by any of these activation functions. - */ - - void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, - arm_nn_activation_type type); - -/** - * @defgroup Pooling Pooling Functions - * - * Perform pooling functions, including max pooling and average pooling - * - */ - - /** - * @brief Q7 max pooling function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * @return none. - * - */ - - void arm_maxpool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const uint16_t dim_im_out, - q7_t * bufferA, - q7_t * Im_out); - - /** - * @brief Q7 average pooling function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * @return none. - * - */ - - void arm_avepool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const uint16_t dim_im_out, - q7_t * bufferA, - q7_t * Im_out); - - /** - * @brief s8 average pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * ARM_MATH_SUCCESS - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ - arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - - /** - * @brief Get the required buffer size for S8 average pooling function - * @param[in] dim_dst_width output tensor dimension - * @param[in] ch_src number of input tensor channels - * @return The function returns required buffer size in bytes - * - */ - int32_t arm_avgpool_s8_get_buffer_size(const int dim_dst_width, - const int ch_src); - - /** - * @brief s8 max pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * ARM_MATH_SUCCESS - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ - arm_status arm_max_pool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *output_data); -/** - * @defgroup Softmax Softmax Functions - * - * EXP(2) based softmax functions. - * - */ - - /** - * @brief Q7 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out); - - /** - * @brief Q7 softmax function with batch parameter - * @param[in] vec_in pointer to input vector - * @param[in] nb_batches number of batches - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * @return none. - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_with_batch_q7(const q7_t * vec_in, const uint16_t nb_batches,const uint16_t dim_vec, q7_t * p_out ); - /** - * @brief Q15 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * @return none. - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out); - - /** - * @brief S8 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ - -void arm_softmax_s8(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - int8_t *output); - - /** - * @brief U8 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ - -void arm_softmax_u8(const uint8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - uint8_t *output); - - /** - * @brief uint8 depthwise convolution function with asymmetric quantization - * Unless specified otherwise, arguments are mandatory. - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_ch Channels in input tensor - * @param[in] kernel Pointer to kernel weights - * @param[in] kernel_x Width of kernel - * @param[in] kernel_y Height of kernel - * @param[in] ch_mult Number of channel multiplier - * @param[in] pad_x Padding sizes x - * @param[in] pad_y Padding sizes y - * @param[in] stride_x stride along the width - * @param[in] stride_y stride along the height - * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement. - * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement. - * @param[in] bias Pointer to optional bias values. If no bias is - * availble, NULL is expected - * @param[in] input_offset Input tensor zero offset - * @param[in] filter_offset Kernel tensor zero offset - * @param[in] output_offset Output tensor zero offset - * @param[in,out] output Pointer to output tensor - * @param[in] output_x Width of output tensor - * @param[in] output_y Height of output tensor - * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255} - * @param[in] out_shift Amount of right-shift for output - * @param[in] out_mult Output multiplier for requantization - * @return The function returns the following - * ARM_MATH_SUCCESS - Successful operation - * - */ - arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const uint8_t *kernel, - const uint16_t kernel_x, - const uint16_t kernel_y, - const int16_t ch_mult, - const int16_t pad_x, - const int16_t pad_y, - const int16_t stride_x, - const int16_t stride_y, - const int16_t dilation_x, - const int16_t dilation_y, - const int32_t *bias, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_offset, - uint8_t *output, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_activation_min, - const int32_t output_activation_max, - const int32_t out_shift, - const int32_t out_mult); - -/** - * @defgroup Reshape Reshape Functions - * - */ - - /** - * @brief Reshape a s8 vector into another with different shape - * @param[in] input points to the s8 input vector - * @param[out] output points to the s8 output vector - * @param[in] total_size total size of the input and output vectors in bytes - * - * @note The output is expected to be in a memory area that does not overlap with the input's - * - */ - void arm_reshape_s8(const int8_t *input, - int8_t *output, - const uint32_t total_size); - -/** - * @defgroup Concatenation Concatenation Functions - * - */ - - /** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis - * This function should be called for each input tensor to concatenate. The argument offset_x - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_x = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_x(&input[i], ..., &output, ..., ..., offset_x) - * offset_x += input_x[i] - * } - * - * This function assumes that the output tensor has: - * -# The same height of the input tensor - * -# The same number of channels of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor - * @param[in] output_x Width of output tensor - * @param[in] offset_x The offset (in number of elements) on the X axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * Input constraints - * offset_x is less than output_x - * - */ - void arm_concatenation_s8_x(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_x, - const uint32_t offset_x); - - /** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis - * This function should be called for each input tensor to concatenate. The argument offset_y - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_y = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_y(&input[i], ..., &output, ..., ..., offset_y) - * offset_y += input_y[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same number of channels of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor - * @param[in] output_y Height of output tensor - * @param[in] offset_y The offset on the Y axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * Input constraints - * offset_y is less than output_y - * - */ - void arm_concatenation_s8_y(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_y, - const uint32_t offset_y); - - /** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis - * This function should be called for each input tensor to concatenate. The argument offset_z - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_z = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_z(&input[i], ..., &output, ..., ..., offset_z) - * offset_z += input_z[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same height of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor - * @param[in] output_z Channels in output tensor - * @param[in] offset_z The offset on the Z axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * Input constraints - * offset_z is less than output_z - * - */ - void arm_concatenation_s8_z(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_z, - const uint32_t offset_z); - - /** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) - * This function should be called for each input tensor to concatenate. The argument offset_w - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_w = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_w(&input[i], ..., &output, ..., ..., offset_w) - * offset_w += input_w[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same height of the input tensor - * -# The same number o channels of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor - * @param[in] offset_w The offset on the W axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - */ - void arm_concatenation_s8_w(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint32_t offset_w); -#ifdef __cplusplus -} -#endif - -#endif diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h deleted file mode 100644 index c14f382b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h +++ /dev/null @@ -1,905 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nnsupportfunctions.h - * Description: Public header file of support functions for CMSIS NN Library - * - * $Date: May 11, 2020 - * $Revision: V.4.0.4 - * - * Target Processor: Cortex-M CPUs - * -------------------------------------------------------------------- */ - -#ifndef _ARM_NNSUPPORTFUNCTIONS_H_ -#define _ARM_NNSUPPORTFUNCTIONS_H_ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" - -#ifdef __cplusplus -extern "C" -{ -#endif - -#define LEFT_SHIFT(_shift) (_shift > 0 ? _shift : 0) -#define RIGHT_SHIFT(_shift) (_shift > 0 ? 0 : -_shift) -#define MASK_IF_ZERO(x) (x) == 0 ? ~0 : 0 -#define MASK_IF_NON_ZERO(x) (x) != 0 ? ~0 : 0 -#define SELECT_USING_MASK(mask, a, b) ((mask) & (a)) ^ (~(mask) & (b)) - -#define MAX(A,B) ((A) > (B) ? (A) : (B)) -#define MIN(A,B) ((A) < (B) ? (A) : (B)) -#define CLAMP(x, h, l) MAX(MIN((x), (h)), (l)) - -/** - * @brief Union for SIMD access of q31/q15/q7 types - */ -union arm_nnword -{ - q31_t word; - /**< q31 type */ - q15_t half_words[2]; - /**< q15 type */ - q7_t bytes[4]; - /**< q7 type */ -}; - -/** - * @brief Struct for specifying activation function types - * - */ -typedef enum -{ - ARM_SIGMOID = 0, - /**< Sigmoid activation function */ - ARM_TANH = 1, - /**< Tanh activation function */ -} arm_nn_activation_type; - -/** - * @defgroup nndata_convert Neural Network Data Conversion Functions - * - * Perform data type conversion in-between neural network operations - * - */ - -/** - * @brief Converts the elements of the q7 vector to q15 vector without left-shift - * @param[in] *pSrc points to the q7 input vector - * @param[out] *pDst points to the q15 output vector - * @param[in] blockSize length of the input vector - * - */ -void arm_q7_to_q15_no_shift(const q7_t *pSrc, q15_t *pDst, uint32_t blockSize); - -/** - * @brief Non-saturating addition of elements of a q7 vector - * @param[in] *input Pointer to the q7 input vector - * @param[out] *output Pointer to the q31 output variable. - * @param[in] block_size length of the input vector - * \par Description: - * - * 2^24 samples can be added without saturating the result. - * - * The equation used for the conversion process is: - * - * 
- *  sum = input[0] + input[1] + .. + input[block_size -1]
- *
- * - * */ -void arm_nn_add_q7(const q7_t *input, q31_t *output, uint32_t block_size); - -/** - * @brief Converts the elements of the q7 vector to reordered q15 vector without left-shift - * @param[in] *pSrc points to the q7 input vector - * @param[out] *pDst points to the q15 output vector - * @param[in] blockSize length of the input vector - * @return none. - * - */ -void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize); - -/** - * @brief Converts the elements from a q7 vector to a q15 vector with an added offset - * @param[in] src pointer to the q7 input vector - * @param[out] dst pointer to the q15 output vector - * @param[in] block_size length of the input vector - * @param[in] offset q7 offset to be added to each input vector element. - * - * \par Description: - * - * The equation used for the conversion process is: - * - * 
- *  dst[n] = (q15_t) src[n] + offset;   0 <= n < block_size.
- *
- * - */ -void arm_q7_to_q15_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset); - -/** - * @brief Converts the elements of the q7 vector to reordered q15 vector with an added offset - * @param[in] src pointer to the q7 input vector - * @param[out] dst pointer to the q15 output vector - * @param[in] block_size length of the input vector - * @param[in] offset offset to be added to each input vector element. - * @return none. - * - * @details This function does the q7 to q15 expansion with re-ordering of bytes. Re-ordering is a consequence of - * the sign extension intrinsic(DSP extension). The tail (i.e., last (N % 4) elements) retains its original - * order. - * - */ -void arm_q7_to_q15_reordered_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset); - -/** - * @brief Converts the elements from a q7 vector and accumulate to a q15 vector - * @param[in] *src points to the q7 input vector - * @param[out] *dst points to the q15 output vector - * @param[in] block_size length of the input vector - * - * \par Description: - * - * The equation used for the conversion process is: - * - * 
- *  dst[n] += (q15_t) src[n] ;   0 <= n < block_size.
- *
- * - */ -void arm_nn_accumulate_q7_to_q15(q15_t *dst, const q7_t *src, uint32_t block_size); - -/** - * @brief Depthwise conv on an im2col buffer where the input channel equals output channel. - * @param[in] row pointer to row - * @param[in] col pointer to im2col buffer, always consists of 2 columns. - * @param[in] num_ch number of channels - * @param[in] out_shift pointer to per output channel requantization shift parameter. - * @param[in] out_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] activation_min minimum value to clamp the output to. Range : int8 - * @param[in] activation_max maximum value to clamp the output to. Range : int8 - * @param[in] kernel_size number of elements in one column. - * @param[in] output_bias per output channel bias. Range : int32 - * @param[out] out pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details Supported framework: TensorFlow Lite micro. - */ -q7_t *arm_nn_depthwise_conv_s8_core(const q7_t *row, - const q15_t *col, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t kernel_size, - const int32_t *const output_bias, - q7_t *out); - -/** - * @brief General Matrix-multiplication function with per-channel requantization. - * @param[in] input_row pointer to row operand - * @param[in] input_col pointer to col operand - * @param[in] output_ch number of rows of input_row - * @param[in] col_batches number of column batches. Range: 1 to 4 - * @param[in] output_shift pointer to per output channel requantization shift parameter. - * @param[in] output_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] col_offset input tensor(col) offset. - * @param[in] row_offset kernel offset(row). Not used. - * @param[in] out_activation_min minimum value to clamp the output to. Range : int8 - * @param[in] out_activation_max maximum value to clamp the output to. Range : int8 - * @param[in] row_len number of elements in each row - * @param[in] bias per output channel bias. Range : int32 - * @param[in,out] out pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details Supported framework: TensorFlow Lite -*/ -q7_t *arm_nn_mat_mult_s8(const q7_t *input_row, - const q7_t *input_col, - const uint16_t output_ch, - const uint16_t col_batches, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t out_offset, - const int32_t col_offset, - const int32_t row_offset, - const int16_t out_activation_min, - const int16_t out_activation_max, - const uint16_t row_len, - const int32_t *const bias, - q7_t *out); - -/** - * @brief General Matrix-multiplication without requantization for one row & one column - * @param[in] row_elements number of row elements - * @param[in] row_base pointer to row operand - * @param[in] col_base pointer to col operand - * @param[out] sum_col pointer to store sum of column elements - * @param[out] output pointer to store result of multiply-accumulate - * @return The function returns the multiply-accumulated result of the row by column. - * - * @details Pseudo-code - * *output = 0 - * sum_col = 0 - * for (i = 0; i < row_elements; i++) - * *output += row_base[i] * col_base[i] - * sum_col += col_base[i] - * -*/ -arm_status arm_nn_mat_mul_core_1x_s8(int32_t row_elements, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output); - -/** - * @brief General Matrix-multiplication without requantization for four rows and one column - * @param[in] row_elements number of row elements - * @param[in] offset offset between rows. Can be the same as row_elements. - * For e.g, in a 1x1 conv scenario with stride as 1. - * @param[in] row_base pointer to row operand - * @param[in] col_base pointer to col operand - * @param[out] sum_col pointer to store sum of column elements - * @param[out] output pointer to store result(4 int32's) of multiply-accumulate - * @return The function returns the multiply-accumulated result of the row by column - * - * @details Pseudo-code - * output[0] = 0 - * .. - * output[3] = 0 - * sum_col = 0 - * for (i = 0; i < row_elements; i++) - * output[0] += row_base[i] * col_base[i] - * .. - * output[3] += row_base[i + (row_elements * 3)] * col_base[i] - * sum_col += col_base[i] -*/ -arm_status arm_nn_mat_mul_core_4x_s8(const int32_t row_elements, - const int32_t offset, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output); - -/** -* @brief General Matrix-multiplication function with per-channel requantization. -* This function assumes: -* - LHS input matrix NOT transposed (nt) -* - RHS input matrix transposed (t) -* -* @note This operation also performs the broadcast bias addition before the requantization -* -* @param[in] lhs Pointer to the LHS input matrix -* @param[in] rhs Pointer to the RHS input matrix -* @param[in] bias Pointer to the bias vector. The length of this vector is equal to the number of output columns (or RHS input rows) -* @param[out] dst Pointer to the output matrix with "m" rows and "n" columns -* @param[in] dst_multipliers Pointer to the multipliers vector needed for the per-channel requantization. The length of this vector is equal to -* the number of output columns (or RHS input rows) -* @param[in] dst_shifts Pointer to the shifts vector needed for the per-channel requantization. The length of this vector is equal to -* the number of output columns (or RHS input rows) -* @param[in] lhs_rows Number of LHS input rows -* @param[in] rhs_rows Number of RHS input rows -* @param[in] rhs_cols Number of LHS/RHS input columns -* @param[in] lhs_offset Offset to be applied to the LHS input value -* @param[in] dst_offset Offset to be applied the output result -* @param[in] activation_min Minimum value to clamp down the output. Range : int8 -* @param[in] activation_max Maximum value to clamp up the output. Range : int8 -* -* @return The function returns ARM_MATH_SUCCESS -* -*/ -arm_status arm_nn_mat_mult_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t *dst_multipliers, - const int32_t *dst_shifts, - const int32_t lhs_rows, - const int32_t rhs_rows, - const int32_t rhs_cols, - const int32_t lhs_offset, - const int32_t dst_offset, - const int32_t activation_min, - const int32_t activation_max); - -/** - * @brief s8 Vector by Matrix (transposed) multiplication - * - * @param[in] lhs Input left-hand side vector - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] bias Input bias - * @param[out] dst Output vector - * @param[in] lhs_offset Offset to be added to the input values of the left-hand side vector. Range: -127 to 128 - * @param[in] rhs_offset Offset to be added to the input values of the right-hand side matrix. Range: -127 to 128 - * @param[in] dst_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] dst_multiplier Output multiplier - * @param[in] dst_shift Output shift - * @param[in] rhs_cols Number of columns in the right-hand side input matrix - * @param[in] rhs_rows Number of rows in the right-hand side input matrix - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * - * @return The function returns ARM_MATH_SUCCESS - * - */ -arm_status arm_nn_vec_mat_mult_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t lhs_offset, - const int32_t rhs_offset, - const int32_t dst_offset, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max); - -/** - * @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in padded cases where - * the padding is -lhs_offset(Range: int8). Dimensions are the same for lhs and rhs. - * - * @param[in] lhs Input left-hand side matrix - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128 - * @param[in] num_ch Number of channels in LHS/RHS - * @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels - * @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels - * @param[in] out_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix - * @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels - * @param[in] out Output pointer - * - * @return The function returns one of the two - * - Updated output pointer if an implementaiton is available - * - NULL if no implementation is available. - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read out - * for the following. - * - Output shift - * - Output multiplier - * - Output bias - * - rhs - */ -q7_t *arm_nn_depthwise_conv_nt_t_padded_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t lhs_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out); - -/** - * @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in non-padded cases. - * Dimensions are the same for lhs and rhs. - * - * @param[in] lhs Input left-hand side matrix - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128 - * @param[in] num_ch Number of channels in LHS/RHS - * @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels. - * @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels. - * @param[in] out_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix - * @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels. - * @param[in] out Output pointer - * - * @return The function returns one of the two - * - Updated output pointer if an implementaiton is available - * - NULL if no implementation is available. - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read out - * for the following. - * - Output shift - * - Output multiplier - * - Output bias - * - rhs - */ -q7_t *arm_nn_depthwise_conv_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t lhs_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out); - -/** - @brief Read 2 q15 elements and post increment pointer. - @param[in] in_q15 Pointer to pointer that holds address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2_ia(const q15_t **in_q15) -{ - q31_t val; - - memcpy(&val, *in_q15, 4); - *in_q15 += 2; - - return (val); -} - -/** - @brief Read 4 q7 from q7 pointer and post increment pointer. - @param[in] in_q7 Pointer to pointer that holds address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4_ia(const q7_t **in_q7) -{ - q31_t val; - memcpy(&val, *in_q7, 4); - *in_q7 += 4; - - return (val); -} - -/** - @brief Read 2 q15 from q15 pointer. - @param[in] in_q15 pointer to address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2(const q15_t *in_q15) -{ - q31_t val; - memcpy(&val, in_q15, 4); - - return (val); -} - -/** - @brief Read 4 q7 values. - @param[in] in_q7 pointer to address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4(const q7_t *in_q7) -{ - q31_t val; - memcpy(&val, in_q7, 4); - - return (val); -} - -/** - * @brief memset optimized for MVE - * @param[in, out] dst Destination pointer - * @param[in] val Value to set - * @param[in] block_size Number of bytes to copy. - * - */ -__STATIC_FORCEINLINE void arm_memset_q7(q7_t *dst, - const q7_t val, - uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - __asm volatile ( - " vdup.8 q0, %[set_val] \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vstrb.8 q0, [%[in]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[in] "+r"(dst) - :[cnt] "r"(block_size), [set_val] "r"(val) - :"q0", "memory", "r14"); -#else - memset(dst, val, block_size); -#endif -} - -#if defined (ARM_MATH_DSP) - -/** - * @brief read and expand one q7 word into two q15 words - */ - -__STATIC_FORCEINLINE const q7_t *read_and_pad(const q7_t *source, q31_t * out1, q31_t * out2) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); - q31_t inAbuf1 = __SXTB16(__ROR((uint32_t)inA, 8)); - q31_t inAbuf2 = __SXTB16(inA); - -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = (int32_t) (__PKHTB (inAbuf1, inAbuf2, 16)); - *out1 = (int32_t) (__PKHBT (inAbuf2, inAbuf1, 16)); -#else - *out1 = (int32_t) (__PKHTB(inAbuf1, inAbuf2, 16)); - *out2 = (int32_t) (__PKHBT(inAbuf2, inAbuf1, 16)); -#endif - - return source; -} - -/** - * @brief read and expand one q7 word into two q15 words with reordering - */ - -__STATIC_FORCEINLINE const q7_t *read_and_pad_reordered(const q7_t *source, q31_t * out1, q31_t * out2) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out1 = __SXTB16(inA); -#else - *out1 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out2 = __SXTB16(inA); -#endif - - return source; -} - -/** - * @brief read and expand one q7 word into two q15 words with reordering and add an offset - */ -__STATIC_FORCEINLINE const q7_t *read_and_pad_reordered_with_offset(const q7_t *source, q31_t * out1, q31_t * out2, q31_t offset) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); - -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out1 = __SXTB16(inA); -#else - *out1 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out2 = __SXTB16(inA); -#endif - *out1 = __QADD16(*out1,offset); - *out2 = __QADD16(*out2,offset); - - return source; -} - -#endif - - - -/** - * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation - * - * Basic Math Functions for Neural Network Computation - * - */ - -/** - * @brief q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * @return none. - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable q15 range [0x8000 0x7FFF] will be saturated. - */ - -void arm_nn_mult_q15( - q15_t * pSrcA, - q15_t * pSrcB, - q15_t * pDst, - const uint16_t out_shift, - uint32_t blockSize); - -/** - * @brief q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * @return none. - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable q7 range [0x80 0x7F] will be saturated. - */ - -void arm_nn_mult_q7( - q7_t * pSrcA, - q7_t * pSrcB, - q7_t * pDst, - const uint16_t out_shift, - uint32_t blockSize); - -/** - * @brief macro for adding rounding offset - */ -#ifndef ARM_NN_TRUNCATE - #define NN_ROUND(out_shift) ( (0x1u << out_shift) >> 1 ) -#else - #define NN_ROUND(out_shift) 0 -#endif - -// Macros for shortening quantization functions' names and avoid long lines -#define MUL_SAT(a, b) arm_nn_sat_doubling_high_mult((a), (b)) -#define MUL_SAT_MVE(a, b) arm_sat_doubling_high_mult_mve_32x4((a), (b)) -#define MUL_POW2(a, b) arm_nn_mult_by_power_of_two((a), (b)) - - -#define DIV_POW2(a, b) arm_nn_divide_by_power_of_two((a), (b)) -#define DIV_POW2_MVE(a, b) arm_divide_by_power_of_two_mve((a), (b)) - - -#define EXP_ON_NEG(x) arm_nn_exp_on_negative_values((x)) -#define ONE_OVER1(x) arm_nn_one_over_one_plus_x_for_x_in_0_1((x)) - -/** - * @brief Saturating doubling high multiply. Result matches - * NEON instruction VQRDMULH. - * @param[in] m1 Multiplicand - * @param[in] m2 Multiplier - * @return Result of multiplication. - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_sat_doubling_high_mult(const q31_t m1, const q31_t m2) -{ - q31_t result = 0; - // Rounding offset to add for a right shift of 31 - q63_t mult = 1 << 30; - - if ((m1 < 0) ^ (m2 < 0)) - { - mult = 1 - mult; - } - // Gets resolved as a SMLAL instruction - mult = mult + (q63_t)m1 * m2; - - // Utilize all of the upper 32 bits. This is the doubling step - // as well. - result = (int32_t) (mult / (1ll << 31)); - - if ((m1 == m2) && (m1 == (int32_t)Q31_MIN)) - { - result = Q31_MAX; - } - return result; -} - -/** - * @brief Rounding divide by power of two. - * @param[in] dividend - Dividend - * @param[in] exponent - Divisor = power(2, exponent) - * Range: [0, 31] - * @return Rounded result of division. Midpoint is rounded away from zero. - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_divide_by_power_of_two(const q31_t dividend, const q31_t exponent) -{ - q31_t result = 0; - const q31_t remainder_mask = (1 << exponent) - 1; - int32_t remainder = remainder_mask & dividend; - - // Basic division - result = dividend >> exponent; - - // Adjust 'result' for rounding (mid point away from zero) - q31_t threshold = remainder_mask >> 1; - if (result < 0) - { - threshold++; - } - if (remainder > threshold) - { - result++; - } - - return result; -} - -/** - * @brief Requantize a given value. - * @param[in] val Value to be requantized - * @param[in] multiplier multiplier - * @param[in] shift left or right shift for 'val * multiplier' - * - * @return Returns (val * multiplier)/(2 ^ shift) - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_requantize(const q31_t val, const q31_t multiplier, const q31_t shift) -{ - return arm_nn_divide_by_power_of_two(arm_nn_sat_doubling_high_mult(val * (1 << LEFT_SHIFT(shift)), multiplier), - RIGHT_SHIFT(shift)); -} - -/** - * @brief memcpy optimized for MVE - * @param[in, out] dst Destination pointer - * @param[in] src Source pointer. - * @param[in] block_size Number of bytes to copy. - * - */ -__STATIC_FORCEINLINE void arm_memcpy_q7(q7_t *__RESTRICT dst, - const q7_t *__RESTRICT src, - uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - __asm volatile ( - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vldrb.8 q0, [%[in]], 16 \n" - " vstrb.8 q0, [%[out]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[in] "+r"(src) - ,[out] "+r"(dst) - :[cnt] "r"(block_size) - :"q0", "memory", "r14"); -#else - memcpy(dst, src, block_size); -#endif -} - -#if defined(ARM_MATH_MVEI) -/** - * @brief Vector saturating doubling high multiply returning high half. - * @param[in] m1 Multiplicand - * @param[in] m2 Multiplier - * @return Result of multiplication. - * - */ -__STATIC_FORCEINLINE int32x4_t arm_sat_doubling_high_mult_mve(const int32x4_t m1, const q31_t m2) -{ - return vqrdmulhq_n_s32(m1, m2); -} - -/** - * @brief Vector rounding divide by power of two. - * @param[in] dividend - Dividend vector - * @param[in] exponent - Divisor = power(2, exponent) - * Range: [0, 31] - * @return Rounded result of division. Midpoint is rounded away from zero. - * - */ -__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve(const int32x4_t dividend, const q31_t exponent) -{ - const int32x4_t shift = vdupq_n_s32(-exponent); - const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31); - const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup); - return vrshlq_s32(fixed_up_dividend, shift); -} - -/** - * @brief Requantize a given vector. - * @param[in] val Vector to be requantized - * @param[in] multiplier multiplier - * @param[in] shift shift - * - * @return Returns (val * multiplier)/(2 ^ shift) - * - */ -__STATIC_FORCEINLINE int32x4_t arm_requantize_mve(const int32x4_t val, const q31_t multiplier, const q31_t shift) -{ - return arm_divide_by_power_of_two_mve( - arm_sat_doubling_high_mult_mve(vshlq_s32(val, vdupq_n_s32(LEFT_SHIFT(shift))), multiplier), - RIGHT_SHIFT(shift)); -} - -__STATIC_FORCEINLINE int32x4_t arm_sat_doubling_high_mult_mve_32x4(const int32x4_t m1, const int32x4_t m2) -{ - return vqrdmulhq_s32(m1, m2); -} - -__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve_32x4(const int32x4_t dividend, const int32x4_t exponent) -{ - const int32x4_t shift = -exponent; - const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31); - const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup); - return vrshlq_s32(fixed_up_dividend, shift); -} - -__STATIC_FORCEINLINE int32x4_t arm_requantize_mve_32x4(const int32x4_t val, const int32x4_t multiplier, const int32x4_t shift) -{ - const int32x4_t zz = vdupq_n_s32(0); - const mve_pred16_t p = vcmpgtq_n_s32(shift, 0); - - const int32x4_t left_shift = vpselq_s32(shift, zz, p); - const int32x4_t right_shift = -vpselq_s32(zz, shift, p); - - return arm_divide_by_power_of_two_mve_32x4(arm_sat_doubling_high_mult_mve_32x4(vshlq_s32(val, left_shift), multiplier), right_shift); -} -#endif - -// @note The following functions are used only for softmax layer, scaled bits = 5 assumed - -__STATIC_FORCEINLINE int32_t arm_nn_exp_on_negative_values(int32_t val) -{ - int32_t mask = 0; - int32_t shift = 24; - - const int32_t val_mod_minus_quarter = (val & ((1 << shift) - 1)) - (1 << shift); - const int32_t remainder = val_mod_minus_quarter - val; - const int32_t x = (val_mod_minus_quarter << 5) + (1 << 28); - const int32_t x2 = MUL_SAT(x, x); - - int32_t result = 1895147668 + MUL_SAT(1895147668, x + - DIV_POW2(MUL_SAT(DIV_POW2(MUL_SAT(x2, x2), 2) + MUL_SAT(x2, x), 715827883) + x2, 1)); - -#define SELECT_IF_NON_ZERO(x) \ -{ \ - mask = MASK_IF_NON_ZERO(remainder & (1 << shift++)); \ - result = SELECT_USING_MASK(mask, MUL_SAT(result, x), result); \ -} - - SELECT_IF_NON_ZERO(1672461947) - SELECT_IF_NON_ZERO(1302514674) - SELECT_IF_NON_ZERO(790015084) - SELECT_IF_NON_ZERO(290630308) - SELECT_IF_NON_ZERO(39332535) - SELECT_IF_NON_ZERO(720401) - SELECT_IF_NON_ZERO(242) - -#undef SELECT_IF_NON_ZERO - - mask = MASK_IF_ZERO(val); - return SELECT_USING_MASK(mask, Q31_MAX, result); -} - -__STATIC_FORCEINLINE q31_t arm_nn_mult_by_power_of_two(const int32_t val, const int32_t exp) -{ - const int32_t thresh = ((1 << (31 - exp)) - 1); - int32_t result = val << exp; - result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val > thresh), Q31_MAX, result); - result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val < -thresh), Q31_MIN, result); - return result; -} - -__STATIC_FORCEINLINE int32_t arm_nn_one_over_one_plus_x_for_x_in_0_1(int32_t val) -{ - const int64_t sum = (int64_t)val + (int64_t)Q31_MAX; - const int32_t half_denominator = (int32_t)((sum + (sum >= 0 ? 1 : -1)) / 2L); - int32_t x = 1515870810 + MUL_SAT(half_denominator, -1010580540); - - const int32_t shift = (1 << 29); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - - return MUL_POW2(x, 1); -} - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c deleted file mode 100644 index 69c68777..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_activations_q15.c - * Description: Q15 neural network activation function using direct table look-up - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - -/** - * @brief neural network activation function using direct table look-up - * - * @note Refer header file for details. - * - */ - -void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type) -{ - uint16_t i = size; - q15_t *pIn = data; - q15_t *pOut = data; - uint16_t shift_size = 8 + 3 - int_width; - uint32_t bit_mask = 0x7FF >> int_width; - uint32_t full_frac = bit_mask + 1; - const q15_t *lookup_table; - - switch (type) - { - case ARM_SIGMOID: - lookup_table = sigmoidTable_q15; - break; - case ARM_TANH: - default: - lookup_table = tanhTable_q15; - break; - } - - while (i) - { - q15_t out; - q15_t in = *pIn++; - q15_t frac = (uint32_t) in & bit_mask; - q15_t value = lookup_table[(uint8_t)(in >> shift_size)]; - if ((in >> shift_size) != 0x7f) - { - q15_t value2 = lookup_table[(uint8_t)(1 + ((uint8_t)(in >> shift_size)))]; - /* doing the interpolation here for better accuracy */ - out = ((q31_t) (full_frac - frac) * value + (q31_t) value2 * frac) >> shift_size; - } else - { - /* the largest positive value does not have a right side for linear interpolation */ - out = value; - } - - *pOut++ = out; - i--; - } - -} - -/** - * @} end of Acti group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c deleted file mode 100644 index 19476ed5..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_activations_q7.c - * Description: Q7 neural network activation function using direct table look-up - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /** - * @brief Q7 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * - * @details - * - * This is the direct table look-up approach. - * - * Assume here the integer part of the fixed-point is <= 3. - * More than 3 just not making much sense, makes no difference with - * saturation followed by any of these activation functions. - */ - -void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type) -{ - uint16_t i = size; - q7_t *pIn = data; - q7_t *pOut = data; - q7_t in; - q7_t out; - uint16_t shift_size = 3 - int_width; - const q7_t *lookup_table; - switch (type) - { - case ARM_SIGMOID: - lookup_table = sigmoidTable_q7; - break; - case ARM_TANH: - default: - lookup_table = tanhTable_q7; - break; - } - while (i) - { - in = *pIn++; - out = lookup_table[(uint8_t) (in >> shift_size)]; - *pOut++ = out; - i--; - } -} - -/** - * @} end of Acti group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c deleted file mode 100644 index 63f29f48..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu6_s8.c - * Description: Basic s8 version of ReLU6 - * - * $Date: Spetember 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /* - * Basic ReLU6 function - * - * Refer to header file for details. - * - */ - -void arm_relu6_s8(q7_t *data, uint16_t size) -{ - int32_t i; - - for (i = 0; i < size; i++) - { - int32_t ip = data[i]; - - ip = MAX(ip, 0); - data[i] = MIN(ip, 6); - } -} - -/** - * @} end of Acti group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c deleted file mode 100644 index be5450de..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu_q15.c - * Description: Q15 version of ReLU - * - * $Date: February 27, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - -/** - * @brief Q15 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * - * @details - * - * Optimized relu with QSUB instructions. - * - */ - -void arm_relu_q15(q15_t *data, uint16_t size) -{ - -#if defined(ARM_MATH_DSP) - /* Run the following code for M cores with DSP extension */ - - uint16_t i = size >> 1; - q15_t *input = data; - q15_t *output = data; - q31_t in; - q31_t buf; - q31_t mask; - - while (i) - { - in = read_q15x2_ia(&input); - - /* extract the first bit */ - buf = __ROR(in & 0x80008000, 15); - - /* if MSB=1, mask will be 0xFF, 0x0 otherwise */ - mask = __QSUB16(0x00000000, buf); - - write_q15x2_ia(&output, in & (~mask)); - i--; - } - - if (size & 0x1) - { - if (*input < 0) - { - *input = 0; - } - input++; - } -#else - /* Run the following code as reference implementation for M cores without DSP extension */ - uint16_t i; - - for (i = 0; i < size; i++) - { - if (data[i] < 0) - data[i] = 0; - } - -#endif /* ARM_MATH_DSP */ -} - -/** - * @} end of Acti group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c deleted file mode 100644 index 724d7b49..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu_q7.c - * Description: Q7 version of ReLU - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /** - * @brief Q7 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * - * @details - * - * Optimized relu with QSUB instructions. - * - */ - -void arm_relu_q7(q7_t *data, uint16_t size) -{ - -#if defined(ARM_MATH_DSP) - /* Run the following code for M cores with DSP extension */ - - uint16_t i = size >> 2; - q7_t *input = data; - q7_t *output = data; - q31_t in; - q31_t buf; - q31_t mask; - - while (i) - { - in = read_q7x4_ia(&input); - - /* extract the first bit */ - buf = (int32_t)__ROR((uint32_t)in & 0x80808080, 7); - - /* if MSB=1, mask will be 0xFF, 0x0 otherwise */ - mask = __QSUB8(0x00000000, buf); - - write_q7x4_ia(&output, in & (~mask)); - - i--; - } - - i = size & 0x3; - while (i) - { - if (*input < 0) - { - *input = 0; - } - input++; - i--; - } - -#else - /* Run the following code as reference implementation for cores without DSP extension */ - - uint16_t i; - - for (i = 0; i < size; i++) - { - if (data[i] < 0) - data[i] = 0; - } - -#endif -} - -/** - * @} end of Acti group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c deleted file mode 100644 index 3e42638d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c +++ /dev/null @@ -1,252 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_elementwise_add_s8 - * Description: Element wise add - * - * $Date: February 27, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#if defined(ARM_MATH_MVEI) -#include "cmsis/CMSIS/DSP/Include/arm_helium_utils.h" -#endif - -#if defined(ARM_MATH_MVEI) -#define SAT_INPUT_VECT(__INPUT_V, __MULT, __SHIFT) \ - __INPUT_V = arm_sat_doubling_high_mult_mve(__INPUT_V, __MULT); \ - __INPUT_V = arm_divide_by_power_of_two_mve(__INPUT_V, -__SHIFT); -#endif - -#define SAT_INPUT(__INPUT, __MULT, __SHIFT) \ - __INPUT = arm_nn_sat_doubling_high_mult(__INPUT, __MULT); \ - __INPUT = arm_nn_divide_by_power_of_two(__INPUT, -__SHIFT); - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup BasicMath - * @{ - */ - -/* - * s8 element wise add - * - * Refer header file for details. - * - */ - -/* Note: __SHIFT is expected to be <=0 */ - - -arm_status -arm_elementwise_add_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_1_mult, - const int32_t input_1_shift, - const int32_t input_2_offset, - const int32_t input_2_mult, - const int32_t input_2_shift, - const int32_t left_shift, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - int32_t count = (int32_t)block_size; - - while (count > 0) - { - int32x4_t vect_1; - int32x4_t vect_2; - - mve_pred16_t p = vctp32q((uint32_t)count); - - vect_1 = vldrbq_z_s32(input_1_vect, p); - vect_2 = vldrbq_z_s32(input_2_vect, p); - - vect_1 = vaddq_s32(vect_1, vdupq_n_s32(input_1_offset)); - vect_2 = vaddq_s32(vect_2, vdupq_n_s32(input_2_offset)); - - vect_1 = vshlq_r_s32(vect_1, left_shift); - vect_2 = vshlq_r_s32(vect_2, left_shift); - - SAT_INPUT_VECT(vect_1, input_1_mult, input_1_shift); - SAT_INPUT_VECT(vect_2, input_2_mult, input_2_shift); - - vect_1 = vaddq_s32(vect_1, vect_2); - SAT_INPUT_VECT(vect_1, out_mult, out_shift); - - vect_1 = vaddq_n_s32(vect_1, out_offset); - - vect_1 = vmaxq_s32(vect_1, vdupq_n_s32(out_activation_min)); - vect_1 = vminq_s32(vect_1, vdupq_n_s32(out_activation_max)); - - input_1_vect += 4; - input_2_vect += 4; - vstrbq_p_s32(output, vect_1, p); - - output += 4; - count -= 4; - } -#else - uint32_t loop_count; - int32_t input_1; - int32_t input_2; - int32_t sum; - -#if defined(ARM_MATH_DSP) - int32_t a_1, b_1, a_2, b_2; - - int32_t offset_1_packed, offset_2_packed; - - int8_t r1, r2, r3, r4; - - offset_1_packed = (input_1_offset << 16U) | (input_1_offset & 0x0FFFFL); - offset_2_packed = (input_2_offset << 16U) | (input_2_offset & 0x0FFFFL); - - loop_count = block_size >> 2; - - while (loop_count > 0U) - { - /* 4 outputs are calculated in one loop. The order of calculation is follows the order of output sign extension - intrinsic */ - input_1_vect = read_and_pad_reordered(input_1_vect, &b_1, &a_1); - input_2_vect = read_and_pad_reordered(input_2_vect, &b_2, &a_2); - - a_1 = __SADD16(a_1, offset_1_packed); - b_1 = __SADD16(b_1, offset_1_packed); - - a_2 = __SADD16(a_2, offset_2_packed); - b_2 = __SADD16(b_2, offset_2_packed); - - /* Sum 1 */ - input_1 = (int16_t)(b_1 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)(b_2 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r1 = (q7_t)sum; - - /* Sum 3 */ - input_1 = (int16_t)((b_1 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)((b_2 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r3 = (q7_t)sum; - - /* Sum 2 */ - input_1 = (int16_t)(a_1 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)(a_2 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r2 = (q7_t)sum; - - /* Sum 4 */ - input_1 = (int16_t)((a_1 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)((a_2 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r4 = (q7_t)sum; - - write_q7x4_ia(&output, __PACKq7(r1, r2, r3, r4)); - - loop_count--; - } - - loop_count = block_size & 0x3; -#else - loop_count = block_size; -#endif - - while (loop_count > 0U) - { - /* C = A + B */ - - input_1 = (*input_1_vect++ + input_1_offset) << left_shift; - input_2 = (*input_2_vect++ + input_2_offset) << left_shift; - - input_1 = arm_nn_sat_doubling_high_mult(input_1, input_1_mult); - input_1 = arm_nn_divide_by_power_of_two(input_1, -input_1_shift); - - input_2 = arm_nn_sat_doubling_high_mult(input_2, input_2_mult); - input_2 = arm_nn_divide_by_power_of_two(input_2, -input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - - *output++ = (q7_t)sum; - - /* Decrement loop counter */ - loop_count--; - } - -#endif /* ARM_MATH_MVEI */ - - return (ARM_MATH_SUCCESS); -} - -/** - * @} end of BasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c deleted file mode 100644 index 55303b77..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c +++ /dev/null @@ -1,202 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_elementwise_mul_s8 - * Description: Element wise multiplication - * - * $Date: May 29, 2020 - * $Revision: V.1.0.3 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup BasicMath - * @{ - */ - -/** - * @brief s8 element wise multiplication of two vectors - * - * @note Refer header file for details. - * - */ - -arm_status -arm_elementwise_mul_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_2_offset, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size) -{ - - int32_t loop_count; -#if defined(ARM_MATH_MVEI) - - loop_count = (block_size + 3) / 4; - uint32_t num_elements = block_size; - - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp32q(num_elements); - - int32x4_t input_1 = vldrbq_z_s32(input_1_vect, p); - input_1 = vaddq_n_s32(input_1, input_1_offset); - - int32x4_t input_2 = vldrbq_z_s32(input_2_vect, p); - input_2 = vaddq_n_s32(input_2, input_2_offset); - - int32x4_t res_0 = vmulq_s32(input_1, input_2); - - res_0 = arm_requantize_mve_32x4(res_0, vdupq_n_s32(out_mult), vdupq_n_s32(out_shift)); - - res_0 += vdupq_n_s32(out_offset); - - res_0 = vmaxq_s32(res_0, vdupq_n_s32(out_activation_min)); - res_0 = vminq_s32(res_0, vdupq_n_s32(out_activation_max)); - - vstrbq_p_s32(output, res_0, p); - input_1_vect += 4; - input_2_vect += 4; - output += 4; - num_elements -= 4; - } - -#else - int32_t input_1; - int32_t input_2; - int32_t mul_res; - -#if defined(ARM_MATH_DSP) - int32_t a_1, b_1, a_2, b_2; - - int32_t offset_1_packed, offset_2_packed; - - int8_t r1, r2, r3, r4; - - offset_1_packed = (input_1_offset << 16U) | (input_1_offset & 0x0FFFFL); - offset_2_packed = (input_2_offset << 16U) | (input_2_offset & 0x0FFFFL); - - loop_count = block_size >> 2; - - while (loop_count > 0U) - { - /* 4 outputs are calculated in one loop. The order of calculation is follows the order of output sign extension - intrinsic */ - input_1_vect = read_and_pad_reordered(input_1_vect, &b_1, &a_1); - input_2_vect = read_and_pad_reordered(input_2_vect, &b_2, &a_2); - - a_1 = __SADD16(a_1, offset_1_packed); - b_1 = __SADD16(b_1, offset_1_packed); - - a_2 = __SADD16(a_2, offset_2_packed); - b_2 = __SADD16(b_2, offset_2_packed); - - /* Mul 1 */ - input_1 = (int16_t)(b_1 & 0x0FFFFL); - input_2 = (int16_t)(b_2 & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r1 = (q7_t)mul_res; - - /* Mul 3 */ - input_1 = (int16_t)((b_1 >> 16U) & 0x0FFFFL); - input_2 = (int16_t)((b_2 >> 16U) & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r3 = (q7_t)mul_res; - - /* Mul 2 */ - input_1 = (int16_t)(a_1 & 0x0FFFFL); - input_2 = (int16_t)(a_2 & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r2 = (q7_t)mul_res; - - /* Mul 4 */ - input_1 = (int16_t)((a_1 >> 16U) & 0x0FFFFL); - input_2 = (int16_t)((a_2 >> 16U) & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r4 = (q7_t)mul_res; - - write_q7x4_ia(&output, __PACKq7(r1, r2, r3, r4)); - - loop_count--; - } - - loop_count = block_size & 0x3; -#else - loop_count = block_size; -#endif - - while (loop_count > 0U) - { - /* C = A * B */ - - input_1 = *input_1_vect++ + input_1_offset; - input_2 = *input_2_vect++ + input_2_offset; - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - - *output++ = (q7_t)mul_res; - - /* Decrement loop counter */ - loop_count--; - } -#endif - return ARM_MATH_SUCCESS; -} - -/** - * @} end of BasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c deleted file mode 100644 index 20b68bc7..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_w.c - * Description: s8 version of concatenation along the W axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the W axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_w(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint32_t offset_w) -{ - const uint32_t input_copy_size = input_x * input_y * input_z * input_w; - - output += offset_w * (input_x * input_y * input_z); - - memcpy(output, input, input_copy_size); -} - -/** - * @} end of Concatenation group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c deleted file mode 100644 index b1ff364a..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_x.c - * Description: s8 version of concatenation along the X axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the X axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_x(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_x, - const uint32_t offset_x) -{ - const uint32_t num_iterations = input_y * input_z * input_w; - - output += offset_x; - - uint32_t i; - - // Copy per row - for (i = 0; i < num_iterations; ++i) - { - memcpy(output, input, input_x); - input += input_x; - output += output_x; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c deleted file mode 100644 index 47913687..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_y.c - * Description: s8 version of concatenation along the Y axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the Y axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_y(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_y, - const uint32_t offset_y) -{ - const uint32_t num_iterations = input_z * input_w; - const uint32_t input_copy_size = input_x * input_y; - const uint32_t output_stride = input_x * output_y; - - output += offset_y * input_x; - uint32_t i; - - // Copy per tile - for (i = 0; i < num_iterations; ++i) - { - memcpy(output, input, input_copy_size); - input += input_copy_size; - output += output_stride; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c deleted file mode 100644 index 0d0bf500..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_z.c - * Description: s8 version of concatenation along the Z axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the Z axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_z(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_z, - const uint32_t offset_z) -{ - const uint32_t input_copy_size = input_x * input_y * input_z; - const uint32_t output_stride = input_x * input_y * output_z; - - output += offset_z * (input_x * input_y); - - uint32_t i; - - for (i = 0; i < input_w; ++i) - { - memcpy(output, input, input_copy_size); - input += input_copy_size; - output += output_stride; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c deleted file mode 100644 index f2af65bb..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c +++ /dev/null @@ -1,199 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1_x_n_s8.c - * Description: s8 version of 1xN convolution using symmetric quantization. - * - * $Date: May 18, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * 1xN s8 convolution function. - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_1_x_n_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - (void)bias_dims; - arm_status status = ARM_MATH_SUCCESS; - if (output_dims->w % 4 != 0) - { - status = ARM_MATH_SIZE_MISMATCH; - goto out; - } - -#if defined(ARM_MATH_MVEI) - q15_t *buffer_a = (q15_t *)ctx->buf; - - const uint16_t input_x = input_dims->w; - const uint16_t kernel_x = filter_dims->w; - const uint16_t output_x = output_dims->w; - const uint16_t output_ch = output_dims->c; - const uint16_t input_ch = input_dims->c; - const uint16_t pad_x = conv_params->padding.w; - const uint16_t stride_x = conv_params->stride.w; - - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - for (int i_out_x = 0; i_out_x <= (output_x - 4); i_out_x += 4) - { - int32_t input_begin_idx[4]; - int32_t ker_begin_idx[4]; - int32_t ker_end_idx[4]; - - for (int i = 0; i < 4; i++) - { - const int32_t est_input_x_idx = stride_x * (i_out_x + i) - pad_x; - input_begin_idx[i] = MAX(0, est_input_x_idx); - ker_begin_idx[i] = MAX(0, -est_input_x_idx); - ker_end_idx[i] = MIN(kernel_x, input_x - est_input_x_idx); - } - - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32x4_t s_offset; - int32_t acc[4]; - if ((ker_begin_idx[0] != 0) || (ker_end_idx[3] != kernel_x)) - { - int32_t sum_row[4]; - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[0] - ker_begin_idx[0]) * input_ch, - input_data + input_begin_idx[0] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[0] * input_ch), - &sum_row[0], - &acc[0]); - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[1] - ker_begin_idx[1]) * input_ch, - input_data + input_begin_idx[1] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[1] * input_ch), - &sum_row[1], - &acc[1]); - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[2] - ker_begin_idx[2]) * input_ch, - input_data + input_begin_idx[2] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[2] * input_ch), - &sum_row[2], - &acc[2]); - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[3] - ker_begin_idx[3]) * input_ch, - input_data + input_begin_idx[3] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[3] * input_ch), - &sum_row[3], - &acc[3]); - - s_offset = vldrwq_s32(sum_row); - } - else - { - int32_t sum_row; - (void)arm_nn_mat_mul_core_4x_s8(kernel_x * input_ch, - stride_x * input_ch, - input_data + input_begin_idx[0] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch), - &sum_row, - acc); - - s_offset = vdupq_n_s32(sum_row); - } - int32x4_t res = vldrwq_s32(acc); - s_offset = vmulq_n_s32(s_offset, input_offset); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - res = vaddq_s32(res, s_offset); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(output_data, scatter_offset, res); - output_data++; - } - output_data += (3 * output_ch); - } - -#else - status = arm_convolve_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); -#endif - -out: - /* Return to application */ - return status; -} - -int32_t arm_convolve_1_x_n_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims) -{ -#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 0725e615..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,236 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1x1_HWC_q7_fast_nonsquare.c - * Description: Fast Q7 version of 1x1 convolution (non-square shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1 - * and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise - * separable convolution. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - * - * [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications - * https://arxiv.org/abs/1704.04861 - */ - -arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - (void)dim_im_in_y; - int16_t i_out_y, i_out_x; - int16_t i_ch_out; - - /* ----------------------- - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1 - || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer, - ch_im_in); - pBuffer += ch_im_in; - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - q31_t sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1 - || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - // if-for implementation - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c deleted file mode 100644 index 75e4c8e3..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c +++ /dev/null @@ -1,182 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1x1_s8_fast.c - * Description: Fast q7 version of 1x1 convolution (non-square shape) - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" - -#define DIM_KER_X (1U) -#define DIM_KER_Y (1U) - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Fast s8 version for 1x1 convolution (non-square shape) - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_1x1_s8_fast(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data) -{ - if (input_dims->c % 4 != 0 || - conv_params->padding.w != 0 || conv_params->padding.h != 0 || - conv_params->stride.w != 1 || conv_params->stride.h != 1) - { - return ARM_MATH_SIZE_MISMATCH; - } - - (void)ctx; - (void)filter_dims; - (void)bias_dims; - -#if defined(ARM_MATH_MVEI) - - const int32_t col_len = input_dims->w * input_dims->h * input_dims->n; - const int32_t output_ch = output_dims->c; - const int32_t input_ch = input_dims->c; - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - for (int i_items = 0; i_items <= (col_len - 4); i_items += 4) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row = 0; - int32_t temp_out[4]; - - (void)arm_nn_mat_mul_core_4x_s8(input_ch, - input_ch, - input_data + i_items * input_ch, - filter_data + i_out_ch * input_ch, - &sum_row, - temp_out); - int32x4_t res = vldrwq_s32(temp_out); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - sum_row = sum_row * input_offset; - res = vaddq_n_s32(res, sum_row); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, (uint32_t)output_ch, - (uint32_t)output_ch * 2, - (uint32_t)output_ch * 3}; - vstrbq_scatter_offset_s32(output_data, scatter_offset, res); - output_data++; - } - output_data += (3 * output_ch); - } - - /* Handle left over elements */ - for (int i_items = (col_len & ~0x3); i_items < col_len; i_items++) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row = 0; - - int32_t acc; - (void)arm_nn_mat_mul_core_1x_s8(input_ch, - input_data + i_items * input_ch, - filter_data + i_out_ch * input_ch, - &sum_row, - &acc); - - acc += bias_data[i_out_ch]; - sum_row = (sum_row * input_offset); - acc += sum_row; - acc = arm_nn_requantize(acc, output_mult[i_out_ch], output_shift[i_out_ch]); - acc += out_offset; - - acc = MAX(acc, out_activation_min); - acc = MIN(acc, out_activation_max); - *output_data++ = acc; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M processors with or without DSP extension */ - - const int32_t lhs_rows = input_dims->w * input_dims->h * input_dims->n; - const int32_t rhs_rows = output_dims->c; - const int32_t rhs_cols = input_dims->c; - - arm_nn_mat_mult_nt_t_s8(input_data, - filter_data, - bias_data, - output_data, - quant_params->multiplier, - quant_params->shift, - lhs_rows, - rhs_rows, - rhs_cols, - conv_params->input_offset, - conv_params->output_offset, - conv_params->activation.min, - conv_params->activation.max); - -#endif - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_convolve_1x1_s8_fast_get_buffer_size(const cmsis_nn_dims *input_dims) -{ - (void)input_dims; - return 0; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c deleted file mode 100644 index d7d25f44..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c +++ /dev/null @@ -1,207 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_basic.c - * Description: Q15 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * bufferA size: ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * This basic version is designed to work for any input tensor and weight - * dimension. - */ - -arm_status -arm_convolve_HWC_q15_basic(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - uint16_t im2col_out_pixel_index = 0; - q15_t *pBuffer = bufferA; - q15_t *pOut = Im_out; - q15_t *im_buffer = bufferA; - const q15_t *pA; - int i; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - pA = wt; - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = im_buffer; - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA1, inB1, sum); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q15_t) __SSAT((sum >> out_shift), 16); - pOut++; - } - - /* counter reset */ - pBuffer = im_buffer; - im2col_out_pixel_index++; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c deleted file mode 100644 index 221d64f0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c +++ /dev/null @@ -1,255 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_fast.c - * Description: Fast Q15 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - -arm_status -arm_convolve_HWC_q15_fast(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - q15_t *pBuffer = bufferA; - q15_t *im_buffer = bufferA; - q15_t *pOut = Im_out; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (i_out_x & 0x1) - { - int i; - /* initialize the matrix pointers for A */ - const q15_t *pA = wt; - - /* set up the second output pointers */ - q15_t *pOut2 = pOut + ch_im_out; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = im_buffer; - const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel; - - /* aling the second pointer for A */ - const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel; - - /* init the sum with bias */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 1; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA2); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA1, inB1, sum); - sum2 = __SMLAD(inA1, inB2, sum2); - sum3 = __SMLAD(inA2, inB1, sum3); - sum4 = __SMLAD(inA2, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16); - *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16); - - /* skip the row computed with A2 */ - pA += ch_im_in * dim_kernel * dim_kernel; - } /* for over ch_im_out */ - - pOut += ch_im_out; - /* counter reset */ - pBuffer = im_buffer; - } - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c deleted file mode 100644 index 8b1fdc7d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_fast.c - * Description: Fast Q15 version of convolution - * - * $Date: 24. May 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q15 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - -arm_status -arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - q15_t *pBuffer = bufferA; - q15_t *im_buffer = bufferA; - q15_t *pOut = Im_out; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (i_out_x & 0x1) - { - int i; - /* initialize the matrix pointers for A */ - const q15_t *pA = wt; - - /* set up the second output pointers */ - q15_t *pOut2 = pOut + ch_im_out; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = im_buffer; - const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x; - - /* aling the second pointer for A */ - const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x; - - /* init the sum with bias */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA2); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA1, inB1, sum); - sum2 = __SMLAD(inA1, inB2, sum2); - sum3 = __SMLAD(inA2, inB1, sum3); - sum4 = __SMLAD(inA2, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16); - *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16); - - /* skip the row computed with A2 */ - pA += ch_im_in * dim_kernel_y * dim_kernel_x; - } /* for over ch_im_out */ - - pOut += ch_im_out; - /* counter reset */ - pBuffer = im_buffer; - } - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c deleted file mode 100644 index 912f8347..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c +++ /dev/null @@ -1,279 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_RGB.c - * Description: Q7 version of convolution for RGB image - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Q7 convolution function for RGB image - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in equals 3 - * - * This kernel is written exclusively for convolution with ch_im_in - * equals 3. This applies on the first layer of CNNs which has input - * image with RGB format. - */ - -arm_status -arm_convolve_HWC_q7_RGB(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, const uint16_t dim_im_out, q15_t * bufferA, q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - // check if number of input channels is 3 - if (ch_im_in != 3) - { - return ARM_MATH_SIZE_MISMATCH; - } - // This part implements the im2col function - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */ - *__SIMD32(pBuffer) = 0x0; - *(pBuffer + 2) = 0; - pBuffer += 3; - } else - { - /* - * Equivalent to: - * arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3); - */ - - const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3; - q31_t buf = arm_nn_read_q7x4(pPixel); - - union arm_nnword top; - union arm_nnword bottom; - - top.word = __SXTB16(buf); - bottom.word = __SXTB16(__ROR(buf, 8)); - -#ifndef ARM_MATH_BIG_ENDIAN - /* - * little-endian, | omit | 3rd | 2nd | 1st | - * MSB LSB - * top | 3rd | 1st |; bottom | omit | 2nd | - * - * version 1, need to swap 2nd and 3rd weight - * *__SIMD32(pBuffer) = top.word; - * *(pBuffer+2) = bottom.half_words[0]; - * - * version 2, no weight shuffling required - */ - *pBuffer++ = top.half_words[0]; - *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0); -#else - /* - * big-endian, | 1st | 2nd | 3rd | omit | - * MSB LSB - * top | 2nd | omit |; bottom | 1st | 3rd | - * - * version 1, need to swap 2nd and 3rd weight - * *__SIMD32(pBuffer) = bottom.word; - * *(pBuffer+2) = top.half_words[1]; - * - * version 2, no weight shuffling required - */ - *pBuffer++ = bottom.half_words[0]; - *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0); -#endif - pBuffer += 2; - } - } - } - - if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - 3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = 3 * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia((const q15_t **)&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia((const q15_t **)&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = 3 * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - // check if number of input channels is 3 - if (ch_im_in != 3) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - /* if-for implementation */ - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return (ARM_MATH_SUCCESS); -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c deleted file mode 100644 index 2e6147b0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c +++ /dev/null @@ -1,231 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_basic.c - * Description: Q7 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * This basic version is designed to work for any input tensor and weight - * dimension. - */ - -arm_status -arm_convolve_HWC_q7_basic(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_no_shift((q7_t *) - Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* Computation is filed for every 2 columns */ - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - ch_im_in * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - - /* Point to the beging of the im2col buffer */ - const q15_t *pB = bufferA; - - /* Each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - // if-for implementation - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c deleted file mode 100644 index d50318db..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c +++ /dev/null @@ -1,229 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_basic.c - * Description: Q7 version of convolution - * - * $Date: 13. July 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - */ - -arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_no_shift((q7_t *) - Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* Computation is filed for every 2 columns */ - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - ch_im_in * - dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - - /* Point to the beging of the im2col buffer */ - const q15_t *pB = bufferA; - - /* Each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2; - - while (colCnt) - { - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - // if-for implementation - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + - (m * dim_kernel_x + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c deleted file mode 100644 index 0352d6f8..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c +++ /dev/null @@ -1,408 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_fast.c - * Description: Fast Q7 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap ) - * - * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel ) - * - * The im2col converts the Q7 tensor input into Q15 column, which is stored in - * bufferA. There is reordering happenning during this im2col process with - * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and - * third elements are swapped. - * - * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the - * GEMM computation with the reordered columns. - * - * To speed-up the determination of the padding condition, we split the - * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. - * This reduces the total number of boundary condition checks and improves - * the data copying performance. - */ - -arm_status -arm_convolve_HWC_q7_fast(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* - * Here we split the entire matrix into three regions depending on the padding situation - * Top: i_out_y from 0 to padding - 1 - * Middle: i_out_y from padding to dim_im_out-padding-1 - * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 - */ - - /* top part */ - for (i_out_y = 0; i_out_y < padding; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* middle part, here we also divide the x into left, mid and right */ - for (; i_out_y < dim_im_out - padding; i_out_y++) - { - - /* left part */ - for (i_out_x = 0; i_out_x < padding; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* mid part */ - for (; i_out_x < dim_im_out - padding; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in - + - (i_ker_y * - dim_im_in + - i_out_x * - stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel); - pBuffer += ch_im_in * dim_kernel; - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* right part */ - for (; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - for (; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - // if-for implementation - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 31b4fdc2..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,379 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_fast_nonsquare.c - * Description: Fast Q7 version of convolution (non-sqaure shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Fast Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - -arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* ----------------------- - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* - * Here we split the entire matrix into three regions depending on the padding situation - * Top: i_out_y from 0 to padding - 1 - * Middle: i_out_y from padding to dim_im_out-padding-1 - * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 - */ - - /* top part */ - for (i_out_y = 0; i_out_y < padding_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* middle part, here we also divide the x into left, mid and right */ - for (; i_out_y < dim_im_out_y - padding_y; i_out_y++) - { - - /* left part */ - for (i_out_x = 0; i_out_x < padding_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* mid part */ - for (; i_out_x < dim_im_out_x - padding_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + - (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in, - pBuffer, ch_im_in * dim_kernel_x); - pBuffer += ch_im_in * dim_kernel_x; - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* right part */ - for (; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - for (; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = (ch_im_in * dim_kernel_y * dim_kernel_x) & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - /* if-for implementation */ - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c deleted file mode 100644 index fd304734..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c +++ /dev/null @@ -1,372 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_s8.c - * Description: s8 version of convolution using symmetric quantization. - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Basic s8 convolution function. - * - * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels - * are multiples of 4 or atleast greater than 4. - * - */ - -arm_status arm_convolve_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - q15_t *buffer_a = (q15_t *)ctx->buf; - - const uint16_t input_batches = input_dims->n; - const uint16_t input_x = input_dims->w; - const uint16_t input_y = input_dims->h; - const uint16_t input_ch = input_dims->c; - const uint16_t kernel_x = filter_dims->w; - const uint16_t kernel_y = filter_dims->h; - const uint16_t output_x = output_dims->w; - const uint16_t output_y = output_dims->h; - const uint16_t output_ch = output_dims->c; - - const uint16_t pad_x = conv_params->padding.w; - const uint16_t pad_y = conv_params->padding.h; - const uint16_t stride_x = conv_params->stride.w; - const uint16_t stride_y = conv_params->stride.h; - - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - int i_batch; - for (i_batch = 0; i_batch < input_batches; i_batch++) - { -#if defined(ARM_MATH_MVEI) - (void)bias_dims; - /* Generate upto four columns from the input tensor a GEMM computation */ - q7_t *im2col_buf = (q7_t *)buffer_a; - q7_t *out = output_data; - int32_t buffer_fill_cnt = 0; - int32_t padded = 0; - const int32_t num_elem = kernel_x * kernel_y * input_ch; - - /* This part implements the im2col function */ - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - for (int i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y; i_ker_y++) - { - for (int i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - memset(im2col_buf, (int8_t)-input_offset, sizeof(q7_t) * input_ch); - padded = 1; - } - else - { - arm_memcpy_q7(im2col_buf, input_data + (i_ker_y * input_x + i_ker_x) * input_ch, input_ch); - } - im2col_buf += input_ch; - } - } - - buffer_fill_cnt++; - - /* Computation is filed for every 4 columns */ - if (buffer_fill_cnt == 4 && (padded == 0)) - { - buffer_fill_cnt = 0; - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row; - int32_t acc[4]; - - (void)arm_nn_mat_mul_core_4x_s8(num_elem, - num_elem, - (q7_t *)buffer_a, - filter_data + num_elem * i_out_ch, - &sum_row, - acc); - int32x4_t s_offset = vdupq_n_s32(sum_row); - - int32x4_t res = vldrwq_s32(acc); - s_offset = vmulq_n_s32(s_offset, input_offset); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - res = vaddq_s32(res, s_offset); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(out, scatter_offset, res); - out++; - } - out += (3 * output_ch); - im2col_buf = (q7_t *)buffer_a; - } - else if (buffer_fill_cnt == 4 && (padded != 0)) - { - buffer_fill_cnt = 0; - out = arm_nn_mat_mult_s8(filter_data, - (q7_t *)buffer_a, - output_ch, - 4, - output_shift, - output_mult, - out_offset, - input_offset, - 0, - out_activation_min, - out_activation_max, - num_elem, - bias_data, - out); - - im2col_buf = (q7_t *)buffer_a; - padded = 0; - } - } - } - /* Handle left over columns */ - if (buffer_fill_cnt != 0) - { - out = arm_nn_mat_mult_s8(filter_data, - (q7_t *)buffer_a, - output_ch, - buffer_fill_cnt, - output_shift, - output_mult, - out_offset, - input_offset, - 0, - out_activation_min, - out_activation_max, - num_elem, - bias_data, - out); - } - -#elif defined(ARM_MATH_DSP) - (void)bias_dims; - int32_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* Generate two columns from the input tensor a GEMM computation */ - q15_t *two_column_buf = buffer_a; - q7_t *out = output_data; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < output_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - /* Filling 0 for out-of-bound paddings */ - memset(two_column_buf, 0, sizeof(q15_t) * input_ch); - } - else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_with_offset(input_data + (i_ker_y * input_x + i_ker_x) * input_ch, two_column_buf, input_ch, input_offset); - } - two_column_buf += input_ch; - } - } - - /* Computation is filed for every 2 columns */ - if (two_column_buf == buffer_a + 2 * input_ch * kernel_y * kernel_x) - { - out = - arm_nn_mat_mult_kernel_s8_s16(filter_data, - buffer_a, - output_ch, - output_shift, - output_mult, - out_offset, - out_activation_min, - out_activation_max, - input_ch * kernel_y * kernel_x, - bias_data, - out); - - /* counter reset */ - two_column_buf = buffer_a; - } - } - } - - /* left-over because odd number of output pixels */ - if (two_column_buf != buffer_a) - { - const q7_t *ker_a = filter_data; - int i; - - for (i = 0; i < output_ch; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = bias_data[i]; - - /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */ - const q15_t *ip_as_col = buffer_a; - - /* 4 multiply and accumulates are done in one loop. */ - uint16_t col_count = (input_ch * kernel_y * kernel_x) >> 2; - - while (col_count) - { - q31_t ker_a1, ker_a2; - q31_t ip_b1, ip_b2; - - ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2); - - ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col); - sum = __SMLAD(ker_a1, ip_b1, sum); - ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col); - sum = __SMLAD(ker_a2, ip_b2, sum); - - col_count--; - } - /* Handle left over mac */ - col_count = input_ch * kernel_y * kernel_x & 0x3; - while (col_count) - { - q7_t ker_a1 = *ker_a++; - q15_t ip_b1 = *ip_as_col++; - sum += ker_a1 * ip_b1; - col_count--; - } - - sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - *out++ = (q7_t)sum; - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - (void)buffer_a; - int32_t i_out_ch, i_out_y, i_out_x, i_input_ch, i_ker_y, i_ker_x; - int32_t conv_out; - - for (i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - for (i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < output_x; i_out_x++) - { - conv_out = bias_data[i_out_ch]; - - const int32_t base_idx_y = stride_y * i_out_y - pad_y; - const int32_t base_idx_x = stride_x * i_out_x - pad_x; - - const int32_t ker_y_start = MAX(0, -base_idx_y); - const int32_t ker_x_start = MAX(0, -base_idx_x); - - const int32_t ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int32_t ker_x_end = MIN(kernel_x, input_x - base_idx_x); - - for (i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - for (i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t in_row = base_idx_y + i_ker_y; - const int32_t in_col = base_idx_x + i_ker_x; - for (i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - conv_out += - (input_data[(in_row * input_x + in_col) * input_ch + i_input_ch] + input_offset) * - filter_data[i_out_ch * input_ch * kernel_y * kernel_x + - (i_ker_y * kernel_x + i_ker_x) * input_ch + i_input_ch]; - } - } - } - conv_out = arm_nn_requantize(conv_out, output_mult[i_out_ch], output_shift[i_out_ch]); - conv_out += out_offset; - conv_out = MAX(conv_out, out_activation_min); - conv_out = MIN(conv_out, out_activation_max); - output_data[i_out_ch + (i_out_y * output_x + i_out_x) * output_ch] = (int8_t)conv_out; - } - } - } -#endif - /* Advance to the next batch */ - input_data += (input_x * input_y * input_ch); - output_data += (output_x * output_y * output_ch); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims) -{ -#if defined(ARM_MATH_DSP) - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c deleted file mode 100644 index 5cef066c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c +++ /dev/null @@ -1,148 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_wrapper_s8.c - * Description: s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution. - * - * $Date: May 18, 2020 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Convolution layer - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_wrapper_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - if ((conv_params->padding.w == 0) && - (conv_params->padding.h == 0) && - (input_dims->c % 4 == 0) && - (conv_params->stride.w == 1) && - (conv_params->stride.h == 1) && - (filter_dims->w == 1) && - (filter_dims->h == 1)) - { - return arm_convolve_1x1_s8_fast(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } - else if ((output_dims->h == 1) && - (input_dims->h == 1) && - (filter_dims->h == 1) && - (output_dims->w % 4 == 0) && - (input_dims->n == 1)) - { - return arm_convolve_1_x_n_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } - else - { - return arm_convolve_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } -} - -int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params* conv_params, - const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims, - const cmsis_nn_dims* output_dims) -{ - if ((conv_params->padding.w == 0) && - (conv_params->padding.h == 0) && - (input_dims->c % 4 == 0) && - (conv_params->stride.w == 1) && - (conv_params->stride.h == 1) && - (filter_dims->w == 1) && - (filter_dims->h == 1)) - { - return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims); - } - else if ((output_dims->h == 1) && - (input_dims->h == 1) && - (filter_dims->h == 1) && - (output_dims->w % 4 == 0) && - (input_dims->n == 1)) - { - return arm_convolve_1_x_n_s8_get_buffer_size(input_dims, filter_dims); - } - else - { - return arm_convolve_s8_get_buffer_size(input_dims, filter_dims); - } -} - -/** - * @} end of NNConv group - */ - diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c deleted file mode 100644 index 5705b4bb..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c +++ /dev/null @@ -1,213 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_3x3_s8.c - * Description: Optimized s8 depthwise convolution function for channel - * multiplier of 1 and 3x3 kernel size. - * - * $Date: May 14, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Optimized s8 depthwise convolution function with constraint that - * in_channel == out_channel and kernel_x == kernel_y == 3 with pads at most 1 - * - * Refer prototype header file for details. - * - */ - -arm_status arm_depthwise_conv_3x3_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)ctx; - (void)bias_dims; - - const int32_t input_x = input_dims->w; - const int32_t input_y = input_dims->h; - const int32_t input_ch = input_dims->c; - const int32_t output_ch = output_dims->c; - const int32_t pad_x = dw_conv_params->padding.w; - const int32_t pad_y = dw_conv_params->padding.h; - const int32_t stride_x = dw_conv_params->stride.w; - const int32_t stride_y = dw_conv_params->stride.h; - const int32_t *output_shift = quant_params->shift; - const int32_t *output_mult = quant_params->multiplier; - const int32_t output_x = output_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_offset = dw_conv_params->output_offset; - const int32_t input_offset = dw_conv_params->input_offset; - const int32_t output_activation_min = dw_conv_params->activation.min; - const int32_t output_activation_max = dw_conv_params->activation.max; - - /* Check input constraints input_ch == output_ch */ - if (input_ch != output_ch) - { - return ARM_MATH_SIZE_MISMATCH; - } - /* Check input constraints pad_x <= 1 */ - if (pad_x > 1 || filter_dims->w != 3 || filter_dims->h != 3) - { - return ARM_MATH_ARGUMENT_ERROR; - } - - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - int32_t in_ch = 0; - int32_t ker_w_start = MAX(0, -in_w); - - for (; in_ch <= (input_ch - 4); in_ch += 4) - { - int32_t out_buff0 = bias[in_ch + 0]; - int32_t out_buff1 = bias[in_ch + 1]; - int32_t out_buff2 = bias[in_ch + 2]; - int32_t out_buff3 = bias[in_ch + 3]; - - const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch; - const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h) - { - int32_t in_val = 0; - int32_t ker_val = 0; - - if (ker_w_start == 0) - { - in_val = arm_nn_read_q7x4(input_ptr); - ker_val = arm_nn_read_q7x4(kernel_ptr); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - } - - in_val = arm_nn_read_q7x4(input_ptr + input_ch); - ker_val = arm_nn_read_q7x4(kernel_ptr + input_ch); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - - if ((input_x - in_w) >= 3) - { - in_val = arm_nn_read_q7x4(input_ptr + (input_ch << 1)); - ker_val = arm_nn_read_q7x4(kernel_ptr + (input_ch << 1)); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - } - - input_ptr += (input_ch * input_x); - kernel_ptr += (input_ch * 3); - } - - out_buff0 = arm_nn_requantize(out_buff0, output_mult[in_ch + 0], output_shift[in_ch + 0]); - out_buff1 = arm_nn_requantize(out_buff1, output_mult[in_ch + 1], output_shift[in_ch + 1]); - out_buff2 = arm_nn_requantize(out_buff2, output_mult[in_ch + 2], output_shift[in_ch + 2]); - out_buff3 = arm_nn_requantize(out_buff3, output_mult[in_ch + 3], output_shift[in_ch + 3]); - - out_buff0 += output_offset; - out_buff1 += output_offset; - out_buff2 += output_offset; - out_buff3 += output_offset; - - out_buff0 = MIN(MAX(out_buff0, output_activation_min), output_activation_max); - out_buff1 = MIN(MAX(out_buff1, output_activation_min), output_activation_max); - out_buff2 = MIN(MAX(out_buff2, output_activation_min), output_activation_max); - out_buff3 = MIN(MAX(out_buff3, output_activation_min), output_activation_max); - - output[out_idx++] = (int8_t)out_buff0; - output[out_idx++] = (int8_t)out_buff1; - output[out_idx++] = (int8_t)out_buff2; - output[out_idx++] = (int8_t)out_buff3; - } - - // Leftover - for (; in_ch < input_ch; ++in_ch) - { - int32_t out_buff = bias[in_ch]; - - const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch; - const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h) - { - if (ker_w_start == 0) - { - out_buff += (*(input_ptr) + input_offset) * *(kernel_ptr); - } - - out_buff += (*(input_ptr + input_ch) + input_offset) * *(kernel_ptr + input_ch); - - if ((input_x - in_w) >= 3) - { - out_buff += (*(input_ptr + (input_ch << 1)) + input_offset) * *(kernel_ptr + (input_ch << 1)); - } - - input_ptr += (input_ch * input_x); - kernel_ptr += (input_ch * 3); - } - - out_buff = arm_nn_requantize(out_buff, output_mult[in_ch], output_shift[in_ch]); - out_buff += output_offset; - out_buff = MIN(MAX(out_buff, output_activation_min), output_activation_max); - output[out_idx++] = (int8_t)out_buff; - } - } - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c deleted file mode 100644 index 4819a0cc..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c +++ /dev/null @@ -1,249 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_s8.c - * Description: s8 version of depthwise convolution. - * - * $Date: May 14, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -static void depthwise_conv_s8_mult_4(const int8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const int8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - int8_t *output, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch; ++in_ch, out_ch += ch_mult) - { - for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4) - { - int32_t out_buff[4]; - - out_buff[0] = bias[out_ch + 0 + mult_tile]; - out_buff[1] = bias[out_ch + 1 + mult_tile]; - out_buff[2] = bias[out_ch + 2 + mult_tile]; - out_buff[3] = bias[out_ch + 3 + mult_tile]; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h) - { - int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch; - int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch; - - for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w); ++ker_w, ker_idx += output_ch) - { - int32_t in_val = input[in_idx + ker_w * input_ch] + input_offset; - out_buff[0] += in_val * kernel[ker_idx + 0 + mult_tile]; - out_buff[1] += in_val * kernel[ker_idx + 1 + mult_tile]; - out_buff[2] += in_val * kernel[ker_idx + 2 + mult_tile]; - out_buff[3] += in_val * kernel[ker_idx + 3 + mult_tile]; - } - } -#if defined(ARM_MATH_MVEI) - (void)out_idx; - int32x4_t res = vldrwq_s32(out_buff); - res = arm_requantize_mve_32x4(res, vldrwq_s32(&output_mult[out_ch + mult_tile]), vldrwq_s32(&output_shift[out_ch + mult_tile])); - res = vaddq_n_s32(res, output_offset); - - res = vmaxq_s32(res, vdupq_n_s32(output_activation_min)); - res = vminq_s32(res, vdupq_n_s32(output_activation_max)); - vstrbq_s32(output, res); - output += 4; -#else - out_buff[0] = arm_nn_requantize(out_buff[0], output_mult[out_ch + 0 + mult_tile], output_shift[out_ch + 0 + mult_tile]); - out_buff[1] = arm_nn_requantize(out_buff[1], output_mult[out_ch + 1 + mult_tile], output_shift[out_ch + 1 + mult_tile]); - out_buff[2] = arm_nn_requantize(out_buff[2], output_mult[out_ch + 2 + mult_tile], output_shift[out_ch + 2 + mult_tile]); - out_buff[3] = arm_nn_requantize(out_buff[3], output_mult[out_ch + 3 + mult_tile], output_shift[out_ch + 3 + mult_tile]); - - out_buff[0] += output_offset; - out_buff[1] += output_offset; - out_buff[2] += output_offset; - out_buff[3] += output_offset; - - out_buff[0] = MIN(MAX(out_buff[0], output_activation_min), output_activation_max); - out_buff[1] = MIN(MAX(out_buff[1], output_activation_min), output_activation_max); - out_buff[2] = MIN(MAX(out_buff[2], output_activation_min), output_activation_max); - out_buff[3] = MIN(MAX(out_buff[3], output_activation_min), output_activation_max); - - output[out_idx++] = (int8_t)out_buff[0]; - output[out_idx++] = (int8_t)out_buff[1]; - output[out_idx++] = (int8_t)out_buff[2]; - output[out_idx++] = (int8_t)out_buff[3]; - -#endif - } - } - } - } -} - -static void depthwise_conv_s8_generic(const q7_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const q7_t *kernel, - const uint16_t output_ch, - const uint16_t ch_mult, - const uint16_t kernel_x, - const uint16_t kernel_y, - const uint16_t pad_x, - const uint16_t pad_y, - const uint16_t stride_x, - const uint16_t stride_y, - const int32_t *bias, - q7_t *output, - const int32_t *output_shift, - const int32_t *output_mult, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - (void)output_ch; - int i_out = 0; - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) - { - const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult; - int32_t acc_0; - /* Condition for kernel start dimension: (base_idx_ + ker__start) >= 0 */ - const int ker_y_start = MAX(0, -base_idx_y); - const int ker_x_start = MAX(0, -base_idx_x); - /* Condition for kernel end dimension: (base_idx_ + ker__end) < input_ */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int ker_x_end = MIN(kernel_x, input_x - base_idx_x); - acc_0 = bias[idx_out_ch]; - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch; - int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch; - - acc_0 += (input[idx_0] + input_offset) * kernel[ker_idx_0]; - } - } - - /* Requantize and clamp output to provided range */ - acc_0 = arm_nn_requantize(acc_0, output_mult[idx_out_ch], output_shift[idx_out_ch]); - acc_0 += output_offset; - acc_0 = MAX(acc_0, output_activation_min); - acc_0 = MIN(acc_0, output_activation_max); - - output[i_out++] = acc_0; - } - } - } - } -} - -/* - * Basic s8 depthwise convolution function. - * - * Refer header file for details. - * Optimization using DSP extension is not available for the generic case where channel multiplier is > 1. - * - */ -arm_status arm_depthwise_conv_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)dw_conv_params->dilation; - (void)ctx; - - if (dw_conv_params->ch_mult % 4 == 0) - { - depthwise_conv_s8_mult_4(input, input_dims->w, input_dims->h, input_dims->c, kernel, output_dims->c, dw_conv_params->ch_mult, filter_dims->w, filter_dims->h, - dw_conv_params->padding.w, dw_conv_params->padding.h, dw_conv_params->stride.w, dw_conv_params->stride.h, bias, output, - quant_params->shift, quant_params->multiplier, output_dims->w, output_dims->h, dw_conv_params->output_offset, - dw_conv_params->input_offset, dw_conv_params->activation.min, dw_conv_params->activation.max); - } - else - { - depthwise_conv_s8_generic(input, input_dims->w, input_dims->h, input_dims->c, kernel, output_dims->c, dw_conv_params->ch_mult, filter_dims->w, filter_dims->h, - dw_conv_params->padding.w, dw_conv_params->padding.h, dw_conv_params->stride.w, dw_conv_params->stride.h, bias, output, - quant_params->shift, quant_params->multiplier, output_dims->w, output_dims->h, dw_conv_params->output_offset, - dw_conv_params->input_offset, dw_conv_params->activation.min, dw_conv_params->activation.max); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c deleted file mode 100644 index 3587b9c9..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c +++ /dev/null @@ -1,425 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_s8_opt.c - * Description: Optimized s8 depthwise separable convolution function for - * channel multiplier of 1. - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel - * - * Refer prototype header file for details. - * - */ - -arm_status arm_depthwise_conv_s8_opt(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - const int32_t input_x = input_dims->w; - const int32_t input_y = input_dims->h; - const int32_t input_ch = input_dims->c; - const int32_t output_ch = output_dims->c; - const int32_t kernel_x = filter_dims->w; - const int32_t kernel_y = filter_dims->h; - const int32_t pad_x = dw_conv_params->padding.w; - const int32_t pad_y = dw_conv_params->padding.h; - const int32_t stride_x = dw_conv_params->stride.w; - const int32_t stride_y = dw_conv_params->stride.h; - const int32_t *output_shift = quant_params->shift; - const int32_t *output_mult = quant_params->multiplier; - const int32_t output_x = output_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_offset = dw_conv_params->output_offset; - const int32_t input_offset = dw_conv_params->input_offset; - const int32_t output_activation_min = dw_conv_params->activation.min; - const int32_t output_activation_max = dw_conv_params->activation.max; - q15_t *buffer_a = (q15_t *)ctx->buf; - - /* Check input constraints input_ch == output_ch */ - if (input_ch != output_ch) - { - return ARM_MATH_SIZE_MISMATCH; - } -#ifdef ARM_MATH_MVEI - (void)bias_dims; - /* Generate two columns from the input tensor */ - q7_t *lhs_buffer = (q7_t *)buffer_a; - q7_t *out = output; - int padded = 0; - int buffer_count = 0; - const int32_t kernel_size = kernel_x * kernel_y; - - /* This part implements the im2col function */ - for (int i_out_y = 0, base_idx_y = -pad_y; i_out_y < output_y; base_idx_y += stride_y, i_out_y++) - { - for (int i_out_x = 0, base_idx_x = -pad_x; i_out_x < output_x; base_idx_x += stride_x, i_out_x++) - { - for (int i_ker_y = base_idx_y; i_ker_y < base_idx_y + kernel_y; i_ker_y++) - { - for (int i_ker_x = base_idx_x; i_ker_x < base_idx_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - arm_memset_q7(lhs_buffer, (int8_t)-input_offset, (uint32_t)input_ch); - padded = 1; - } - else - { - arm_memcpy_q7(lhs_buffer, input + (i_ker_y * input_x + i_ker_x) * input_ch, (uint32_t)input_ch); - } - lhs_buffer += input_ch; - } - } - buffer_count++; - - if (buffer_count == 4) - { - lhs_buffer = (q7_t *)buffer_a; - if (padded == 0) - { - out = arm_nn_depthwise_conv_nt_t_s8(lhs_buffer, - kernel, - input_offset, - input_ch, - output_shift, - output_mult, - output_offset, - output_activation_min, - output_activation_max, - kernel_size, - bias, - out); - } - else - { - out = arm_nn_depthwise_conv_nt_t_padded_s8(lhs_buffer, - kernel, - input_offset, - input_ch, - output_shift, - output_mult, - output_offset, - output_activation_min, - output_activation_max, - kernel_size, - bias, - out); - padded = 0; - } - buffer_count = 0; - } - } - } - - /* Handle left over buffers */ - lhs_buffer = (q7_t *)buffer_a; - - for (int i_buf = 0; i_buf < buffer_count; i_buf++) - { - int32_t loop_count = (input_ch + 3) / 4; - - int32_t num_ch_to_process = input_ch; - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, offset += 4, i_loop_cnt++) - { - const int8_t *col_0 = lhs_buffer + (kernel_size * input_ch * i_buf) + offset; - const int8_t *row_0 = kernel + offset; - int32x4_t out_0 = vldrwq_s32(&bias[offset]); - - for (int i_ker = 0; i_ker < kernel_size; i_ker++) - { - const int32x4_t ker_0 = vldrbq_s32(row_0); - - int32x4_t ip_0 = vldrbq_s32(col_0); - ip_0 = vaddq_n_s32(ip_0, input_offset); - out_0 += vmulq_s32(ip_0, ker_0); - - col_0 += input_ch; - row_0 += input_ch; - } - - const int32x4_t mult = vldrwq_s32(&output_mult[offset]); - const int32x4_t shift = vldrwq_s32(&output_shift[offset]); - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, output_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(output_activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(output_activation_max)); - mve_pred16_t p = vctp32q((uint32_t)num_ch_to_process); - vstrbq_p_s32(out, out_0, p); - - out += 4; - } - - const int tail_ch = input_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - } - -#elif defined(ARM_MATH_DSP) - (void)bias_dims; - /* Run the following code in cores using DSP extension */ - q15_t *const col_buffer_start = buffer_a; - q15_t *col_buffer = col_buffer_start; - const int32_t *const bias_start_pos = bias; - const q31_t *const out_mult_start_pos = output_mult; - const q31_t *const out_shift_start_pos = output_shift; - uint16_t row_count; - uint16_t row_shift; - - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - - /* Out of bounds is only considered for the y axis as it provides a contiguous zero'ing opportunity than along - the x axis */ - const int ker_y_start = MAX(0, -base_idx_y); - /* Condition for kernel end dimension: (base_idx_y + ker_y_end) < input_y */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - - int32_t index = 0; - if (ker_y_start != 0) - { - memset(&col_buffer[index], 0, (kernel_x * input_ch) * ker_y_start * sizeof(q15_t)); - index += (kernel_x * input_ch) * ker_y_start; - } - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - - for (int i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - if (idx_x < 0 || idx_x >= input_x) - { - memset(&col_buffer[index], 0, input_ch * sizeof(q15_t)); - } - else - { - arm_q7_to_q15_with_offset((q7_t *)input + (idx_y * input_x + idx_x) * input_ch, &col_buffer[index], input_ch, input_offset); - } - index += input_ch; - } - } - - const int diff = kernel_y - ker_y_end; - if (diff != 0) - { - memset(&col_buffer[index], 0, (kernel_x * input_ch) * diff * sizeof(q15_t)); - } - - row_count = output_ch / 4; - row_shift = 0; - bias = bias_start_pos; - output_mult = out_mult_start_pos; - output_shift = out_shift_start_pos; - - while (row_count) - { - q31_t sum = *bias++; - q31_t sum_2 = *bias++; - q31_t sum_3 = *bias++; - q31_t sum_4 = *bias++; - - uint16_t col_count = (kernel_x * kernel_y) / 2; - q15_t *col_pos = col_buffer_start + row_shift; - const q7_t *row_pos = kernel + row_shift; - row_shift += 4; - - while (col_count) - { - /* General idea is to read 4 + 4 (input, kernel) pair and re-arrange them in the right order to - use in a SMLAD instruction . One run of this loop produces 4 partial outputs with 8 MACs. */ - /* Note: variable names can be improved here to align with rows and columns. */ - q31_t ip_a1, ip_a2, ip_b1, ip_b2, op_a, op_b, op_c; - /* Read 4 weights */ - ip_b1 = arm_nn_read_q7x4(row_pos); - ip_a1 = arm_nn_read_q7x4(row_pos + input_ch); - op_a = arm_nn_read_q15x2(col_pos); - op_b = arm_nn_read_q15x2(col_pos + input_ch); - - ip_a2 = __SXTB16(ip_b1); - ip_b1 = __SXTB16(__ROR(ip_b1, 8)); - - ip_b2 = __SXTB16(ip_a1); - ip_a1 = __SXTB16(__ROR(ip_a1, 8)); - - op_c = __PKHBT(op_b, op_a, 16); - op_a = __PKHTB(op_b, op_a, 16); - op_b = __PKHBT(ip_b2, ip_a2, 16); - sum = __SMLAD(op_c, op_b, sum); - - op_b = __PKHBT(ip_b1, ip_a1, 16); - sum_2 = __SMLAD(op_a, op_b, sum_2); - - op_a = arm_nn_read_q15x2(col_pos + 2); - op_b = arm_nn_read_q15x2(col_pos + input_ch + 2); - - op_c = __PKHBT(op_b, op_a, 16); - op_a = __PKHTB(op_b, op_a, 16); - op_b = __PKHTB(ip_a2, ip_b2, 16); - sum_3 = __SMLAD(op_c, op_b, sum_3); - - op_b = __PKHTB(ip_a1, ip_b1, 16); - sum_4 = __SMLAD(op_a, op_b, sum_4); - - row_pos += input_ch << 1; - col_pos += input_ch << 1; - col_count--; - } - - col_count = (kernel_x * kernel_y) & 0x1; - while (col_count) - { - sum += row_pos[0] * col_pos[0]; - sum_2 += row_pos[1] * col_pos[1]; - sum_3 += row_pos[2] * col_pos[2]; - sum_4 += row_pos[3] * col_pos[3]; - - row_pos += input_ch; - col_pos += input_ch; - - col_count--; - } - sum = arm_nn_requantize(sum, *output_mult++, *output_shift++); - sum += output_offset; - sum = MAX(sum, output_activation_min); - sum = MIN(sum, output_activation_max); - *output++ = (q7_t)sum; - - sum_2 = arm_nn_requantize(sum_2, *output_mult++, *output_shift++); - sum_2 += output_offset; - sum_2 = MAX(sum_2, output_activation_min); - sum_2 = MIN(sum_2, output_activation_max); - *output++ = (q7_t)sum_2; - sum_3 = arm_nn_requantize(sum_3, *output_mult++, *output_shift++); - sum_3 += output_offset; - sum_3 = MAX(sum_3, output_activation_min); - sum_3 = MIN(sum_3, output_activation_max); - *output++ = (q7_t)sum_3; - - sum_4 = arm_nn_requantize(sum_4, *output_mult++, *output_shift++); - sum_4 += output_offset; - sum_4 = MAX(sum_4, output_activation_min); - sum_4 = MIN(sum_4, output_activation_max); - *output++ = (q7_t)sum_4; - - row_count--; - } - - row_count = output_ch & 0x3; - while (row_count) - { - q15_t *col_pos = col_buffer_start + row_shift; - const q7_t *row_pos = kernel + row_shift; - q31_t sum = *bias++; - const uint16_t col_count = (kernel_x * kernel_y); - row_shift += 1; - - for (int i = 0; i < col_count; i++) - { - sum += row_pos[i * input_ch] * col_pos[i * input_ch]; - } - sum = arm_nn_requantize(sum, *output_mult++, *output_shift++); - sum += output_offset; - sum = MAX(sum, output_activation_min); - sum = MIN(sum, output_activation_max); - *output++ = (q7_t)sum; - - row_count--; - } - - // clear counter and pointers - col_buffer = col_buffer_start; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - return arm_depthwise_conv_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - kernel, - bias_dims, - bias, - output_dims, - output); -#endif /* ARM_MATH_MVEI | ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_depthwise_conv_s8_opt_get_buffer_size(const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims) -{ -#if defined(ARM_MATH_MVEI) - /* The + 4 accounts for out of bounds read of the lhs buffers in the *_nt_t_* functions. */ - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t) + 4; -#elif defined(ARM_MATH_DSP) - return (input_dims->c * filter_dims->w * filter_dims->h) * sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c deleted file mode 100644 index d5fa36c6..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c +++ /dev/null @@ -1,294 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_u8_basic_ver1.c - * Description: u8 depthwise convolution function - * - * $Date: May 29, 2020 - * $Revision: V.1.1.0 - * - * Target : Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -static void depthwise_conv_u8_mult_4(const uint8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const uint8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - uint8_t *output, - const int32_t output_shift, - const int32_t output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch; ++in_ch, out_ch += ch_mult) - { - for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4) - { - int32_t out_buff[4]; - - out_buff[0] = 0; - out_buff[1] = 0; - out_buff[2] = 0; - out_buff[3] = 0; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h) - { - int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch; - int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch; - - for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w); ++ker_w, ker_idx += output_ch) - { - int32_t in_val = input[in_idx + ker_w * input_ch] + input_offset; - out_buff[0] += in_val * (kernel[ker_idx + 0 + mult_tile] + filter_offset); - out_buff[1] += in_val * (kernel[ker_idx + 1 + mult_tile] + filter_offset); - out_buff[2] += in_val * (kernel[ker_idx + 2 + mult_tile] + filter_offset); - out_buff[3] += in_val * (kernel[ker_idx + 3 + mult_tile] + filter_offset); - } - } - - if (bias != NULL) - { - out_buff[0] += bias[out_ch + 0 + mult_tile]; - out_buff[1] += bias[out_ch + 1 + mult_tile]; - out_buff[2] += bias[out_ch + 2 + mult_tile]; - out_buff[3] += bias[out_ch + 3 + mult_tile]; - } - out_buff[0] = arm_nn_requantize(out_buff[0], output_mult, output_shift); - out_buff[1] = arm_nn_requantize(out_buff[1], output_mult, output_shift); - out_buff[2] = arm_nn_requantize(out_buff[2], output_mult, output_shift); - out_buff[3] = arm_nn_requantize(out_buff[3], output_mult, output_shift); - - out_buff[0] += output_offset; - out_buff[1] += output_offset; - out_buff[2] += output_offset; - out_buff[3] += output_offset; - - out_buff[0] = MIN(MAX(out_buff[0], output_activation_min), output_activation_max); - out_buff[1] = MIN(MAX(out_buff[1], output_activation_min), output_activation_max); - out_buff[2] = MIN(MAX(out_buff[2], output_activation_min), output_activation_max); - out_buff[3] = MIN(MAX(out_buff[3], output_activation_min), output_activation_max); - - output[out_idx++] = (uint8_t)out_buff[0]; - output[out_idx++] = (uint8_t)out_buff[1]; - output[out_idx++] = (uint8_t)out_buff[2]; - output[out_idx++] = (uint8_t)out_buff[3]; - } - } - } - } -} - -static void depthwise_conv_u8_generic(const uint8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const uint8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - uint8_t *output, - const int32_t output_shift, - const int32_t output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - (void)output_ch; - int i_out = 0; - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) - { - const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult; - int32_t acc_0; - /* Condition for kernel start dimension: (base_idx_ + ker__start) >= 0 */ - const int ker_y_start = MAX(0, -base_idx_y); - const int ker_x_start = MAX(0, -base_idx_x); - /* Condition for kernel end dimension: (base_idx_ + ker__end) < input_ */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int ker_x_end = MIN(kernel_x, input_x - base_idx_x); - acc_0 = 0; - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch; - int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch; - - acc_0 += (input[idx_0] + input_offset) * (kernel[ker_idx_0] + filter_offset); - } - } - if (bias != NULL) - { - acc_0 += bias[idx_out_ch]; - } - - /* Requantize and clamp output to provided range */ - acc_0 = arm_nn_requantize(acc_0, output_mult, output_shift); - acc_0 += output_offset; - acc_0 = MAX(acc_0, output_activation_min); - acc_0 = MIN(acc_0, output_activation_max); - - output[i_out++] = acc_0; - } - } - } - } -} - -/** - * @brief uint8 depthwise convolution function with asymmetric quantization - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_ch Channels in input tensor - * @param[in] kernel Pointer to kernel weights - * @param[in] kernel_x Width of kernel - * @param[in] kernel_y Height of kernel - * @param[in] ch_mult Number of channel multiplier - * @param[in] pad_x Padding sizes x - * @param[in] pad_y Padding sizes y - * @param[in] stride_x Convolution stride along the width - * @param[in] stride_y Convolution stride along the height - * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement. - * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement. - * @param[in] bias Pointer to optional bias values. If no bias is - * availble, NULL is expected - * @param[in] input_offset Input tensor zero offset - * @param[in] filter_offset Kernel tensor zero offset - * @param[in] output_offset Output tensor zero offset - * @param[in,out] output Pointer to output tensor - * @param[in] output_x Width of output tensor - * @param[in] output_y Height of output tensor - * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_shift Amount of right-shift for output - * @param[in] output_mult Output multiplier for requantization - * @return The function returns one of the following - * ARM_MATH_SIZE_MISMATCH - Not supported dimension of tensors - * ARM_MATH_SUCCESS - Successful operation - * ARM_MATH_ARGUMENT_ERROR - Implementation not available - * - * - */ - -arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const uint8_t *kernel, - const uint16_t kernel_x, - const uint16_t kernel_y, - const int16_t ch_mult, - const int16_t pad_x, - const int16_t pad_y, - const int16_t stride_x, - const int16_t stride_y, - const int16_t dilation_x, - const int16_t dilation_y, - const int32_t *bias, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_offset, - uint8_t *output, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_activation_min, - const int32_t output_activation_max, - const int32_t output_shift, - const int32_t output_mult) -{ - (void)dilation_x; - (void)dilation_y; - - if (ch_mult % 4 == 0) - { - depthwise_conv_u8_mult_4(input, input_x, input_y, input_ch, kernel, ch_mult * input_ch, ch_mult, - kernel_x, kernel_y, pad_x, pad_y, stride_x, stride_y, bias, output, - output_shift, output_mult, output_x, output_y, output_offset, input_offset, - filter_offset, output_activation_min, output_activation_max); - } - else - { - depthwise_conv_u8_generic(input, input_x, input_y, input_ch, kernel, ch_mult * input_ch, ch_mult, - kernel_x, kernel_y, pad_x, pad_y, stride_x, stride_y, bias, - output, output_shift, output_mult, output_x, output_y, output_offset, - input_offset, filter_offset, output_activation_min, output_activation_max); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c deleted file mode 100644 index 42d425f0..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c +++ /dev/null @@ -1,133 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_wrapper_s8.c - * Description: Wrapper API to select appropriate depthwise conv API based - * on dimensions. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * s8 Depthwise conv wrapper function - * - * Refer header file for details. - * - */ -arm_status arm_depthwise_conv_wrapper_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *filter, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - arm_status status = ARM_MATH_SUCCESS; - if (1 == dw_conv_params->ch_mult) - { -#if !defined(ARM_MATH_MVEI) - if ((filter_dims->w == 3) && (filter_dims->h == 3) && (dw_conv_params->padding.h <= 1)) - { - status = arm_depthwise_conv_3x3_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - else -#endif - { - status = arm_depthwise_conv_s8_opt(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - } - else - { - status = arm_depthwise_conv_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - - /* Return to application */ - return status; -} - -int32_t arm_depthwise_conv_wrapper_s8_get_buffer_size(const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims) -{ - (void)dw_conv_params; - int32_t size = 0; - - if (input_dims->c == output_dims->c) - { - size = arm_depthwise_conv_s8_opt_get_buffer_size(input_dims, filter_dims); - } - - return size; -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c deleted file mode 100644 index 2767ff47..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c +++ /dev/null @@ -1,418 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_separable_conv_HWC_q7.c - * Description: Q7 depthwise separable convolution function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Q7 depthwise separable convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in equals ch_im_out - * - * Implementation: - * There are 3 nested loop here: - * Inner loop: calculate each output value with MAC instruction over an accumulator - * Mid loop: loop over different output channel - * Outer loop: loop over different output (x, y) - */ - -arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x; - int16_t i_ker_y, i_ker_x; - q7_t *colBuffer = (q7_t *) bufferA; - q7_t *pBuffer = colBuffer; - const q7_t *pBias = bias; - q7_t *pOut = Im_out; - uint16_t rowCnt; - uint16_t row_shift; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* we first do im2col here */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q7(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, ch_im_in); - } else - { - /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* we will do the computation here for each channel */ - rowCnt = ch_im_out >> 2; - row_shift = 0; - pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = (dim_kernel * dim_kernel) >> 1; - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - row_shift += 4; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHTB(opB, inB1, 16); - inB1 = __PKHBT(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHTB(opB, inA1, 16); - inA1 = __PKHBT(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum3 = __SMLAD(opA, opB, sum3); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum4 = __SMLAD(opA, opB, sum4); - colCnt--; - } -#else - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHBT(opB, inB1, 16); - inB1 = __PKHTB(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHBT(opB, inA1, 16); - inA1 = __PKHTB(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum4 = __SMLAD(opA, opB, sum4); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum3 = __SMLAD(opA, opB, sum3); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - -#ifndef ARM_MATH_BIG_ENDIAN - /* - * r0 r1 r2 r3 r4 r5 - * inA1, inA2, inB1, inB2, opA, opB - */ - - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhtb r3, r5, r2, ASR #16\n" - "pkhbt r2, r2, r5, LSL #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhtb r1, r5, r0, ASR #16\n" - "pkhbt r0, r0, r5, LSL #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum], r4, r5, %[sum]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] - "+r"(sum),[sum2] "+r"(sum2), - [sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB), - [pA] "+r"(pA):[colCnt] - "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#else - /* - * r0 r1 r2 r3 r4 r5 - * inA1, inA2, inB1, inB2, opA, opB - */ - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhbt r3, r5, r2, LSL #16\n" - "pkhtb r2, r2, r5, ASR #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhbt r1, r5, r0, LSL #16\n" - "pkhtb r0, r0, r5, ASR #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum], r4, r5, %[sum]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] - "+r"(sum),[sum2] "+r"(sum2), - [sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB), - [pA] "+r"(pA):[colCnt] - "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = (dim_kernel * dim_kernel) & 0x1; - while (colCnt) - { - union arm_nnword inA, inB; - inA.word = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inB.word = arm_nn_read_q7x4(pB); - pB += ch_im_in; - sum += inA.bytes[0] * inB.bytes[0]; - sum2 += inA.bytes[1] * inB.bytes[1]; - sum3 += inA.bytes[2] * inB.bytes[2]; - sum4 += inA.bytes[3] * inB.bytes[3]; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = ch_im_out & 0x3; - while (rowCnt) - { - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = (dim_kernel * dim_kernel); - - row_shift += 1; - - while (colCnt) - { - q7_t A1 = *pA; - q7_t B1 = *pB; - pA += ch_im_in; - pB += ch_im_in; - sum += A1 * B1; - - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - rowCnt--; - } - - /* clear counter and pointers */ - pBuffer = colBuffer; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y; - int conv_out; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - // for each output - conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++) - { - for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++) - { - int in_row = stride * i_out_y + i_ker_y - padding; - int in_col = stride * i_out_x + i_ker_x - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - conv_out += - Im_in[(in_row * - dim_im_in + - in_col) * - ch_im_in + - i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out]; - } - } - } - Im_out[(i_out_y * dim_im_out + - i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; - -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c deleted file mode 100644 index adeb8c45..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c +++ /dev/null @@ -1,413 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_separable_conv_HWC_q7_nonsquare.c - * Description: Q7 depthwise separable convolution function (non-square shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Q7 depthwise separable convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding sizes x - * @param[in] padding_y padding sizes y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is equal to ch_im_out - * - */ - -arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - - (void)bufferB; - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - -/* - * Implementation: - * There are 3 nested loop here: - * Inner loop: calculate each output value with MAC instruction over an accumulator - * Mid loop: loop over different output channel - * Outer loop: loop over different output (x, y) - * - */ - - int16_t i_out_y, i_out_x; - int16_t i_ker_y, i_ker_x; - q7_t *colBuffer = (q7_t *) bufferA; - q7_t *pBuffer = colBuffer; - const q7_t *pBias = bias; - q7_t *pOut = Im_out; - uint16_t rowCnt; - uint16_t row_shift; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* we first do im2col here */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q7(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, ch_im_in); - } else - { - /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* we will do the computation here for each channel */ - rowCnt = ch_im_out >> 2; - row_shift = 0; - pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = (dim_kernel_x * dim_kernel_y) >> 1; - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - row_shift += 4; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHTB(opB, inB1, 16); - inB1 = __PKHBT(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHTB(opB, inA1, 16); - inA1 = __PKHBT(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum3 = __SMLAD(opA, opB, sum3); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum4 = __SMLAD(opA, opB, sum4); - colCnt--; - } -#else - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHBT(opB, inB1, 16); - inB1 = __PKHTB(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHBT(opB, inA1, 16); - inA1 = __PKHTB(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum4 = __SMLAD(opA, opB, sum4); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum3 = __SMLAD(opA, opB, sum3); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - -#ifndef ARM_MATH_BIG_ENDIAN - // r0 r1 r2 r3 r4 r5 - // inA1, inA2, inB1, inB2, opA, opB - asm volatile ("COL_LOOP:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhtb r3, r5, r2, ASR #16\n" - "pkhbt r2, r2, r5, LSL #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhtb r1, r5, r0, ASR #16\n" - "pkhbt r0, r0, r5, LSL #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum], r4, r5, %[sum]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt), - [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#else - // r0 r1 r2 r3 r4 r5 - // inA1, inA2, inB1, inB2, opA, opB - asm volatile ("COL_LOOP:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhbt r3, r5, r2, LSL #16\n" - "pkhtb r2, r2, r5, ASR #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhbt r1, r5, r0, LSL #16\n" - "pkhtb r0, r0, r5, ASR #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum], r4, r5, %[sum]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt), - [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#endif /*ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = (dim_kernel_x * dim_kernel_y) & 0x1; - while (colCnt) - { - union arm_nnword inA, inB; - inA.word = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inB.word = arm_nn_read_q7x4(pB); - pB += ch_im_in; - sum += inA.bytes[0] * inB.bytes[0]; - sum2 += inA.bytes[1] * inB.bytes[1]; - sum3 += inA.bytes[2] * inB.bytes[2]; - sum4 += inA.bytes[3] * inB.bytes[3]; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = ch_im_out & 0x3; - while (rowCnt) - { - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = (dim_kernel_x * dim_kernel_y); - - row_shift += 1; - - while (colCnt) - { - q7_t A1 = *pA; - q7_t B1 = *pB; - pA += ch_im_in; - pB += ch_im_in; - sum += A1 * B1; - - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - rowCnt--; - } - - // clear counter and pointers - pBuffer = colBuffer; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i_out_y, i_out_x, i_ch_out; - int i_ker_y, i_ker_x; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - // for each output - int conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++) - { - for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++) - { - int in_row = stride_y * i_out_y + i_ker_y - padding_y; - int in_col = stride_x * i_out_x + i_ker_x - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] * - wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out]; - } - } - } - Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] = - (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - - /* Return to application */ - return ARM_MATH_SUCCESS; - -} - -/** - * @} end of NNConv group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c deleted file mode 100644 index f9106341..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c +++ /dev/null @@ -1,219 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_s8_core.c - * Description: Depthwise convolution on im2col buffers. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.3 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * Depthwise conv on an im2col buffer where the input channel equals - * output channel. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_s8_core(const q7_t *row, - const q15_t *col, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t kernel_size, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - int32_t ch_per_loop = num_ch / 4; - - const int32_t *bias = output_bias; - int8_t *out_tmp = out; - - int32_t idx = 0; - - while (ch_per_loop > 0) - { - int32x4_t ip_0; - int32x4_t ip_1; - int32_t ker_loop = kernel_size / 3; - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - bias += 4; - - const int32_t offset = idx * 4; - const int8_t *row_0 = row + offset; - const int16_t *col_0 = col + offset; - const int16_t *col_1 = col + kernel_size * num_ch + offset; - - int32x4_t ker_0 = vldrbq_s32(row_0); - - while (ker_loop > 0) - { - const int8_t *row_1 = row_0 + num_ch; - const int8_t *row_2 = row_0 + 2 * num_ch; - const int32x4_t ker_1 = vldrbq_s32(row_1); - const int32x4_t ker_2 = vldrbq_s32(row_2); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_0); - out_1 += vmulq_s32(ip_1, ker_0); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_1); - out_1 += vmulq_s32(ip_1, ker_1); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_2); - out_1 += vmulq_s32(ip_1, ker_2); - row_0 += 3 * num_ch; - - ker_0 = vldrbq_s32(row_0); - ker_loop--; - } - - idx++; - /* Handle tail kernel elements */ - ker_loop = kernel_size - ((kernel_size / 3) * 3); - while (ker_loop > 0) - { - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - - out_0 += vmulq_s32(ip_0, ker_0); - out_1 += vmulq_s32(ip_1, ker_0); - - col_0 += num_ch; - col_1 += num_ch; - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - - row_0 += num_ch; - ker_0 = vldrbq_s32(row_0); - ker_loop--; - } - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - vstrbq_s32(out_tmp, out_0); - - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_s32(out_tmp + num_ch, out_1); - - out_tmp += 4; - ch_per_loop--; - } - - int32_t tail_ch = num_ch & 3; - if (tail_ch != 0) - { - int32_t ch_idx = (num_ch & ~3); - int32x4_t col_0_sum; - int32x4_t col_1_sum; - - const int32_t single_buffer_size = kernel_size * num_ch; - for (int i = 0; i < tail_ch; i++) - { - const int16_t *col_pos_0 = col + ch_idx; - const int16_t *col_pos_1 = col_pos_0 + single_buffer_size; - - const int8_t *row_pos = row + ch_idx; - int32_t sum_0 = bias[i]; - int32_t sum_1 = bias[i]; - - for (int j = 0; j < kernel_size; j++) - { - const int8_t row_val = row_pos[j * num_ch]; - sum_0 += row_val * col_pos_0[j * num_ch]; - sum_1 += row_val * col_pos_1[j * num_ch]; - } - col_0_sum[i] = sum_0; - col_1_sum[i] = sum_1; - - ch_idx++; - } - const mve_pred16_t p = vctp32q((uint32_t)tail_ch); - const int32x4_t mult = vldrwq_z_s32(out_mult, p); - const int32x4_t shift = vldrwq_z_s32(out_shift, p); - - col_0_sum = arm_requantize_mve_32x4(col_0_sum, mult, shift); - col_1_sum = arm_requantize_mve_32x4(col_1_sum, mult, shift); - - col_0_sum = vaddq_n_s32(col_0_sum, out_offset); - col_0_sum = vmaxq_s32(col_0_sum, vdupq_n_s32(activation_min)); - col_0_sum = vminq_s32(col_0_sum, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_tmp, col_0_sum, p); - - col_1_sum = vaddq_n_s32(col_1_sum, out_offset); - col_1_sum = vmaxq_s32(col_1_sum, vdupq_n_s32(activation_min)); - col_1_sum = vminq_s32(col_1_sum, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_tmp + num_ch, col_1_sum, p); - - out_tmp += tail_ch; - } - - return out_tmp + num_ch; -#else - (void)row; - (void)col; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)kernel_size; - (void)output_bias; - (void)out; - return NULL; -#endif -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c deleted file mode 100644 index 49b3ba47..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c +++ /dev/null @@ -1,179 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_q7_q15.c - * Description: Matrix-multiplication function for convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - - /** - * @brief Matrix-multiplication function for convolution. - * - * @details Refer to header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut) -{ -#if defined (ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *pOut2 = pOut + ch_im_out; - const q7_t *pBias = bias; - - uint16_t rowCnt = ch_im_out >> 1; - /* this loop over rows in A */ - while (rowCnt) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* align the second pointer for A */ - const q7_t *pA2 = pA + numCol_A; - - /* init the sum with bias */ - q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA11, inA12, inA21, inA22; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad(pA, &inA11, &inA12); - pA2 = read_and_pad(pA2, &inA21, &inA22); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - sum3 = __SMLAD(inA21, inB1, sum3); - sum4 = __SMLAD(inA21, inB2, sum4); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - sum3 = __SMLAD(inA22, inB1, sum3); - sum4 = __SMLAD(inA22, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q7_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - /* skip the row computed with A2 */ - pA += numCol_A; - rowCnt--; - } /* for over ch_im_out */ - - /* compute left-over row if any */ - if (ch_im_out & 0x1) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* load the bias */ - q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - while (colCnt) - { - q31_t inA11, inA12; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad(pA, &inA11, &inA12); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - - colCnt--; - } - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - } - - pOut += ch_im_out; - - /* return the new output pointer with offset */ - return pOut; -#else - /* To be completed */ - return NULL; -#endif /* ARM_MATH_DSP */ - -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c deleted file mode 100644 index b5bbc397..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_q7_q15_reordered.c - * Description: Matrix-multiplication function for convolution with reordered columns - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - - /** - * @brief Matrix-multiplication function for convolution with re-ordered input. - * - * @details Refer to header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut) -{ - -#if defined (ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *pOut2 = pOut + ch_im_out; - int i; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* align the second pointer for A */ - const q7_t *pA2 = pA + numCol_A; - - /* init the sum with bias */ - q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA11, inA12, inA21, inA22; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad_reordered(pA, &inA11, &inA12); - pA2 = read_and_pad_reordered(pA2, &inA21, &inA22); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - sum3 = __SMLAD(inA21, inB1, sum3); - sum4 = __SMLAD(inA21, inB2, sum4); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - sum3 = __SMLAD(inA22, inB1, sum3); - sum4 = __SMLAD(inA22, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q7_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - /* skip the row computed with A2 */ - pA += numCol_A; - } /* for over ch_im_out */ - - pOut += ch_im_out; - - /* return the new output pointer with offset */ - return pOut; -#else - /* To be completed */ - return NULL; -#endif /* ARM_MATH_DSP */ -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c deleted file mode 100644 index 9aa17c96..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c +++ /dev/null @@ -1,391 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_s8_s16.c - * Description: Matrix-multiplication function for convolution - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * Matrix-multiplication function for convolution with per-channel requantization. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_s8_s16(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0) -{ -#if defined(ARM_MATH_MVEI) -#define ROW_PER_LOOP (4) -#define COL_PER_LOOP (8) - - const q7_t *ip_a0_s8 = input_a; - q7_t *out_1 = out_0 + output_ch; - - const int32_t *bias = output_bias; - - int32_t row_count = output_ch / ROW_PER_LOOP; - - while (row_count) - { - const q15_t *ip_b0_s16 = input_b; - const q15_t *ip_b1_s16 = input_b + num_col_a; - - const q7_t *ip_a1_s8 = ip_a0_s8 + num_col_a; - const q7_t *ip_a2_s8 = ip_a0_s8 + num_col_a * 2; - const q7_t *ip_a3_s8 = ip_a0_s8 + num_col_a * 3; - - q31_t ch_0_out_n = bias[0]; - q31_t ch_1_out_n = bias[1]; - q31_t ch_2_out_n = bias[2]; - q31_t ch_3_out_n = bias[3]; - - q31_t ch_0_out_n1 = ch_0_out_n; - q31_t ch_1_out_n1 = ch_1_out_n; - q31_t ch_2_out_n1 = ch_2_out_n; - q31_t ch_3_out_n1 = ch_3_out_n; - bias += 4; - - int32_t col_count = num_col_a / COL_PER_LOOP; - - while (col_count) - { - // Load inputs - const int16x8_t ip_b0 = vld1q_s16(ip_b0_s16); - ip_b0_s16 += COL_PER_LOOP; - const int16x8_t ip_b1 = vld1q_s16(ip_b1_s16); - ip_b1_s16 += COL_PER_LOOP; - - // Load filters - const int16x8_t ip_a0 = vldrbq_s16(ip_a0_s8); - ip_a0_s8 += COL_PER_LOOP; - const int16x8_t ip_a1 = vldrbq_s16(ip_a1_s8); - ip_a1_s8 += COL_PER_LOOP; - const int16x8_t ip_a2 = vldrbq_s16(ip_a2_s8); - ip_a2_s8 += COL_PER_LOOP; - const int16x8_t ip_a3 = vldrbq_s16(ip_a3_s8); - ip_a3_s8 += COL_PER_LOOP; - - // MAC - ch_0_out_n += vmladavq_s16(ip_b0, ip_a0); - ch_1_out_n += vmladavq_s16(ip_b0, ip_a1); - ch_2_out_n += vmladavq_s16(ip_b0, ip_a2); - ch_3_out_n += vmladavq_s16(ip_b0, ip_a3); - ch_0_out_n1 += vmladavq_s16(ip_b1, ip_a0); - ch_1_out_n1 += vmladavq_s16(ip_b1, ip_a1); - ch_2_out_n1 += vmladavq_s16(ip_b1, ip_a2); - ch_3_out_n1 += vmladavq_s16(ip_b1, ip_a3); - - col_count--; - } - - /* Handle tail */ - col_count = (num_col_a & (COL_PER_LOOP - 1)) - 1; - while (col_count >= 0) - { - const int32_t b0 = ip_b0_s16[col_count]; - const int32_t b1 = ip_b1_s16[col_count]; - - ch_0_out_n += b0 * ip_a0_s8[col_count]; - ch_1_out_n += b0 * ip_a1_s8[col_count]; - ch_2_out_n += b0 * ip_a2_s8[col_count]; - ch_3_out_n += b0 * ip_a3_s8[col_count]; - - ch_0_out_n1 += b1 * ip_a0_s8[col_count]; - ch_1_out_n1 += b1 * ip_a1_s8[col_count]; - ch_2_out_n1 += b1 * ip_a2_s8[col_count]; - ch_3_out_n1 += b1 * ip_a3_s8[col_count]; - col_count--; - } - ip_a0_s8 += (num_col_a & (COL_PER_LOOP - 1)); - - int32x4_t out_vec_0; - int32x4_t out_vec_1; - out_vec_0[0] = ch_0_out_n; - out_vec_0[1] = ch_1_out_n; - out_vec_0[2] = ch_2_out_n; - out_vec_0[3] = ch_3_out_n; - - out_vec_1[0] = ch_0_out_n1; - out_vec_1[1] = ch_1_out_n1; - out_vec_1[2] = ch_2_out_n1; - out_vec_1[3] = ch_3_out_n1; - - int32x4_t mult = vldrwq_s32(out_mult); - int32x4_t shift = vldrwq_s32(out_shift); - out_mult += ROW_PER_LOOP; - out_shift += ROW_PER_LOOP; - - out_vec_0 = arm_requantize_mve_32x4(out_vec_0, mult, shift); - out_vec_1 = arm_requantize_mve_32x4(out_vec_1, mult, shift); - - out_vec_0 = vaddq_n_s32(out_vec_0, out_offset); - out_vec_0 = vmaxq_s32(out_vec_0, vdupq_n_s32(activation_min)); - out_vec_0 = vminq_s32(out_vec_0, vdupq_n_s32(activation_max)); - vstrbq_s32(out_0, out_vec_0); - out_0 += ROW_PER_LOOP; - - out_vec_1 = vaddq_n_s32(out_vec_1, out_offset); - out_vec_1 = vmaxq_s32(out_vec_1, vdupq_n_s32(activation_min)); - out_vec_1 = vminq_s32(out_vec_1, vdupq_n_s32(activation_max)); - vstrbq_s32(out_1, out_vec_1); - out_1 += ROW_PER_LOOP; - row_count--; - ip_a0_s8 += (num_col_a * 3); - } - - row_count = output_ch & (ROW_PER_LOOP - 1); - - if (row_count) - { - ip_a0_s8 = input_a + num_col_a * (output_ch & ~3); - const mve_pred16_t p = vctp32q((uint32_t)row_count); - int32x4_t out_vec_0 = vdupq_n_s32(0); - int32x4_t out_vec_1 = vdupq_n_s32(0); - int32x4_t mult_tail; - int32x4_t shift_tail; - - for (int i_ch = 0; i_ch < row_count; i_ch++) - { - int32_t output_0 = bias[i_ch]; - int32_t output_1 = bias[i_ch]; - const q15_t *ip_b0_s16 = input_b; - const q15_t *ip_b1_s16 = input_b + num_col_a; - - for (int i_idx = 0; i_idx < num_col_a; i_idx++) - { - output_0 += ip_b0_s16[i_idx] * ip_a0_s8[i_idx]; - output_1 += ip_b1_s16[i_idx] * ip_a0_s8[i_idx]; - } - - ip_a0_s8 += num_col_a; - out_vec_0[i_ch] = output_0; - out_vec_1[i_ch] = output_1; - mult_tail[i_ch] = out_mult[i_ch]; - shift_tail[i_ch] = out_shift[i_ch]; - } - out_vec_0 = arm_requantize_mve_32x4(out_vec_0, mult_tail, shift_tail); - out_vec_1 = arm_requantize_mve_32x4(out_vec_1, mult_tail, shift_tail); - - out_vec_0 = vaddq_n_s32(out_vec_0, out_offset); - out_vec_0 = vmaxq_s32(out_vec_0, vdupq_n_s32(activation_min)); - out_vec_0 = vminq_s32(out_vec_0, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_0, out_vec_0, p); - - out_vec_1 = vaddq_n_s32(out_vec_1, out_offset); - out_vec_1 = vmaxq_s32(out_vec_1, vdupq_n_s32(activation_min)); - out_vec_1 = vminq_s32(out_vec_1, vdupq_n_s32(activation_max)); - - vstrbq_p_s32(out_1, out_vec_1, p); - out_1 += row_count; - } - - return out_1; - -#elif defined(ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *out_1 = out_0 + output_ch; - const int32_t *bias = output_bias; - - uint16_t row_count = output_ch / 2; - const q7_t *ip_a0 = input_a; - /* this loop over rows in A */ - while (row_count) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* align the second pointer for A */ - const q7_t *ip_a1 = ip_a0 + num_col_a; - - /* Init accumulator with bias for channel N and N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - q31_t ch_1_out_0 = *bias; - q31_t ch_1_out_1 = *bias++; - - uint16_t col_count = num_col_a / 4; - /* accumulate over the vector */ - while (col_count) - { - q31_t a01, a02, a11, a12; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad(ip_a0, &a01, &a02); - ip_a1 = read_and_pad(ip_a1, &a11, &a12); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a11, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a11, b1, ch_1_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a12, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a12, b1, ch_1_out_1); - - col_count--; - } /* while over col_count */ - col_count = num_col_a & 0x3; - while (col_count) - { - q7_t a0 = *ip_a0++; - q15_t b0 = *ip_b0++; - q7_t a1 = *ip_a1++; - q15_t b1 = *ip_b1++; - - ch_0_out_0 += a0 * b0; - ch_0_out_1 += a0 * b1; - ch_1_out_0 += a1 * b0; - ch_1_out_1 += a1 * b1; - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - - ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift); - ch_1_out_0 += out_offset; - ch_1_out_0 = MAX(ch_1_out_0, activation_min); - ch_1_out_0 = MIN(ch_1_out_0, activation_max); - *out_0++ = (q7_t)ch_1_out_0; - - ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift); - ch_1_out_1 += out_offset; - ch_1_out_1 = MAX(ch_1_out_1, activation_min); - ch_1_out_1 = MIN(ch_1_out_1, activation_max); - *out_1++ = (q7_t)ch_1_out_1; - out_mult++; - out_shift++; - - /* skip row */ - ip_a0 += num_col_a; - row_count--; - } - - /* compute the last odd numbered row if any */ - if (output_ch & 0x1) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* load the bias */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - - uint16_t col_count = num_col_a >> 2; - while (col_count) - { - q31_t a01, a02; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad(ip_a0, &a01, &a02); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - - col_count--; - } - col_count = num_col_a & 0x3; - while (col_count) - { - q7_t a0 = *ip_a0++; - q15_t b0 = *ip_b0++; - q15_t b1 = *ip_b1++; - - ch_0_out_0 += a0 * b0; - ch_0_out_1 += a0 * b1; - col_count--; - } - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - } - - out_0 += output_ch; - - /* return the new output pointer with offset */ - return out_0; -#else - (void)input_a; - (void)input_b; - (void)output_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)num_col_a; - (void)output_bias; - (void)out_0; - /* To be completed */ - return NULL; -#endif -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c deleted file mode 100644 index dc5d36d4..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c +++ /dev/null @@ -1,201 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_s8_s16_reordered.c - * Description: Matrix-multiplication function for convolution with reordered columns - * - * $Date: February 27, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - -/* - * Matrix-multiplication with re-ordered input and bias inputs for convolution with per-channel - * requantization. The re-ordering is a consequence of sign extension is done by the SXTB16 command. - * - * Refer header file for details. This function differs from arm_nn_mat_mult_kernel_s8_s16(), in that it uses - * read_and_pad_reordered() instead of arm_nn_mat_mult_kernel_s8_s16(). Investigating the cycles impact and - * unifying these two functions is a potential future improvement. - * - */ - -q7_t *arm_nn_mat_mult_kernel_s8_s16_reordered(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0) -{ -#if defined(ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *out_1 = out_0 + output_ch; - const int32_t *bias = output_bias; - - uint16_t row_count = output_ch / 2; - const q7_t *ip_a0 = input_a; - /* this loop over rows in A */ - while (row_count) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* align the second pointer for A */ - const q7_t *ip_a1 = ip_a0 + num_col_a; - - /* Init accumulator with bias for channel N and N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - q31_t ch_1_out_0 = *bias; - q31_t ch_1_out_1 = *bias++; - - uint16_t col_count = num_col_a / 4; - /* accumulate over the vector */ - while (col_count) - { - q31_t a01, a02, a11, a12; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad_reordered(ip_a0, &a01, &a02); - ip_a1 = read_and_pad_reordered(ip_a1, &a11, &a12); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a11, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a11, b1, ch_1_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a12, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a12, b1, ch_1_out_1); - - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - - ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift); - ch_1_out_0 += out_offset; - ch_1_out_0 = MAX(ch_1_out_0, activation_min); - ch_1_out_0 = MIN(ch_1_out_0, activation_max); - *out_0++ = (q7_t)ch_1_out_0; - - ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift); - ch_1_out_1 += out_offset; - ch_1_out_1 = MAX(ch_1_out_1, activation_min); - ch_1_out_1 = MIN(ch_1_out_1, activation_max); - *out_1++ = (q7_t)ch_1_out_1; - out_mult++; - out_shift++; - - /* skip row */ - ip_a0 += num_col_a; - row_count--; - } - - if (output_ch & 1) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* Init accumulator with bias for channel N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = ch_0_out_0; - - int32_t col_count = num_col_a / 4; - while (col_count) - { - q31_t a01, a02; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad_reordered(ip_a0, &a01, &a02); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - } - - out_0 += output_ch; - - /* return the new output pointer with offset */ - return out_0; -#else - (void)input_a; - (void)input_b; - (void)output_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)num_col_a; - (void)output_bias; - (void)out_0; - /* To be completed */ - return NULL; -#endif -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c deleted file mode 100644 index d0271d06..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c +++ /dev/null @@ -1,173 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_s8.c - * Description: General Matrix-multiplication function - * - * $Date: May 29, 2020 - * $Revision: V.2.0.3 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * s8 General matrix multiplication function with per-channel requantization for upto 4 column batches. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_mat_mult_s8(const q7_t *input_row, - const q7_t *input_col, - const uint16_t output_ch, - const uint16_t col_batches, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t out_offset, - const int32_t col_offset, - const int32_t row_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t row_len, - const int32_t *const bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - (void)row_offset; - if (col_batches == 4) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t row_len_tmp = row_len; - const int8_t *ip_r0 = input_row + (i_out_ch * row_len); - const int8_t *ip_c0 = input_col; - const int8_t *ip_c1 = input_col + row_len; - const int8_t *ip_c2 = input_col + (2 * row_len); - const int8_t *ip_c3 = input_col + (3 * row_len); - - int32_t acc_0 = bias[i_out_ch]; - int32_t acc_1 = bias[i_out_ch]; - int32_t acc_2 = bias[i_out_ch]; - int32_t acc_3 = bias[i_out_ch]; - const int32_t row_loop_cnt = (row_len + 7) / 8; - - for (int i_row_loop = 0; i_row_loop < row_loop_cnt; i_row_loop++) - { - mve_pred16_t p = vctp16q((uint32_t)row_len_tmp); - const int16x8_t offset = vdupq_m_n_s16(vuninitializedq_s16(), col_offset, p); - row_len_tmp -= 8; - - int16x8_t r0 = vldrbq_z_s16(ip_r0, p); - ip_r0 += 8; - - int16x8_t c0 = vldrbq_z_s16(ip_c0, p); - ip_c0 += 8; - c0 = vaddq_m_s16(vuninitializedq_s16(), c0, offset, p); - - int16x8_t c1 = vldrbq_z_s16(ip_c1, p); - ip_c1 += 8; - c1 = vaddq_m_s16(vuninitializedq_s16(), c1, offset, p); - - int16x8_t c2 = vldrbq_z_s16(ip_c2, p); - ip_c2 += 8; - c2 = vaddq_m_s16(vuninitializedq_s16(), c2, offset, p); - - int16x8_t c3 = vldrbq_z_s16(ip_c3, p); - ip_c3 += 8; - c3 = vaddq_m_s16(vuninitializedq_s16(), c3, offset, p); - - acc_0 = vmladavaq_p_s16(acc_0, r0, c0, p); - acc_1 = vmladavaq_p_s16(acc_1, r0, c1, p); - acc_2 = vmladavaq_p_s16(acc_2, r0, c2, p); - acc_3 = vmladavaq_p_s16(acc_3, r0, c3, p); - } - - int32x4_t res = {acc_0, acc_1, acc_2, acc_3}; - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(activation_min)); - res = vminq_s32(res, vdupq_n_s32(activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(&out[i_out_ch], scatter_offset, res); - } - out += 4 * output_ch; - } - else - { - for (int i_col_batch = (col_batches & ~0x3); i_col_batch < (col_batches & 0x3); i_col_batch++) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t row_len_tmp = row_len; - - const int8_t *ip_r0 = input_row + (i_out_ch * row_len); - const int8_t *ip_c0 = input_col + (i_col_batch * row_len); - int32_t acc_0 = bias[i_out_ch]; - const int32_t row_loop_cnt = (row_len + 7) / 8; - - for (int i_row_loop = 0; i_row_loop < row_loop_cnt; i_row_loop++) - { - const mve_pred16_t p = vctp16q((uint32_t)row_len_tmp); - const int16x8_t offset = vdupq_m_n_s16(vuninitializedq_s16(), col_offset, p); - row_len_tmp -= 8; - - int16x8_t r0 = vldrbq_z_s16(ip_r0, p); - ip_r0 += 8; - int16x8_t c0 = vldrbq_z_s16(ip_c0, p); - ip_c0 += 8; - - c0 = vaddq_m_s16(vuninitializedq_s16(), c0, offset, p); - acc_0 = vmladavaq_p_s16(acc_0, r0, c0, p); - } - - acc_0 = arm_nn_requantize(acc_0, output_mult[i_out_ch], output_shift[i_out_ch]); - acc_0 += out_offset; - acc_0 = MAX(acc_0, activation_min); - acc_0 = MIN(acc_0, activation_max); - out[i_out_ch] = (q7_t)acc_0; - } - out += output_ch; - } - } - return out; - -#else - (void)input_row; - (void)input_col; - (void)output_ch; - (void)col_batches; - (void)output_shift; - (void)output_mult; - (void)out_offset; - (void)col_offset; - (void)row_offset; - (void)activation_min; - (void)activation_max; - (void)row_len; - (void)bias; - (void)out; - return NULL; -#endif -} diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c deleted file mode 100644 index 92bffbfd..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c +++ /dev/null @@ -1,199 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_mat_q7_vec_q15.c - * Description: Mixed Q15-Q7 fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Mixed Q15-Q7 fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Q7_Q15 version of the fully connected layer - * - * Weights are in q7_t and Activations are in q15_t - * - */ - -arm_status -arm_fully_connected_mat_q7_vec_q15(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - const q7_t *pB2; - q15_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 1; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV, inM11, inM12, inM21, inM22; - pB = read_and_pad(pB, &inM11, &inM12); - pB2 = read_and_pad(pB2, &inM21, &inM22); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM11, sum); - sum2 = __SMLAD(inV, inM21, sum2); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM12, sum); - sum2 = __SMLAD(inV, inM22, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - - /*adjust the pointers and counters */ - pB += dim_vec; - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x1; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - int i, j; - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c deleted file mode 100644 index f2debef1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c +++ /dev/null @@ -1,404 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_mat_q7_vec_q15_opt.c - * Description: Mixed Q15-Q7 opt fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Mixed Q15-Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Q7_Q15 version of the fully connected layer - * - * Weights are in q7_t and Activations are in q15_t - * - * Limitation: x4 version requires weight reordering to work - * - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original q7_t matrix looks like this: - * - * | a11 | a12 | a13 | a14 | a15 | a16 | a17 | - * - * | a21 | a22 | a23 | a24 | a25 | a26 | a27 | - * - * | a31 | a32 | a33 | a34 | a35 | a36 | a37 | - * - * | a41 | a42 | a43 | a44 | a45 | a46 | a47 | - * - * | a51 | a52 | a53 | a54 | a55 | a56 | a57 | - * - * | a61 | a62 | a63 | a64 | a65 | a66 | a67 | - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 | - * - * | a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 | - * - * | a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 | - * - * The column left over will be in-order. - * which is: - * | a17 | a27 | a37 | a47 | - * - * For the left-over rows, we do 1x1 computation, so the data remains - * as its original order. - * - * So the stored weight matrix looks like this: - * - * | a11 | a21 | a12 | a22 | a31 | a41 | - * - * | a32 | a42 | a13 | a23 | a14 | a24 | - * - * | a33 | a43 | a34 | a44 | a15 | a25 | - * - * | a16 | a26 | a35 | a45 | a36 | a46 | - * - * | a17 | a27 | a37 | a47 | a51 | a52 | - * - * | a53 | a54 | a55 | a56 | a57 | a61 | - * - * | a62 | a63 | a64 | a65 | a66 | a67 | - * - */ - -arm_status -arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, const q7_t * bias, q15_t * pOut, q15_t * vec_buffer) -{ - - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - q15_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 2; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - colCnt--; - } - -#else - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = *__SIMD32(pA)++; - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - -#ifndef ARM_MATH_BIG_ENDIAN - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r1, [%[pB]], #8\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#else - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r1, [%[pB]], #8\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB++; - q7_t inM3 = *pB++; - q7_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q7_t *pB = pM; - const q15_t *pA; - q15_t *pO = pOut; - const q7_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inA2 = *pA++; - - q7_t inB1 = *pB++; - q7_t inB3 = *pB++; - q7_t inB2 = *pB++; - q7_t inB4 = *pB++; - - sum += inA1 * inB1 + inA2 * inB2; - sum2 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA1 * inB1 + inA2 * inB2; - sum4 += inA1 * inB3 + inA2 * inB4; - - colCnt--; - } - - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inA = *pA++; - q7_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - - colCnt--; - } - *pO++ = (q15_t) __SSAT((sum >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16); - - rowCnt--; - } - - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q15_t inA = *pA++; - q7_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c deleted file mode 100644 index 79413cbd..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c +++ /dev/null @@ -1,193 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q15.c - * Description: Q15 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - */ - -arm_status -arm_fully_connected_q15(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q15_t *pB = pM; - const q15_t *pB2 = pB + dim_vec; - q15_t *pO = pOut; - const q15_t *pA; - const q15_t *pBias = bias; - uint16_t rowCnt = num_of_rows >> 1; - - /* this loop loops over different output */ - while (rowCnt) { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV1, inM1, inM2; - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - inM2 = arm_nn_read_q15x2_ia(&pB2); - sum2 = __SMLAD(inV1, inM2, sum2); - - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - inM2 = arm_nn_read_q15x2_ia(&pB2); - sum2 = __SMLAD(inV1, inM2, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2>> out_shift), 16)); - - /* adjust the pointers and counters */ - pB = pB + dim_vec; - rowCnt --; - } - - rowCnt = num_of_rows & 0x1; - - while (rowCnt) { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) { - q31_t inV1, inM1; - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while(colCnt) { - q15_t inV = *pA++; - q15_t inM = *pB++; - - sum += inV * inM; - - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt --; - } - -#else - int i, j; - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c deleted file mode 100644 index d495342e..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c +++ /dev/null @@ -1,332 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q15_opt.c - * Description: Q15 opt fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original matrix looks like this: - * - * | a11 | a12 | a13 | - * - * | a21 | a22 | a23 | - * - * | a31 | a32 | a33 | - * - * | a41 | a42 | a43 | - * - * | a51 | a52 | a53 | - * - * | a61 | a62 | a63 | - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | - * - * | a13 | a23 | a33 | a43 | - * - * Remaining rows are kept the same original order. - * - * So the stored weight matrix looks like this: - * - * - * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | - * - * | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 | - * - * | a62 | a63 | - */ - -arm_status -arm_fully_connected_q15_opt(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q15_t *pB = pM; - q15_t *pO = pOut; - const q15_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 2; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - -#ifdef USE_INTRINSIC - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV, inM11, sum); - inM12 = arm_nn_read_q15x2_ia(&pB); - sum2 = __SMLAD(inV, inM12, sum2); - inM13 = arm_nn_read_q15x2_ia(&pB); - sum3 = __SMLAD(inV, inM13, sum3); - inM14 = arm_nn_read_q15x2_ia(&pB); - sum4 = __SMLAD(inV, inM14, sum4); - colCnt--; - } - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r0, [%[pB]], #16\n" - "smlad %[sum], r4, r0, %[sum]\n" - "ldr.w r1, [%[pB] , #-12]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r2, [%[pB] , #-8]\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "ldr.w r3, [%[pB] , #-4]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x1; - while (colCnt) - { - - q15_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB++; - q15_t inM3 = *pB++; - q15_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM1, inM2; - - inM1 = arm_nn_read_q15x2_ia(&pB); - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM1, sum); - - inM2 = arm_nn_read_q15x2_ia(&pB); - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM2, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q15_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q15_t *pB = pM; - const q15_t *pA; - q15_t *pO = pOut; - const q15_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inA2 = *pA++; - - q15_t inB1 = *pB++; - q15_t inB2 = *pB++; - sum += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum2 += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum3 += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum4 += inA1 * inB1 + inA2 * inB2; - - colCnt--; - } - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inA = *pA++; - q15_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - colCnt--; - } - *pO++ = (q15_t) __SSAT((sum >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16); - - rowCnt--; - } - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q15_t inA = *pA++; - q15_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c deleted file mode 100644 index 12dc6f1c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c +++ /dev/null @@ -1,198 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q7.c - * Description: Q7 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q7 basic fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: dim_vec - * - * This basic function is designed to work with regular weight - * matrix without interleaving. - * - */ - -arm_status -arm_fully_connected_q7(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, const q7_t * bias, q7_t * pOut, q15_t * vec_buffer) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - const q7_t *pB2; - q7_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA; - uint16_t rowCnt = num_of_rows >> 1; - - /* expand the vector into the buffer */ - arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec); - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV, inM11, inM12, inM21, inM22; - pB = read_and_pad_reordered(pB, &inM11, &inM12); - pB2 = read_and_pad_reordered(pB2, &inM21, &inM22); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM11, sum); - sum2 = __SMLAD(inV, inM21, sum2); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM12, sum); - sum2 = __SMLAD(inV, inM22, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8)); - - /* adjust the pointers and counters */ - pB += dim_vec; - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x1; - - while (rowCnt) - { - uint16_t colCnt = dim_vec >> 2; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - pA = vec_buffer; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad_reordered(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inV = *pA++; - q15_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - - rowCnt--; - } - -#else - int i, j; - - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c deleted file mode 100644 index ee7faa9d..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c +++ /dev/null @@ -1,484 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q7_opt.c - * Description: Q7 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: dim_vec - * - * This opt function is designed to work with interleaved weight - * matrix. The vector input is assumed in q7_t format, we call - * arm_q7_to_q15_no_shift_shuffle function to expand into - * q15_t format with certain weight re-ordering, refer to the function - * comments for more details. - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original q7_t matrix looks like this: - * - * | a11 | a12 | a13 | a14 | a15 | a16 | a17 | - * - * | a21 | a22 | a23 | a24 | a25 | a26 | a27 | - * - * | a31 | a32 | a33 | a34 | a35 | a36 | a37 | - * - * | a41 | a42 | a43 | a44 | a45 | a46 | a47 | - * - * | a51 | a52 | a53 | a54 | a55 | a56 | a57 | - * - * | a61 | a62 | a63 | a64 | a65 | a66 | a67 | - * - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 | - * - * | a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 | - * - * | a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 | - * - * So within the kernel, we first read the re-ordered vector in as: - * - * | b1 | b3 | and | b2 | b4 | - * - * the four q31_t weights will look like - * - * | a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 | - * - * | a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 | - * - * The column left over will be in-order. - * which is: - * - * | a17 | a27 | a37 | a47 | - * - * For the left-over rows, we do 1x1 computation, so the data remains - * as its original order. - * - * So the stored weight matrix looks like this: - * - * | a11 | a21 | a13 | a23 | a31 | a41 | - * - * | a33 | a43 | a12 | a22 | a14 | a24 | - * - * | a32 | a42 | a34 | a44 | a15 | a25 | - * - * | a35 | a45 | a16 | a26 | a36 | a46 | - * - * | a17 | a27 | a37 | a47 | a51 | a52 | - * - * | a53 | a54 | a55 | a56 | a57 | a61 | - * - * | a62 | a63 | a64 | a65 | a66 | a67 | - * - * - */ - -arm_status -arm_fully_connected_q7_opt(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut, - q15_t * vec_buffer) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - q7_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA; - uint16_t rowCnt = num_of_rows >> 2; - - arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec); - - while (rowCnt) - { - - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - colCnt--; - } -#else - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - colCnt--; - } -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - -#ifndef ARM_MATH_BIG_ENDIAN - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #8\n" - "ldr.w r1, [%[pB]], #16\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-12]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "ldr.w r4, [%[pA], #-4]\n" - "ldr.w r1, [%[pB], #-8]\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#else - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #8\n" - "ldr.w r1, [%[pB]], #16\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-12]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "ldr.w r4, [%[pA], #-4]\n" - "ldr.w r1, [%[pB], #-8]\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB++; - q7_t inM3 = *pB++; - q7_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum3 >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum4 >> out_shift), 8)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad_reordered(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q7_t *pB = pM; - const q7_t *pA; - q7_t *pO = pOut; - const q7_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q7_t inA1 = *pA++; - q7_t inA3 = *pA++; - q7_t inA2 = *pA++; - q7_t inA4 = *pA++; - - q7_t inB1 = *pB++; - q7_t inB3 = *pB++; - q7_t inB2 = *pB++; - q7_t inB4 = *pB++; - - sum += inA1 * inB1 + inA2 * inB2; - sum2 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA1 * inB1 + inA2 * inB2; - sum4 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum += inA3 * inB1 + inA4 * inB2; - sum2 += inA3 * inB3 + inA4 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA3 * inB1 + inA4 * inB2; - sum4 += inA3 * inB3 + inA4 * inB4; - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inA = *pA++; - q7_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - - colCnt--; - } - *pO++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q7_t inA = *pA++; - q7_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c deleted file mode 100644 index 9775aaaa..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_s8 - * Description: Fully connected function compatible with TF Lite. - * - * $Date: May 2, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M and Cortex-A cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - -/* - * S8 basic fully-connected and matrix multiplication layer function for TensorFlow Lite - * - * Refer header file for details. - * - */ - -arm_status -arm_fully_connected_s8(const cmsis_nn_context *ctx, - const cmsis_nn_fc_params *fc_params, - const cmsis_nn_per_tensor_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)bias_dims; - (void)ctx; - int32_t batch_cnt = input_dims->n; - - while (batch_cnt) - { - arm_nn_vec_mat_mult_t_s8(input, - kernel, - bias, - output, - fc_params->input_offset, - fc_params->filter_offset, - fc_params->output_offset, - quant_params->multiplier, - quant_params->shift, - filter_dims->n, /* col_dim or accum_depth */ - output_dims->c, /* row_dim or output_depth */ - fc_params->activation.min, - fc_params->activation.max); - input += filter_dims->n; - output += output_dims->c; - batch_cnt--; - } - return (ARM_MATH_SUCCESS); -} - -int32_t arm_fully_connected_s8_get_buffer_size(const cmsis_nn_dims *filter_dims) -{ - (void)filter_dims; - return 0; -} - -/** - * @} end of FC group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c deleted file mode 100644 index 7d14834e..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_accumulate_q7_to_q15.c - * Description: Accumulate q7 vector into q15 one. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * pSrc Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -void arm_nn_accumulate_q7_to_q15(q15_t *pDst, const q7_t *pSrc, uint32_t length) -{ - q15_t *pCnt = pDst; - const q7_t *pV = pSrc; - q31_t v1, v2, vo1, vo2; - int32_t cnt = length >> 2; - q31_t in; - - while (cnt > 0l) - { - q31_t value = arm_nn_read_q7x4_ia(&pV); - v1 = __SXTB16(__ROR((uint32_t)value, 8)); - v2 = __SXTB16(value); -#ifndef ARM_MATH_BIG_ENDIAN - vo2 = (q31_t)__PKHTB(v1, v2, 16); - vo1 = (q31_t)__PKHBT(v2, v1, 16); -#else - vo1 = (q31_t)__PKHTB(v1, v2, 16); - vo2 = (q31_t)__PKHBT(v2, v1, 16); -#endif - - in = arm_nn_read_q15x2(pCnt); - write_q15x2_ia(&pCnt, __QADD16(vo1, in)); - - in = arm_nn_read_q15x2(pCnt); - write_q15x2_ia(&pCnt, __QADD16(vo2, in)); - - cnt--; - } - cnt = length & 0x3; - while (cnt > 0l) - { - *pCnt++ += *pV++; - cnt--; - } -} - -/** - * @} end of NNBasicMath group - */ \ No newline at end of file diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c deleted file mode 100644 index ea549f94..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_add_q7.c - * Description: Non saturating addition of elements of a q7 vector. - * - * $Date: July 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -void arm_nn_add_q7(const q7_t *input, q31_t *output, uint32_t block_size) -{ - uint32_t block_count; - q31_t result = 0; -#if defined(ARM_MATH_DSP) - /* Loop unrolling: Compute 4 outputs at a time */ - block_count = block_size >> 2U; - - while (block_count > 0U) - { - const int32_t mult_q15x2 = (1UL << 16) | 1UL; - q31_t in_q7x4 = arm_nn_read_q7x4_ia(&input); - q31_t temp_q15x2 = __SXTAB16(__SXTB16(in_q7x4), - __ROR((uint32_t)in_q7x4, 8)); - - result = __SMLAD(temp_q15x2, mult_q15x2, result); - - /* Decrement loop counter */ - block_count--; - } - - /* Loop unrolling: Compute remaining outputs */ - block_count = block_size & 0x3; -#else - block_count = block_size; -#endif - while (block_count > 0U) - { - /* Add and store result in destination buffer. */ - result += *input++; - - /* Decrement loop counter */ - block_count--; - } - - *output = result; -} - -/** - * @} end of NNBasicMath group - */ \ No newline at end of file diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c deleted file mode 100644 index 15e6b6dd..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c +++ /dev/null @@ -1,169 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_nt_t_padded_s8.c - * Description: Depthwise convolution with padded matrices. - * - * $Date: March 17, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M processors with MVE extension - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * Depthwise convolution of transposed rhs matrix with 4 lhs matrices. One or more of the rhs matrices are padded. - * Dimensions are the same for lhs and rhs. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_nt_t_padded_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t input_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - int32_t loop_count = (num_ch + 3) / 4; - const int32_t *bias = output_bias; - uint32_t num_ch_to_process = num_ch; - - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, out += 4, offset += 4, i_loop_cnt++) - { - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - int32x4_t out_2 = out_0; - int32x4_t out_3 = out_0; - bias += 4; - - const int8_t *rhs_0 = rhs + offset; - const int8_t *lhs_0 = lhs + offset; - const int8_t *lhs_1 = lhs + row_x_col * num_ch + offset; - const int8_t *lhs_2 = lhs + (row_x_col * num_ch * 2) + offset; - const int8_t *lhs_3 = lhs + (row_x_col * num_ch * 3) + offset; - - for (int i_row_x_col = 0; i_row_x_col < row_x_col; i_row_x_col++) - { - const int32x4_t ker_0 = vldrbq_s32(rhs_0); - - int32x4_t ip_0 = vldrbq_s32(lhs_0); - ip_0 = vaddq_n_s32(ip_0, input_offset); - out_0 += vmulq_s32(ip_0, ker_0); - - int32x4_t ip_1 = vldrbq_s32(lhs_1); - ip_1 = vaddq_n_s32(ip_1, input_offset); - out_1 += vmulq_s32(ip_1, ker_0); - - int32x4_t ip_2 = vldrbq_s32(lhs_2); - ip_2 = vaddq_n_s32(ip_2, input_offset); - out_2 += vmulq_s32(ip_2, ker_0); - - int32x4_t ip_3 = vldrbq_s32(lhs_3); - ip_3 = vaddq_n_s32(ip_3, input_offset); - - out_3 += vmulq_s32(ip_3, ker_0); - - lhs_0 += num_ch; - lhs_1 += num_ch; - lhs_2 += num_ch; - lhs_3 += num_ch; - - rhs_0 += num_ch; - } - - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - mve_pred16_t p = vctp32q(num_ch_to_process); - vstrbq_p_s32(out, out_0, p); - - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + num_ch, out_1, p); - - out_2 = arm_requantize_mve_32x4(out_2, mult, shift); - out_2 = vaddq_n_s32(out_2, out_offset); - out_2 = vmaxq_s32(out_2, vdupq_n_s32(activation_min)); - out_2 = vminq_s32(out_2, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 2 * num_ch, out_2, p); - - out_3 = arm_requantize_mve_32x4(out_3, mult, shift); - out_3 = vaddq_n_s32(out_3, out_offset); - out_3 = vmaxq_s32(out_3, vdupq_n_s32(activation_min)); - out_3 = vminq_s32(out_3, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 3 * num_ch, out_3, p); - } - - const int tail_ch = num_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - return out + (3 * num_ch); - -#else - (void)lhs; - (void)rhs; - (void)input_offset; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)row_x_col; - (void)output_bias; - (void)out; - return NULL; -#endif -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c deleted file mode 100644 index 2cfdef44..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c +++ /dev/null @@ -1,171 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_nt_t_s8.c - * Description: Depthwise convolution on matrices with no padding. - * - * $Date: March 17, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M processors with MVE extension. - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * Depthwise convolution of rhs matrix with 4 lhs matrices with no padding. Dimensions are the same for lhs and rhs. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t input_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - const int32_t *bias = output_bias; - int32_t loop_count = (num_ch + 3) / 4; - uint32_t num_ch_to_process = num_ch; - - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, offset += 4, out += 4, i_loop_cnt++) - { - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - int32x4_t out_2 = out_0; - int32x4_t out_3 = out_0; - bias += 4; - - const int8_t *rhs_0 = rhs + offset; - const int8_t *lhs_0 = lhs + offset; - const int8_t *lhs_1 = lhs + row_x_col * num_ch + offset; - const int8_t *lhs_2 = lhs + (row_x_col * num_ch * 2) + offset; - const int8_t *lhs_3 = lhs + (row_x_col * num_ch * 3) + offset; - int32x4_t ker_sum = vdupq_n_s32(0); - - for (int i_row_x_col = 0; i_row_x_col < row_x_col; i_row_x_col++) - { - const int32x4_t ker_0 = vldrbq_s32(rhs_0); - ker_sum = vaddq_s32(ker_sum, ker_0); - - int32x4_t ip_0 = vldrbq_s32(lhs_0); - out_0 += vmulq_s32(ip_0, ker_0); - - int32x4_t ip_1 = vldrbq_s32(lhs_1); - out_1 += vmulq_s32(ip_1, ker_0); - - int32x4_t ip_2 = vldrbq_s32(lhs_2); - out_2 += vmulq_s32(ip_2, ker_0); - - int32x4_t ip_3 = vldrbq_s32(lhs_3); - out_3 += vmulq_s32(ip_3, ker_0); - - lhs_0 += num_ch; - lhs_1 += num_ch; - lhs_2 += num_ch; - lhs_3 += num_ch; - - rhs_0 += num_ch; - } - - ker_sum = vmulq_n_s32(ker_sum, input_offset); - out_0 = ker_sum + out_0; - out_1 = ker_sum + out_1; - out_2 = ker_sum + out_2; - out_3 = ker_sum + out_3; - - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - mve_pred16_t p = vctp32q(num_ch_to_process); - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out, out_0, p); - - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + num_ch, out_1, p); - - out_2 = arm_requantize_mve_32x4(out_2, mult, shift); - out_2 = vaddq_n_s32(out_2, out_offset); - out_2 = vmaxq_s32(out_2, vdupq_n_s32(activation_min)); - out_2 = vminq_s32(out_2, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 2 * num_ch, out_2, p); - - out_3 = arm_requantize_mve_32x4(out_3, mult, shift); - out_3 = vaddq_n_s32(out_3, out_offset); - out_3 = vmaxq_s32(out_3, vdupq_n_s32(activation_min)); - out_3 = vminq_s32(out_3, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 3 * num_ch, out_3, p); - } - - const int tail_ch = num_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - - return out + (3 * num_ch); -#else - (void)lhs; - (void)rhs; - (void)input_offset; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)row_x_col; - (void)output_bias; - (void)out; - return NULL; -#endif -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c deleted file mode 100644 index f57ee07f..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mul_core_1x_s8.c - * Description: General Matrix-multiplication function - * - * $Date: January 20, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication to process 1 row - * - * Refer header file for details. - * - */ - -arm_status arm_nn_mat_mul_core_1x_s8(int32_t row_elements, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output) -{ - int32_t acc_n0 = 0; - int32_t sum_tmp = 0; - -#if defined(ARM_MATH_MVEI) && !defined(ARM_MATH_AUTOVECTORIZE) - - __asm volatile ( - " vldrb.8 q0, [%[col]], 16 \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vaddva.s8 %[sum], q0 \n" - " vldrb.8 q1, [%[row0]], 16 \n" - " vmladava.s8 %[out0], q0, q1 \n" - " vldrb.8 q0, [%[col]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[col] "+r"(col_base) - ,[sum] "+Te"(sum_tmp) - ,[row0] "+r"(row_base) - ,[out0] "+Te"(acc_n0) - :[cnt] "r"(row_elements) - :"q0","q1", "memory", "r14"); -#else - for (int i = 0; i < row_elements; i++) - { - sum_tmp += col_base[i]; - acc_n0 += row_base[i] * col_base[i]; - } -#endif - - *sum_col = sum_tmp; - *output = acc_n0; - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c deleted file mode 100644 index 0b240b70..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mul_core_4x_s8.c - * Description: General matrix multiplication function for MVE extension - * - * $Date: January 20, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication to process 4 rows and one column - * - * Refer header file for details. - * - */ -arm_status arm_nn_mat_mul_core_4x_s8(const int32_t row_elements, - const int32_t offset, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output) -{ - int32_t acc_n0 = 0; - int32_t acc_n1 = 0; - int32_t acc_n2 = 0; - int32_t acc_n3 = 0; - - const int8_t *ip_row_0 = row_base; - const int8_t *ip_row_1 = row_base + offset; - const int8_t *ip_row_2 = row_base + (2 * offset); - const int8_t *ip_row_3 = row_base + (3 * offset); - int32_t sum_tmp = 0; - -#if defined(ARM_MATH_MVEI) && !defined(ARM_MATH_AUTOVECTORIZE) - __asm volatile( - " vldrb.8 q0, [%[col]], 16 \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vaddva.s8 %[sum], q0 \n" - " vldrb.8 q1, [%[row0]], 16 \n" - " vmladava.s8 %[out0], q0, q1 \n" - " vldrb.8 q2, [%[row1]], 16 \n" - " vmladava.s8 %[out1], q0, q2 \n" - " vldrb.8 q3, [%[row2]], 16 \n" - " vmladava.s8 %[out2], q0, q3 \n" - " vldrb.8 q4, [%[row3]], 16 \n" - " vmladava.s8 %[out3], q0, q4 \n" - " vldrb.8 q0, [%[col]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[col] "+r"(col_base) - ,[sum] "+Te"(sum_tmp) - ,[row0] "+r"(ip_row_0) - ,[row1] "+r"(ip_row_1) - ,[row2] "+r"(ip_row_2) - ,[row3] "+r"(ip_row_3) - ,[out0] "+Te"(acc_n0) - ,[out1] "+Te"(acc_n1) - ,[out2] "+Te"(acc_n2) - ,[out3] "+Te"(acc_n3) - : [cnt] "r"(row_elements) - : "q0", "q1", "q2", "q3", "q4", "memory", "r14"); -#else - for (int i = 0; i < row_elements; i++) - { - int32_t col = col_base[i]; - sum_tmp += col; - acc_n0 += ip_row_0[i] * col; - acc_n1 += ip_row_1[i] * col; - acc_n2 += ip_row_2[i] * col; - acc_n3 += ip_row_3[i] * col; - } -#endif - output[0] = acc_n0; - output[1] = acc_n1; - output[2] = acc_n2; - output[3] = acc_n3; - - *sum_col = sum_tmp; - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c deleted file mode 100644 index 392ab58c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c +++ /dev/null @@ -1,579 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_s8_nt_t_s8 - * Description: Matrix multiplication support function with the right-hand-side (rhs) matrix transposed - * - * $Date: March 17 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -// Work around for https://github.com/ARMmbed/mbed-os/issues/12568 -#define __patched_SXTB16_RORn(op1, rotate) \ -({ \ - uint32_t result; \ - __ASM ("sxtb16 %0, %1, ROR %2" : "=r" (result) : "r" (op1), "i" (rotate) ); \ - result; \ -}) -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication with the right-hand-side matrix transposed - * - * Refer header file for details. - * - */ -arm_status arm_nn_mat_mult_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t *dst_multipliers, - const int32_t *dst_shifts, - const int32_t lhs_rows, - const int32_t rhs_rows, - const int32_t rhs_cols, - const int32_t lhs_offset, - const int32_t dst_offset, - const int32_t activation_min, - const int32_t activation_max) -{ -#if defined(ARM_MATH_DSP) - const int32_t off0 = rhs_cols - 4; - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - q31_t lhs_offset_contribution0 = 0; - q31_t lhs_offset_contribution1 = 0; - - for (int32_t x = 0; x < rhs_cols; ++x) - { - lhs_offset_contribution0 += rhs[x]; - lhs_offset_contribution1 += rhs[x + rhs_cols]; - } - - lhs_offset_contribution0 *= lhs_offset; - lhs_offset_contribution1 *= lhs_offset; - - lhs_offset_contribution0 += bias[rhs_rows_idx]; - lhs_offset_contribution1 += bias[rhs_rows_idx + 1]; - - int32_t lhs_rows_idx = lhs_rows >> 1; - - while (lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - q31_t res10 = lhs_offset_contribution0; - q31_t res11 = lhs_offset_contribution1; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - res11 = __SMLAD(val0, val4, res11); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - lhs_value = lhs_ptr[rhs_cols]; - res10 += lhs_value * rhs_value0; - res11 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - res10 = arm_nn_requantize(res10, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res11 = arm_nn_requantize(res11, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - res10 += dst_offset; - res11 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - res10 = MAX(res10, activation_min); - res10 = MIN(res10, activation_max); - res11 = MAX(res11, activation_min); - res11 = MIN(res11, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - dst_ptr += rhs_rows; - dst_ptr[0] = (q7_t)res10; - dst_ptr[1] = (q7_t)res11; - dst_ptr += rhs_rows; - - lhs_ptr += rhs_cols; - - lhs_rows_idx--; - } - - // Left-over rows - if (lhs_rows % 2) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - } - - // Left-over accumulations - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - } - - rhs += 2 * rhs_cols; - dst += 2; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - for (int32_t lhs_rows_idx = 0; lhs_rows_idx < lhs_rows; ++lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - q31_t res00 = bias[rhs_rows - 1]; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value = rhs_ptr[0]; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows - 1], dst_shifts[rhs_rows - 1]); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr += rhs_rows; - } - } -#else - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - q31_t lhs_offset_contribution0 = 0; - q31_t lhs_offset_contribution1 = 0; - - for (int32_t x = 0; x < rhs_cols; ++x) - { - lhs_offset_contribution0 += rhs[x]; - lhs_offset_contribution1 += rhs[x + rhs_cols]; - } - - lhs_offset_contribution0 *= lhs_offset; - lhs_offset_contribution1 *= lhs_offset; - - lhs_offset_contribution0 += bias[rhs_rows_idx]; - lhs_offset_contribution1 += bias[rhs_rows_idx + 1]; - - int32_t lhs_rows_idx = lhs_rows >> 1; - - while (lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - q31_t res10 = lhs_offset_contribution0; - q31_t res11 = lhs_offset_contribution1; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - lhs_value = lhs_ptr[rhs_cols]; - res10 += lhs_value * rhs_value0; - res11 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - res10 = arm_nn_requantize(res10, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res11 = arm_nn_requantize(res11, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - res10 += dst_offset; - res11 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - res10 = MAX(res10, activation_min); - res10 = MIN(res10, activation_max); - res11 = MAX(res11, activation_min); - res11 = MIN(res11, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - dst_ptr += rhs_rows; - dst_ptr[0] = (q7_t)res10; - dst_ptr[1] = (q7_t)res11; - dst_ptr += rhs_rows; - - lhs_ptr += rhs_cols; - - lhs_rows_idx--; - } - - // Left-over rows - if (lhs_rows % 2) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - } - - rhs += 2 * rhs_cols; - dst += 2; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - for (int32_t lhs_rows_idx = 0; lhs_rows_idx < lhs_rows; ++lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - q31_t res00 = bias[rhs_rows - 1]; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value = rhs_ptr[0]; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows - 1], dst_shifts[rhs_rows - 1]); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr += rhs_rows; - } - } -#endif - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c deleted file mode 100644 index d84bc18b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c +++ /dev/null @@ -1,145 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mult_q15.c - * Description: Q15 vector multiplication with variable output shifts - * - * $Date: 29. April 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - - -/** - * @brief Q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated. - */ - -void arm_nn_mult_q15( - q15_t * pSrcA, - q15_t * pSrcB, - q15_t * pDst, - const uint16_t out_shift, - uint32_t blockSize) -{ - uint32_t blkCnt; /* loop counters */ - -#if defined (ARM_MATH_DSP) - -/* Run the below code for Cortex-M4 and Cortex-M3 */ - q31_t inA1, inA2, inB1, inB2; /* temporary input variables */ - q15_t out1, out2, out3, out4; /* temporary output variables */ - q31_t mul1, mul2, mul3, mul4; /* temporary variables */ - - /* loop Unrolling */ - blkCnt = blockSize >> 2U; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0U) - { - /* read two samples at a time from sourceA */ - inA1 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcA); - /* read two samples at a time from sourceB */ - inB1 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcB); - /* read two samples at a time from sourceA */ - inA2 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcA); - /* read two samples at a time from sourceB */ - inB2 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcB); - - /* multiply mul = sourceA * sourceB */ - mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16)); - mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1); - mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16)); - mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2); - - /* saturate result to 16 bit */ - out1 = (q15_t) __SSAT((q31_t) (mul1 + NN_ROUND(out_shift)) >> out_shift, 16); - out2 = (q15_t) __SSAT((q31_t) (mul2 + NN_ROUND(out_shift)) >> out_shift, 16); - out3 = (q15_t) __SSAT((q31_t) (mul3 + NN_ROUND(out_shift)) >> out_shift, 16); - out4 = (q15_t) __SSAT((q31_t) (mul4 + NN_ROUND(out_shift)) >> out_shift, 16); - - /* store the result */ -#ifndef ARM_MATH_BIG_ENDIAN - - *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); - *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); - -#else - - *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); - *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); - -#endif /* #ifndef ARM_MATH_BIG_ENDIAN */ - - /* Decrement the blockSize loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4U; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Initialize blkCnt with number of samples */ - blkCnt = blockSize; - -#endif /* #if defined (ARM_MATH_DSP) */ - - - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the result in the destination buffer */ - *pDst++ = (q15_t) __SSAT(((q31_t) ((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c deleted file mode 100644 index d99c42a3..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mult_q7.c - * Description: Q7 vector multiplication with variable output shifts - * - * $Date: 29. April 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/** - * @brief Q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated. - */ - -void arm_nn_mult_q7( - q7_t * pSrcA, - q7_t * pSrcB, - q7_t * pDst, - const uint16_t out_shift, - uint32_t blockSize) -{ - uint32_t blkCnt; /* loop counters */ - -#if defined (ARM_MATH_DSP) - -/* Run the below code for Cortex-M4 and Cortex-M3 */ - q7_t out1, out2, out3, out4; /* Temporary variables to store the product */ - - /* loop Unrolling */ - blkCnt = blockSize >> 2U; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the results in temporary variables */ - out1 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out2 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out3 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out4 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - - /* Store the results of 4 inputs in the destination buffer in single cycle by packing */ - *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4U; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Initialize blkCnt with number of samples */ - blkCnt = blockSize; - -#endif /* #if defined (ARM_MATH_DSP) */ - - - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the result in the destination buffer */ - *pDst++ = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c deleted file mode 100644 index 2551b189..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c +++ /dev/null @@ -1,462 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_vec_mat_mult_t_s8 - * Description: s8 vector by matrix (transposed) multiplication - * - * $Date: April 2, 2020 - * $Revision: V.1.5.0 - * - * Target Processor: Cortex-M - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 vector(lhs) by matrix (transposed) multiplication - * - * Refer header file for details. - * - */ -arm_status arm_nn_vec_mat_mult_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t lhs_offset, - const int32_t rhs_offset, - const int32_t dst_offset, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max) -{ -#if defined(ARM_MATH_MVEI) - - const int16x8_t rhs_offset_vec = vdupq_n_s16((int16_t)rhs_offset); - const int16x8_t lhs_offset_vec = vdupq_n_s16((int16_t)lhs_offset); - - int32_t row_loop_cnt = rhs_rows / 4; - - for (int i_row_loop_cnt = 0; i_row_loop_cnt < row_loop_cnt; i_row_loop_cnt++) - { - int32_t acc1 = bias[0]; - int32_t acc2 = bias[1]; - int32_t acc3 = bias[2]; - int32_t acc4 = bias[3]; - bias += 4; - - int32x4_t acc; - const int32_t col_loop_cnt = (rhs_cols + 7) / 8; - - const int8_t *vec = lhs; - const int8_t *rhs_0 = rhs; - const int8_t *rhs_1 = rhs + rhs_cols; - const int8_t *rhs_2 = rhs + 2 * rhs_cols; - const int8_t *rhs_3 = rhs + 3 * rhs_cols; - - uint32_t col_cnt = (uint32_t)rhs_cols; - - for (int i = 0; i < col_loop_cnt; i++) - { - mve_pred16_t p = vctp16q(col_cnt); - col_cnt -= 8; - const int16x8_t tmp_b = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(vec, p), lhs_offset_vec, p); - - const int16x8_t tmp_a0 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_0, p), rhs_offset_vec, p); - acc1 = vmladavaq_p_s16(acc1, tmp_a0, tmp_b, p); - - const int16x8_t tmp_a1 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_1, p), rhs_offset_vec, p); - acc2 = vmladavaq_p_s16(acc2, tmp_a1, tmp_b, p); - - const int16x8_t tmp_a2 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_2, p), rhs_offset_vec, p); - acc3 = vmladavaq_p_s16(acc3, tmp_a2, tmp_b, p); - - const int16x8_t tmp_a3 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_3, p), rhs_offset_vec, p); - acc4 = vmladavaq_p_s16(acc4, tmp_a3, tmp_b, p); - - vec += 8; - rhs_0 += 8; - rhs_1 += 8; - rhs_2 += 8; - rhs_3 += 8; - } - rhs += 4 * rhs_cols; - - acc[0] = acc1; - acc[1] = acc2; - acc[2] = acc3; - acc[3] = acc4; - - acc = arm_requantize_mve(acc, dst_multiplier, dst_shift); - acc = vaddq_s32(acc, vdupq_n_s32(dst_offset)); - acc = vmaxq_s32(acc, vdupq_n_s32(activation_min)); - acc = vminq_s32(acc, vdupq_n_s32(activation_max)); - - vstrbq_s32(dst, acc); - dst += 4; - } - - row_loop_cnt = rhs_rows & 3; - - for (int i_row_loop_cnt = 0; i_row_loop_cnt < row_loop_cnt; - i_row_loop_cnt++) - { - int32_t acc = *bias++; - const int32_t col_loop_cnt = (rhs_cols + 7) / 8; - const int8_t *vec = lhs; - const int8_t *kernel_cur = rhs; - - uint32_t col_cnt = (uint32_t)rhs_cols; - - for (int i = 0; i < col_loop_cnt; i++) - { - mve_pred16_t p = vctp16q(col_cnt); - col_cnt -= 8; - const int16x8_t tmp_b = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(vec, p), lhs_offset_vec, p); - - const int16x8_t tmp_a = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(kernel_cur, p), rhs_offset_vec, p); - acc = vmladavaq_p_s16(acc, tmp_a, tmp_b, p); - vec += 8; - kernel_cur += 8; - } - rhs += rhs_cols; - - acc = arm_nn_requantize(acc, dst_multiplier, dst_shift); - acc += dst_offset; - - acc = MAX(acc, activation_min); - acc = MIN(acc, activation_max); - *dst++ = (int8_t)(acc); - } - -#elif defined(ARM_MATH_DSP) - const int32_t off0 = rhs_cols - 4; - const int16_t lhs_offset_s16 = lhs_offset; - const int16_t rhs_offset_s16 = rhs_offset; - - const uint32_t lhs_offset_s16x2 = __PKHBT(lhs_offset_s16, lhs_offset_s16, 16); - const uint32_t rhs_offset_s16x2 = __PKHBT(rhs_offset_s16, rhs_offset_s16, 16); - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - q31_t res01 = *bias++; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t rhs_value1 = rhs_ptr[rhs_cols] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - res01 = arm_nn_requantize(res01, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - *dst++ = (q7_t)res00; - *dst++ = (q7_t)res01; - - rhs += 2 * rhs_cols; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - *dst = (q7_t)res00; - } - -#else - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - q31_t res01 = *bias++; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t rhs_value1 = rhs_ptr[rhs_cols] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - res01 = arm_nn_requantize(res01, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - *dst++ = (q7_t)res00; - *dst++ = (q7_t)res01; - - rhs += 2 * rhs_cols; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - *dst = (q7_t)res00; - } -#endif - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c deleted file mode 100644 index 57f97f68..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c +++ /dev/null @@ -1,297 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nntables.c - * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @brief tables for various activation functions - * - * This file include the declaration of common tables. - * Most of them are used for activation functions - * - * Assumption: - * Unified table: input is 3.x format, i.e, range of [-8, 8) - * sigmoid(8) = 0.9996646498695336 - * tanh(8) = 0.9999997749296758 - * The accuracy here should be good enough - * - * 2-stage HL table: - * - * The entire input range is divided into two parts: - * - * Low range table: 0x000x xxxx or 0x111x xxxx - * table entry will be the binary number excluding the first - * two digits, i.e., 0x0x xxxx or 0x1x xxxx - * - * - * - * High range table 0x0010 0000 -- 0x0111 1111 - * 0x1000 0000 -- 0x1101 1111 - * - * For positive numbers, table entry will be - * 0x0010 0000 -- 0x0111 1111 minus 0x0010 0000 - * i.e., 0x0000 0000 - 0x0101 11111 - * - * same thing for the negative numbers, table entry will be - * 0x1000 0000 -- 0x1101 1111 minux 0x0010 0000 - * i.e., 0x0110 0000 - 0x1011 1111 - */ - -const q7_t sigmoidTable_q7[256] = { - 0x40, 0x42, 0x44, 0x46, 0x48, 0x4a, 0x4c, 0x4e, - 0x50, 0x52, 0x53, 0x55, 0x57, 0x59, 0x5a, 0x5c, - 0x5e, 0x5f, 0x61, 0x62, 0x63, 0x65, 0x66, 0x67, - 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f, 0x70, - 0x71, 0x72, 0x72, 0x73, 0x74, 0x74, 0x75, 0x76, - 0x76, 0x77, 0x77, 0x78, 0x78, 0x79, 0x79, 0x7a, - 0x7a, 0x7a, 0x7b, 0x7b, 0x7b, 0x7c, 0x7c, 0x7c, - 0x7c, 0x7c, 0x7d, 0x7d, 0x7d, 0x7d, 0x7d, 0x7e, - 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x04, - 0x04, 0x04, 0x04, 0x04, 0x05, 0x05, 0x05, 0x06, - 0x06, 0x06, 0x07, 0x07, 0x08, 0x08, 0x09, 0x09, - 0x0a, 0x0a, 0x0b, 0x0c, 0x0c, 0x0d, 0x0e, 0x0e, - 0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, - 0x17, 0x19, 0x1a, 0x1b, 0x1d, 0x1e, 0x1f, 0x21, - 0x22, 0x24, 0x26, 0x27, 0x29, 0x2b, 0x2d, 0x2e, - 0x30, 0x32, 0x34, 0x36, 0x38, 0x3a, 0x3c, 0x3e, -}; - -const q15_t sigmoidTable_q15[256] = { - 0x4000, 0x4200, 0x43ff, 0x45fc, 0x47f5, 0x49eb, 0x4bdc, 0x4dc8, - 0x4fad, 0x518a, 0x5360, 0x552c, 0x56ef, 0x58a8, 0x5a57, 0x5bfb, - 0x5d93, 0x5f20, 0x60a1, 0x6216, 0x637f, 0x64db, 0x662b, 0x676f, - 0x68a6, 0x69d2, 0x6af1, 0x6c05, 0x6d0d, 0x6e09, 0x6efb, 0x6fe2, - 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7, - 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f, - 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03, - 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d, - 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81, - 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17, - 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72, - 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa, - 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc, - 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0, - 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed, - 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4, - 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011, - 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c, - 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e, - 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c, - 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d, - 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce, - 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152, - 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a, - 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388, - 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8, - 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a, - 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70, - 0x0f42, 0x101e, 0x1105, 0x11f7, 0x12f3, 0x13fb, 0x150f, 0x162e, - 0x175a, 0x1891, 0x19d5, 0x1b25, 0x1c81, 0x1dea, 0x1f5f, 0x20e0, - 0x226d, 0x2405, 0x25a9, 0x2758, 0x2911, 0x2ad4, 0x2ca0, 0x2e76, - 0x3053, 0x3238, 0x3424, 0x3615, 0x380b, 0x3a04, 0x3c01, 0x3e00, -}; - -const q15_t sigmoidLTable_q15[128] = { - 0x4000, 0x4100, 0x4200, 0x42ff, 0x43ff, 0x44fd, 0x45fc, 0x46f9, - 0x47f5, 0x48f1, 0x49eb, 0x4ae5, 0x4bdc, 0x4cd3, 0x4dc8, 0x4ebb, - 0x4fad, 0x509c, 0x518a, 0x5276, 0x5360, 0x5447, 0x552c, 0x560f, - 0x56ef, 0x57cd, 0x58a8, 0x5981, 0x5a57, 0x5b2a, 0x5bfb, 0x5cc9, - 0x5d93, 0x5e5b, 0x5f20, 0x5fe2, 0x60a1, 0x615d, 0x6216, 0x62cc, - 0x637f, 0x642e, 0x64db, 0x6584, 0x662b, 0x66ce, 0x676f, 0x680c, - 0x68a6, 0x693d, 0x69d2, 0x6a63, 0x6af1, 0x6b7c, 0x6c05, 0x6c8a, - 0x6d0d, 0x6d8d, 0x6e09, 0x6e84, 0x6efb, 0x6f70, 0x6fe2, 0x7051, - 0x0f42, 0x0faf, 0x101e, 0x1090, 0x1105, 0x117c, 0x11f7, 0x1273, - 0x12f3, 0x1376, 0x13fb, 0x1484, 0x150f, 0x159d, 0x162e, 0x16c3, - 0x175a, 0x17f4, 0x1891, 0x1932, 0x19d5, 0x1a7c, 0x1b25, 0x1bd2, - 0x1c81, 0x1d34, 0x1dea, 0x1ea3, 0x1f5f, 0x201e, 0x20e0, 0x21a5, - 0x226d, 0x2337, 0x2405, 0x24d6, 0x25a9, 0x267f, 0x2758, 0x2833, - 0x2911, 0x29f1, 0x2ad4, 0x2bb9, 0x2ca0, 0x2d8a, 0x2e76, 0x2f64, - 0x3053, 0x3145, 0x3238, 0x332d, 0x3424, 0x351b, 0x3615, 0x370f, - 0x380b, 0x3907, 0x3a04, 0x3b03, 0x3c01, 0x3d01, 0x3e00, 0x3f00, -}; - -const q15_t sigmoidHTable_q15[192] = { - 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7, - 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f, - 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03, - 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d, - 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81, - 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17, - 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72, - 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa, - 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc, - 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0, - 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed, - 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4, - 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011, - 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c, - 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e, - 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c, - 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d, - 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce, - 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152, - 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a, - 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388, - 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8, - 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a, - 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70, -}; - -const q7_t tanhTable_q7[256] = { - 0x00, 0x08, 0x10, 0x18, 0x1f, 0x27, 0x2e, 0x35, - 0x3b, 0x41, 0x47, 0x4c, 0x51, 0x56, 0x5a, 0x5e, - 0x61, 0x65, 0x68, 0x6a, 0x6d, 0x6f, 0x71, 0x72, - 0x74, 0x75, 0x76, 0x78, 0x78, 0x79, 0x7a, 0x7b, - 0x7b, 0x7c, 0x7c, 0x7d, 0x7d, 0x7e, 0x7e, 0x7e, - 0x7e, 0x7e, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x81, - 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x82, - 0x82, 0x82, 0x82, 0x82, 0x83, 0x83, 0x84, 0x84, - 0x85, 0x85, 0x86, 0x87, 0x88, 0x88, 0x8a, 0x8b, - 0x8c, 0x8e, 0x8f, 0x91, 0x93, 0x96, 0x98, 0x9b, - 0x9f, 0xa2, 0xa6, 0xaa, 0xaf, 0xb4, 0xb9, 0xbf, - 0xc5, 0xcb, 0xd2, 0xd9, 0xe1, 0xe8, 0xf0, 0xf8, -}; - -const q15_t tanhTable_q15[256] = { - 0x0000, 0x07fd, 0x0feb, 0x17b9, 0x1f59, 0x26bf, 0x2ddf, 0x34ae, - 0x3b27, 0x4142, 0x46fd, 0x4c56, 0x514d, 0x55e2, 0x5a1a, 0x5df6, - 0x617c, 0x64b0, 0x6797, 0x6a37, 0x6c95, 0x6eb5, 0x709e, 0x7254, - 0x73dc, 0x753a, 0x7672, 0x7788, 0x787f, 0x795b, 0x7a1e, 0x7acb, - 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f, - 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48, - 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc, - 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7, - 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7, - 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd, - 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, - 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003, - 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007, - 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013, - 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035, - 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f, - 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183, - 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412, - 0x849b, 0x8535, 0x85e2, 0x86a5, 0x8781, 0x8878, 0x898e, 0x8ac6, - 0x8c24, 0x8dac, 0x8f62, 0x914b, 0x936b, 0x95c9, 0x9869, 0x9b50, - 0x9e84, 0xa20a, 0xa5e6, 0xaa1e, 0xaeb3, 0xb3aa, 0xb903, 0xbebe, - 0xc4d9, 0xcb52, 0xd221, 0xd941, 0xe0a7, 0xe847, 0xf015, 0xf803, -}; - -const q15_t tanhLTable_q15[128] = { - 0x0000, 0x0400, 0x07fd, 0x0bf7, 0x0feb, 0x13d7, 0x17b9, 0x1b90, - 0x1f59, 0x2314, 0x26bf, 0x2a58, 0x2ddf, 0x3151, 0x34ae, 0x37f6, - 0x3b27, 0x3e40, 0x4142, 0x442c, 0x46fd, 0x49b6, 0x4c56, 0x4edd, - 0x514d, 0x53a3, 0x55e2, 0x580a, 0x5a1a, 0x5c13, 0x5df6, 0x5fc4, - 0x617c, 0x6320, 0x64b0, 0x662d, 0x6797, 0x68f0, 0x6a37, 0x6b6e, - 0x6c95, 0x6dac, 0x6eb5, 0x6fb0, 0x709e, 0x717f, 0x7254, 0x731e, - 0x73dc, 0x7490, 0x753a, 0x75da, 0x7672, 0x7701, 0x7788, 0x7807, - 0x787f, 0x78f0, 0x795b, 0x79bf, 0x7a1e, 0x7a77, 0x7acb, 0x7b1b, - 0x849b, 0x84e5, 0x8535, 0x8589, 0x85e2, 0x8641, 0x86a5, 0x8710, - 0x8781, 0x87f9, 0x8878, 0x88ff, 0x898e, 0x8a26, 0x8ac6, 0x8b70, - 0x8c24, 0x8ce2, 0x8dac, 0x8e81, 0x8f62, 0x9050, 0x914b, 0x9254, - 0x936b, 0x9492, 0x95c9, 0x9710, 0x9869, 0x99d3, 0x9b50, 0x9ce0, - 0x9e84, 0xa03c, 0xa20a, 0xa3ed, 0xa5e6, 0xa7f6, 0xaa1e, 0xac5d, - 0xaeb3, 0xb123, 0xb3aa, 0xb64a, 0xb903, 0xbbd4, 0xbebe, 0xc1c0, - 0xc4d9, 0xc80a, 0xcb52, 0xceaf, 0xd221, 0xd5a8, 0xd941, 0xdcec, - 0xe0a7, 0xe470, 0xe847, 0xec29, 0xf015, 0xf409, 0xf803, 0xfc00, -}; - -const q15_t tanhHTable_q15[192] = { - 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f, - 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48, - 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc, - 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7, - 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7, - 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd, - 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, - 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003, - 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007, - 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013, - 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035, - 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f, - 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183, - 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412, -}; diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c deleted file mode 100644 index 5cdac6ce..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_no_shift.c - * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift - * @param[in] *pSrc points to the Q7 input vector - * @param[out] *pDst points to the Q15 output vector - * @param[in] blockSize length of the input vector - * - * \par Description: - * - * The equation used for the conversion process is: - * - * 
- * 	pDst[n] = (q15_t) pSrc[n];   0 <= n < blockSize.
- *
- * - */ - -void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize) -{ - const q7_t *pIn = pSrc; - uint32_t blkCnt; - -#if defined(ARM_MATH_DSP) - q31_t in; - q31_t in1, in2; - q31_t out1, out2; - - /*loop Unrolling */ - blkCnt = blockSize >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - while (blkCnt > 0u) - { - in = arm_nn_read_q7x4_ia(&pIn); - - /* rotatate in by 8 and extend two q7_t values to q15_t values */ - in1 = __SXTB16(__ROR((uint32_t)in, 8)); - - /* extend remaining two q7_t values to q15_t values */ - in2 = __SXTB16(in); - -#ifndef ARM_MATH_BIG_ENDIAN - out2 = (int32_t)__PKHTB(in1, in2, 16); - out1 = (int32_t)__PKHBT(in2, in1, 16); -#else - out1 = (int32_t)__PKHTB(in1, in2, 16); - out2 = (int32_t)__PKHBT(in2, in1, 16); -#endif - write_q15x2_ia(&pDst, out1); - write_q15x2_ia(&pDst, out2); - - /* Decrement the loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4u; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Loop over blockSize number of values */ - blkCnt = blockSize; - -#endif /* #ifndef ARM_MATH_CM0_FAMILY */ - - while (blkCnt > 0u) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - *pDst++ = (q15_t)*pIn++; - - /* Decrement the loop counter */ - blkCnt--; - } - -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c deleted file mode 100644 index 8128aaa2..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c +++ /dev/null @@ -1,144 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_reordered_no_shift.c - * Description: Converts the elements of the Q7 vector to reordered Q15 vector without left-shift - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift - * @param[in] *pSrc points to the Q7 input vector - * @param[out] *pDst points to the Q15 output vector - * @param[in] blockSize length of the input vector - * - * @details - * - * This function does the q7 to q15 expansion with re-ordering - * - * 
- *                          |   A1   |   A2   |   A3   |   A4   |
- *
- *                           0      7 8     15 16    23 24    31
- *
- * - * is converted into: - * - * 
- *  |       A1       |       A3       |   and  |       A2       |       A4       |
- *
- *   0             15 16            31          0             15 16            31
- *
- * - * - * This looks strange but is natural considering how sign-extension is done at - * assembly level. - * - * The expansion of other other oprand will follow the same rule so that the end - * results are the same. - * - * The tail (i.e., last (N % 4) elements) will still be in original order. - * - */ - -void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize) -{ - const q7_t *pIn = pSrc; /* Src pointer */ - uint32_t blkCnt; /* loop counter */ - -#ifndef ARM_MATH_CM0_FAMILY - q31_t in; - q31_t in1, in2; - - /* Run the below code for Cortex-M4 and Cortex-M3 */ - - /*loop Unrolling */ - blkCnt = blockSize >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0u) - { - /* C = (q15_t) A << 8 */ - /* convert from q7 to q15 and then store the results in the destination buffer */ - in = arm_nn_read_q7x4_ia(&pIn); - - /* rotatate in by 8 and extend two q7_t values to q15_t values */ - in1 = __SXTB16(__ROR((uint32_t)in, 8)); - - /* extend remainig two q7_t values to q15_t values */ - in2 = __SXTB16(in); - -#ifndef ARM_MATH_BIG_ENDIAN - *__SIMD32(pDst)++ = in2; - *__SIMD32(pDst)++ = in1; -#else - *__SIMD32(pDst)++ = in1; - *__SIMD32(pDst)++ = in2; -#endif - - /* Decrement the loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4u; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Loop over blockSize number of values */ - blkCnt = blockSize; - -#endif /* #ifndef ARM_MATH_CM0_FAMILY */ - - while (blkCnt > 0u) - { - /* C = (q15_t) A << 8 */ - /* convert from q7 to q15 and then store the results in the destination buffer */ - *pDst++ = (q15_t) * pIn++; - - /* Decrement the loop counter */ - blkCnt--; - } - -} - -/** - * @} end of q7_to_x group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c deleted file mode 100644 index 34e13ca1..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c +++ /dev/null @@ -1,100 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_reordered_with_offset.c - * Description: Converts the elements of the Q7 vector to a reordered Q15 vector with an added offset. The re-ordering - * is a signature of sign extension intrinsic(DSP extension). - * - * $Date: May 29, 2020 - * $Revision: V.2.0.3 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to a reordered Q15 vector with an added offset. - * - * @note Refer header file for details. - * - */ - -void arm_q7_to_q15_reordered_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset) -{ - -#if defined(ARM_MATH_DSP) - uint32_t block_cnt; - /* Run the below code for cores that support SIMD instructions */ - q31_t in_q7x4; - q31_t out_q15x2_1; - q31_t out_q15x2_2; - - /*loop unrolling */ - block_cnt = block_size >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - const q31_t offset_q15x2 = (q31_t)__PKHBT(offset, offset, 16); - while (block_cnt > 0u) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - in_q7x4 = arm_nn_read_q7x4_ia(&src); - - /* Extract and sign extend each of the four q7 values to q15 */ - out_q15x2_1 = __SXTAB16(offset_q15x2, __ROR((uint32_t)in_q7x4, 8)); - out_q15x2_2 = __SXTAB16(offset_q15x2, in_q7x4); - - write_q15x2_ia(&dst, out_q15x2_2); - write_q15x2_ia(&dst, out_q15x2_1); - - block_cnt--; - } - /* Handle left over samples */ - block_cnt = block_size % 0x4u; - - while (block_cnt > 0u) - { - *dst++ = (q15_t)*src++ + offset; - - /* Decrement the loop counter */ - block_cnt--; - } -#else - (void)src; - (void)dst; - (void)block_size; - (void)offset; - /* Not available */ -#endif -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c deleted file mode 100644 index 4b69f556..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c +++ /dev/null @@ -1,117 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in_q7x4 compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in_q7x4 writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_with_offset.c - * Description: Converts the elements of the Q7 vector to Q15 vector with an added offset - * - * $Date: March 3, 2020 - * $Revision: V.2.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -void arm_q7_to_q15_with_offset(const q7_t *src, - q15_t *dst, - uint32_t block_size, - q15_t offset) -{ - int block_cnt; - -#if defined(ARM_MATH_MVEI) - - int16x8_t source; - const int16x8_t source_offset = vdupq_n_s16(offset); - block_cnt = block_size / 8; - - while (block_cnt > 0) - { - source = vldrbq_s16(src); - source = vaddq_s16(source, source_offset); - vstrhq_s16(dst, source); - dst += 8; - src += 8; - block_cnt--; - } - - block_cnt = block_size & 0x7; - -#elif defined(ARM_MATH_DSP) - /* Run the below code for cores that support SIMD instructions */ - q31_t in_q7x4; - q31_t in_q15x2_1; - q31_t in_q15x2_2; - q31_t out_q15x2_1; - q31_t out_q15x2_2; - - /*loop unrolling */ - block_cnt = block_size >> 2; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - const q31_t offset_q15x2 = __PKHBT(offset, offset, 16); - while (block_cnt > 0) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - in_q7x4 = arm_nn_read_q7x4_ia(&src); - - /* Extract and sign extend each of the four q7 values to q15 */ - in_q15x2_1 = __SXTAB16(offset_q15x2, __ROR(in_q7x4, 8)); - in_q15x2_2 = __SXTAB16(offset_q15x2, in_q7x4); - - out_q15x2_2 = __PKHTB(in_q15x2_1, in_q15x2_2, 16); - out_q15x2_1 = __PKHBT(in_q15x2_2, in_q15x2_1, 16); - - write_q15x2_ia(&dst, out_q15x2_1); - write_q15x2_ia(&dst, out_q15x2_2); - - block_cnt--; - } - /* Handle left over samples */ - block_cnt = block_size % 0x4; - -#else - /* Run the below code for Cortex-M0 */ - /* Loop over block_size number of values */ - block_cnt = block_size; -#endif - - while (block_cnt > 0) - { - *dst++ = (q15_t)*src++ + offset; - - /* Decrement the loop counter */ - block_cnt--; - } -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c deleted file mode 100644 index fbcfdf6c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c +++ /dev/null @@ -1,361 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_avgpool_s8.c - * Description: Pooling function implementations - * - * $Date: May 29,2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - - -#if defined (ARM_MATH_DSP) && !defined (ARM_MATH_MVEI) - -static void buffer_scale_back_q15_to_q7_and_clamp(q15_t *buffer, q7_t *target, uint16_t length, - uint16_t count,const int act_min, const int act_max) -{ - int i; - int sum; - - for (i = 0; i < length; i++) - { - sum = buffer[i] > 0 ? (buffer[i] + count / 2) / count : (buffer[i] - count / 2) / count; - - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - target[i] = (q7_t) (sum); - } -} -#endif - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - -/* - * s8 average pooling function - * - * Refer to header file for details. - * - */ - -#if defined(ARM_MATH_MVEI) - -arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - (void)ctx; - const int32_t dim_src_height = input_dims->h; - const int32_t dim_src_width = input_dims->w; - const int32_t dim_dst_height = output_dims->h; - const int32_t dim_dst_width = output_dims->w; - const int32_t stride_height = pool_params->stride.h; - const int32_t stride_width = pool_params->stride.w; - const int32_t dim_kernel_height = filter_dims->h; - const int32_t dim_kernel_width = filter_dims->w; - const int32_t padding_height = pool_params->padding.h; - const int32_t padding_width = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t ch_src = input_dims->c; - - int32_t i_x, i_y; - int32_t k_x, k_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - - int32_t k_y_start,k_y_end; - int32_t k_x_start,k_x_end; - int32_t chCnt; - const int8_t *pTmp, *pTmpInner; - int8_t *pDst; - - k_y_start = MAX(0, i_y * stride_height - padding_height); - k_y_end = MIN(i_y * stride_height - padding_height + dim_kernel_height,dim_src_height); - - k_x_start = MAX(0,i_x * stride_width - padding_width); - k_x_end = MIN(i_x * stride_width - padding_width + dim_kernel_width, dim_src_width); - - - pTmp = src; - pDst = &dst[ch_src * (i_x + i_y * dim_dst_width)]; - - chCnt = ch_src >> 4; - while(chCnt > 0) - { - int32x4_t sumV1,sumV2,sumV3,sumV4; - - int8x16_t tempV; - int16x8_t tempVLO, tempVHI; - int32x4_t tempVLOLO, tempVLOHI, tempVHILO, tempVHIHI; - int32_t count = 0; - - sumV1 = vdupq_n_s32(0); - sumV2 = vdupq_n_s32(0); - sumV3 = vdupq_n_s32(0); - sumV4 = vdupq_n_s32(0); - - for (k_y = k_y_start; k_y < k_y_end; k_y++) - { - for (k_x = k_x_start; k_x < k_x_end; k_x++) - { - pTmpInner = pTmp + (ch_src * (k_x + k_y * dim_src_width)); - tempV = vldrbq_s8 (pTmpInner); - - tempVLO = vmovlbq_s8(tempV); - tempVHI = vmovltq_s8(tempV); - - tempVLOLO = vmovlbq_s16(tempVLO); - tempVLOHI = vmovltq_s16(tempVLO); - - tempVHILO = vmovlbq_s16(tempVHI); - tempVHIHI = vmovltq_s16(tempVHI); - - sumV1 = vaddq_s32(sumV1,tempVLOLO); - sumV2 = vaddq_s32(sumV2,tempVLOHI); - sumV3 = vaddq_s32(sumV3,tempVHILO); - sumV4 = vaddq_s32(sumV4,tempVHIHI); - - count++; - } - } - - - sumV1[0] = sumV1[0] > 0 ? (sumV1[0] + count / 2) / count : (sumV1[0] - count / 2) / count; - sumV1[1] = sumV1[1] > 0 ? (sumV1[1] + count / 2) / count : (sumV1[1] - count / 2) / count; - sumV1[2] = sumV1[2] > 0 ? (sumV1[2] + count / 2) / count : (sumV1[2] - count / 2) / count; - sumV1[3] = sumV1[3] > 0 ? (sumV1[3] + count / 2) / count : (sumV1[3] - count / 2) / count; - - sumV2[0] = sumV2[0] > 0 ? (sumV2[0] + count / 2) / count : (sumV2[0] - count / 2) / count; - sumV2[1] = sumV2[1] > 0 ? (sumV2[1] + count / 2) / count : (sumV2[1] - count / 2) / count; - sumV2[2] = sumV2[2] > 0 ? (sumV2[2] + count / 2) / count : (sumV2[2] - count / 2) / count; - sumV2[3] = sumV2[3] > 0 ? (sumV2[3] + count / 2) / count : (sumV2[3] - count / 2) / count; - - sumV3[0] = sumV3[0] > 0 ? (sumV3[0] + count / 2) / count : (sumV3[0] - count / 2) / count; - sumV3[1] = sumV3[1] > 0 ? (sumV3[1] + count / 2) / count : (sumV3[1] - count / 2) / count; - sumV3[2] = sumV3[2] > 0 ? (sumV3[2] + count / 2) / count : (sumV3[2] - count / 2) / count; - sumV3[3] = sumV3[3] > 0 ? (sumV3[3] + count / 2) / count : (sumV3[3] - count / 2) / count; - - sumV4[0] = sumV4[0] > 0 ? (sumV4[0] + count / 2) / count : (sumV4[0] - count / 2) / count; - sumV4[1] = sumV4[1] > 0 ? (sumV4[1] + count / 2) / count : (sumV4[1] - count / 2) / count; - sumV4[2] = sumV4[2] > 0 ? (sumV4[2] + count / 2) / count : (sumV4[2] - count / 2) / count; - sumV4[3] = sumV4[3] > 0 ? (sumV4[3] + count / 2) / count : (sumV4[3] - count / 2) / count; - - sumV1 = vmaxq_s32(sumV1, vdupq_n_s32(act_min)); - sumV1 = vminq_s32(sumV1, vdupq_n_s32(act_max)); - - sumV2 = vmaxq_s32(sumV2, vdupq_n_s32(act_min)); - sumV2 = vminq_s32(sumV2, vdupq_n_s32(act_max)); - - sumV3 = vmaxq_s32(sumV3, vdupq_n_s32(act_min)); - sumV3 = vminq_s32(sumV3, vdupq_n_s32(act_max)); - - sumV4 = vmaxq_s32(sumV4, vdupq_n_s32(act_min)); - sumV4 = vminq_s32(sumV4, vdupq_n_s32(act_max)); - - tempVLO = vmovnbq_s32(tempVLO,sumV1); - tempVLO = vmovntq_s32(tempVLO,sumV2); - - tempVHI = vmovnbq_s32(tempVHI,sumV3); - tempVHI = vmovntq_s32(tempVHI,sumV4); - - - tempV = vmovnbq_s16(tempV,tempVLO); - tempV = vmovntq_s16(tempV,tempVHI); - - vstrbq_s8(pDst,tempV); - pDst += 16; - - chCnt --; - pTmp += 16; - } - - chCnt = ch_src & 0xF; - while(chCnt > 0) - { - int32_t sum = 0; - int32_t count = 0; - - for (k_y = k_y_start; k_y < k_y_end; k_y++) - { - for (k_x = k_x_start; k_x < k_x_end; k_x++) - { - sum += pTmp[ch_src * (k_x + k_y * dim_src_width)]; - count++; - } - } - sum = sum > 0 ? (sum + count / 2) / count : (sum - count / 2) / count; - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - *pDst++ = sum; - - chCnt --; - pTmp++; - } - } - } - return ARM_MATH_SUCCESS; -} - -#else -arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - const int32_t dim_src_height = input_dims->h; - const int32_t dim_src_width = input_dims->w; - const int32_t dim_dst_height = output_dims->h; - const int32_t dim_dst_width = output_dims->w; - const int32_t stride_height = pool_params->stride.h; - const int32_t stride_width = pool_params->stride.w; - const int32_t dim_kernel_height = filter_dims->h; - const int32_t dim_kernel_width = filter_dims->w; - const int32_t padding_height = pool_params->padding.h; - const int32_t padding_width = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t ch_src = input_dims->c; - q15_t *bufferA = (q15_t *)ctx->buf; - -#if defined (ARM_MATH_DSP) - - /* Run the following code for Cortex-M4 and Cortex-M7 - */ - int32_t k_x, k_y, i_x, i_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - /* Condition for kernel start dimension: (base_idx_ + kernel__start) >= 0 */ - const int32_t base_idx_y = (i_y * stride_height) - padding_height; - const int32_t base_idx_x = (i_x * stride_width) - padding_width; - const int32_t kernel_y_start = MAX(0, -base_idx_y); - const int32_t kernel_x_start = MAX(0, -base_idx_x); - - /* Condition for kernel end dimension: (base_idx_ + kernel__end) < dim_src_ */ - const int32_t kernel_y_end = MIN(dim_kernel_height, dim_src_height - base_idx_y); - const int32_t kernel_x_end = MIN(dim_kernel_width, dim_src_width - base_idx_x); - - int count = 0; - - for (k_y = kernel_y_start; k_y < kernel_y_end; k_y++) - { - for (k_x = kernel_x_start; k_x < kernel_x_end; k_x++) - { - const q7_t *start = src + ch_src * (k_x + base_idx_x + (k_y + base_idx_y) * dim_src_width); - - if (count == 0) - { - arm_q7_to_q15_no_shift(start, bufferA, ch_src); - } - else - { - arm_nn_accumulate_q7_to_q15(bufferA, start, ch_src); - } - count++; - } - } - buffer_scale_back_q15_to_q7_and_clamp(bufferA, dst, ch_src, count, act_min, act_max); - dst += ch_src; - } - } -#else - - /* Reference C code adapted from CMSIS-NN arm_avepool_q7_HWC. - */ - (void)bufferA; - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - for (i_ch_in = 0; i_ch_in < ch_src; i_ch_in++) - { - int sum = 0; - int count = 0; - for (k_y = i_y * stride_height - padding_height; k_y < i_y * stride_height - padding_height + dim_kernel_height; k_y++) - { - for (k_x = i_x * stride_width - padding_width; k_x < i_x * stride_width - padding_width + dim_kernel_width; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_src_height && k_x < dim_src_width) - { - sum += src[i_ch_in + ch_src * (k_x + k_y * dim_src_width)]; - count++; - } - } - } - sum = sum > 0 ? (sum + count / 2) / count : (sum - count / 2) / count; - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - dst[i_ch_in + ch_src * (i_x + i_y * dim_dst_width)] = sum; - } - } - } - -#endif - return ARM_MATH_SUCCESS; -} - -#endif /* ARM_MATH_MVEI */ - -int32_t arm_avgpool_s8_get_buffer_size(const int dim_dst_width, - const int ch_src) -{ - (void)dim_dst_width; - -#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) - return (ch_src * sizeof(int16_t)); -#else - (void)ch_src; - return 0; -#endif -} -/** - * @} end of Pooling group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c deleted file mode 100644 index df87ad98..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c +++ /dev/null @@ -1,235 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_max_pool_s8.c - * Description: Pooling function implementations - * - * $Date: June 11, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -static void compare_and_replace_if_larger_q7(q7_t *base, - const q7_t *target, - int32_t length) -{ -#if defined(ARM_MATH_MVEI) - int32_t loop_count = (length + 15) / 16; - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp16q((uint32_t)length); - const int8x16_t op_1 = vldrbq_z_s8(base, p); - const int8x16_t op_2 = vldrbq_z_s8(target, p); - const int8x16_t max = vmaxq_m_s8(vuninitializedq_s8(), op_1, op_2, p); - vstrbq_p_s8(base, max, p); - base += 16; - target += 16; - length -= 16; - } -#else - q7_t *dst = base; - const q7_t *src = target; - union arm_nnword ref_max; - union arm_nnword comp_max; - int32_t cnt = length >> 2; - - while (cnt > 0l) - { - ref_max.word = arm_nn_read_q7x4(dst); - comp_max.word = arm_nn_read_q7x4_ia(&src); - - if (comp_max.bytes[0] > ref_max.bytes[0]) - { - ref_max.bytes[0] = comp_max.bytes[0]; - } - if (comp_max.bytes[1] > ref_max.bytes[1]) - { - ref_max.bytes[1] = comp_max.bytes[1]; - } - if (comp_max.bytes[2] > ref_max.bytes[2]) - { - ref_max.bytes[2] = comp_max.bytes[2]; - } - if (comp_max.bytes[3] > ref_max.bytes[3]) - { - ref_max.bytes[3] = comp_max.bytes[3]; - } - - write_q7x4_ia(&dst, ref_max.word); - - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0l) - { - if (*src > *dst) - { - *dst = *src; - } - dst++; - src++; - cnt--; - } -#endif -} - -static void -clamp_output(q7_t *source, int32_t length, const int32_t act_min, const int32_t act_max) -{ -#if defined(ARM_MATH_MVEI) - int32_t - loop_count = (length + 15) / 16; - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp16q((uint32_t)length); - length -= 16; - const int8x16_t src = vldrbq_z_s8(source, p); - const int8x16_t predicated_min = vdupq_m_n_s8(vuninitializedq_s8(), (int8_t)act_min, p); - const int8x16_t predicated_max = vdupq_m_n_s8(vuninitializedq_s8(), (int8_t)act_max, p); - int8x16_t - res = vmaxq_m_s8(vuninitializedq_s8(), src, predicated_min, p); - res = vminq_m_s8(vuninitializedq_s8(), src, predicated_max, p); - vstrbq_p_s8(source, res, p); - source += 16; - } -#else - union arm_nnword in; - int32_t cnt = length >> 2; - - while (cnt > 0l) - { - in.word = arm_nn_read_q7x4(source); - - in.bytes[0] = MAX(in.bytes[0], act_min); - in.bytes[0] = MIN(in.bytes[0], act_max); - in.bytes[1] = MAX(in.bytes[1], act_min); - in.bytes[1] = MIN(in.bytes[1], act_max); - in.bytes[2] = MAX(in.bytes[2], act_min); - in.bytes[2] = MIN(in.bytes[2], act_max); - in.bytes[3] = MAX(in.bytes[3], act_min); - in.bytes[3] = MIN(in.bytes[3], act_max); - - write_q7x4_ia(&source, in.word); - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0l) - { - int32_t comp = *source; - comp = MAX(comp, act_min); - comp = MIN(comp, act_max); - *source++ = (int8_t)comp; - cnt--; - } -#endif -} - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - -/* - * Optimized s8 max pooling function - * - * Refer to header file for details. - * - */ - -arm_status -arm_max_pool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - const int32_t input_y = input_dims->h; - const int32_t input_x = input_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_x = output_dims->w; - const int32_t stride_y = pool_params->stride.h; - const int32_t stride_x = pool_params->stride.w; - const int32_t kernel_y = filter_dims->h; - const int32_t kernel_x = filter_dims->w; - const int32_t pad_y = pool_params->padding.h; - const int32_t pad_x = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t channel_in = input_dims->c; - (void)ctx; - q7_t *dst_base = dst; - - for (int i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) - { - for (int i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) - { - /* Condition for kernel start dimension: (base_idx_ + kernel__start) >= 0 */ - const int32_t ker_y_start = MAX(0, -base_idx_y); - const int32_t ker_x_start = MAX(0, -base_idx_x); - - /* Condition for kernel end dimension: (base_idx_ + kernel__end) < dim_src_ */ - const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); - const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); - - int count = 0; - - for (int k_y = ker_y_start; k_y < kernel_y_end; k_y++) - { - for (int k_x = ker_x_start; k_x < kernel_x_end; k_x++) - { - const q7_t *start = src + channel_in * (k_x + base_idx_x + (k_y + base_idx_y) * input_x); - - if (count == 0) - { - memcpy(dst, start, channel_in); - count++; - } - else - { - compare_and_replace_if_larger_q7(dst, start, channel_in); - } - } - } - /* 'count' is expected to be non-zero here. */ - dst += channel_in; - } - } - - clamp_output(dst_base, output_x * output_y * channel_in, act_min, act_max); - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of Pooling group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c deleted file mode 100644 index 4f1e2a51..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c +++ /dev/null @@ -1,454 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_pool_q7_HWC.c - * Description: Pooling function implementations - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -#if defined (ARM_MATH_DSP) - -/** - * @brief A few utility functions used by pooling functions - * - * - */ - -static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale) -{ - int i; - - for (i = 0; i < length; i++) - { - target[i] = (q7_t) (buffer[i] / scale); - } -} - -static void compare_and_replace_if_larger_q7(q7_t * base, // base data - const q7_t * target, // compare target - const uint16_t length // data length - ) -{ - q7_t *pIn = base; - const q7_t *pCom = target; - union arm_nnword in; - union arm_nnword com; - uint16_t cnt = length >> 2; - - while (cnt > 0u) - { - in.word = arm_nn_read_q7x4((const q7_t*)pIn); - com.word = arm_nn_read_q7x4_ia((const q7_t**)&pCom); - - // if version - if (com.bytes[0] > in.bytes[0]) - in.bytes[0] = com.bytes[0]; - if (com.bytes[1] > in.bytes[1]) - in.bytes[1] = com.bytes[1]; - if (com.bytes[2] > in.bytes[2]) - in.bytes[2] = com.bytes[2]; - if (com.bytes[3] > in.bytes[3]) - in.bytes[3] = com.bytes[3]; - - *__SIMD32(pIn)++ = in.word; - - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0u) - { - if (*pCom > *pIn) - { - *pIn = *pCom; - } - pIn++; - pCom++; - cnt--; - } -} - -static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length) -{ - q15_t *pCnt = base; - q7_t *pV = target; - q31_t v1, v2, vo1, vo2; - uint16_t cnt = length >> 2; - q31_t in; - - while (cnt > 0u) - { - q31_t value = arm_nn_read_q7x4_ia((const q7_t**)&pV); - v1 = __SXTB16(__ROR(value, 8)); - v2 = __SXTB16(value); -#ifndef ARM_MATH_BIG_ENDIAN - - vo2 = __PKHTB(v1, v2, 16); - vo1 = __PKHBT(v2, v1, 16); - -#else - - vo1 = __PKHTB(v1, v2, 16); - vo2 = __PKHBT(v2, v1, 16); - -#endif - - in = arm_nn_read_q15x2(pCnt); - *__SIMD32(pCnt)++ = __QADD16(vo1, in); - - in = arm_nn_read_q15x2(pCnt); - *__SIMD32(pCnt)++ = __QADD16(vo2, in); - - cnt--; - } - cnt = length & 0x3; - while (cnt > 0u) - { - *pCnt++ += *pV++; - cnt--; - } -} - -#endif // ARM_MATH_DSP - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - - /** - * @brief Q7 max pooling function - * @param[in, out] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA Not used - * @param[in,out] Im_out pointer to output tensor - * - * @details - * - * The pooling function is implemented as split x-pooling then - * y-pooling. - * - * This pooling function is input-destructive. Input data is undefined - * after calling this function. - * - */ - -void -arm_maxpool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out) -{ - (void)bufferA; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_x, i_y; - - /* first does the pooling along x axis */ - for (i_y = 0; i_y < dim_im_in; i_y++) - { - - for (i_x = 0; i_x < dim_im_out; i_x++) - { - /* for each output pixel */ - q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in; - q7_t *win_start; - q7_t *win_stop; - if (i_x * stride - padding < 0) - { - win_start = target; - } else - { - win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in; - } - - if (i_x * stride - padding + dim_kernel >= dim_im_in) - { - win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in; - } else - { - win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in; - } - - /* first step is to copy over initial data */ - /* arm_copy_q7(win_start, target, ch_im_in); */ - memmove(target, win_start, ch_im_in); - - /* start the max operation from the second part */ - win_start += ch_im_in; - for (; win_start < win_stop; win_start += ch_im_in) - { - compare_and_replace_if_larger_q7(target, win_start, ch_im_in); - } - } - } - - /* then does the pooling along y axis */ - for (i_y = 0; i_y < dim_im_out; i_y++) - { - - /* for each output row */ - q7_t *target = Im_out + i_y * dim_im_out * ch_im_in; - q7_t *row_start; - q7_t *row_end; - /* setting the starting row */ - if (i_y * stride - padding < 0) - { - row_start = Im_in; - } else - { - row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in; - } - /* setting the stopping row */ - if (i_y * stride - padding + dim_kernel >= dim_im_in) - { - row_end = Im_in + dim_im_in * dim_im_in * ch_im_in; - } else - { - row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in; - } - - /* copy over the first row */ - /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */ - memmove(target, row_start, dim_im_out * ch_im_in); - - /* move over to next row */ - row_start += ch_im_in * dim_im_in; - - for (; row_start < row_end; row_start += dim_im_in * ch_im_in) - { - compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in); - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++) - { - for (i_y = 0; i_y < dim_im_out; i_y++) - { - for (i_x = 0; i_x < dim_im_out; i_x++) - { - int max = -129; - for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++) - { - for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in) - { - if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max) - { - max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)]; - } - } - } - } - Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max; - } - } - } - -#endif /* ARM_MATH_DSP */ - -} - - /** - * @brief Q7 average pooling function - * @param[in,out] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * - * @details - * - * Buffer size: - * - * bufferA size: 2*dim_im_out*ch_im_in - * - * The pooling function is implemented as split x-pooling then - * y-pooling. - * - * This pooling function is input-destructive. Input data is undefined - * after calling this function. - * - */ - -void -arm_avepool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - q15_t *buffer = (q15_t *) bufferA; - int16_t i_x, i_y; - int16_t count = 0; - - /* first does the pooling along x axis */ - for (i_y = 0; i_y < dim_im_in; i_y++) - { - - for (i_x = 0; i_x < dim_im_out; i_x++) - { - /* for each output pixel */ - q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in; - q7_t *win_start; - q7_t *win_stop; - if (i_x * stride - padding < 0) - { - win_start = target; - } else - { - win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in; - } - - if (i_x * stride - padding + dim_kernel >= dim_im_in) - { - win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in; - } else - { - win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in; - } - - /* first step is to copy over initial data */ - arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in); - count = 1; - - /* start the max operation from the second part */ - win_start += ch_im_in; - for (; win_start < win_stop; win_start += ch_im_in) - { - accumulate_q7_to_q15(buffer, win_start, ch_im_in); - count++; - } - buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count); - } - } - - /* then does the pooling along y axis */ - for (i_y = 0; i_y < dim_im_out; i_y++) - { - /* for each output row */ - q7_t *target = Im_out + i_y * dim_im_out * ch_im_in; - q7_t *row_start; - q7_t *row_end; - /* setting the starting row */ - if (i_y * stride - padding < 0) - { - row_start = Im_in; - } else - { - row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in; - } - /* setting the stopping row */ - if (i_y * stride - padding + dim_kernel >= dim_im_in) - { - row_end = Im_in + dim_im_in * dim_im_in * ch_im_in; - } else - { - row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in; - } - - /* copy over the first row */ - arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in); - count = 1; - - /* move over to next row */ - row_start += ch_im_in * dim_im_in; - - for (; row_start < row_end; row_start += dim_im_in * ch_im_in) - { - accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in); - count++; - } - buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count); - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - (void)bufferA; - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++) - { - for (i_y = 0; i_y < dim_im_out; i_y++) - { - for (i_x = 0; i_x < dim_im_out; i_x++) - { - int sum = 0; - int count = 0; - for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++) - { - for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in) - { - sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)]; - count++; - } - } - } - Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count; - } - } - } - -#endif /* ARM_MATH_DSP */ - -} - -/** - * @} end of Pooling group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c deleted file mode 100644 index f943976c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_reshape_s8.c - * Description: Reshape a s8 vector - * - * $Date: September 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Reshape - * @{ - */ - -/** - * Basic s8 reshape function. - * - * Refer header file for details. - * - */ - -void arm_reshape_s8(const int8_t *input, - int8_t *output, - const uint32_t total_size) -{ - memcpy(output, input, total_size); -} - -/** - * @} end of Reshape group - */ \ No newline at end of file diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c deleted file mode 100644 index 30000431..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c +++ /dev/null @@ -1,119 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_q15.c - * Description: Q15 softmax function - * - * $Date: 20. February 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q15 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical e based softmax, we use - * 2-based softmax, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out) -{ - q31_t sum; - int16_t i; - uint8_t shift; - q31_t base; - base = -1 * 0x100000; - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - base = vec_in[i]; - } - } - - /* we ignore really small values - * anyway, they will be 0 after shrinking - * to q15_t - */ - base = base - 16; - - sum = 0; - - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - shift = (uint8_t)__USAT(vec_in[i] - base, 5); - sum += 0x1 << shift; - } - } - - /* This is effectively (0x1 << 32) / sum */ - int64_t div_base = 0x100000000LL; - int output_base = (int32_t)(div_base / sum); - - /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) ) - * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16 - * and vec_in[i]-base = 16 - */ - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - /* Here minimum value of 17+base-vec[i] will be 1 */ - shift = (uint8_t)__USAT(17+base-vec_in[i], 5); - p_out[i] = (q15_t) __SSAT((output_base >> shift), 16); - } else - { - p_out[i] = 0; - } - } - -} - -/** - * @} end of Softmax group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c deleted file mode 100644 index c2032271..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_q7.c - * Description: Q7 softmax function - * - * $Date: June 8, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q7 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical natural logarithm e based softmax, we use - * 2-based softmax here, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out ) -{ - q31_t sum; - int16_t i; - uint8_t shift; - q15_t base; - base = -128; - - /* We first search for the maximum */ - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - base = vec_in[i]; - } - } - - /* - * So the base is set to max-8, meaning - * that we ignore really small values. - * anyway, they will be 0 after shrinking to q7_t. - */ - base = base - (1 << 3); - - sum = 0; - - for (i = 0; i < dim_vec; i++) - { - shift = (uint8_t)__USAT(vec_in[i] - base, 3); - sum += 0x1 << shift; - } - - /* This is effectively (0x1 << 20) / sum */ - int output_base = (1 << 20) / sum; - - for (i = 0; i < dim_vec; i++) - { - - /* Here minimum value of 13+base-vec_in[i] will be 5 */ - shift = (uint8_t)__USAT(13 + base - vec_in[i], 5); - p_out[i] = (q7_t)__SSAT((output_base >> shift), 8); - } -} - -/** - * @} end of Softmax group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c deleted file mode 100644 index 8639f706..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c +++ /dev/null @@ -1,257 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_s8.c - * Description: S8 softmax function - * - * $Date: April 6, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -#define ACCUM_BITS 12 - -#ifdef ARM_MATH_MVEI -static int32x4_t arm_exp_on_negative_values_mve_32x4(int32x4_t val) -{ -#define SHIFT_START (24) - int32_t shift = SHIFT_START; - int32x4_t mask; - - const int32x4_t val_mod_minus_quarter = vandq_s32(val, vdupq_n_s32((1 << SHIFT_START) - 1)) - vdupq_n_s32(1 << SHIFT_START); - const int32x4_t remainder = vsubq_s32(val_mod_minus_quarter, val); - const int32x4_t x = vaddq_n_s32(val_mod_minus_quarter << 5, 1 << 28); - const int32x4_t x2 = MUL_SAT_MVE(x, x); - const int32x4_t op_1 = DIV_POW2_MVE(MUL_SAT_MVE(x2, x2), 2) + MUL_SAT_MVE(x2, x); - const int32x4_t op_2 = x + DIV_POW2_MVE(MUL_SAT_MVE(op_1, vdupq_n_s32(715827883)) + x2, 1); - int32x4_t result = vdupq_n_s32(1895147668) + MUL_SAT_MVE(vdupq_n_s32(1895147668), op_2); - -#define SELECT_IF_NON_ZERO(x) \ - { \ - mve_pred16_t p = vcmpneq_n_s32(remainder & vdupq_n_s32(1 << shift++), 0); \ - mask = vmvnq_m_s32(vdupq_n_s32(0), vdupq_n_s32(0), p); \ - result = SELECT_USING_MASK(mask, MUL_SAT_MVE(result, vdupq_n_s32(x)), result); \ - } - - SELECT_IF_NON_ZERO(1672461947) - SELECT_IF_NON_ZERO(1302514674) - SELECT_IF_NON_ZERO(790015084) - SELECT_IF_NON_ZERO(290630308) - SELECT_IF_NON_ZERO(39332535) - SELECT_IF_NON_ZERO(720401) - SELECT_IF_NON_ZERO(242) - -#undef SELECT_IF_NON_ZERO - - mve_pred16_t p = vcmpeqq_n_s32(val, 0); - mask = vmvnq_m_s32(vdupq_n_s32(0), vdupq_n_s32(0), p); - - result = SELECT_USING_MASK(mask, vdupq_n_s32(Q31_MAX), result); - return result; -} -#endif - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - -void arm_softmax_s8(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - int8_t *output) -{ -#ifdef ARM_MATH_MVEI - -#define ACT_MIN ((int8_t)Q7_MIN) -#define ACT_MAX ((int8_t)Q7_MAX) - - const int32_t mask = (1 << shift); - - for (int i_num_rows = 0; i_num_rows < num_rows; ++i_num_rows) - { - int8_t max = ACT_MIN; - - int32_t vec_count = (row_size + 15) / 16; - uint32_t r_count = (uint32_t)row_size; - for (int i = 0; i < vec_count; i++) - { - mve_pred16_t p = vctp8q(r_count); - const int8x16_t ip = vldrbq_z_s8(&input[i * 16], p); - max = vmaxvq_p_s8(max, ip, p); - r_count -= 16; - } - - vec_count = row_size / 4; - int32_t idx = 0; - int32_t sum = 0; - - while (vec_count) - { - int32x4_t ip = vldrbq_s32(&input[idx * 4]); - ip = vsubq_n_s32(ip, max); - mve_pred16_t p = vcmpgeq_n_s32(ip, diff_min); - if (p != 0) - { - ip = vmulq_n_s32(ip, mask); - - int32x4_t res = MUL_SAT_MVE(ip, vdupq_n_s32(mult)); - - res = arm_exp_on_negative_values_mve_32x4(res); - res = DIV_POW2_MVE(res, ACCUM_BITS); - res = vpselq_s32(res, vdupq_n_s32(0), p); - sum += vaddvq_s32(res); - } - - vec_count--; - idx++; - } - - const int32_t tail_idx = row_size & ~3; - for (int i = 0; i < (row_size & 3); i++) - { - const int32_t diff = input[tail_idx + i] - max; - if (diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ((uint32_t)sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - vec_count = row_size / 4; - idx = 0; - - while (vec_count) - { - int32x4_t ip = vldrbq_s32(&input[idx]); - ip = vsubq_n_s32(ip, max); - - mve_pred16_t p = vcmpgeq_n_s32(ip, diff_min); - - int32x4_t tmp_res; - - if (p != 0) - { - ip = vmulq_n_s32(ip, mask); - - tmp_res = MUL_SAT_MVE(ip, vdupq_n_s32(mult)); - tmp_res = arm_exp_on_negative_values_mve_32x4(tmp_res); - tmp_res = MUL_SAT_MVE(vdupq_n_s32(shifted_scale), tmp_res); - tmp_res = DIV_POW2_MVE(tmp_res, bits_over_unit); - tmp_res += vdupq_n_s32(ACT_MIN); - - tmp_res = vmaxq_s32(tmp_res, vdupq_n_s32(ACT_MIN)); - tmp_res = vminq_s32(tmp_res, vdupq_n_s32(ACT_MAX)); - tmp_res = vpselq_s32(tmp_res, vdupq_n_s32(ACT_MIN), p); - } - else - { - tmp_res = vdupq_n_s32(ACT_MIN); - } - vstrbq_s32(&output[idx], tmp_res); - vec_count--; - idx += 4; - } - - for (int i = 0; i < (row_size & 3); i++) - { - int32_t diff = input[tail_idx + i] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit) - 128; - output[tail_idx + i] = (int8_t)CLAMP(res, (int32_t)ACT_MAX, (int32_t)ACT_MIN); - } - else - { - output[tail_idx + i] = ACT_MIN; - } - } - - input += row_size; - output += row_size; - } -#else - const int32_t mask = (1 << shift); - - int32_t col = 0; - int32_t row_idx; - - for (row_idx = 0; row_idx < num_rows; ++row_idx) - { - // Find the maximum value in order to ensure numerical stability - int8_t max = *input; - - for (col = 1; col < row_size; ++col) - { - max = MAX(max, input[col]); - } - - int32_t diff = 0; - int32_t sum = 0; - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ(sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit) - 128; - output[col] = (int8_t)CLAMP(res, (int32_t)127, (int32_t)-128); - } - else - { - output[col] = -128; - } - } - input += row_size; - output += row_size; - } - -#endif -} -/** - * @} end of Softmax group - */ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c deleted file mode 100644 index 2820af64..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_u8.c - * Description: U8 softmax function - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -#define ACCUM_BITS 12 - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ -void arm_softmax_u8(const uint8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - uint8_t *output) -{ - const int32_t mask = (1 << shift); - - int32_t col = 0; - int32_t row_idx; - - for(row_idx = 0; row_idx < num_rows; ++row_idx) - { - // Find the maximum value in order to ensure numerical stability - uint8_t max = *input; - - for (col = 1; col < row_size; ++col) - { - max = MAX(max, input[col]); - } - - int32_t diff = 0; - int32_t sum = 0; - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if(diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ((uint32_t)sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit); - output[col] = (uint8_t) CLAMP(res, (int32_t)255, (int32_t)0); - } - else - { - output[col] = 0; - } - } - input += row_size; - output += row_size; - } -} -/** - * @} end of Softmax group - */ \ No newline at end of file diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c deleted file mode 100644 index e4a634d6..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_with_batch_q7.c - * Description: Q7 softmax function - * - * $Date: 05. August 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M and Cortex-A cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q7 softmax function with batch parameter - * @param[in] vec_in pointer to input vector - * @param[in] nb_batches number of batches - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical natural logarithm e based softmax, we use - * 2-based softmax here, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_with_batch_q7(const q7_t * vec_in, const uint16_t nb_batches,const uint16_t dim_vec, q7_t * p_out ) -{ - for(int i=0; i(e); - return EnumNamesTensorType()[index]; -} - -enum QuantizationDetails { - QuantizationDetails_NONE = 0, - QuantizationDetails_CustomQuantization = 1, - QuantizationDetails_MIN = QuantizationDetails_NONE, - QuantizationDetails_MAX = QuantizationDetails_CustomQuantization -}; - -inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] { - static const QuantizationDetails values[] = { - QuantizationDetails_NONE, - QuantizationDetails_CustomQuantization - }; - return values; -} - -inline const char * const *EnumNamesQuantizationDetails() { - static const char * const names[3] = { - "NONE", - "CustomQuantization", - nullptr - }; - return names; -} - -inline const char *EnumNameQuantizationDetails(QuantizationDetails e) { - if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return ""; - const size_t index = static_cast(e); - return EnumNamesQuantizationDetails()[index]; -} - -template struct QuantizationDetailsTraits { - static const QuantizationDetails enum_value = QuantizationDetails_NONE; -}; - -template<> struct QuantizationDetailsTraits { - static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; -}; - -struct QuantizationDetailsUnion { - QuantizationDetails type; - void *value; - - QuantizationDetailsUnion() : type(QuantizationDetails_NONE), value(nullptr) {} - QuantizationDetailsUnion(QuantizationDetailsUnion&& u) FLATBUFFERS_NOEXCEPT : - type(QuantizationDetails_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - QuantizationDetailsUnion(const QuantizationDetailsUnion &) FLATBUFFERS_NOEXCEPT; - QuantizationDetailsUnion &operator=(const QuantizationDetailsUnion &u) FLATBUFFERS_NOEXCEPT - { QuantizationDetailsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - QuantizationDetailsUnion &operator=(QuantizationDetailsUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~QuantizationDetailsUnion() { Reset(); } - - void Reset(); - -#ifndef FLATBUFFERS_CPP98_STL - template - void Set(T&& val) { - using RT = typename std::remove_reference::type; - Reset(); - type = QuantizationDetailsTraits::enum_value; - if (type != QuantizationDetails_NONE) { - value = new RT(std::forward(val)); - } - } -#endif // FLATBUFFERS_CPP98_STL - - static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite::CustomQuantizationT *AsCustomQuantization() { - return type == QuantizationDetails_CustomQuantization ? - reinterpret_cast(value) : nullptr; - } - const tflite::CustomQuantizationT *AsCustomQuantization() const { - return type == QuantizationDetails_CustomQuantization ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type); -bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum DimensionType { - DimensionType_DENSE = 0, - DimensionType_SPARSE_CSR = 1, - DimensionType_MIN = DimensionType_DENSE, - DimensionType_MAX = DimensionType_SPARSE_CSR -}; - -inline const DimensionType (&EnumValuesDimensionType())[2] { - static const DimensionType values[] = { - DimensionType_DENSE, - DimensionType_SPARSE_CSR - }; - return values; -} - -inline const char * const *EnumNamesDimensionType() { - static const char * const names[3] = { - "DENSE", - "SPARSE_CSR", - nullptr - }; - return names; -} - -inline const char *EnumNameDimensionType(DimensionType e) { - if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return ""; - const size_t index = static_cast(e); - return EnumNamesDimensionType()[index]; -} - -enum SparseIndexVector { - SparseIndexVector_NONE = 0, - SparseIndexVector_Int32Vector = 1, - SparseIndexVector_Uint16Vector = 2, - SparseIndexVector_Uint8Vector = 3, - SparseIndexVector_MIN = SparseIndexVector_NONE, - SparseIndexVector_MAX = SparseIndexVector_Uint8Vector -}; - -inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] { - static const SparseIndexVector values[] = { - SparseIndexVector_NONE, - SparseIndexVector_Int32Vector, - SparseIndexVector_Uint16Vector, - SparseIndexVector_Uint8Vector - }; - return values; -} - -inline const char * const *EnumNamesSparseIndexVector() { - static const char * const names[5] = { - "NONE", - "Int32Vector", - "Uint16Vector", - "Uint8Vector", - nullptr - }; - return names; -} - -inline const char *EnumNameSparseIndexVector(SparseIndexVector e) { - if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return ""; - const size_t index = static_cast(e); - return EnumNamesSparseIndexVector()[index]; -} - -template struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_NONE; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; -}; - -struct SparseIndexVectorUnion { - SparseIndexVector type; - void *value; - - SparseIndexVectorUnion() : type(SparseIndexVector_NONE), value(nullptr) {} - SparseIndexVectorUnion(SparseIndexVectorUnion&& u) FLATBUFFERS_NOEXCEPT : - type(SparseIndexVector_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - SparseIndexVectorUnion(const SparseIndexVectorUnion &) FLATBUFFERS_NOEXCEPT; - SparseIndexVectorUnion &operator=(const SparseIndexVectorUnion &u) FLATBUFFERS_NOEXCEPT - { SparseIndexVectorUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - SparseIndexVectorUnion &operator=(SparseIndexVectorUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~SparseIndexVectorUnion() { Reset(); } - - void Reset(); - -#ifndef FLATBUFFERS_CPP98_STL - template - void Set(T&& val) { - using RT = typename std::remove_reference::type; - Reset(); - type = SparseIndexVectorTraits::enum_value; - if (type != SparseIndexVector_NONE) { - value = new RT(std::forward(val)); - } - } -#endif // FLATBUFFERS_CPP98_STL - - static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite::Int32VectorT *AsInt32Vector() { - return type == SparseIndexVector_Int32Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite::Int32VectorT *AsInt32Vector() const { - return type == SparseIndexVector_Int32Vector ? - reinterpret_cast(value) : nullptr; - } - tflite::Uint16VectorT *AsUint16Vector() { - return type == SparseIndexVector_Uint16Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite::Uint16VectorT *AsUint16Vector() const { - return type == SparseIndexVector_Uint16Vector ? - reinterpret_cast(value) : nullptr; - } - tflite::Uint8VectorT *AsUint8Vector() { - return type == SparseIndexVector_Uint8Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite::Uint8VectorT *AsUint8Vector() const { - return type == SparseIndexVector_Uint8Vector ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type); -bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum BuiltinOperator { - BuiltinOperator_ADD = 0, - BuiltinOperator_AVERAGE_POOL_2D = 1, - BuiltinOperator_CONCATENATION = 2, - BuiltinOperator_CONV_2D = 3, - BuiltinOperator_DEPTHWISE_CONV_2D = 4, - BuiltinOperator_DEPTH_TO_SPACE = 5, - BuiltinOperator_DEQUANTIZE = 6, - BuiltinOperator_EMBEDDING_LOOKUP = 7, - BuiltinOperator_FLOOR = 8, - BuiltinOperator_FULLY_CONNECTED = 9, - BuiltinOperator_HASHTABLE_LOOKUP = 10, - BuiltinOperator_L2_NORMALIZATION = 11, - BuiltinOperator_L2_POOL_2D = 12, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, - BuiltinOperator_LOGISTIC = 14, - BuiltinOperator_LSH_PROJECTION = 15, - BuiltinOperator_LSTM = 16, - BuiltinOperator_MAX_POOL_2D = 17, - BuiltinOperator_MUL = 18, - BuiltinOperator_RELU = 19, - BuiltinOperator_RELU_N1_TO_1 = 20, - BuiltinOperator_RELU6 = 21, - BuiltinOperator_RESHAPE = 22, - BuiltinOperator_RESIZE_BILINEAR = 23, - BuiltinOperator_RNN = 24, - BuiltinOperator_SOFTMAX = 25, - BuiltinOperator_SPACE_TO_DEPTH = 26, - BuiltinOperator_SVDF = 27, - BuiltinOperator_TANH = 28, - BuiltinOperator_CONCAT_EMBEDDINGS = 29, - BuiltinOperator_SKIP_GRAM = 30, - BuiltinOperator_CALL = 31, - BuiltinOperator_CUSTOM = 32, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, - BuiltinOperator_PAD = 34, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, - BuiltinOperator_GATHER = 36, - BuiltinOperator_BATCH_TO_SPACE_ND = 37, - BuiltinOperator_SPACE_TO_BATCH_ND = 38, - BuiltinOperator_TRANSPOSE = 39, - BuiltinOperator_MEAN = 40, - BuiltinOperator_SUB = 41, - BuiltinOperator_DIV = 42, - BuiltinOperator_SQUEEZE = 43, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - BuiltinOperator_STRIDED_SLICE = 45, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, - BuiltinOperator_EXP = 47, - BuiltinOperator_TOPK_V2 = 48, - BuiltinOperator_SPLIT = 49, - BuiltinOperator_LOG_SOFTMAX = 50, - BuiltinOperator_DELEGATE = 51, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, - BuiltinOperator_CAST = 53, - BuiltinOperator_PRELU = 54, - BuiltinOperator_MAXIMUM = 55, - BuiltinOperator_ARG_MAX = 56, - BuiltinOperator_MINIMUM = 57, - BuiltinOperator_LESS = 58, - BuiltinOperator_NEG = 59, - BuiltinOperator_PADV2 = 60, - BuiltinOperator_GREATER = 61, - BuiltinOperator_GREATER_EQUAL = 62, - BuiltinOperator_LESS_EQUAL = 63, - BuiltinOperator_SELECT = 64, - BuiltinOperator_SLICE = 65, - BuiltinOperator_SIN = 66, - BuiltinOperator_TRANSPOSE_CONV = 67, - BuiltinOperator_SPARSE_TO_DENSE = 68, - BuiltinOperator_TILE = 69, - BuiltinOperator_EXPAND_DIMS = 70, - BuiltinOperator_EQUAL = 71, - BuiltinOperator_NOT_EQUAL = 72, - BuiltinOperator_LOG = 73, - BuiltinOperator_SUM = 74, - BuiltinOperator_SQRT = 75, - BuiltinOperator_RSQRT = 76, - BuiltinOperator_SHAPE = 77, - BuiltinOperator_POW = 78, - BuiltinOperator_ARG_MIN = 79, - BuiltinOperator_FAKE_QUANT = 80, - BuiltinOperator_REDUCE_PROD = 81, - BuiltinOperator_REDUCE_MAX = 82, - BuiltinOperator_PACK = 83, - BuiltinOperator_LOGICAL_OR = 84, - BuiltinOperator_ONE_HOT = 85, - BuiltinOperator_LOGICAL_AND = 86, - BuiltinOperator_LOGICAL_NOT = 87, - BuiltinOperator_UNPACK = 88, - BuiltinOperator_REDUCE_MIN = 89, - BuiltinOperator_FLOOR_DIV = 90, - BuiltinOperator_REDUCE_ANY = 91, - BuiltinOperator_SQUARE = 92, - BuiltinOperator_ZEROS_LIKE = 93, - BuiltinOperator_FILL = 94, - BuiltinOperator_FLOOR_MOD = 95, - BuiltinOperator_RANGE = 96, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, - BuiltinOperator_LEAKY_RELU = 98, - BuiltinOperator_SQUARED_DIFFERENCE = 99, - BuiltinOperator_MIRROR_PAD = 100, - BuiltinOperator_ABS = 101, - BuiltinOperator_SPLIT_V = 102, - BuiltinOperator_UNIQUE = 103, - BuiltinOperator_CEIL = 104, - BuiltinOperator_REVERSE_V2 = 105, - BuiltinOperator_ADD_N = 106, - BuiltinOperator_GATHER_ND = 107, - BuiltinOperator_COS = 108, - BuiltinOperator_WHERE = 109, - BuiltinOperator_RANK = 110, - BuiltinOperator_ELU = 111, - BuiltinOperator_REVERSE_SEQUENCE = 112, - BuiltinOperator_MATRIX_DIAG = 113, - BuiltinOperator_QUANTIZE = 114, - BuiltinOperator_MATRIX_SET_DIAG = 115, - BuiltinOperator_ROUND = 116, - BuiltinOperator_HARD_SWISH = 117, - BuiltinOperator_IF = 118, - BuiltinOperator_WHILE = 119, - BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, - BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, - BuiltinOperator_SCATTER_ND = 122, - BuiltinOperator_SELECT_V2 = 123, - BuiltinOperator_DENSIFY = 124, - BuiltinOperator_SEGMENT_SUM = 125, - BuiltinOperator_BATCH_MATMUL = 126, - BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_BATCH_MATMUL -}; - -inline const BuiltinOperator (&EnumValuesBuiltinOperator())[127] { - static const BuiltinOperator values[] = { - BuiltinOperator_ADD, - BuiltinOperator_AVERAGE_POOL_2D, - BuiltinOperator_CONCATENATION, - BuiltinOperator_CONV_2D, - BuiltinOperator_DEPTHWISE_CONV_2D, - BuiltinOperator_DEPTH_TO_SPACE, - BuiltinOperator_DEQUANTIZE, - BuiltinOperator_EMBEDDING_LOOKUP, - BuiltinOperator_FLOOR, - BuiltinOperator_FULLY_CONNECTED, - BuiltinOperator_HASHTABLE_LOOKUP, - BuiltinOperator_L2_NORMALIZATION, - BuiltinOperator_L2_POOL_2D, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - BuiltinOperator_LOGISTIC, - BuiltinOperator_LSH_PROJECTION, - BuiltinOperator_LSTM, - BuiltinOperator_MAX_POOL_2D, - BuiltinOperator_MUL, - BuiltinOperator_RELU, - BuiltinOperator_RELU_N1_TO_1, - BuiltinOperator_RELU6, - BuiltinOperator_RESHAPE, - BuiltinOperator_RESIZE_BILINEAR, - BuiltinOperator_RNN, - BuiltinOperator_SOFTMAX, - BuiltinOperator_SPACE_TO_DEPTH, - BuiltinOperator_SVDF, - BuiltinOperator_TANH, - BuiltinOperator_CONCAT_EMBEDDINGS, - BuiltinOperator_SKIP_GRAM, - BuiltinOperator_CALL, - BuiltinOperator_CUSTOM, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - BuiltinOperator_PAD, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_GATHER, - BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOperator_TRANSPOSE, - BuiltinOperator_MEAN, - BuiltinOperator_SUB, - BuiltinOperator_DIV, - BuiltinOperator_SQUEEZE, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_EXP, - BuiltinOperator_TOPK_V2, - BuiltinOperator_SPLIT, - BuiltinOperator_LOG_SOFTMAX, - BuiltinOperator_DELEGATE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_CAST, - BuiltinOperator_PRELU, - BuiltinOperator_MAXIMUM, - BuiltinOperator_ARG_MAX, - BuiltinOperator_MINIMUM, - BuiltinOperator_LESS, - BuiltinOperator_NEG, - BuiltinOperator_PADV2, - BuiltinOperator_GREATER, - BuiltinOperator_GREATER_EQUAL, - BuiltinOperator_LESS_EQUAL, - BuiltinOperator_SELECT, - BuiltinOperator_SLICE, - BuiltinOperator_SIN, - BuiltinOperator_TRANSPOSE_CONV, - BuiltinOperator_SPARSE_TO_DENSE, - BuiltinOperator_TILE, - BuiltinOperator_EXPAND_DIMS, - BuiltinOperator_EQUAL, - BuiltinOperator_NOT_EQUAL, - BuiltinOperator_LOG, - BuiltinOperator_SUM, - BuiltinOperator_SQRT, - BuiltinOperator_RSQRT, - BuiltinOperator_SHAPE, - BuiltinOperator_POW, - BuiltinOperator_ARG_MIN, - BuiltinOperator_FAKE_QUANT, - BuiltinOperator_REDUCE_PROD, - BuiltinOperator_REDUCE_MAX, - BuiltinOperator_PACK, - BuiltinOperator_LOGICAL_OR, - BuiltinOperator_ONE_HOT, - BuiltinOperator_LOGICAL_AND, - BuiltinOperator_LOGICAL_NOT, - BuiltinOperator_UNPACK, - BuiltinOperator_REDUCE_MIN, - BuiltinOperator_FLOOR_DIV, - BuiltinOperator_REDUCE_ANY, - BuiltinOperator_SQUARE, - BuiltinOperator_ZEROS_LIKE, - BuiltinOperator_FILL, - BuiltinOperator_FLOOR_MOD, - BuiltinOperator_RANGE, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - BuiltinOperator_LEAKY_RELU, - BuiltinOperator_SQUARED_DIFFERENCE, - BuiltinOperator_MIRROR_PAD, - BuiltinOperator_ABS, - BuiltinOperator_SPLIT_V, - BuiltinOperator_UNIQUE, - BuiltinOperator_CEIL, - BuiltinOperator_REVERSE_V2, - BuiltinOperator_ADD_N, - BuiltinOperator_GATHER_ND, - BuiltinOperator_COS, - BuiltinOperator_WHERE, - BuiltinOperator_RANK, - BuiltinOperator_ELU, - BuiltinOperator_REVERSE_SEQUENCE, - BuiltinOperator_MATRIX_DIAG, - BuiltinOperator_QUANTIZE, - BuiltinOperator_MATRIX_SET_DIAG, - BuiltinOperator_ROUND, - BuiltinOperator_HARD_SWISH, - BuiltinOperator_IF, - BuiltinOperator_WHILE, - BuiltinOperator_NON_MAX_SUPPRESSION_V4, - BuiltinOperator_NON_MAX_SUPPRESSION_V5, - BuiltinOperator_SCATTER_ND, - BuiltinOperator_SELECT_V2, - BuiltinOperator_DENSIFY, - BuiltinOperator_SEGMENT_SUM, - BuiltinOperator_BATCH_MATMUL - }; - return values; -} - -inline const char * const *EnumNamesBuiltinOperator() { - static const char * const names[128] = { - "ADD", - "AVERAGE_POOL_2D", - "CONCATENATION", - "CONV_2D", - "DEPTHWISE_CONV_2D", - "DEPTH_TO_SPACE", - "DEQUANTIZE", - "EMBEDDING_LOOKUP", - "FLOOR", - "FULLY_CONNECTED", - "HASHTABLE_LOOKUP", - "L2_NORMALIZATION", - "L2_POOL_2D", - "LOCAL_RESPONSE_NORMALIZATION", - "LOGISTIC", - "LSH_PROJECTION", - "LSTM", - "MAX_POOL_2D", - "MUL", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "RESHAPE", - "RESIZE_BILINEAR", - "RNN", - "SOFTMAX", - "SPACE_TO_DEPTH", - "SVDF", - "TANH", - "CONCAT_EMBEDDINGS", - "SKIP_GRAM", - "CALL", - "CUSTOM", - "EMBEDDING_LOOKUP_SPARSE", - "PAD", - "UNIDIRECTIONAL_SEQUENCE_RNN", - "GATHER", - "BATCH_TO_SPACE_ND", - "SPACE_TO_BATCH_ND", - "TRANSPOSE", - "MEAN", - "SUB", - "DIV", - "SQUEEZE", - "UNIDIRECTIONAL_SEQUENCE_LSTM", - "STRIDED_SLICE", - "BIDIRECTIONAL_SEQUENCE_RNN", - "EXP", - "TOPK_V2", - "SPLIT", - "LOG_SOFTMAX", - "DELEGATE", - "BIDIRECTIONAL_SEQUENCE_LSTM", - "CAST", - "PRELU", - "MAXIMUM", - "ARG_MAX", - "MINIMUM", - "LESS", - "NEG", - "PADV2", - "GREATER", - "GREATER_EQUAL", - "LESS_EQUAL", - "SELECT", - "SLICE", - "SIN", - "TRANSPOSE_CONV", - "SPARSE_TO_DENSE", - "TILE", - "EXPAND_DIMS", - "EQUAL", - "NOT_EQUAL", - "LOG", - "SUM", - "SQRT", - "RSQRT", - "SHAPE", - "POW", - "ARG_MIN", - "FAKE_QUANT", - "REDUCE_PROD", - "REDUCE_MAX", - "PACK", - "LOGICAL_OR", - "ONE_HOT", - "LOGICAL_AND", - "LOGICAL_NOT", - "UNPACK", - "REDUCE_MIN", - "FLOOR_DIV", - "REDUCE_ANY", - "SQUARE", - "ZEROS_LIKE", - "FILL", - "FLOOR_MOD", - "RANGE", - "RESIZE_NEAREST_NEIGHBOR", - "LEAKY_RELU", - "SQUARED_DIFFERENCE", - "MIRROR_PAD", - "ABS", - "SPLIT_V", - "UNIQUE", - "CEIL", - "REVERSE_V2", - "ADD_N", - "GATHER_ND", - "COS", - "WHERE", - "RANK", - "ELU", - "REVERSE_SEQUENCE", - "MATRIX_DIAG", - "QUANTIZE", - "MATRIX_SET_DIAG", - "ROUND", - "HARD_SWISH", - "IF", - "WHILE", - "NON_MAX_SUPPRESSION_V4", - "NON_MAX_SUPPRESSION_V5", - "SCATTER_ND", - "SELECT_V2", - "DENSIFY", - "SEGMENT_SUM", - "BATCH_MATMUL", - nullptr - }; - return names; -} - -inline const char *EnumNameBuiltinOperator(BuiltinOperator e) { - if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_BATCH_MATMUL)) return ""; - const size_t index = static_cast(e); - return EnumNamesBuiltinOperator()[index]; -} - -enum BuiltinOptions { - BuiltinOptions_NONE = 0, - BuiltinOptions_Conv2DOptions = 1, - BuiltinOptions_DepthwiseConv2DOptions = 2, - BuiltinOptions_ConcatEmbeddingsOptions = 3, - BuiltinOptions_LSHProjectionOptions = 4, - BuiltinOptions_Pool2DOptions = 5, - BuiltinOptions_SVDFOptions = 6, - BuiltinOptions_RNNOptions = 7, - BuiltinOptions_FullyConnectedOptions = 8, - BuiltinOptions_SoftmaxOptions = 9, - BuiltinOptions_ConcatenationOptions = 10, - BuiltinOptions_AddOptions = 11, - BuiltinOptions_L2NormOptions = 12, - BuiltinOptions_LocalResponseNormalizationOptions = 13, - BuiltinOptions_LSTMOptions = 14, - BuiltinOptions_ResizeBilinearOptions = 15, - BuiltinOptions_CallOptions = 16, - BuiltinOptions_ReshapeOptions = 17, - BuiltinOptions_SkipGramOptions = 18, - BuiltinOptions_SpaceToDepthOptions = 19, - BuiltinOptions_EmbeddingLookupSparseOptions = 20, - BuiltinOptions_MulOptions = 21, - BuiltinOptions_PadOptions = 22, - BuiltinOptions_GatherOptions = 23, - BuiltinOptions_BatchToSpaceNDOptions = 24, - BuiltinOptions_SpaceToBatchNDOptions = 25, - BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_ReducerOptions = 27, - BuiltinOptions_SubOptions = 28, - BuiltinOptions_DivOptions = 29, - BuiltinOptions_SqueezeOptions = 30, - BuiltinOptions_SequenceRNNOptions = 31, - BuiltinOptions_StridedSliceOptions = 32, - BuiltinOptions_ExpOptions = 33, - BuiltinOptions_TopKV2Options = 34, - BuiltinOptions_SplitOptions = 35, - BuiltinOptions_LogSoftmaxOptions = 36, - BuiltinOptions_CastOptions = 37, - BuiltinOptions_DequantizeOptions = 38, - BuiltinOptions_MaximumMinimumOptions = 39, - BuiltinOptions_ArgMaxOptions = 40, - BuiltinOptions_LessOptions = 41, - BuiltinOptions_NegOptions = 42, - BuiltinOptions_PadV2Options = 43, - BuiltinOptions_GreaterOptions = 44, - BuiltinOptions_GreaterEqualOptions = 45, - BuiltinOptions_LessEqualOptions = 46, - BuiltinOptions_SelectOptions = 47, - BuiltinOptions_SliceOptions = 48, - BuiltinOptions_TransposeConvOptions = 49, - BuiltinOptions_SparseToDenseOptions = 50, - BuiltinOptions_TileOptions = 51, - BuiltinOptions_ExpandDimsOptions = 52, - BuiltinOptions_EqualOptions = 53, - BuiltinOptions_NotEqualOptions = 54, - BuiltinOptions_ShapeOptions = 55, - BuiltinOptions_PowOptions = 56, - BuiltinOptions_ArgMinOptions = 57, - BuiltinOptions_FakeQuantOptions = 58, - BuiltinOptions_PackOptions = 59, - BuiltinOptions_LogicalOrOptions = 60, - BuiltinOptions_OneHotOptions = 61, - BuiltinOptions_LogicalAndOptions = 62, - BuiltinOptions_LogicalNotOptions = 63, - BuiltinOptions_UnpackOptions = 64, - BuiltinOptions_FloorDivOptions = 65, - BuiltinOptions_SquareOptions = 66, - BuiltinOptions_ZerosLikeOptions = 67, - BuiltinOptions_FillOptions = 68, - BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, - BuiltinOptions_BidirectionalSequenceRNNOptions = 70, - BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, - BuiltinOptions_FloorModOptions = 72, - BuiltinOptions_RangeOptions = 73, - BuiltinOptions_ResizeNearestNeighborOptions = 74, - BuiltinOptions_LeakyReluOptions = 75, - BuiltinOptions_SquaredDifferenceOptions = 76, - BuiltinOptions_MirrorPadOptions = 77, - BuiltinOptions_AbsOptions = 78, - BuiltinOptions_SplitVOptions = 79, - BuiltinOptions_UniqueOptions = 80, - BuiltinOptions_ReverseV2Options = 81, - BuiltinOptions_AddNOptions = 82, - BuiltinOptions_GatherNdOptions = 83, - BuiltinOptions_CosOptions = 84, - BuiltinOptions_WhereOptions = 85, - BuiltinOptions_RankOptions = 86, - BuiltinOptions_ReverseSequenceOptions = 87, - BuiltinOptions_MatrixDiagOptions = 88, - BuiltinOptions_QuantizeOptions = 89, - BuiltinOptions_MatrixSetDiagOptions = 90, - BuiltinOptions_HardSwishOptions = 91, - BuiltinOptions_IfOptions = 92, - BuiltinOptions_WhileOptions = 93, - BuiltinOptions_DepthToSpaceOptions = 94, - BuiltinOptions_NonMaxSuppressionV4Options = 95, - BuiltinOptions_NonMaxSuppressionV5Options = 96, - BuiltinOptions_ScatterNdOptions = 97, - BuiltinOptions_SelectV2Options = 98, - BuiltinOptions_DensifyOptions = 99, - BuiltinOptions_SegmentSumOptions = 100, - BuiltinOptions_BatchMatMulOptions = 101, - BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_BatchMatMulOptions -}; - -inline const BuiltinOptions (&EnumValuesBuiltinOptions())[102] { - static const BuiltinOptions values[] = { - BuiltinOptions_NONE, - BuiltinOptions_Conv2DOptions, - BuiltinOptions_DepthwiseConv2DOptions, - BuiltinOptions_ConcatEmbeddingsOptions, - BuiltinOptions_LSHProjectionOptions, - BuiltinOptions_Pool2DOptions, - BuiltinOptions_SVDFOptions, - BuiltinOptions_RNNOptions, - BuiltinOptions_FullyConnectedOptions, - BuiltinOptions_SoftmaxOptions, - BuiltinOptions_ConcatenationOptions, - BuiltinOptions_AddOptions, - BuiltinOptions_L2NormOptions, - BuiltinOptions_LocalResponseNormalizationOptions, - BuiltinOptions_LSTMOptions, - BuiltinOptions_ResizeBilinearOptions, - BuiltinOptions_CallOptions, - BuiltinOptions_ReshapeOptions, - BuiltinOptions_SkipGramOptions, - BuiltinOptions_SpaceToDepthOptions, - BuiltinOptions_EmbeddingLookupSparseOptions, - BuiltinOptions_MulOptions, - BuiltinOptions_PadOptions, - BuiltinOptions_GatherOptions, - BuiltinOptions_BatchToSpaceNDOptions, - BuiltinOptions_SpaceToBatchNDOptions, - BuiltinOptions_TransposeOptions, - BuiltinOptions_ReducerOptions, - BuiltinOptions_SubOptions, - BuiltinOptions_DivOptions, - BuiltinOptions_SqueezeOptions, - BuiltinOptions_SequenceRNNOptions, - BuiltinOptions_StridedSliceOptions, - BuiltinOptions_ExpOptions, - BuiltinOptions_TopKV2Options, - BuiltinOptions_SplitOptions, - BuiltinOptions_LogSoftmaxOptions, - BuiltinOptions_CastOptions, - BuiltinOptions_DequantizeOptions, - BuiltinOptions_MaximumMinimumOptions, - BuiltinOptions_ArgMaxOptions, - BuiltinOptions_LessOptions, - BuiltinOptions_NegOptions, - BuiltinOptions_PadV2Options, - BuiltinOptions_GreaterOptions, - BuiltinOptions_GreaterEqualOptions, - BuiltinOptions_LessEqualOptions, - BuiltinOptions_SelectOptions, - BuiltinOptions_SliceOptions, - BuiltinOptions_TransposeConvOptions, - BuiltinOptions_SparseToDenseOptions, - BuiltinOptions_TileOptions, - BuiltinOptions_ExpandDimsOptions, - BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions, - BuiltinOptions_ShapeOptions, - BuiltinOptions_PowOptions, - BuiltinOptions_ArgMinOptions, - BuiltinOptions_FakeQuantOptions, - BuiltinOptions_PackOptions, - BuiltinOptions_LogicalOrOptions, - BuiltinOptions_OneHotOptions, - BuiltinOptions_LogicalAndOptions, - BuiltinOptions_LogicalNotOptions, - BuiltinOptions_UnpackOptions, - BuiltinOptions_FloorDivOptions, - BuiltinOptions_SquareOptions, - BuiltinOptions_ZerosLikeOptions, - BuiltinOptions_FillOptions, - BuiltinOptions_BidirectionalSequenceLSTMOptions, - BuiltinOptions_BidirectionalSequenceRNNOptions, - BuiltinOptions_UnidirectionalSequenceLSTMOptions, - BuiltinOptions_FloorModOptions, - BuiltinOptions_RangeOptions, - BuiltinOptions_ResizeNearestNeighborOptions, - BuiltinOptions_LeakyReluOptions, - BuiltinOptions_SquaredDifferenceOptions, - BuiltinOptions_MirrorPadOptions, - BuiltinOptions_AbsOptions, - BuiltinOptions_SplitVOptions, - BuiltinOptions_UniqueOptions, - BuiltinOptions_ReverseV2Options, - BuiltinOptions_AddNOptions, - BuiltinOptions_GatherNdOptions, - BuiltinOptions_CosOptions, - BuiltinOptions_WhereOptions, - BuiltinOptions_RankOptions, - BuiltinOptions_ReverseSequenceOptions, - BuiltinOptions_MatrixDiagOptions, - BuiltinOptions_QuantizeOptions, - BuiltinOptions_MatrixSetDiagOptions, - BuiltinOptions_HardSwishOptions, - BuiltinOptions_IfOptions, - BuiltinOptions_WhileOptions, - BuiltinOptions_DepthToSpaceOptions, - BuiltinOptions_NonMaxSuppressionV4Options, - BuiltinOptions_NonMaxSuppressionV5Options, - BuiltinOptions_ScatterNdOptions, - BuiltinOptions_SelectV2Options, - BuiltinOptions_DensifyOptions, - BuiltinOptions_SegmentSumOptions, - BuiltinOptions_BatchMatMulOptions - }; - return values; -} - -inline const char * const *EnumNamesBuiltinOptions() { - static const char * const names[103] = { - "NONE", - "Conv2DOptions", - "DepthwiseConv2DOptions", - "ConcatEmbeddingsOptions", - "LSHProjectionOptions", - "Pool2DOptions", - "SVDFOptions", - "RNNOptions", - "FullyConnectedOptions", - "SoftmaxOptions", - "ConcatenationOptions", - "AddOptions", - "L2NormOptions", - "LocalResponseNormalizationOptions", - "LSTMOptions", - "ResizeBilinearOptions", - "CallOptions", - "ReshapeOptions", - "SkipGramOptions", - "SpaceToDepthOptions", - "EmbeddingLookupSparseOptions", - "MulOptions", - "PadOptions", - "GatherOptions", - "BatchToSpaceNDOptions", - "SpaceToBatchNDOptions", - "TransposeOptions", - "ReducerOptions", - "SubOptions", - "DivOptions", - "SqueezeOptions", - "SequenceRNNOptions", - "StridedSliceOptions", - "ExpOptions", - "TopKV2Options", - "SplitOptions", - "LogSoftmaxOptions", - "CastOptions", - "DequantizeOptions", - "MaximumMinimumOptions", - "ArgMaxOptions", - "LessOptions", - "NegOptions", - "PadV2Options", - "GreaterOptions", - "GreaterEqualOptions", - "LessEqualOptions", - "SelectOptions", - "SliceOptions", - "TransposeConvOptions", - "SparseToDenseOptions", - "TileOptions", - "ExpandDimsOptions", - "EqualOptions", - "NotEqualOptions", - "ShapeOptions", - "PowOptions", - "ArgMinOptions", - "FakeQuantOptions", - "PackOptions", - "LogicalOrOptions", - "OneHotOptions", - "LogicalAndOptions", - "LogicalNotOptions", - "UnpackOptions", - "FloorDivOptions", - "SquareOptions", - "ZerosLikeOptions", - "FillOptions", - "BidirectionalSequenceLSTMOptions", - "BidirectionalSequenceRNNOptions", - "UnidirectionalSequenceLSTMOptions", - "FloorModOptions", - "RangeOptions", - "ResizeNearestNeighborOptions", - "LeakyReluOptions", - "SquaredDifferenceOptions", - "MirrorPadOptions", - "AbsOptions", - "SplitVOptions", - "UniqueOptions", - "ReverseV2Options", - "AddNOptions", - "GatherNdOptions", - "CosOptions", - "WhereOptions", - "RankOptions", - "ReverseSequenceOptions", - "MatrixDiagOptions", - "QuantizeOptions", - "MatrixSetDiagOptions", - "HardSwishOptions", - "IfOptions", - "WhileOptions", - "DepthToSpaceOptions", - "NonMaxSuppressionV4Options", - "NonMaxSuppressionV5Options", - "ScatterNdOptions", - "SelectV2Options", - "DensifyOptions", - "SegmentSumOptions", - "BatchMatMulOptions", - nullptr - }; - return names; -} - -inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { - if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_BatchMatMulOptions)) return ""; - const size_t index = static_cast(e); - return EnumNamesBuiltinOptions()[index]; -} - -template struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NONE; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; -}; - -struct BuiltinOptionsUnion { - BuiltinOptions type; - void *value; - - BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {} - BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT : - type(BuiltinOptions_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - BuiltinOptionsUnion(const BuiltinOptionsUnion &) FLATBUFFERS_NOEXCEPT; - BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) FLATBUFFERS_NOEXCEPT - { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~BuiltinOptionsUnion() { Reset(); } - - void Reset(); - -#ifndef FLATBUFFERS_CPP98_STL - template - void Set(T&& val) { - using RT = typename std::remove_reference::type; - Reset(); - type = BuiltinOptionsTraits::enum_value; - if (type != BuiltinOptions_NONE) { - value = new RT(std::forward(val)); - } - } -#endif // FLATBUFFERS_CPP98_STL - - static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite::Conv2DOptionsT *AsConv2DOptions() { - return type == BuiltinOptions_Conv2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::Conv2DOptionsT *AsConv2DOptions() const { - return type == BuiltinOptions_Conv2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() { - return type == BuiltinOptions_DepthwiseConv2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const { - return type == BuiltinOptions_DepthwiseConv2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() { - return type == BuiltinOptions_ConcatEmbeddingsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const { - return type == BuiltinOptions_ConcatEmbeddingsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() { - return type == BuiltinOptions_LSHProjectionOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() const { - return type == BuiltinOptions_LSHProjectionOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::Pool2DOptionsT *AsPool2DOptions() { - return type == BuiltinOptions_Pool2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::Pool2DOptionsT *AsPool2DOptions() const { - return type == BuiltinOptions_Pool2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SVDFOptionsT *AsSVDFOptions() { - return type == BuiltinOptions_SVDFOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SVDFOptionsT *AsSVDFOptions() const { - return type == BuiltinOptions_SVDFOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::RNNOptionsT *AsRNNOptions() { - return type == BuiltinOptions_RNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::RNNOptionsT *AsRNNOptions() const { - return type == BuiltinOptions_RNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() { - return type == BuiltinOptions_FullyConnectedOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() const { - return type == BuiltinOptions_FullyConnectedOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SoftmaxOptionsT *AsSoftmaxOptions() { - return type == BuiltinOptions_SoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SoftmaxOptionsT *AsSoftmaxOptions() const { - return type == BuiltinOptions_SoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ConcatenationOptionsT *AsConcatenationOptions() { - return type == BuiltinOptions_ConcatenationOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ConcatenationOptionsT *AsConcatenationOptions() const { - return type == BuiltinOptions_ConcatenationOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::AddOptionsT *AsAddOptions() { - return type == BuiltinOptions_AddOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::AddOptionsT *AsAddOptions() const { - return type == BuiltinOptions_AddOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::L2NormOptionsT *AsL2NormOptions() { - return type == BuiltinOptions_L2NormOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::L2NormOptionsT *AsL2NormOptions() const { - return type == BuiltinOptions_L2NormOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() { - return type == BuiltinOptions_LocalResponseNormalizationOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const { - return type == BuiltinOptions_LocalResponseNormalizationOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LSTMOptionsT *AsLSTMOptions() { - return type == BuiltinOptions_LSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LSTMOptionsT *AsLSTMOptions() const { - return type == BuiltinOptions_LSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() { - return type == BuiltinOptions_ResizeBilinearOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() const { - return type == BuiltinOptions_ResizeBilinearOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::CallOptionsT *AsCallOptions() { - return type == BuiltinOptions_CallOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::CallOptionsT *AsCallOptions() const { - return type == BuiltinOptions_CallOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ReshapeOptionsT *AsReshapeOptions() { - return type == BuiltinOptions_ReshapeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ReshapeOptionsT *AsReshapeOptions() const { - return type == BuiltinOptions_ReshapeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SkipGramOptionsT *AsSkipGramOptions() { - return type == BuiltinOptions_SkipGramOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SkipGramOptionsT *AsSkipGramOptions() const { - return type == BuiltinOptions_SkipGramOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() { - return type == BuiltinOptions_SpaceToDepthOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() const { - return type == BuiltinOptions_SpaceToDepthOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() { - return type == BuiltinOptions_EmbeddingLookupSparseOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const { - return type == BuiltinOptions_EmbeddingLookupSparseOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::MulOptionsT *AsMulOptions() { - return type == BuiltinOptions_MulOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::MulOptionsT *AsMulOptions() const { - return type == BuiltinOptions_MulOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::PadOptionsT *AsPadOptions() { - return type == BuiltinOptions_PadOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::PadOptionsT *AsPadOptions() const { - return type == BuiltinOptions_PadOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::GatherOptionsT *AsGatherOptions() { - return type == BuiltinOptions_GatherOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::GatherOptionsT *AsGatherOptions() const { - return type == BuiltinOptions_GatherOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() { - return type == BuiltinOptions_BatchToSpaceNDOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const { - return type == BuiltinOptions_BatchToSpaceNDOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() { - return type == BuiltinOptions_SpaceToBatchNDOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const { - return type == BuiltinOptions_SpaceToBatchNDOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::TransposeOptionsT *AsTransposeOptions() { - return type == BuiltinOptions_TransposeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::TransposeOptionsT *AsTransposeOptions() const { - return type == BuiltinOptions_TransposeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ReducerOptionsT *AsReducerOptions() { - return type == BuiltinOptions_ReducerOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ReducerOptionsT *AsReducerOptions() const { - return type == BuiltinOptions_ReducerOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SubOptionsT *AsSubOptions() { - return type == BuiltinOptions_SubOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SubOptionsT *AsSubOptions() const { - return type == BuiltinOptions_SubOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::DivOptionsT *AsDivOptions() { - return type == BuiltinOptions_DivOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::DivOptionsT *AsDivOptions() const { - return type == BuiltinOptions_DivOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SqueezeOptionsT *AsSqueezeOptions() { - return type == BuiltinOptions_SqueezeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SqueezeOptionsT *AsSqueezeOptions() const { - return type == BuiltinOptions_SqueezeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() { - return type == BuiltinOptions_SequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() const { - return type == BuiltinOptions_SequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::StridedSliceOptionsT *AsStridedSliceOptions() { - return type == BuiltinOptions_StridedSliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::StridedSliceOptionsT *AsStridedSliceOptions() const { - return type == BuiltinOptions_StridedSliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ExpOptionsT *AsExpOptions() { - return type == BuiltinOptions_ExpOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ExpOptionsT *AsExpOptions() const { - return type == BuiltinOptions_ExpOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::TopKV2OptionsT *AsTopKV2Options() { - return type == BuiltinOptions_TopKV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::TopKV2OptionsT *AsTopKV2Options() const { - return type == BuiltinOptions_TopKV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite::SplitOptionsT *AsSplitOptions() { - return type == BuiltinOptions_SplitOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SplitOptionsT *AsSplitOptions() const { - return type == BuiltinOptions_SplitOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() { - return type == BuiltinOptions_LogSoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() const { - return type == BuiltinOptions_LogSoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::CastOptionsT *AsCastOptions() { - return type == BuiltinOptions_CastOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::CastOptionsT *AsCastOptions() const { - return type == BuiltinOptions_CastOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::DequantizeOptionsT *AsDequantizeOptions() { - return type == BuiltinOptions_DequantizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::DequantizeOptionsT *AsDequantizeOptions() const { - return type == BuiltinOptions_DequantizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() { - return type == BuiltinOptions_MaximumMinimumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() const { - return type == BuiltinOptions_MaximumMinimumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ArgMaxOptionsT *AsArgMaxOptions() { - return type == BuiltinOptions_ArgMaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ArgMaxOptionsT *AsArgMaxOptions() const { - return type == BuiltinOptions_ArgMaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LessOptionsT *AsLessOptions() { - return type == BuiltinOptions_LessOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LessOptionsT *AsLessOptions() const { - return type == BuiltinOptions_LessOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::NegOptionsT *AsNegOptions() { - return type == BuiltinOptions_NegOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::NegOptionsT *AsNegOptions() const { - return type == BuiltinOptions_NegOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::PadV2OptionsT *AsPadV2Options() { - return type == BuiltinOptions_PadV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::PadV2OptionsT *AsPadV2Options() const { - return type == BuiltinOptions_PadV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite::GreaterOptionsT *AsGreaterOptions() { - return type == BuiltinOptions_GreaterOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::GreaterOptionsT *AsGreaterOptions() const { - return type == BuiltinOptions_GreaterOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() { - return type == BuiltinOptions_GreaterEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() const { - return type == BuiltinOptions_GreaterEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LessEqualOptionsT *AsLessEqualOptions() { - return type == BuiltinOptions_LessEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LessEqualOptionsT *AsLessEqualOptions() const { - return type == BuiltinOptions_LessEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SelectOptionsT *AsSelectOptions() { - return type == BuiltinOptions_SelectOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SelectOptionsT *AsSelectOptions() const { - return type == BuiltinOptions_SelectOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SliceOptionsT *AsSliceOptions() { - return type == BuiltinOptions_SliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SliceOptionsT *AsSliceOptions() const { - return type == BuiltinOptions_SliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::TransposeConvOptionsT *AsTransposeConvOptions() { - return type == BuiltinOptions_TransposeConvOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::TransposeConvOptionsT *AsTransposeConvOptions() const { - return type == BuiltinOptions_TransposeConvOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() { - return type == BuiltinOptions_SparseToDenseOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() const { - return type == BuiltinOptions_SparseToDenseOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::TileOptionsT *AsTileOptions() { - return type == BuiltinOptions_TileOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::TileOptionsT *AsTileOptions() const { - return type == BuiltinOptions_TileOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ExpandDimsOptionsT *AsExpandDimsOptions() { - return type == BuiltinOptions_ExpandDimsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ExpandDimsOptionsT *AsExpandDimsOptions() const { - return type == BuiltinOptions_ExpandDimsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::EqualOptionsT *AsEqualOptions() { - return type == BuiltinOptions_EqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::EqualOptionsT *AsEqualOptions() const { - return type == BuiltinOptions_EqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::NotEqualOptionsT *AsNotEqualOptions() { - return type == BuiltinOptions_NotEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::NotEqualOptionsT *AsNotEqualOptions() const { - return type == BuiltinOptions_NotEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ShapeOptionsT *AsShapeOptions() { - return type == BuiltinOptions_ShapeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ShapeOptionsT *AsShapeOptions() const { - return type == BuiltinOptions_ShapeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::PowOptionsT *AsPowOptions() { - return type == BuiltinOptions_PowOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::PowOptionsT *AsPowOptions() const { - return type == BuiltinOptions_PowOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ArgMinOptionsT *AsArgMinOptions() { - return type == BuiltinOptions_ArgMinOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ArgMinOptionsT *AsArgMinOptions() const { - return type == BuiltinOptions_ArgMinOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::FakeQuantOptionsT *AsFakeQuantOptions() { - return type == BuiltinOptions_FakeQuantOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::FakeQuantOptionsT *AsFakeQuantOptions() const { - return type == BuiltinOptions_FakeQuantOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::PackOptionsT *AsPackOptions() { - return type == BuiltinOptions_PackOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::PackOptionsT *AsPackOptions() const { - return type == BuiltinOptions_PackOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LogicalOrOptionsT *AsLogicalOrOptions() { - return type == BuiltinOptions_LogicalOrOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LogicalOrOptionsT *AsLogicalOrOptions() const { - return type == BuiltinOptions_LogicalOrOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::OneHotOptionsT *AsOneHotOptions() { - return type == BuiltinOptions_OneHotOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::OneHotOptionsT *AsOneHotOptions() const { - return type == BuiltinOptions_OneHotOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LogicalAndOptionsT *AsLogicalAndOptions() { - return type == BuiltinOptions_LogicalAndOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LogicalAndOptionsT *AsLogicalAndOptions() const { - return type == BuiltinOptions_LogicalAndOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LogicalNotOptionsT *AsLogicalNotOptions() { - return type == BuiltinOptions_LogicalNotOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LogicalNotOptionsT *AsLogicalNotOptions() const { - return type == BuiltinOptions_LogicalNotOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::UnpackOptionsT *AsUnpackOptions() { - return type == BuiltinOptions_UnpackOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::UnpackOptionsT *AsUnpackOptions() const { - return type == BuiltinOptions_UnpackOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::FloorDivOptionsT *AsFloorDivOptions() { - return type == BuiltinOptions_FloorDivOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::FloorDivOptionsT *AsFloorDivOptions() const { - return type == BuiltinOptions_FloorDivOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SquareOptionsT *AsSquareOptions() { - return type == BuiltinOptions_SquareOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SquareOptionsT *AsSquareOptions() const { - return type == BuiltinOptions_SquareOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ZerosLikeOptionsT *AsZerosLikeOptions() { - return type == BuiltinOptions_ZerosLikeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ZerosLikeOptionsT *AsZerosLikeOptions() const { - return type == BuiltinOptions_ZerosLikeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::FillOptionsT *AsFillOptions() { - return type == BuiltinOptions_FillOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::FillOptionsT *AsFillOptions() const { - return type == BuiltinOptions_FillOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() { - return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() const { - return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() { - return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() const { - return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() { - return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() const { - return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::FloorModOptionsT *AsFloorModOptions() { - return type == BuiltinOptions_FloorModOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::FloorModOptionsT *AsFloorModOptions() const { - return type == BuiltinOptions_FloorModOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::RangeOptionsT *AsRangeOptions() { - return type == BuiltinOptions_RangeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::RangeOptionsT *AsRangeOptions() const { - return type == BuiltinOptions_RangeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() { - return type == BuiltinOptions_ResizeNearestNeighborOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() const { - return type == BuiltinOptions_ResizeNearestNeighborOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::LeakyReluOptionsT *AsLeakyReluOptions() { - return type == BuiltinOptions_LeakyReluOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::LeakyReluOptionsT *AsLeakyReluOptions() const { - return type == BuiltinOptions_LeakyReluOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() { - return type == BuiltinOptions_SquaredDifferenceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() const { - return type == BuiltinOptions_SquaredDifferenceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::MirrorPadOptionsT *AsMirrorPadOptions() { - return type == BuiltinOptions_MirrorPadOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::MirrorPadOptionsT *AsMirrorPadOptions() const { - return type == BuiltinOptions_MirrorPadOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::AbsOptionsT *AsAbsOptions() { - return type == BuiltinOptions_AbsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::AbsOptionsT *AsAbsOptions() const { - return type == BuiltinOptions_AbsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SplitVOptionsT *AsSplitVOptions() { - return type == BuiltinOptions_SplitVOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SplitVOptionsT *AsSplitVOptions() const { - return type == BuiltinOptions_SplitVOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::UniqueOptionsT *AsUniqueOptions() { - return type == BuiltinOptions_UniqueOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::UniqueOptionsT *AsUniqueOptions() const { - return type == BuiltinOptions_UniqueOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ReverseV2OptionsT *AsReverseV2Options() { - return type == BuiltinOptions_ReverseV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::ReverseV2OptionsT *AsReverseV2Options() const { - return type == BuiltinOptions_ReverseV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite::AddNOptionsT *AsAddNOptions() { - return type == BuiltinOptions_AddNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::AddNOptionsT *AsAddNOptions() const { - return type == BuiltinOptions_AddNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::GatherNdOptionsT *AsGatherNdOptions() { - return type == BuiltinOptions_GatherNdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::GatherNdOptionsT *AsGatherNdOptions() const { - return type == BuiltinOptions_GatherNdOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::CosOptionsT *AsCosOptions() { - return type == BuiltinOptions_CosOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::CosOptionsT *AsCosOptions() const { - return type == BuiltinOptions_CosOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::WhereOptionsT *AsWhereOptions() { - return type == BuiltinOptions_WhereOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::WhereOptionsT *AsWhereOptions() const { - return type == BuiltinOptions_WhereOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::RankOptionsT *AsRankOptions() { - return type == BuiltinOptions_RankOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::RankOptionsT *AsRankOptions() const { - return type == BuiltinOptions_RankOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() { - return type == BuiltinOptions_ReverseSequenceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() const { - return type == BuiltinOptions_ReverseSequenceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() { - return type == BuiltinOptions_MatrixDiagOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() const { - return type == BuiltinOptions_MatrixDiagOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::QuantizeOptionsT *AsQuantizeOptions() { - return type == BuiltinOptions_QuantizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::QuantizeOptionsT *AsQuantizeOptions() const { - return type == BuiltinOptions_QuantizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() { - return type == BuiltinOptions_MatrixSetDiagOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() const { - return type == BuiltinOptions_MatrixSetDiagOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::HardSwishOptionsT *AsHardSwishOptions() { - return type == BuiltinOptions_HardSwishOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::HardSwishOptionsT *AsHardSwishOptions() const { - return type == BuiltinOptions_HardSwishOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::IfOptionsT *AsIfOptions() { - return type == BuiltinOptions_IfOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::IfOptionsT *AsIfOptions() const { - return type == BuiltinOptions_IfOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::WhileOptionsT *AsWhileOptions() { - return type == BuiltinOptions_WhileOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::WhileOptionsT *AsWhileOptions() const { - return type == BuiltinOptions_WhileOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() { - return type == BuiltinOptions_DepthToSpaceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() const { - return type == BuiltinOptions_DepthToSpaceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() { - return type == BuiltinOptions_NonMaxSuppressionV4Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() const { - return type == BuiltinOptions_NonMaxSuppressionV4Options ? - reinterpret_cast(value) : nullptr; - } - tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() { - return type == BuiltinOptions_NonMaxSuppressionV5Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() const { - return type == BuiltinOptions_NonMaxSuppressionV5Options ? - reinterpret_cast(value) : nullptr; - } - tflite::ScatterNdOptionsT *AsScatterNdOptions() { - return type == BuiltinOptions_ScatterNdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::ScatterNdOptionsT *AsScatterNdOptions() const { - return type == BuiltinOptions_ScatterNdOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SelectV2OptionsT *AsSelectV2Options() { - return type == BuiltinOptions_SelectV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite::SelectV2OptionsT *AsSelectV2Options() const { - return type == BuiltinOptions_SelectV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite::DensifyOptionsT *AsDensifyOptions() { - return type == BuiltinOptions_DensifyOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::DensifyOptionsT *AsDensifyOptions() const { - return type == BuiltinOptions_DensifyOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::SegmentSumOptionsT *AsSegmentSumOptions() { - return type == BuiltinOptions_SegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::SegmentSumOptionsT *AsSegmentSumOptions() const { - return type == BuiltinOptions_SegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() { - return type == BuiltinOptions_BatchMatMulOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() const { - return type == BuiltinOptions_BatchMatMulOptions ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); -bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum Padding { - Padding_SAME = 0, - Padding_VALID = 1, - Padding_MIN = Padding_SAME, - Padding_MAX = Padding_VALID -}; - -inline const Padding (&EnumValuesPadding())[2] { - static const Padding values[] = { - Padding_SAME, - Padding_VALID - }; - return values; -} - -inline const char * const *EnumNamesPadding() { - static const char * const names[3] = { - "SAME", - "VALID", - nullptr - }; - return names; -} - -inline const char *EnumNamePadding(Padding e) { - if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return ""; - const size_t index = static_cast(e); - return EnumNamesPadding()[index]; -} - -enum ActivationFunctionType { - ActivationFunctionType_NONE = 0, - ActivationFunctionType_RELU = 1, - ActivationFunctionType_RELU_N1_TO_1 = 2, - ActivationFunctionType_RELU6 = 3, - ActivationFunctionType_TANH = 4, - ActivationFunctionType_SIGN_BIT = 5, - ActivationFunctionType_MIN = ActivationFunctionType_NONE, - ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT -}; - -inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] { - static const ActivationFunctionType values[] = { - ActivationFunctionType_NONE, - ActivationFunctionType_RELU, - ActivationFunctionType_RELU_N1_TO_1, - ActivationFunctionType_RELU6, - ActivationFunctionType_TANH, - ActivationFunctionType_SIGN_BIT - }; - return values; -} - -inline const char * const *EnumNamesActivationFunctionType() { - static const char * const names[7] = { - "NONE", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "TANH", - "SIGN_BIT", - nullptr - }; - return names; -} - -inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) { - if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return ""; - const size_t index = static_cast(e); - return EnumNamesActivationFunctionType()[index]; -} - -enum LSHProjectionType { - LSHProjectionType_UNKNOWN = 0, - LSHProjectionType_SPARSE = 1, - LSHProjectionType_DENSE = 2, - LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, - LSHProjectionType_MAX = LSHProjectionType_DENSE -}; - -inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] { - static const LSHProjectionType values[] = { - LSHProjectionType_UNKNOWN, - LSHProjectionType_SPARSE, - LSHProjectionType_DENSE - }; - return values; -} - -inline const char * const *EnumNamesLSHProjectionType() { - static const char * const names[4] = { - "UNKNOWN", - "SPARSE", - "DENSE", - nullptr - }; - return names; -} - -inline const char *EnumNameLSHProjectionType(LSHProjectionType e) { - if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return ""; - const size_t index = static_cast(e); - return EnumNamesLSHProjectionType()[index]; -} - -enum FullyConnectedOptionsWeightsFormat { - FullyConnectedOptionsWeightsFormat_DEFAULT = 0, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, - FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 -}; - -inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] { - static const FullyConnectedOptionsWeightsFormat values[] = { - FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 - }; - return values; -} - -inline const char * const *EnumNamesFullyConnectedOptionsWeightsFormat() { - static const char * const names[3] = { - "DEFAULT", - "SHUFFLED4x16INT8", - nullptr - }; - return names; -} - -inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) { - if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return ""; - const size_t index = static_cast(e); - return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; -} - -enum LSTMKernelType { - LSTMKernelType_FULL = 0, - LSTMKernelType_BASIC = 1, - LSTMKernelType_MIN = LSTMKernelType_FULL, - LSTMKernelType_MAX = LSTMKernelType_BASIC -}; - -inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] { - static const LSTMKernelType values[] = { - LSTMKernelType_FULL, - LSTMKernelType_BASIC - }; - return values; -} - -inline const char * const *EnumNamesLSTMKernelType() { - static const char * const names[3] = { - "FULL", - "BASIC", - nullptr - }; - return names; -} - -inline const char *EnumNameLSTMKernelType(LSTMKernelType e) { - if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return ""; - const size_t index = static_cast(e); - return EnumNamesLSTMKernelType()[index]; -} - -enum CombinerType { - CombinerType_SUM = 0, - CombinerType_MEAN = 1, - CombinerType_SQRTN = 2, - CombinerType_MIN = CombinerType_SUM, - CombinerType_MAX = CombinerType_SQRTN -}; - -inline const CombinerType (&EnumValuesCombinerType())[3] { - static const CombinerType values[] = { - CombinerType_SUM, - CombinerType_MEAN, - CombinerType_SQRTN - }; - return values; -} - -inline const char * const *EnumNamesCombinerType() { - static const char * const names[4] = { - "SUM", - "MEAN", - "SQRTN", - nullptr - }; - return names; -} - -inline const char *EnumNameCombinerType(CombinerType e) { - if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return ""; - const size_t index = static_cast(e); - return EnumNamesCombinerType()[index]; -} - -enum MirrorPadMode { - MirrorPadMode_REFLECT = 0, - MirrorPadMode_SYMMETRIC = 1, - MirrorPadMode_MIN = MirrorPadMode_REFLECT, - MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC -}; - -inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] { - static const MirrorPadMode values[] = { - MirrorPadMode_REFLECT, - MirrorPadMode_SYMMETRIC - }; - return values; -} - -inline const char * const *EnumNamesMirrorPadMode() { - static const char * const names[3] = { - "REFLECT", - "SYMMETRIC", - nullptr - }; - return names; -} - -inline const char *EnumNameMirrorPadMode(MirrorPadMode e) { - if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return ""; - const size_t index = static_cast(e); - return EnumNamesMirrorPadMode()[index]; -} - -enum CustomOptionsFormat { - CustomOptionsFormat_FLEXBUFFERS = 0, - CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, - CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS -}; - -inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] { - static const CustomOptionsFormat values[] = { - CustomOptionsFormat_FLEXBUFFERS - }; - return values; -} - -inline const char * const *EnumNamesCustomOptionsFormat() { - static const char * const names[2] = { - "FLEXBUFFERS", - nullptr - }; - return names; -} - -inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) { - if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return ""; - const size_t index = static_cast(e); - return EnumNamesCustomOptionsFormat()[index]; -} - -struct CustomQuantizationT : public flatbuffers::NativeTable { - typedef CustomQuantization TableType; - std::vector custom; - CustomQuantizationT() { - } -}; - -struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CustomQuantizationT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_CUSTOM = 4 - }; - const flatbuffers::Vector *custom() const { - return GetPointer *>(VT_CUSTOM); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_CUSTOM) && - verifier.VerifyVector(custom()) && - verifier.EndTable(); - } - CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CustomQuantizationBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_custom(flatbuffers::Offset> custom) { - fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); - } - explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - CustomQuantizationBuilder &operator=(const CustomQuantizationBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCustomQuantization( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> custom = 0) { - CustomQuantizationBuilder builder_(_fbb); - builder_.add_custom(custom); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateCustomQuantizationDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *custom = nullptr) { - if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); } - auto custom__ = custom ? _fbb.CreateVector(*custom) : 0; - return tflite::CreateCustomQuantization( - _fbb, - custom__); -} - -flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct QuantizationParametersT : public flatbuffers::NativeTable { - typedef QuantizationParameters TableType; - std::vector min; - std::vector max; - std::vector scale; - std::vector zero_point; - tflite::QuantizationDetailsUnion details; - int32_t quantized_dimension; - QuantizationParametersT() - : quantized_dimension(0) { - } -}; - -struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef QuantizationParametersT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MIN = 4, - VT_MAX = 6, - VT_SCALE = 8, - VT_ZERO_POINT = 10, - VT_DETAILS_TYPE = 12, - VT_DETAILS = 14, - VT_QUANTIZED_DIMENSION = 16 - }; - const flatbuffers::Vector *min() const { - return GetPointer *>(VT_MIN); - } - const flatbuffers::Vector *max() const { - return GetPointer *>(VT_MAX); - } - const flatbuffers::Vector *scale() const { - return GetPointer *>(VT_SCALE); - } - const flatbuffers::Vector *zero_point() const { - return GetPointer *>(VT_ZERO_POINT); - } - tflite::QuantizationDetails details_type() const { - return static_cast(GetField(VT_DETAILS_TYPE, 0)); - } - const void *details() const { - return GetPointer(VT_DETAILS); - } - template const T *details_as() const; - const tflite::CustomQuantization *details_as_CustomQuantization() const { - return details_type() == tflite::QuantizationDetails_CustomQuantization ? static_cast(details()) : nullptr; - } - int32_t quantized_dimension() const { - return GetField(VT_QUANTIZED_DIMENSION, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_MIN) && - verifier.VerifyVector(min()) && - VerifyOffset(verifier, VT_MAX) && - verifier.VerifyVector(max()) && - VerifyOffset(verifier, VT_SCALE) && - verifier.VerifyVector(scale()) && - VerifyOffset(verifier, VT_ZERO_POINT) && - verifier.VerifyVector(zero_point()) && - VerifyField(verifier, VT_DETAILS_TYPE) && - VerifyOffset(verifier, VT_DETAILS) && - VerifyQuantizationDetails(verifier, details(), details_type()) && - VerifyField(verifier, VT_QUANTIZED_DIMENSION) && - verifier.EndTable(); - } - QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite::CustomQuantization *QuantizationParameters::details_as() const { - return details_as_CustomQuantization(); -} - -struct QuantizationParametersBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(flatbuffers::Offset> min) { - fbb_.AddOffset(QuantizationParameters::VT_MIN, min); - } - void add_max(flatbuffers::Offset> max) { - fbb_.AddOffset(QuantizationParameters::VT_MAX, max); - } - void add_scale(flatbuffers::Offset> scale) { - fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); - } - void add_zero_point(flatbuffers::Offset> zero_point) { - fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); - } - void add_details_type(tflite::QuantizationDetails details_type) { - fbb_.AddElement(QuantizationParameters::VT_DETAILS_TYPE, static_cast(details_type), 0); - } - void add_details(flatbuffers::Offset details) { - fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); - } - void add_quantized_dimension(int32_t quantized_dimension) { - fbb_.AddElement(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0); - } - explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateQuantizationParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> min = 0, - flatbuffers::Offset> max = 0, - flatbuffers::Offset> scale = 0, - flatbuffers::Offset> zero_point = 0, - tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE, - flatbuffers::Offset details = 0, - int32_t quantized_dimension = 0) { - QuantizationParametersBuilder builder_(_fbb); - builder_.add_quantized_dimension(quantized_dimension); - builder_.add_details(details); - builder_.add_zero_point(zero_point); - builder_.add_scale(scale); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_details_type(details_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateQuantizationParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *min = nullptr, - const std::vector *max = nullptr, - const std::vector *scale = nullptr, - const std::vector *zero_point = nullptr, - tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE, - flatbuffers::Offset details = 0, - int32_t quantized_dimension = 0) { - auto min__ = min ? _fbb.CreateVector(*min) : 0; - auto max__ = max ? _fbb.CreateVector(*max) : 0; - auto scale__ = scale ? _fbb.CreateVector(*scale) : 0; - auto zero_point__ = zero_point ? _fbb.CreateVector(*zero_point) : 0; - return tflite::CreateQuantizationParameters( - _fbb, - min__, - max__, - scale__, - zero_point__, - details_type, - details, - quantized_dimension); -} - -flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Int32VectorT : public flatbuffers::NativeTable { - typedef Int32Vector TableType; - std::vector values; - Int32VectorT() { - } -}; - -struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Int32VectorT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Int32VectorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Int32Vector::VT_VALUES, values); - } - explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Int32VectorBuilder &operator=(const Int32VectorBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateInt32Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Int32VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateInt32VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite::CreateInt32Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Uint16VectorT : public flatbuffers::NativeTable { - typedef Uint16Vector TableType; - std::vector values; - Uint16VectorT() { - } -}; - -struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Uint16VectorT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Uint16VectorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Uint16Vector::VT_VALUES, values); - } - explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Uint16VectorBuilder &operator=(const Uint16VectorBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUint16Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Uint16VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateUint16VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); } - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite::CreateUint16Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Uint8VectorT : public flatbuffers::NativeTable { - typedef Uint8Vector TableType; - std::vector values; - Uint8VectorT() { - } -}; - -struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Uint8VectorT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Uint8VectorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Uint8Vector::VT_VALUES, values); - } - explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Uint8VectorBuilder &operator=(const Uint8VectorBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUint8Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Uint8VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateUint8VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); } - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite::CreateUint8Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DimensionMetadataT : public flatbuffers::NativeTable { - typedef DimensionMetadata TableType; - tflite::DimensionType format; - int32_t dense_size; - tflite::SparseIndexVectorUnion array_segments; - tflite::SparseIndexVectorUnion array_indices; - DimensionMetadataT() - : format(tflite::DimensionType_DENSE), - dense_size(0) { - } -}; - -struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DimensionMetadataT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FORMAT = 4, - VT_DENSE_SIZE = 6, - VT_ARRAY_SEGMENTS_TYPE = 8, - VT_ARRAY_SEGMENTS = 10, - VT_ARRAY_INDICES_TYPE = 12, - VT_ARRAY_INDICES = 14 - }; - tflite::DimensionType format() const { - return static_cast(GetField(VT_FORMAT, 0)); - } - int32_t dense_size() const { - return GetField(VT_DENSE_SIZE, 0); - } - tflite::SparseIndexVector array_segments_type() const { - return static_cast(GetField(VT_ARRAY_SEGMENTS_TYPE, 0)); - } - const void *array_segments() const { - return GetPointer(VT_ARRAY_SEGMENTS); - } - template const T *array_segments_as() const; - const tflite::Int32Vector *array_segments_as_Int32Vector() const { - return array_segments_type() == tflite::SparseIndexVector_Int32Vector ? static_cast(array_segments()) : nullptr; - } - const tflite::Uint16Vector *array_segments_as_Uint16Vector() const { - return array_segments_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast(array_segments()) : nullptr; - } - const tflite::Uint8Vector *array_segments_as_Uint8Vector() const { - return array_segments_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast(array_segments()) : nullptr; - } - tflite::SparseIndexVector array_indices_type() const { - return static_cast(GetField(VT_ARRAY_INDICES_TYPE, 0)); - } - const void *array_indices() const { - return GetPointer(VT_ARRAY_INDICES); - } - template const T *array_indices_as() const; - const tflite::Int32Vector *array_indices_as_Int32Vector() const { - return array_indices_type() == tflite::SparseIndexVector_Int32Vector ? static_cast(array_indices()) : nullptr; - } - const tflite::Uint16Vector *array_indices_as_Uint16Vector() const { - return array_indices_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast(array_indices()) : nullptr; - } - const tflite::Uint8Vector *array_indices_as_Uint8Vector() const { - return array_indices_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast(array_indices()) : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FORMAT) && - VerifyField(verifier, VT_DENSE_SIZE) && - VerifyField(verifier, VT_ARRAY_SEGMENTS_TYPE) && - VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && - VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && - VerifyField(verifier, VT_ARRAY_INDICES_TYPE) && - VerifyOffset(verifier, VT_ARRAY_INDICES) && - VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && - verifier.EndTable(); - } - DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite::Int32Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Int32Vector(); -} - -template<> inline const tflite::Uint16Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Uint16Vector(); -} - -template<> inline const tflite::Uint8Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Uint8Vector(); -} - -template<> inline const tflite::Int32Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Int32Vector(); -} - -template<> inline const tflite::Uint16Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Uint16Vector(); -} - -template<> inline const tflite::Uint8Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Uint8Vector(); -} - -struct DimensionMetadataBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_format(tflite::DimensionType format) { - fbb_.AddElement(DimensionMetadata::VT_FORMAT, static_cast(format), 0); - } - void add_dense_size(int32_t dense_size) { - fbb_.AddElement(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); - } - void add_array_segments_type(tflite::SparseIndexVector array_segments_type) { - fbb_.AddElement(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast(array_segments_type), 0); - } - void add_array_segments(flatbuffers::Offset array_segments) { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); - } - void add_array_indices_type(tflite::SparseIndexVector array_indices_type) { - fbb_.AddElement(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast(array_indices_type), 0); - } - void add_array_indices(flatbuffers::Offset array_indices) { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); - } - explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DimensionMetadataBuilder &operator=(const DimensionMetadataBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDimensionMetadata( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::DimensionType format = tflite::DimensionType_DENSE, - int32_t dense_size = 0, - tflite::SparseIndexVector array_segments_type = tflite::SparseIndexVector_NONE, - flatbuffers::Offset array_segments = 0, - tflite::SparseIndexVector array_indices_type = tflite::SparseIndexVector_NONE, - flatbuffers::Offset array_indices = 0) { - DimensionMetadataBuilder builder_(_fbb); - builder_.add_array_indices(array_indices); - builder_.add_array_segments(array_segments); - builder_.add_dense_size(dense_size); - builder_.add_array_indices_type(array_indices_type); - builder_.add_array_segments_type(array_segments_type); - builder_.add_format(format); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SparsityParametersT : public flatbuffers::NativeTable { - typedef SparsityParameters TableType; - std::vector traversal_order; - std::vector block_map; - std::vector> dim_metadata; - SparsityParametersT() { - } -}; - -struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SparsityParametersT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TRAVERSAL_ORDER = 4, - VT_BLOCK_MAP = 6, - VT_DIM_METADATA = 8 - }; - const flatbuffers::Vector *traversal_order() const { - return GetPointer *>(VT_TRAVERSAL_ORDER); - } - const flatbuffers::Vector *block_map() const { - return GetPointer *>(VT_BLOCK_MAP); - } - const flatbuffers::Vector> *dim_metadata() const { - return GetPointer> *>(VT_DIM_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && - verifier.VerifyVector(traversal_order()) && - VerifyOffset(verifier, VT_BLOCK_MAP) && - verifier.VerifyVector(block_map()) && - VerifyOffset(verifier, VT_DIM_METADATA) && - verifier.VerifyVector(dim_metadata()) && - verifier.VerifyVectorOfTables(dim_metadata()) && - verifier.EndTable(); - } - SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SparsityParametersBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_traversal_order(flatbuffers::Offset> traversal_order) { - fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); - } - void add_block_map(flatbuffers::Offset> block_map) { - fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); - } - void add_dim_metadata(flatbuffers::Offset>> dim_metadata) { - fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); - } - explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SparsityParametersBuilder &operator=(const SparsityParametersBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSparsityParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> traversal_order = 0, - flatbuffers::Offset> block_map = 0, - flatbuffers::Offset>> dim_metadata = 0) { - SparsityParametersBuilder builder_(_fbb); - builder_.add_dim_metadata(dim_metadata); - builder_.add_block_map(block_map); - builder_.add_traversal_order(traversal_order); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSparsityParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *traversal_order = nullptr, - const std::vector *block_map = nullptr, - const std::vector> *dim_metadata = nullptr) { - auto traversal_order__ = traversal_order ? _fbb.CreateVector(*traversal_order) : 0; - auto block_map__ = block_map ? _fbb.CreateVector(*block_map) : 0; - auto dim_metadata__ = dim_metadata ? _fbb.CreateVector>(*dim_metadata) : 0; - return tflite::CreateSparsityParameters( - _fbb, - traversal_order__, - block_map__, - dim_metadata__); -} - -flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TensorT : public flatbuffers::NativeTable { - typedef Tensor TableType; - std::vector shape; - tflite::TensorType type; - uint32_t buffer; - std::string name; - std::unique_ptr quantization; - bool is_variable; - std::unique_ptr sparsity; - std::vector shape_signature; - TensorT() - : type(tflite::TensorType_FLOAT32), - buffer(0), - is_variable(false) { - } -}; - -struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TensorT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SHAPE = 4, - VT_TYPE = 6, - VT_BUFFER = 8, - VT_NAME = 10, - VT_QUANTIZATION = 12, - VT_IS_VARIABLE = 14, - VT_SPARSITY = 16, - VT_SHAPE_SIGNATURE = 18 - }; - const flatbuffers::Vector *shape() const { - return GetPointer *>(VT_SHAPE); - } - tflite::TensorType type() const { - return static_cast(GetField(VT_TYPE, 0)); - } - uint32_t buffer() const { - return GetField(VT_BUFFER, 0); - } - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - const tflite::QuantizationParameters *quantization() const { - return GetPointer(VT_QUANTIZATION); - } - bool is_variable() const { - return GetField(VT_IS_VARIABLE, 0) != 0; - } - const tflite::SparsityParameters *sparsity() const { - return GetPointer(VT_SPARSITY); - } - const flatbuffers::Vector *shape_signature() const { - return GetPointer *>(VT_SHAPE_SIGNATURE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && - VerifyField(verifier, VT_TYPE) && - VerifyField(verifier, VT_BUFFER) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffset(verifier, VT_QUANTIZATION) && - verifier.VerifyTable(quantization()) && - VerifyField(verifier, VT_IS_VARIABLE) && - VerifyOffset(verifier, VT_SPARSITY) && - verifier.VerifyTable(sparsity()) && - VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && - verifier.VerifyVector(shape_signature()) && - verifier.EndTable(); - } - TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TensorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset> shape) { - fbb_.AddOffset(Tensor::VT_SHAPE, shape); - } - void add_type(tflite::TensorType type) { - fbb_.AddElement(Tensor::VT_TYPE, static_cast(type), 0); - } - void add_buffer(uint32_t buffer) { - fbb_.AddElement(Tensor::VT_BUFFER, buffer, 0); - } - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Tensor::VT_NAME, name); - } - void add_quantization(flatbuffers::Offset quantization) { - fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); - } - void add_is_variable(bool is_variable) { - fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); - } - void add_sparsity(flatbuffers::Offset sparsity) { - fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); - } - void add_shape_signature(flatbuffers::Offset> shape_signature) { - fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); - } - explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TensorBuilder &operator=(const TensorBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTensor( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> shape = 0, - tflite::TensorType type = tflite::TensorType_FLOAT32, - uint32_t buffer = 0, - flatbuffers::Offset name = 0, - flatbuffers::Offset quantization = 0, - bool is_variable = false, - flatbuffers::Offset sparsity = 0, - flatbuffers::Offset> shape_signature = 0) { - TensorBuilder builder_(_fbb); - builder_.add_shape_signature(shape_signature); - builder_.add_sparsity(sparsity); - builder_.add_quantization(quantization); - builder_.add_name(name); - builder_.add_buffer(buffer); - builder_.add_shape(shape); - builder_.add_is_variable(is_variable); - builder_.add_type(type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateTensorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *shape = nullptr, - tflite::TensorType type = tflite::TensorType_FLOAT32, - uint32_t buffer = 0, - const char *name = nullptr, - flatbuffers::Offset quantization = 0, - bool is_variable = false, - flatbuffers::Offset sparsity = 0, - const std::vector *shape_signature = nullptr) { - auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; - auto name__ = name ? _fbb.CreateString(name) : 0; - auto shape_signature__ = shape_signature ? _fbb.CreateVector(*shape_signature) : 0; - return tflite::CreateTensor( - _fbb, - shape__, - type, - buffer, - name__, - quantization, - is_variable, - sparsity, - shape_signature__); -} - -flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Conv2DOptionsT : public flatbuffers::NativeTable { - typedef Conv2DOptions TableType; - tflite::Padding padding; - int32_t stride_w; - int32_t stride_h; - tflite::ActivationFunctionType fused_activation_function; - int32_t dilation_w_factor; - int32_t dilation_h_factor; - Conv2DOptionsT() - : padding(tflite::Padding_SAME), - stride_w(0), - stride_h(0), - fused_activation_function(tflite::ActivationFunctionType_NONE), - dilation_w_factor(1), - dilation_h_factor(1) { - } -}; - -struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Conv2DOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FUSED_ACTIVATION_FUNCTION = 10, - VT_DILATION_W_FACTOR = 12, - VT_DILATION_H_FACTOR = 14 - }; - tflite::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 1); - } - int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 1); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING) && - VerifyField(verifier, VT_STRIDE_W) && - VerifyField(verifier, VT_STRIDE_H) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_DILATION_W_FACTOR) && - VerifyField(verifier, VT_DILATION_H_FACTOR) && - verifier.EndTable(); - } - Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Conv2DOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite::Padding padding) { - fbb_.AddElement(Conv2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(Conv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(Conv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::Padding padding = tflite::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, - int32_t dilation_h_factor = 1) { - Conv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Pool2DOptionsT : public flatbuffers::NativeTable { - typedef Pool2DOptions TableType; - tflite::Padding padding; - int32_t stride_w; - int32_t stride_h; - int32_t filter_width; - int32_t filter_height; - tflite::ActivationFunctionType fused_activation_function; - Pool2DOptionsT() - : padding(tflite::Padding_SAME), - stride_w(0), - stride_h(0), - filter_width(0), - filter_height(0), - fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Pool2DOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FILTER_WIDTH = 10, - VT_FILTER_HEIGHT = 12, - VT_FUSED_ACTIVATION_FUNCTION = 14 - }; - tflite::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - int32_t filter_width() const { - return GetField(VT_FILTER_WIDTH, 0); - } - int32_t filter_height() const { - return GetField(VT_FILTER_HEIGHT, 0); - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING) && - VerifyField(verifier, VT_STRIDE_W) && - VerifyField(verifier, VT_STRIDE_H) && - VerifyField(verifier, VT_FILTER_WIDTH) && - VerifyField(verifier, VT_FILTER_HEIGHT) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Pool2DOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite::Padding padding) { - fbb_.AddElement(Pool2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(Pool2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(Pool2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_filter_width(int32_t filter_width) { - fbb_.AddElement(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); - } - void add_filter_height(int32_t filter_height) { - fbb_.AddElement(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePool2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::Padding padding = tflite::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - int32_t filter_width = 0, - int32_t filter_height = 0, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - Pool2DOptionsBuilder builder_(_fbb); - builder_.add_filter_height(filter_height); - builder_.add_filter_width(filter_width); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { - typedef DepthwiseConv2DOptions TableType; - tflite::Padding padding; - int32_t stride_w; - int32_t stride_h; - int32_t depth_multiplier; - tflite::ActivationFunctionType fused_activation_function; - int32_t dilation_w_factor; - int32_t dilation_h_factor; - DepthwiseConv2DOptionsT() - : padding(tflite::Padding_SAME), - stride_w(0), - stride_h(0), - depth_multiplier(0), - fused_activation_function(tflite::ActivationFunctionType_NONE), - dilation_w_factor(1), - dilation_h_factor(1) { - } -}; - -struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DepthwiseConv2DOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_DEPTH_MULTIPLIER = 10, - VT_FUSED_ACTIVATION_FUNCTION = 12, - VT_DILATION_W_FACTOR = 14, - VT_DILATION_H_FACTOR = 16 - }; - tflite::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - int32_t depth_multiplier() const { - return GetField(VT_DEPTH_MULTIPLIER, 0); - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 1); - } - int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 1); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING) && - VerifyField(verifier, VT_STRIDE_W) && - VerifyField(verifier, VT_STRIDE_H) && - VerifyField(verifier, VT_DEPTH_MULTIPLIER) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_DILATION_W_FACTOR) && - VerifyField(verifier, VT_DILATION_H_FACTOR) && - verifier.EndTable(); - } - DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DepthwiseConv2DOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite::Padding padding) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_depth_multiplier(int32_t depth_multiplier) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::Padding padding = tflite::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - int32_t depth_multiplier = 0, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, - int32_t dilation_h_factor = 1) { - DepthwiseConv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_depth_multiplier(depth_multiplier); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable { - typedef ConcatEmbeddingsOptions TableType; - int32_t num_channels; - std::vector num_columns_per_channel; - std::vector embedding_dim_per_channel; - ConcatEmbeddingsOptionsT() - : num_channels(0) { - } -}; - -struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ConcatEmbeddingsOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_CHANNELS = 4, - VT_NUM_COLUMNS_PER_CHANNEL = 6, - VT_EMBEDDING_DIM_PER_CHANNEL = 8 - }; - int32_t num_channels() const { - return GetField(VT_NUM_CHANNELS, 0); - } - const flatbuffers::Vector *num_columns_per_channel() const { - return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); - } - const flatbuffers::Vector *embedding_dim_per_channel() const { - return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_CHANNELS) && - VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && - verifier.VerifyVector(num_columns_per_channel()) && - VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && - verifier.VerifyVector(embedding_dim_per_channel()) && - verifier.EndTable(); - } - ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ConcatEmbeddingsOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_channels(int32_t num_channels) { - fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); - } - void add_num_columns_per_channel(flatbuffers::Offset> num_columns_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); - } - void add_embedding_dim_per_channel(flatbuffers::Offset> embedding_dim_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel); - } - explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_channels = 0, - flatbuffers::Offset> num_columns_per_channel = 0, - flatbuffers::Offset> embedding_dim_per_channel = 0) { - ConcatEmbeddingsOptionsBuilder builder_(_fbb); - builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); - builder_.add_num_columns_per_channel(num_columns_per_channel); - builder_.add_num_channels(num_channels); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateConcatEmbeddingsOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_channels = 0, - const std::vector *num_columns_per_channel = nullptr, - const std::vector *embedding_dim_per_channel = nullptr) { - auto num_columns_per_channel__ = num_columns_per_channel ? _fbb.CreateVector(*num_columns_per_channel) : 0; - auto embedding_dim_per_channel__ = embedding_dim_per_channel ? _fbb.CreateVector(*embedding_dim_per_channel) : 0; - return tflite::CreateConcatEmbeddingsOptions( - _fbb, - num_channels, - num_columns_per_channel__, - embedding_dim_per_channel__); -} - -flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LSHProjectionOptionsT : public flatbuffers::NativeTable { - typedef LSHProjectionOptions TableType; - tflite::LSHProjectionType type; - LSHProjectionOptionsT() - : type(tflite::LSHProjectionType_UNKNOWN) { - } -}; - -struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LSHProjectionOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TYPE = 4 - }; - tflite::LSHProjectionType type() const { - return static_cast(GetField(VT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TYPE) && - verifier.EndTable(); - } - LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LSHProjectionOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_type(tflite::LSHProjectionType type) { - fbb_.AddElement(LSHProjectionOptions::VT_TYPE, static_cast(type), 0); - } - explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLSHProjectionOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN) { - LSHProjectionOptionsBuilder builder_(_fbb); - builder_.add_type(type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SVDFOptionsT : public flatbuffers::NativeTable { - typedef SVDFOptions TableType; - int32_t rank; - tflite::ActivationFunctionType fused_activation_function; - bool asymmetric_quantize_inputs; - SVDFOptionsT() - : rank(0), - fused_activation_function(tflite::ActivationFunctionType_NONE), - asymmetric_quantize_inputs(false) { - } -}; - -struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SVDFOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_RANK = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - int32_t rank() const { - return GetField(VT_RANK, 0); - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_RANK) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SVDFOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_rank(int32_t rank) { - fbb_.AddElement(SVDFOptions::VT_RANK, rank, 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSVDFOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t rank = 0, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - SVDFOptionsBuilder builder_(_fbb); - builder_.add_rank(rank); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RNNOptionsT : public flatbuffers::NativeTable { - typedef RNNOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - bool asymmetric_quantize_inputs; - RNNOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE), - asymmetric_quantize_inputs(false) { - } -}; - -struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RNNOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RNNOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - RNNOptionsBuilder &operator=(const RNNOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - RNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SequenceRNNOptionsT : public flatbuffers::NativeTable { - typedef SequenceRNNOptions TableType; - bool time_major; - tflite::ActivationFunctionType fused_activation_function; - bool asymmetric_quantize_inputs; - SequenceRNNOptionsT() - : time_major(false), - fused_activation_function(tflite::ActivationFunctionType_NONE), - asymmetric_quantize_inputs(false) { - } -}; - -struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SequenceRNNOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TIME_MAJOR) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SequenceRNNOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) { - fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool time_major = false, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - SequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { - typedef BidirectionalSequenceRNNOptions TableType; - bool time_major; - tflite::ActivationFunctionType fused_activation_function; - bool merge_outputs; - bool asymmetric_quantize_inputs; - BidirectionalSequenceRNNOptionsT() - : time_major(false), - fused_activation_function(tflite::ActivationFunctionType_NONE), - merge_outputs(false), - asymmetric_quantize_inputs(false) { - } -}; - -struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BidirectionalSequenceRNNOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_MERGE_OUTPUTS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool merge_outputs() const { - return GetField(VT_MERGE_OUTPUTS, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TIME_MAJOR) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_MERGE_OUTPUTS) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BidirectionalSequenceRNNOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_merge_outputs(bool merge_outputs) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool time_major = false, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - bool merge_outputs = false, - bool asymmetric_quantize_inputs = false) { - BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FullyConnectedOptionsT : public flatbuffers::NativeTable { - typedef FullyConnectedOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - tflite::FullyConnectedOptionsWeightsFormat weights_format; - bool keep_num_dims; - bool asymmetric_quantize_inputs; - FullyConnectedOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE), - weights_format(tflite::FullyConnectedOptionsWeightsFormat_DEFAULT), - keep_num_dims(false), - asymmetric_quantize_inputs(false) { - } -}; - -struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FullyConnectedOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_WEIGHTS_FORMAT = 6, - VT_KEEP_NUM_DIMS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - tflite::FullyConnectedOptionsWeightsFormat weights_format() const { - return static_cast(GetField(VT_WEIGHTS_FORMAT, 0)); - } - bool keep_num_dims() const { - return GetField(VT_KEEP_NUM_DIMS, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_WEIGHTS_FORMAT) && - VerifyField(verifier, VT_KEEP_NUM_DIMS) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FullyConnectedOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_weights_format(tflite::FullyConnectedOptionsWeightsFormat weights_format) { - fbb_.AddElement(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast(weights_format), 0); - } - void add_keep_num_dims(bool keep_num_dims) { - fbb_.AddElement(FullyConnectedOptions::VT_KEEP_NUM_DIMS, static_cast(keep_num_dims), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT, - bool keep_num_dims = false, - bool asymmetric_quantize_inputs = false) { - FullyConnectedOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_keep_num_dims(keep_num_dims); - builder_.add_weights_format(weights_format); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SoftmaxOptionsT : public flatbuffers::NativeTable { - typedef SoftmaxOptions TableType; - float beta; - SoftmaxOptionsT() - : beta(0.0f) { - } -}; - -struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SoftmaxOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BETA = 4 - }; - float beta() const { - return GetField(VT_BETA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BETA) && - verifier.EndTable(); - } - SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SoftmaxOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_beta(float beta) { - fbb_.AddElement(SoftmaxOptions::VT_BETA, beta, 0.0f); - } - explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float beta = 0.0f) { - SoftmaxOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ConcatenationOptionsT : public flatbuffers::NativeTable { - typedef ConcatenationOptions TableType; - int32_t axis; - tflite::ActivationFunctionType fused_activation_function; - ConcatenationOptionsT() - : axis(0), - fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ConcatenationOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ConcatenationOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(ConcatenationOptions::VT_AXIS, axis, 0); - } - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - ConcatenationOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AddOptionsT : public flatbuffers::NativeTable { - typedef AddOptions TableType; - bool pot_scale_int16; - tflite::ActivationFunctionType fused_activation_function; - AddOptionsT() - : pot_scale_int16(true), - fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AddOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_POT_SCALE_INT16 = 6 - }; - bool pot_scale_int16() const { - return GetField(VT_POT_SCALE_INT16, 0) != 0; - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_POT_SCALE_INT16) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AddOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - AddOptionsBuilder &operator=(const AddOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAddOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - AddOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MulOptionsT : public flatbuffers::NativeTable { - typedef MulOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - MulOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MulOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MulOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MulOptionsBuilder &operator=(const MulOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - MulOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct L2NormOptionsT : public flatbuffers::NativeTable { - typedef L2NormOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - L2NormOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef L2NormOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct L2NormOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateL2NormOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - L2NormOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { - typedef LocalResponseNormalizationOptions TableType; - int32_t radius; - float bias; - float alpha; - float beta; - LocalResponseNormalizationOptionsT() - : radius(0), - bias(0.0f), - alpha(0.0f), - beta(0.0f) { - } -}; - -struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LocalResponseNormalizationOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_RADIUS = 4, - VT_BIAS = 6, - VT_ALPHA = 8, - VT_BETA = 10 - }; - int32_t radius() const { - return GetField(VT_RADIUS, 0); - } - float bias() const { - return GetField(VT_BIAS, 0.0f); - } - float alpha() const { - return GetField(VT_ALPHA, 0.0f); - } - float beta() const { - return GetField(VT_BETA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_RADIUS) && - VerifyField(verifier, VT_BIAS) && - VerifyField(verifier, VT_ALPHA) && - VerifyField(verifier, VT_BETA) && - verifier.EndTable(); - } - LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LocalResponseNormalizationOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_radius(int32_t radius) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); - } - void add_bias(float bias) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); - } - void add_alpha(float alpha) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); - } - void add_beta(float beta) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); - } - explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LocalResponseNormalizationOptionsBuilder &operator=(const LocalResponseNormalizationOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLocalResponseNormalizationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t radius = 0, - float bias = 0.0f, - float alpha = 0.0f, - float beta = 0.0f) { - LocalResponseNormalizationOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - builder_.add_alpha(alpha); - builder_.add_bias(bias); - builder_.add_radius(radius); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LSTMOptionsT : public flatbuffers::NativeTable { - typedef LSTMOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - float cell_clip; - float proj_clip; - tflite::LSTMKernelType kernel_type; - bool asymmetric_quantize_inputs; - LSTMOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE), - cell_clip(0.0f), - proj_clip(0.0f), - kernel_type(tflite::LSTMKernelType_FULL), - asymmetric_quantize_inputs(false) { - } -}; - -struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LSTMOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_KERNEL_TYPE = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - tflite::LSTMKernelType kernel_type() const { - return static_cast(GetField(VT_KERNEL_TYPE, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_CELL_CLIP) && - VerifyField(verifier, VT_PROJ_CLIP) && - VerifyField(verifier, VT_KERNEL_TYPE) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LSTMOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_kernel_type(tflite::LSTMKernelType kernel_type) { - fbb_.AddElement(LSTMOptions::VT_KERNEL_TYPE, static_cast(kernel_type), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - tflite::LSTMKernelType kernel_type = tflite::LSTMKernelType_FULL, - bool asymmetric_quantize_inputs = false) { - LSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_kernel_type(kernel_type); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { - typedef UnidirectionalSequenceLSTMOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - float cell_clip; - float proj_clip; - bool time_major; - bool asymmetric_quantize_inputs; - UnidirectionalSequenceLSTMOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE), - cell_clip(0.0f), - proj_clip(0.0f), - time_major(false), - asymmetric_quantize_inputs(false) { - } -}; - -struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnidirectionalSequenceLSTMOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_TIME_MAJOR = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_CELL_CLIP) && - VerifyField(verifier, VT_PROJ_CLIP) && - VerifyField(verifier, VT_TIME_MAJOR) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnidirectionalSequenceLSTMOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_time_major(bool time_major) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - UnidirectionalSequenceLSTMOptionsBuilder &operator=(const UnidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - bool time_major = false, - bool asymmetric_quantize_inputs = false) { - UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { - typedef BidirectionalSequenceLSTMOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - float cell_clip; - float proj_clip; - bool merge_outputs; - bool time_major; - bool asymmetric_quantize_inputs; - BidirectionalSequenceLSTMOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE), - cell_clip(0.0f), - proj_clip(0.0f), - merge_outputs(false), - time_major(true), - asymmetric_quantize_inputs(false) { - } -}; - -struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BidirectionalSequenceLSTMOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_MERGE_OUTPUTS = 10, - VT_TIME_MAJOR = 12, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - bool merge_outputs() const { - return GetField(VT_MERGE_OUTPUTS, 0) != 0; - } - bool time_major() const { - return GetField(VT_TIME_MAJOR, 1) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField(verifier, VT_CELL_CLIP) && - VerifyField(verifier, VT_PROJ_CLIP) && - VerifyField(verifier, VT_MERGE_OUTPUTS) && - VerifyField(verifier, VT_TIME_MAJOR) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && - verifier.EndTable(); - } - BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BidirectionalSequenceLSTMOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_merge_outputs(bool merge_outputs) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); - } - void add_time_major(bool time_major) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 1); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceLSTMOptionsBuilder &operator=(const BidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - bool merge_outputs = false, - bool time_major = true, - bool asymmetric_quantize_inputs = false) { - BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { - typedef ResizeBilinearOptions TableType; - bool align_corners; - bool half_pixel_centers; - ResizeBilinearOptionsT() - : align_corners(false), - half_pixel_centers(false) { - } -}; - -struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ResizeBilinearOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALIGN_CORNERS = 8, - VT_HALF_PIXEL_CENTERS = 10 - }; - bool align_corners() const { - return GetField(VT_ALIGN_CORNERS, 0) != 0; - } - bool half_pixel_centers() const { - return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALIGN_CORNERS) && - VerifyField(verifier, VT_HALF_PIXEL_CENTERS) && - verifier.EndTable(); - } - ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ResizeBilinearOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) { - fbb_.AddElement(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) { - fbb_.AddElement(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); - } - explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateResizeBilinearOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool align_corners = false, - bool half_pixel_centers = false) { - ResizeBilinearOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable { - typedef ResizeNearestNeighborOptions TableType; - bool align_corners; - bool half_pixel_centers; - ResizeNearestNeighborOptionsT() - : align_corners(false), - half_pixel_centers(false) { - } -}; - -struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ResizeNearestNeighborOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALIGN_CORNERS = 4, - VT_HALF_PIXEL_CENTERS = 6 - }; - bool align_corners() const { - return GetField(VT_ALIGN_CORNERS, 0) != 0; - } - bool half_pixel_centers() const { - return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALIGN_CORNERS) && - VerifyField(verifier, VT_HALF_PIXEL_CENTERS) && - verifier.EndTable(); - } - ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ResizeNearestNeighborOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) { - fbb_.AddElement(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) { - fbb_.AddElement(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); - } - explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ResizeNearestNeighborOptionsBuilder &operator=(const ResizeNearestNeighborOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateResizeNearestNeighborOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool align_corners = false, - bool half_pixel_centers = false) { - ResizeNearestNeighborOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CallOptionsT : public flatbuffers::NativeTable { - typedef CallOptions TableType; - uint32_t subgraph; - CallOptionsT() - : subgraph(0) { - } -}; - -struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CallOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SUBGRAPH = 4 - }; - uint32_t subgraph() const { - return GetField(VT_SUBGRAPH, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SUBGRAPH) && - verifier.EndTable(); - } - CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CallOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_subgraph(uint32_t subgraph) { - fbb_.AddElement(CallOptions::VT_SUBGRAPH, subgraph, 0); - } - explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - CallOptionsBuilder &operator=(const CallOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t subgraph = 0) { - CallOptionsBuilder builder_(_fbb); - builder_.add_subgraph(subgraph); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PadOptionsT : public flatbuffers::NativeTable { - typedef PadOptions TableType; - PadOptionsT() { - } -}; - -struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PadOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PadOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - PadOptionsBuilder &operator=(const PadOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePadOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - PadOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PadV2OptionsT : public flatbuffers::NativeTable { - typedef PadV2Options TableType; - PadV2OptionsT() { - } -}; - -struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PadV2OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PadV2OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - PadV2OptionsBuilder &operator=(const PadV2OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePadV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - PadV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReshapeOptionsT : public flatbuffers::NativeTable { - typedef ReshapeOptions TableType; - std::vector new_shape; - ReshapeOptionsT() { - } -}; - -struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReshapeOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NEW_SHAPE = 4 - }; - const flatbuffers::Vector *new_shape() const { - return GetPointer *>(VT_NEW_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NEW_SHAPE) && - verifier.VerifyVector(new_shape()) && - verifier.EndTable(); - } - ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReshapeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_new_shape(flatbuffers::Offset> new_shape) { - fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); - } - explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReshapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> new_shape = 0) { - ReshapeOptionsBuilder builder_(_fbb); - builder_.add_new_shape(new_shape); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateReshapeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *new_shape = nullptr) { - auto new_shape__ = new_shape ? _fbb.CreateVector(*new_shape) : 0; - return tflite::CreateReshapeOptions( - _fbb, - new_shape__); -} - -flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { - typedef SpaceToBatchNDOptions TableType; - SpaceToBatchNDOptionsT() { - } -}; - -struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SpaceToBatchNDOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SpaceToBatchNDOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSpaceToBatchNDOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SpaceToBatchNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { - typedef BatchToSpaceNDOptions TableType; - BatchToSpaceNDOptionsT() { - } -}; - -struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BatchToSpaceNDOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BatchToSpaceNDOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBatchToSpaceNDOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - BatchToSpaceNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SkipGramOptionsT : public flatbuffers::NativeTable { - typedef SkipGramOptions TableType; - int32_t ngram_size; - int32_t max_skip_size; - bool include_all_ngrams; - SkipGramOptionsT() - : ngram_size(0), - max_skip_size(0), - include_all_ngrams(false) { - } -}; - -struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SkipGramOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NGRAM_SIZE = 4, - VT_MAX_SKIP_SIZE = 6, - VT_INCLUDE_ALL_NGRAMS = 8 - }; - int32_t ngram_size() const { - return GetField(VT_NGRAM_SIZE, 0); - } - int32_t max_skip_size() const { - return GetField(VT_MAX_SKIP_SIZE, 0); - } - bool include_all_ngrams() const { - return GetField(VT_INCLUDE_ALL_NGRAMS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NGRAM_SIZE) && - VerifyField(verifier, VT_MAX_SKIP_SIZE) && - VerifyField(verifier, VT_INCLUDE_ALL_NGRAMS) && - verifier.EndTable(); - } - SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SkipGramOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_ngram_size(int32_t ngram_size) { - fbb_.AddElement(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); - } - void add_max_skip_size(int32_t max_skip_size) { - fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); - } - void add_include_all_ngrams(bool include_all_ngrams) { - fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast(include_all_ngrams), 0); - } - explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSkipGramOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t ngram_size = 0, - int32_t max_skip_size = 0, - bool include_all_ngrams = false) { - SkipGramOptionsBuilder builder_(_fbb); - builder_.add_max_skip_size(max_skip_size); - builder_.add_ngram_size(ngram_size); - builder_.add_include_all_ngrams(include_all_ngrams); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SpaceToDepthOptionsT : public flatbuffers::NativeTable { - typedef SpaceToDepthOptions TableType; - int32_t block_size; - SpaceToDepthOptionsT() - : block_size(0) { - } -}; - -struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SpaceToDepthOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { - return GetField(VT_BLOCK_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } - SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SpaceToDepthOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) { - fbb_.AddElement(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSpaceToDepthOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t block_size = 0) { - SpaceToDepthOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DepthToSpaceOptionsT : public flatbuffers::NativeTable { - typedef DepthToSpaceOptions TableType; - int32_t block_size; - DepthToSpaceOptionsT() - : block_size(0) { - } -}; - -struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DepthToSpaceOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { - return GetField(VT_BLOCK_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } - DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DepthToSpaceOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) { - fbb_.AddElement(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DepthToSpaceOptionsBuilder &operator=(const DepthToSpaceOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDepthToSpaceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t block_size = 0) { - DepthToSpaceOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SubOptionsT : public flatbuffers::NativeTable { - typedef SubOptions TableType; - bool pot_scale_int16; - tflite::ActivationFunctionType fused_activation_function; - SubOptionsT() - : pot_scale_int16(true), - fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SubOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_POT_SCALE_INT16 = 6 - }; - bool pot_scale_int16() const { - return GetField(VT_POT_SCALE_INT16, 0) != 0; - } - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_POT_SCALE_INT16) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SubOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SubOptionsBuilder &operator=(const SubOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSubOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - SubOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DivOptionsT : public flatbuffers::NativeTable { - typedef DivOptions TableType; - tflite::ActivationFunctionType fused_activation_function; - DivOptionsT() - : fused_activation_function(tflite::ActivationFunctionType_NONE) { - } -}; - -struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DivOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - verifier.EndTable(); - } - DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DivOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DivOptionsBuilder &operator=(const DivOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDivOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) { - DivOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TopKV2OptionsT : public flatbuffers::NativeTable { - typedef TopKV2Options TableType; - TopKV2OptionsT() { - } -}; - -struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TopKV2OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TopKV2OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTopKV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - TopKV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable { - typedef EmbeddingLookupSparseOptions TableType; - tflite::CombinerType combiner; - EmbeddingLookupSparseOptionsT() - : combiner(tflite::CombinerType_SUM) { - } -}; - -struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EmbeddingLookupSparseOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COMBINER = 4 - }; - tflite::CombinerType combiner() const { - return static_cast(GetField(VT_COMBINER, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COMBINER) && - verifier.EndTable(); - } - EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct EmbeddingLookupSparseOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_combiner(tflite::CombinerType combiner) { - fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast(combiner), 0); - } - explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::CombinerType combiner = tflite::CombinerType_SUM) { - EmbeddingLookupSparseOptionsBuilder builder_(_fbb); - builder_.add_combiner(combiner); - return builder_.Finish(); -} - -flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GatherOptionsT : public flatbuffers::NativeTable { - typedef GatherOptions TableType; - int32_t axis; - GatherOptionsT() - : axis(0) { - } -}; - -struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GatherOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS) && - verifier.EndTable(); - } - GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GatherOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(GatherOptions::VT_AXIS, axis, 0); - } - explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - GatherOptionsBuilder &operator=(const GatherOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) { - GatherOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TransposeOptionsT : public flatbuffers::NativeTable { - typedef TransposeOptions TableType; - TransposeOptionsT() { - } -}; - -struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TransposeOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TransposeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - TransposeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ExpOptionsT : public flatbuffers::NativeTable { - typedef ExpOptions TableType; - ExpOptionsT() { - } -}; - -struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ExpOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ExpOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ExpOptionsBuilder &operator=(const ExpOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateExpOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ExpOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CosOptionsT : public flatbuffers::NativeTable { - typedef CosOptions TableType; - CosOptionsT() { - } -}; - -struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CosOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CosOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - CosOptionsBuilder &operator=(const CosOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCosOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - CosOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReducerOptionsT : public flatbuffers::NativeTable { - typedef ReducerOptions TableType; - bool keep_dims; - ReducerOptionsT() - : keep_dims(false) { - } -}; - -struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReducerOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_KEEP_DIMS = 4 - }; - bool keep_dims() const { - return GetField(VT_KEEP_DIMS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_KEEP_DIMS) && - verifier.EndTable(); - } - ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReducerOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_keep_dims(bool keep_dims) { - fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); - } - explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReducerOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool keep_dims = false) { - ReducerOptionsBuilder builder_(_fbb); - builder_.add_keep_dims(keep_dims); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SqueezeOptionsT : public flatbuffers::NativeTable { - typedef SqueezeOptions TableType; - std::vector squeeze_dims; - SqueezeOptionsT() { - } -}; - -struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SqueezeOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SQUEEZE_DIMS = 4 - }; - const flatbuffers::Vector *squeeze_dims() const { - return GetPointer *>(VT_SQUEEZE_DIMS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SQUEEZE_DIMS) && - verifier.VerifyVector(squeeze_dims()) && - verifier.EndTable(); - } - SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SqueezeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_squeeze_dims(flatbuffers::Offset> squeeze_dims) { - fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); - } - explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSqueezeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> squeeze_dims = 0) { - SqueezeOptionsBuilder builder_(_fbb); - builder_.add_squeeze_dims(squeeze_dims); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSqueezeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *squeeze_dims = nullptr) { - auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0; - return tflite::CreateSqueezeOptions( - _fbb, - squeeze_dims__); -} - -flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SplitOptionsT : public flatbuffers::NativeTable { - typedef SplitOptions TableType; - int32_t num_splits; - SplitOptionsT() - : num_splits(0) { - } -}; - -struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SplitOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { - return GetField(VT_NUM_SPLITS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } - SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SplitOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) { - fbb_.AddElement(SplitOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SplitOptionsBuilder &operator=(const SplitOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSplitOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) { - SplitOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SplitVOptionsT : public flatbuffers::NativeTable { - typedef SplitVOptions TableType; - int32_t num_splits; - SplitVOptionsT() - : num_splits(0) { - } -}; - -struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SplitVOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { - return GetField(VT_NUM_SPLITS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } - SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SplitVOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) { - fbb_.AddElement(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SplitVOptionsBuilder &operator=(const SplitVOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSplitVOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) { - SplitVOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StridedSliceOptionsT : public flatbuffers::NativeTable { - typedef StridedSliceOptions TableType; - int32_t begin_mask; - int32_t end_mask; - int32_t ellipsis_mask; - int32_t new_axis_mask; - int32_t shrink_axis_mask; - StridedSliceOptionsT() - : begin_mask(0), - end_mask(0), - ellipsis_mask(0), - new_axis_mask(0), - shrink_axis_mask(0) { - } -}; - -struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StridedSliceOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BEGIN_MASK = 4, - VT_END_MASK = 6, - VT_ELLIPSIS_MASK = 8, - VT_NEW_AXIS_MASK = 10, - VT_SHRINK_AXIS_MASK = 12 - }; - int32_t begin_mask() const { - return GetField(VT_BEGIN_MASK, 0); - } - int32_t end_mask() const { - return GetField(VT_END_MASK, 0); - } - int32_t ellipsis_mask() const { - return GetField(VT_ELLIPSIS_MASK, 0); - } - int32_t new_axis_mask() const { - return GetField(VT_NEW_AXIS_MASK, 0); - } - int32_t shrink_axis_mask() const { - return GetField(VT_SHRINK_AXIS_MASK, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BEGIN_MASK) && - VerifyField(verifier, VT_END_MASK) && - VerifyField(verifier, VT_ELLIPSIS_MASK) && - VerifyField(verifier, VT_NEW_AXIS_MASK) && - VerifyField(verifier, VT_SHRINK_AXIS_MASK) && - verifier.EndTable(); - } - StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StridedSliceOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_begin_mask(int32_t begin_mask) { - fbb_.AddElement(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); - } - void add_end_mask(int32_t end_mask) { - fbb_.AddElement(StridedSliceOptions::VT_END_MASK, end_mask, 0); - } - void add_ellipsis_mask(int32_t ellipsis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); - } - void add_new_axis_mask(int32_t new_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); - } - void add_shrink_axis_mask(int32_t shrink_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); - } - explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStridedSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t begin_mask = 0, - int32_t end_mask = 0, - int32_t ellipsis_mask = 0, - int32_t new_axis_mask = 0, - int32_t shrink_axis_mask = 0) { - StridedSliceOptionsBuilder builder_(_fbb); - builder_.add_shrink_axis_mask(shrink_axis_mask); - builder_.add_new_axis_mask(new_axis_mask); - builder_.add_ellipsis_mask(ellipsis_mask); - builder_.add_end_mask(end_mask); - builder_.add_begin_mask(begin_mask); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogSoftmaxOptionsT : public flatbuffers::NativeTable { - typedef LogSoftmaxOptions TableType; - LogSoftmaxOptionsT() { - } -}; - -struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogSoftmaxOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogSoftmaxOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogSoftmaxOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CastOptionsT : public flatbuffers::NativeTable { - typedef CastOptions TableType; - tflite::TensorType in_data_type; - tflite::TensorType out_data_type; - CastOptionsT() - : in_data_type(tflite::TensorType_FLOAT32), - out_data_type(tflite::TensorType_FLOAT32) { - } -}; - -struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CastOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_IN_DATA_TYPE = 4, - VT_OUT_DATA_TYPE = 6 - }; - tflite::TensorType in_data_type() const { - return static_cast(GetField(VT_IN_DATA_TYPE, 0)); - } - tflite::TensorType out_data_type() const { - return static_cast(GetField(VT_OUT_DATA_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_IN_DATA_TYPE) && - VerifyField(verifier, VT_OUT_DATA_TYPE) && - verifier.EndTable(); - } - CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CastOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_in_data_type(tflite::TensorType in_data_type) { - fbb_.AddElement(CastOptions::VT_IN_DATA_TYPE, static_cast(in_data_type), 0); - } - void add_out_data_type(tflite::TensorType out_data_type) { - fbb_.AddElement(CastOptions::VT_OUT_DATA_TYPE, static_cast(out_data_type), 0); - } - explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - CastOptionsBuilder &operator=(const CastOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCastOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::TensorType in_data_type = tflite::TensorType_FLOAT32, - tflite::TensorType out_data_type = tflite::TensorType_FLOAT32) { - CastOptionsBuilder builder_(_fbb); - builder_.add_out_data_type(out_data_type); - builder_.add_in_data_type(in_data_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DequantizeOptionsT : public flatbuffers::NativeTable { - typedef DequantizeOptions TableType; - DequantizeOptionsT() { - } -}; - -struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DequantizeOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DequantizeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDequantizeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DequantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MaximumMinimumOptionsT : public flatbuffers::NativeTable { - typedef MaximumMinimumOptions TableType; - MaximumMinimumOptionsT() { - } -}; - -struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MaximumMinimumOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MaximumMinimumOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MaximumMinimumOptionsBuilder &operator=(const MaximumMinimumOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMaximumMinimumOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MaximumMinimumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TileOptionsT : public flatbuffers::NativeTable { - typedef TileOptions TableType; - TileOptionsT() { - } -}; - -struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TileOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TileOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TileOptionsBuilder &operator=(const TileOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTileOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - TileOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ArgMaxOptionsT : public flatbuffers::NativeTable { - typedef ArgMaxOptions TableType; - tflite::TensorType output_type; - ArgMaxOptionsT() - : output_type(tflite::TensorType_FLOAT32) { - } -}; - -struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ArgMaxOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPUT_TYPE = 4 - }; - tflite::TensorType output_type() const { - return static_cast(GetField(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } - ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ArgMaxOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(tflite::TensorType output_type) { - fbb_.AddElement(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); - } - explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ArgMaxOptionsBuilder &operator=(const ArgMaxOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateArgMaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::TensorType output_type = tflite::TensorType_FLOAT32) { - ArgMaxOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ArgMinOptionsT : public flatbuffers::NativeTable { - typedef ArgMinOptions TableType; - tflite::TensorType output_type; - ArgMinOptionsT() - : output_type(tflite::TensorType_FLOAT32) { - } -}; - -struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ArgMinOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPUT_TYPE = 4 - }; - tflite::TensorType output_type() const { - return static_cast(GetField(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } - ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ArgMinOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(tflite::TensorType output_type) { - fbb_.AddElement(ArgMinOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); - } - explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateArgMinOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::TensorType output_type = tflite::TensorType_FLOAT32) { - ArgMinOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GreaterOptionsT : public flatbuffers::NativeTable { - typedef GreaterOptions TableType; - GreaterOptionsT() { - } -}; - -struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GreaterOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GreaterOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGreaterOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GreaterOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GreaterEqualOptionsT : public flatbuffers::NativeTable { - typedef GreaterEqualOptions TableType; - GreaterEqualOptionsT() { - } -}; - -struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GreaterEqualOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GreaterEqualOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGreaterEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GreaterEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LessOptionsT : public flatbuffers::NativeTable { - typedef LessOptions TableType; - LessOptionsT() { - } -}; - -struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LessOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LessOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LessOptionsBuilder &operator=(const LessOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLessOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LessOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LessEqualOptionsT : public flatbuffers::NativeTable { - typedef LessEqualOptions TableType; - LessEqualOptionsT() { - } -}; - -struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LessEqualOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LessEqualOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLessEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LessEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NegOptionsT : public flatbuffers::NativeTable { - typedef NegOptions TableType; - NegOptionsT() { - } -}; - -struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NegOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NegOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - NegOptionsBuilder &operator=(const NegOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNegOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - NegOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SelectOptionsT : public flatbuffers::NativeTable { - typedef SelectOptions TableType; - SelectOptionsT() { - } -}; - -struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SelectOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SelectOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SelectOptionsBuilder &operator=(const SelectOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSelectOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SelectOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SliceOptionsT : public flatbuffers::NativeTable { - typedef SliceOptions TableType; - SliceOptionsT() { - } -}; - -struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SliceOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SliceOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SliceOptionsBuilder &operator=(const SliceOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SliceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TransposeConvOptionsT : public flatbuffers::NativeTable { - typedef TransposeConvOptions TableType; - tflite::Padding padding; - int32_t stride_w; - int32_t stride_h; - TransposeConvOptionsT() - : padding(tflite::Padding_SAME), - stride_w(0), - stride_h(0) { - } -}; - -struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TransposeConvOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8 - }; - tflite::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING) && - VerifyField(verifier, VT_STRIDE_W) && - VerifyField(verifier, VT_STRIDE_H) && - verifier.EndTable(); - } - TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TransposeConvOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite::Padding padding) { - fbb_.AddElement(TransposeConvOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); - } - explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TransposeConvOptionsBuilder &operator=(const TransposeConvOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTransposeConvOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::Padding padding = tflite::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0) { - TransposeConvOptionsBuilder builder_(_fbb); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ExpandDimsOptionsT : public flatbuffers::NativeTable { - typedef ExpandDimsOptions TableType; - ExpandDimsOptionsT() { - } -}; - -struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ExpandDimsOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ExpandDimsOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ExpandDimsOptionsBuilder &operator=(const ExpandDimsOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateExpandDimsOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ExpandDimsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SparseToDenseOptionsT : public flatbuffers::NativeTable { - typedef SparseToDenseOptions TableType; - bool validate_indices; - SparseToDenseOptionsT() - : validate_indices(false) { - } -}; - -struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SparseToDenseOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALIDATE_INDICES = 4 - }; - bool validate_indices() const { - return GetField(VT_VALIDATE_INDICES, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VALIDATE_INDICES) && - verifier.EndTable(); - } - SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SparseToDenseOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_validate_indices(bool validate_indices) { - fbb_.AddElement(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast(validate_indices), 0); - } - explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SparseToDenseOptionsBuilder &operator=(const SparseToDenseOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSparseToDenseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool validate_indices = false) { - SparseToDenseOptionsBuilder builder_(_fbb); - builder_.add_validate_indices(validate_indices); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct EqualOptionsT : public flatbuffers::NativeTable { - typedef EqualOptions TableType; - EqualOptionsT() { - } -}; - -struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EqualOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct EqualOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - EqualOptionsBuilder &operator=(const EqualOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - EqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NotEqualOptionsT : public flatbuffers::NativeTable { - typedef NotEqualOptions TableType; - NotEqualOptionsT() { - } -}; - -struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NotEqualOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NotEqualOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - NotEqualOptionsBuilder &operator=(const NotEqualOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNotEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - NotEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ShapeOptionsT : public flatbuffers::NativeTable { - typedef ShapeOptions TableType; - tflite::TensorType out_type; - ShapeOptionsT() - : out_type(tflite::TensorType_FLOAT32) { - } -}; - -struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ShapeOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUT_TYPE = 4 - }; - tflite::TensorType out_type() const { - return static_cast(GetField(VT_OUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUT_TYPE) && - verifier.EndTable(); - } - ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ShapeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_out_type(tflite::TensorType out_type) { - fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); - } - explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateShapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::TensorType out_type = tflite::TensorType_FLOAT32) { - ShapeOptionsBuilder builder_(_fbb); - builder_.add_out_type(out_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RankOptionsT : public flatbuffers::NativeTable { - typedef RankOptions TableType; - RankOptionsT() { - } -}; - -struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RankOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RankOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - RankOptionsBuilder &operator=(const RankOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRankOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - RankOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PowOptionsT : public flatbuffers::NativeTable { - typedef PowOptions TableType; - PowOptionsT() { - } -}; - -struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PowOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PowOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - PowOptionsBuilder &operator=(const PowOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePowOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - PowOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FakeQuantOptionsT : public flatbuffers::NativeTable { - typedef FakeQuantOptions TableType; - float min; - float max; - int32_t num_bits; - bool narrow_range; - FakeQuantOptionsT() - : min(0.0f), - max(0.0f), - num_bits(0), - narrow_range(false) { - } -}; - -struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FakeQuantOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MIN = 4, - VT_MAX = 6, - VT_NUM_BITS = 8, - VT_NARROW_RANGE = 10 - }; - float min() const { - return GetField(VT_MIN, 0.0f); - } - float max() const { - return GetField(VT_MAX, 0.0f); - } - int32_t num_bits() const { - return GetField(VT_NUM_BITS, 0); - } - bool narrow_range() const { - return GetField(VT_NARROW_RANGE, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_MIN) && - VerifyField(verifier, VT_MAX) && - VerifyField(verifier, VT_NUM_BITS) && - VerifyField(verifier, VT_NARROW_RANGE) && - verifier.EndTable(); - } - FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FakeQuantOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(float min) { - fbb_.AddElement(FakeQuantOptions::VT_MIN, min, 0.0f); - } - void add_max(float max) { - fbb_.AddElement(FakeQuantOptions::VT_MAX, max, 0.0f); - } - void add_num_bits(int32_t num_bits) { - fbb_.AddElement(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); - } - void add_narrow_range(bool narrow_range) { - fbb_.AddElement(FakeQuantOptions::VT_NARROW_RANGE, static_cast(narrow_range), 0); - } - explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFakeQuantOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float min = 0.0f, - float max = 0.0f, - int32_t num_bits = 0, - bool narrow_range = false) { - FakeQuantOptionsBuilder builder_(_fbb); - builder_.add_num_bits(num_bits); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_narrow_range(narrow_range); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PackOptionsT : public flatbuffers::NativeTable { - typedef PackOptions TableType; - int32_t values_count; - int32_t axis; - PackOptionsT() - : values_count(0), - axis(0) { - } -}; - -struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PackOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES_COUNT = 4, - VT_AXIS = 6 - }; - int32_t values_count() const { - return GetField(VT_VALUES_COUNT, 0); - } - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VALUES_COUNT) && - VerifyField(verifier, VT_AXIS) && - verifier.EndTable(); - } - PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PackOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values_count(int32_t values_count) { - fbb_.AddElement(PackOptions::VT_VALUES_COUNT, values_count, 0); - } - void add_axis(int32_t axis) { - fbb_.AddElement(PackOptions::VT_AXIS, axis, 0); - } - explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - PackOptionsBuilder &operator=(const PackOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePackOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t values_count = 0, - int32_t axis = 0) { - PackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_values_count(values_count); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalOrOptionsT : public flatbuffers::NativeTable { - typedef LogicalOrOptions TableType; - LogicalOrOptionsT() { - } -}; - -struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalOrOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalOrOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalOrOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalOrOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OneHotOptionsT : public flatbuffers::NativeTable { - typedef OneHotOptions TableType; - int32_t axis; - OneHotOptionsT() - : axis(0) { - } -}; - -struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OneHotOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS) && - verifier.EndTable(); - } - OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct OneHotOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); - } - explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOneHotOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) { - OneHotOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AbsOptionsT : public flatbuffers::NativeTable { - typedef AbsOptions TableType; - AbsOptionsT() { - } -}; - -struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AbsOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AbsOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - AbsOptionsBuilder &operator=(const AbsOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAbsOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - AbsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HardSwishOptionsT : public flatbuffers::NativeTable { - typedef HardSwishOptions TableType; - HardSwishOptionsT() { - } -}; - -struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HardSwishOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HardSwishOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - HardSwishOptionsBuilder &operator=(const HardSwishOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHardSwishOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - HardSwishOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalAndOptionsT : public flatbuffers::NativeTable { - typedef LogicalAndOptions TableType; - LogicalAndOptionsT() { - } -}; - -struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalAndOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalAndOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LogicalAndOptionsBuilder &operator=(const LogicalAndOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalAndOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalAndOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalNotOptionsT : public flatbuffers::NativeTable { - typedef LogicalNotOptions TableType; - LogicalNotOptionsT() { - } -}; - -struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalNotOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalNotOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LogicalNotOptionsBuilder &operator=(const LogicalNotOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalNotOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalNotOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnpackOptionsT : public flatbuffers::NativeTable { - typedef UnpackOptions TableType; - int32_t num; - int32_t axis; - UnpackOptionsT() - : num(0), - axis(0) { - } -}; - -struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnpackOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM = 4, - VT_AXIS = 6 - }; - int32_t num() const { - return GetField(VT_NUM, 0); - } - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM) && - VerifyField(verifier, VT_AXIS) && - verifier.EndTable(); - } - UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnpackOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num(int32_t num) { - fbb_.AddElement(UnpackOptions::VT_NUM, num, 0); - } - void add_axis(int32_t axis) { - fbb_.AddElement(UnpackOptions::VT_AXIS, axis, 0); - } - explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - UnpackOptionsBuilder &operator=(const UnpackOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnpackOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num = 0, - int32_t axis = 0) { - UnpackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_num(num); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FloorDivOptionsT : public flatbuffers::NativeTable { - typedef FloorDivOptions TableType; - FloorDivOptionsT() { - } -}; - -struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FloorDivOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FloorDivOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - FloorDivOptionsBuilder &operator=(const FloorDivOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFloorDivOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FloorDivOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SquareOptionsT : public flatbuffers::NativeTable { - typedef SquareOptions TableType; - SquareOptionsT() { - } -}; - -struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SquareOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SquareOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SquareOptionsBuilder &operator=(const SquareOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSquareOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SquareOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ZerosLikeOptionsT : public flatbuffers::NativeTable { - typedef ZerosLikeOptions TableType; - ZerosLikeOptionsT() { - } -}; - -struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ZerosLikeOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ZerosLikeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ZerosLikeOptionsBuilder &operator=(const ZerosLikeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateZerosLikeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ZerosLikeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FillOptionsT : public flatbuffers::NativeTable { - typedef FillOptions TableType; - FillOptionsT() { - } -}; - -struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FillOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FillOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - FillOptionsBuilder &operator=(const FillOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFillOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FillOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FloorModOptionsT : public flatbuffers::NativeTable { - typedef FloorModOptions TableType; - FloorModOptionsT() { - } -}; - -struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FloorModOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FloorModOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - FloorModOptionsBuilder &operator=(const FloorModOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFloorModOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FloorModOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RangeOptionsT : public flatbuffers::NativeTable { - typedef RangeOptions TableType; - RangeOptionsT() { - } -}; - -struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RangeOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RangeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - RangeOptionsBuilder &operator=(const RangeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRangeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - RangeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LeakyReluOptionsT : public flatbuffers::NativeTable { - typedef LeakyReluOptions TableType; - float alpha; - LeakyReluOptionsT() - : alpha(0.0f) { - } -}; - -struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LeakyReluOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALPHA = 4 - }; - float alpha() const { - return GetField(VT_ALPHA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALPHA) && - verifier.EndTable(); - } - LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LeakyReluOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_alpha(float alpha) { - fbb_.AddElement(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); - } - explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - LeakyReluOptionsBuilder &operator=(const LeakyReluOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLeakyReluOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float alpha = 0.0f) { - LeakyReluOptionsBuilder builder_(_fbb); - builder_.add_alpha(alpha); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable { - typedef SquaredDifferenceOptions TableType; - SquaredDifferenceOptionsT() { - } -}; - -struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SquaredDifferenceOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SquaredDifferenceOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SquaredDifferenceOptionsBuilder &operator=(const SquaredDifferenceOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSquaredDifferenceOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SquaredDifferenceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MirrorPadOptionsT : public flatbuffers::NativeTable { - typedef MirrorPadOptions TableType; - tflite::MirrorPadMode mode; - MirrorPadOptionsT() - : mode(tflite::MirrorPadMode_REFLECT) { - } -}; - -struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MirrorPadOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MODE = 4 - }; - tflite::MirrorPadMode mode() const { - return static_cast(GetField(VT_MODE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_MODE) && - verifier.EndTable(); - } - MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MirrorPadOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_mode(tflite::MirrorPadMode mode) { - fbb_.AddElement(MirrorPadOptions::VT_MODE, static_cast(mode), 0); - } - explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MirrorPadOptionsBuilder &operator=(const MirrorPadOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMirrorPadOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT) { - MirrorPadOptionsBuilder builder_(_fbb); - builder_.add_mode(mode); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UniqueOptionsT : public flatbuffers::NativeTable { - typedef UniqueOptions TableType; - tflite::TensorType idx_out_type; - UniqueOptionsT() - : idx_out_type(tflite::TensorType_INT32) { - } -}; - -struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UniqueOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_IDX_OUT_TYPE = 4 - }; - tflite::TensorType idx_out_type() const { - return static_cast(GetField(VT_IDX_OUT_TYPE, 2)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_IDX_OUT_TYPE) && - verifier.EndTable(); - } - UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UniqueOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_idx_out_type(tflite::TensorType idx_out_type) { - fbb_.AddElement(UniqueOptions::VT_IDX_OUT_TYPE, static_cast(idx_out_type), 2); - } - explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - UniqueOptionsBuilder &operator=(const UniqueOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUniqueOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::TensorType idx_out_type = tflite::TensorType_INT32) { - UniqueOptionsBuilder builder_(_fbb); - builder_.add_idx_out_type(idx_out_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReverseV2OptionsT : public flatbuffers::NativeTable { - typedef ReverseV2Options TableType; - ReverseV2OptionsT() { - } -}; - -struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReverseV2OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReverseV2OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReverseV2OptionsBuilder &operator=(const ReverseV2OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReverseV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - ReverseV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AddNOptionsT : public flatbuffers::NativeTable { - typedef AddNOptions TableType; - AddNOptionsT() { - } -}; - -struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AddNOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AddNOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - AddNOptionsBuilder &operator=(const AddNOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAddNOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - AddNOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GatherNdOptionsT : public flatbuffers::NativeTable { - typedef GatherNdOptions TableType; - GatherNdOptionsT() { - } -}; - -struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GatherNdOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GatherNdOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - GatherNdOptionsBuilder &operator=(const GatherNdOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGatherNdOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GatherNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct WhereOptionsT : public flatbuffers::NativeTable { - typedef WhereOptions TableType; - WhereOptionsT() { - } -}; - -struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef WhereOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct WhereOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - WhereOptionsBuilder &operator=(const WhereOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateWhereOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - WhereOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReverseSequenceOptionsT : public flatbuffers::NativeTable { - typedef ReverseSequenceOptions TableType; - int32_t seq_dim; - int32_t batch_dim; - ReverseSequenceOptionsT() - : seq_dim(0), - batch_dim(0) { - } -}; - -struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReverseSequenceOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SEQ_DIM = 4, - VT_BATCH_DIM = 6 - }; - int32_t seq_dim() const { - return GetField(VT_SEQ_DIM, 0); - } - int32_t batch_dim() const { - return GetField(VT_BATCH_DIM, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SEQ_DIM) && - VerifyField(verifier, VT_BATCH_DIM) && - verifier.EndTable(); - } - ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReverseSequenceOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_seq_dim(int32_t seq_dim) { - fbb_.AddElement(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); - } - void add_batch_dim(int32_t batch_dim) { - fbb_.AddElement(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); - } - explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReverseSequenceOptionsBuilder &operator=(const ReverseSequenceOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReverseSequenceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t seq_dim = 0, - int32_t batch_dim = 0) { - ReverseSequenceOptionsBuilder builder_(_fbb); - builder_.add_batch_dim(batch_dim); - builder_.add_seq_dim(seq_dim); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MatrixDiagOptionsT : public flatbuffers::NativeTable { - typedef MatrixDiagOptions TableType; - MatrixDiagOptionsT() { - } -}; - -struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MatrixDiagOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MatrixDiagOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MatrixDiagOptionsBuilder &operator=(const MatrixDiagOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMatrixDiagOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MatrixDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct QuantizeOptionsT : public flatbuffers::NativeTable { - typedef QuantizeOptions TableType; - QuantizeOptionsT() { - } -}; - -struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef QuantizeOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct QuantizeOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - QuantizeOptionsBuilder &operator=(const QuantizeOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateQuantizeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - QuantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable { - typedef MatrixSetDiagOptions TableType; - MatrixSetDiagOptionsT() { - } -}; - -struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MatrixSetDiagOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MatrixSetDiagOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MatrixSetDiagOptionsBuilder &operator=(const MatrixSetDiagOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMatrixSetDiagOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MatrixSetDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct IfOptionsT : public flatbuffers::NativeTable { - typedef IfOptions TableType; - int32_t then_subgraph_index; - int32_t else_subgraph_index; - IfOptionsT() - : then_subgraph_index(0), - else_subgraph_index(0) { - } -}; - -struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef IfOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_THEN_SUBGRAPH_INDEX = 4, - VT_ELSE_SUBGRAPH_INDEX = 6 - }; - int32_t then_subgraph_index() const { - return GetField(VT_THEN_SUBGRAPH_INDEX, 0); - } - int32_t else_subgraph_index() const { - return GetField(VT_ELSE_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_THEN_SUBGRAPH_INDEX) && - VerifyField(verifier, VT_ELSE_SUBGRAPH_INDEX) && - verifier.EndTable(); - } - IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct IfOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_then_subgraph_index(int32_t then_subgraph_index) { - fbb_.AddElement(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); - } - void add_else_subgraph_index(int32_t else_subgraph_index) { - fbb_.AddElement(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); - } - explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - IfOptionsBuilder &operator=(const IfOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateIfOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t then_subgraph_index = 0, - int32_t else_subgraph_index = 0) { - IfOptionsBuilder builder_(_fbb); - builder_.add_else_subgraph_index(else_subgraph_index); - builder_.add_then_subgraph_index(then_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct WhileOptionsT : public flatbuffers::NativeTable { - typedef WhileOptions TableType; - int32_t cond_subgraph_index; - int32_t body_subgraph_index; - WhileOptionsT() - : cond_subgraph_index(0), - body_subgraph_index(0) { - } -}; - -struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef WhileOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COND_SUBGRAPH_INDEX = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - int32_t cond_subgraph_index() const { - return GetField(VT_COND_SUBGRAPH_INDEX, 0); - } - int32_t body_subgraph_index() const { - return GetField(VT_BODY_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COND_SUBGRAPH_INDEX) && - VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX) && - verifier.EndTable(); - } - WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct WhileOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_subgraph_index(int32_t cond_subgraph_index) { - fbb_.AddElement(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); - } - void add_body_subgraph_index(int32_t body_subgraph_index) { - fbb_.AddElement(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - WhileOptionsBuilder &operator=(const WhileOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateWhileOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t cond_subgraph_index = 0, - int32_t body_subgraph_index = 0) { - WhileOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_cond_subgraph_index(cond_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable { - typedef NonMaxSuppressionV4Options TableType; - NonMaxSuppressionV4OptionsT() { - } -}; - -struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NonMaxSuppressionV4OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NonMaxSuppressionV4OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV4OptionsBuilder &operator=(const NonMaxSuppressionV4OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNonMaxSuppressionV4Options( - flatbuffers::FlatBufferBuilder &_fbb) { - NonMaxSuppressionV4OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable { - typedef NonMaxSuppressionV5Options TableType; - NonMaxSuppressionV5OptionsT() { - } -}; - -struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NonMaxSuppressionV5OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NonMaxSuppressionV5OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV5OptionsBuilder &operator=(const NonMaxSuppressionV5OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNonMaxSuppressionV5Options( - flatbuffers::FlatBufferBuilder &_fbb) { - NonMaxSuppressionV5OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ScatterNdOptionsT : public flatbuffers::NativeTable { - typedef ScatterNdOptions TableType; - ScatterNdOptionsT() { - } -}; - -struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ScatterNdOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ScatterNdOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ScatterNdOptionsBuilder &operator=(const ScatterNdOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateScatterNdOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ScatterNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SelectV2OptionsT : public flatbuffers::NativeTable { - typedef SelectV2Options TableType; - SelectV2OptionsT() { - } -}; - -struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SelectV2OptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SelectV2OptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SelectV2OptionsBuilder &operator=(const SelectV2OptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSelectV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - SelectV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DensifyOptionsT : public flatbuffers::NativeTable { - typedef DensifyOptions TableType; - DensifyOptionsT() { - } -}; - -struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DensifyOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DensifyOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - DensifyOptionsBuilder &operator=(const DensifyOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDensifyOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DensifyOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SegmentSumOptionsT : public flatbuffers::NativeTable { - typedef SegmentSumOptions TableType; - SegmentSumOptionsT() { - } -}; - -struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SegmentSumOptionsT NativeTableType; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SegmentSumOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SegmentSumOptionsBuilder &operator=(const SegmentSumOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSegmentSumOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SegmentSumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BatchMatMulOptionsT : public flatbuffers::NativeTable { - typedef BatchMatMulOptions TableType; - bool adj_x; - bool adj_y; - BatchMatMulOptionsT() - : adj_x(false), - adj_y(false) { - } -}; - -struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BatchMatMulOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ADJ_X = 4, - VT_ADJ_Y = 6 - }; - bool adj_x() const { - return GetField(VT_ADJ_X, 0) != 0; - } - bool adj_y() const { - return GetField(VT_ADJ_Y, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ADJ_X) && - VerifyField(verifier, VT_ADJ_Y) && - verifier.EndTable(); - } - BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BatchMatMulOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_adj_x(bool adj_x) { - fbb_.AddElement(BatchMatMulOptions::VT_ADJ_X, static_cast(adj_x), 0); - } - void add_adj_y(bool adj_y) { - fbb_.AddElement(BatchMatMulOptions::VT_ADJ_Y, static_cast(adj_y), 0); - } - explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - BatchMatMulOptionsBuilder &operator=(const BatchMatMulOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBatchMatMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool adj_x = false, - bool adj_y = false) { - BatchMatMulOptionsBuilder builder_(_fbb); - builder_.add_adj_y(adj_y); - builder_.add_adj_x(adj_x); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OperatorCodeT : public flatbuffers::NativeTable { - typedef OperatorCode TableType; - tflite::BuiltinOperator builtin_code; - std::string custom_code; - int32_t version; - OperatorCodeT() - : builtin_code(tflite::BuiltinOperator_ADD), - version(1) { - } -}; - -struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OperatorCodeT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BUILTIN_CODE = 4, - VT_CUSTOM_CODE = 6, - VT_VERSION = 8 - }; - tflite::BuiltinOperator builtin_code() const { - return static_cast(GetField(VT_BUILTIN_CODE, 0)); - } - const flatbuffers::String *custom_code() const { - return GetPointer(VT_CUSTOM_CODE); - } - int32_t version() const { - return GetField(VT_VERSION, 1); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BUILTIN_CODE) && - VerifyOffset(verifier, VT_CUSTOM_CODE) && - verifier.VerifyString(custom_code()) && - VerifyField(verifier, VT_VERSION) && - verifier.EndTable(); - } - OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct OperatorCodeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_builtin_code(tflite::BuiltinOperator builtin_code) { - fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, static_cast(builtin_code), 0); - } - void add_custom_code(flatbuffers::Offset custom_code) { - fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); - } - void add_version(int32_t version) { - fbb_.AddElement(OperatorCode::VT_VERSION, version, 1); - } - explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - OperatorCodeBuilder &operator=(const OperatorCodeBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOperatorCode( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD, - flatbuffers::Offset custom_code = 0, - int32_t version = 1) { - OperatorCodeBuilder builder_(_fbb); - builder_.add_version(version); - builder_.add_custom_code(custom_code); - builder_.add_builtin_code(builtin_code); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateOperatorCodeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD, - const char *custom_code = nullptr, - int32_t version = 1) { - auto custom_code__ = custom_code ? _fbb.CreateString(custom_code) : 0; - return tflite::CreateOperatorCode( - _fbb, - builtin_code, - custom_code__, - version); -} - -flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OperatorT : public flatbuffers::NativeTable { - typedef Operator TableType; - uint32_t opcode_index; - std::vector inputs; - std::vector outputs; - tflite::BuiltinOptionsUnion builtin_options; - std::vector custom_options; - tflite::CustomOptionsFormat custom_options_format; - std::vector mutating_variable_inputs; - std::vector intermediates; - OperatorT() - : opcode_index(0), - custom_options_format(tflite::CustomOptionsFormat_FLEXBUFFERS) { - } -}; - -struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OperatorT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OPCODE_INDEX = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_BUILTIN_OPTIONS_TYPE = 10, - VT_BUILTIN_OPTIONS = 12, - VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16, - VT_MUTATING_VARIABLE_INPUTS = 18, - VT_INTERMEDIATES = 20 - }; - uint32_t opcode_index() const { - return GetField(VT_OPCODE_INDEX, 0); - } - const flatbuffers::Vector *inputs() const { - return GetPointer *>(VT_INPUTS); - } - const flatbuffers::Vector *outputs() const { - return GetPointer *>(VT_OUTPUTS); - } - tflite::BuiltinOptions builtin_options_type() const { - return static_cast(GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); - } - const void *builtin_options() const { - return GetPointer(VT_BUILTIN_OPTIONS); - } - template const T *builtin_options_as() const; - const tflite::Conv2DOptions *builtin_options_as_Conv2DOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_Conv2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_DepthwiseConv2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ConcatEmbeddingsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LSHProjectionOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::Pool2DOptions *builtin_options_as_Pool2DOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_Pool2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SVDFOptions *builtin_options_as_SVDFOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SVDFOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::RNNOptions *builtin_options_as_RNNOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_RNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_FullyConnectedOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SoftmaxOptions *builtin_options_as_SoftmaxOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SoftmaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ConcatenationOptions *builtin_options_as_ConcatenationOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ConcatenationOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::AddOptions *builtin_options_as_AddOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_AddOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::L2NormOptions *builtin_options_as_L2NormOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_L2NormOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LocalResponseNormalizationOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LSTMOptions *builtin_options_as_LSTMOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ResizeBilinearOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::CallOptions *builtin_options_as_CallOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_CallOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ReshapeOptions *builtin_options_as_ReshapeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ReshapeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SkipGramOptions *builtin_options_as_SkipGramOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SkipGramOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SpaceToDepthOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::MulOptions *builtin_options_as_MulOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_MulOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::PadOptions *builtin_options_as_PadOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::GatherOptions *builtin_options_as_GatherOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_BatchToSpaceNDOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SpaceToBatchNDOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::TransposeOptions *builtin_options_as_TransposeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ReducerOptions *builtin_options_as_ReducerOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SubOptions *builtin_options_as_SubOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::DivOptions *builtin_options_as_DivOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_DivOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SqueezeOptions *builtin_options_as_SqueezeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SqueezeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SequenceRNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::StridedSliceOptions *builtin_options_as_StridedSliceOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_StridedSliceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ExpOptions *builtin_options_as_ExpOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ExpOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::TopKV2Options *builtin_options_as_TopKV2Options() const { - return builtin_options_type() == tflite::BuiltinOptions_TopKV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::SplitOptions *builtin_options_as_SplitOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SplitOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::CastOptions *builtin_options_as_CastOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::DequantizeOptions *builtin_options_as_DequantizeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_MaximumMinimumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ArgMaxOptions *builtin_options_as_ArgMaxOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LessOptions *builtin_options_as_LessOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::NegOptions *builtin_options_as_NegOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::PadV2Options *builtin_options_as_PadV2Options() const { - return builtin_options_type() == tflite::BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::GreaterOptions *builtin_options_as_GreaterOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LessEqualOptions *builtin_options_as_LessEqualOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SelectOptions *builtin_options_as_SelectOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SliceOptions *builtin_options_as_SliceOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SliceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::TransposeConvOptions *builtin_options_as_TransposeConvOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_TransposeConvOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SparseToDenseOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::TileOptions *builtin_options_as_TileOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_TileOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ExpandDimsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::EqualOptions *builtin_options_as_EqualOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_EqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::NotEqualOptions *builtin_options_as_NotEqualOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ShapeOptions *builtin_options_as_ShapeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::PowOptions *builtin_options_as_PowOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ArgMinOptions *builtin_options_as_ArgMinOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ArgMinOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::FakeQuantOptions *builtin_options_as_FakeQuantOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_FakeQuantOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::PackOptions *builtin_options_as_PackOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::OneHotOptions *builtin_options_as_OneHotOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LogicalAndOptions *builtin_options_as_LogicalAndOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LogicalAndOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LogicalNotOptions *builtin_options_as_LogicalNotOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LogicalNotOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::UnpackOptions *builtin_options_as_UnpackOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_UnpackOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::FloorDivOptions *builtin_options_as_FloorDivOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_FloorDivOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SquareOptions *builtin_options_as_SquareOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SquareOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ZerosLikeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::FillOptions *builtin_options_as_FillOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_FillOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::BidirectionalSequenceLSTMOptions *builtin_options_as_BidirectionalSequenceLSTMOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceRNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::UnidirectionalSequenceLSTMOptions *builtin_options_as_UnidirectionalSequenceLSTMOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_UnidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::FloorModOptions *builtin_options_as_FloorModOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_FloorModOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::RangeOptions *builtin_options_as_RangeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_RangeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ResizeNearestNeighborOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::LeakyReluOptions *builtin_options_as_LeakyReluOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_LeakyReluOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SquaredDifferenceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::MirrorPadOptions *builtin_options_as_MirrorPadOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_MirrorPadOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::AbsOptions *builtin_options_as_AbsOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_AbsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SplitVOptions *builtin_options_as_SplitVOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SplitVOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::UniqueOptions *builtin_options_as_UniqueOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_UniqueOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ReverseV2Options *builtin_options_as_ReverseV2Options() const { - return builtin_options_type() == tflite::BuiltinOptions_ReverseV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::AddNOptions *builtin_options_as_AddNOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_AddNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::GatherNdOptions *builtin_options_as_GatherNdOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_GatherNdOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::CosOptions *builtin_options_as_CosOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_CosOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::WhereOptions *builtin_options_as_WhereOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_WhereOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::RankOptions *builtin_options_as_RankOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_RankOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ReverseSequenceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_MatrixDiagOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::QuantizeOptions *builtin_options_as_QuantizeOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_QuantizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_MatrixSetDiagOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::HardSwishOptions *builtin_options_as_HardSwishOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_HardSwishOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::IfOptions *builtin_options_as_IfOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_IfOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::WhileOptions *builtin_options_as_WhileOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_WhileOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_DepthToSpaceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const { - return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV4Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const { - return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV5Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::ScatterNdOptions *builtin_options_as_ScatterNdOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_ScatterNdOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SelectV2Options *builtin_options_as_SelectV2Options() const { - return builtin_options_type() == tflite::BuiltinOptions_SelectV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite::DensifyOptions *builtin_options_as_DensifyOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_DensifyOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::SegmentSumOptions *builtin_options_as_SegmentSumOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_SegmentSumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_BatchMatMulOptions ? static_cast(builtin_options()) : nullptr; - } - const flatbuffers::Vector *custom_options() const { - return GetPointer *>(VT_CUSTOM_OPTIONS); - } - tflite::CustomOptionsFormat custom_options_format() const { - return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); - } - const flatbuffers::Vector *mutating_variable_inputs() const { - return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); - } - const flatbuffers::Vector *intermediates() const { - return GetPointer *>(VT_INTERMEDIATES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OPCODE_INDEX) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - VerifyField(verifier, VT_BUILTIN_OPTIONS_TYPE) && - VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && - VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && - VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && - verifier.VerifyVector(custom_options()) && - VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT) && - VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && - verifier.VerifyVector(mutating_variable_inputs()) && - VerifyOffset(verifier, VT_INTERMEDIATES) && - verifier.VerifyVector(intermediates()) && - verifier.EndTable(); - } - OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite::Conv2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_Conv2DOptions(); -} - -template<> inline const tflite::DepthwiseConv2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_DepthwiseConv2DOptions(); -} - -template<> inline const tflite::ConcatEmbeddingsOptions *Operator::builtin_options_as() const { - return builtin_options_as_ConcatEmbeddingsOptions(); -} - -template<> inline const tflite::LSHProjectionOptions *Operator::builtin_options_as() const { - return builtin_options_as_LSHProjectionOptions(); -} - -template<> inline const tflite::Pool2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_Pool2DOptions(); -} - -template<> inline const tflite::SVDFOptions *Operator::builtin_options_as() const { - return builtin_options_as_SVDFOptions(); -} - -template<> inline const tflite::RNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_RNNOptions(); -} - -template<> inline const tflite::FullyConnectedOptions *Operator::builtin_options_as() const { - return builtin_options_as_FullyConnectedOptions(); -} - -template<> inline const tflite::SoftmaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_SoftmaxOptions(); -} - -template<> inline const tflite::ConcatenationOptions *Operator::builtin_options_as() const { - return builtin_options_as_ConcatenationOptions(); -} - -template<> inline const tflite::AddOptions *Operator::builtin_options_as() const { - return builtin_options_as_AddOptions(); -} - -template<> inline const tflite::L2NormOptions *Operator::builtin_options_as() const { - return builtin_options_as_L2NormOptions(); -} - -template<> inline const tflite::LocalResponseNormalizationOptions *Operator::builtin_options_as() const { - return builtin_options_as_LocalResponseNormalizationOptions(); -} - -template<> inline const tflite::LSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_LSTMOptions(); -} - -template<> inline const tflite::ResizeBilinearOptions *Operator::builtin_options_as() const { - return builtin_options_as_ResizeBilinearOptions(); -} - -template<> inline const tflite::CallOptions *Operator::builtin_options_as() const { - return builtin_options_as_CallOptions(); -} - -template<> inline const tflite::ReshapeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReshapeOptions(); -} - -template<> inline const tflite::SkipGramOptions *Operator::builtin_options_as() const { - return builtin_options_as_SkipGramOptions(); -} - -template<> inline const tflite::SpaceToDepthOptions *Operator::builtin_options_as() const { - return builtin_options_as_SpaceToDepthOptions(); -} - -template<> inline const tflite::EmbeddingLookupSparseOptions *Operator::builtin_options_as() const { - return builtin_options_as_EmbeddingLookupSparseOptions(); -} - -template<> inline const tflite::MulOptions *Operator::builtin_options_as() const { - return builtin_options_as_MulOptions(); -} - -template<> inline const tflite::PadOptions *Operator::builtin_options_as() const { - return builtin_options_as_PadOptions(); -} - -template<> inline const tflite::GatherOptions *Operator::builtin_options_as() const { - return builtin_options_as_GatherOptions(); -} - -template<> inline const tflite::BatchToSpaceNDOptions *Operator::builtin_options_as() const { - return builtin_options_as_BatchToSpaceNDOptions(); -} - -template<> inline const tflite::SpaceToBatchNDOptions *Operator::builtin_options_as() const { - return builtin_options_as_SpaceToBatchNDOptions(); -} - -template<> inline const tflite::TransposeOptions *Operator::builtin_options_as() const { - return builtin_options_as_TransposeOptions(); -} - -template<> inline const tflite::ReducerOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReducerOptions(); -} - -template<> inline const tflite::SubOptions *Operator::builtin_options_as() const { - return builtin_options_as_SubOptions(); -} - -template<> inline const tflite::DivOptions *Operator::builtin_options_as() const { - return builtin_options_as_DivOptions(); -} - -template<> inline const tflite::SqueezeOptions *Operator::builtin_options_as() const { - return builtin_options_as_SqueezeOptions(); -} - -template<> inline const tflite::SequenceRNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_SequenceRNNOptions(); -} - -template<> inline const tflite::StridedSliceOptions *Operator::builtin_options_as() const { - return builtin_options_as_StridedSliceOptions(); -} - -template<> inline const tflite::ExpOptions *Operator::builtin_options_as() const { - return builtin_options_as_ExpOptions(); -} - -template<> inline const tflite::TopKV2Options *Operator::builtin_options_as() const { - return builtin_options_as_TopKV2Options(); -} - -template<> inline const tflite::SplitOptions *Operator::builtin_options_as() const { - return builtin_options_as_SplitOptions(); -} - -template<> inline const tflite::LogSoftmaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogSoftmaxOptions(); -} - -template<> inline const tflite::CastOptions *Operator::builtin_options_as() const { - return builtin_options_as_CastOptions(); -} - -template<> inline const tflite::DequantizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_DequantizeOptions(); -} - -template<> inline const tflite::MaximumMinimumOptions *Operator::builtin_options_as() const { - return builtin_options_as_MaximumMinimumOptions(); -} - -template<> inline const tflite::ArgMaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_ArgMaxOptions(); -} - -template<> inline const tflite::LessOptions *Operator::builtin_options_as() const { - return builtin_options_as_LessOptions(); -} - -template<> inline const tflite::NegOptions *Operator::builtin_options_as() const { - return builtin_options_as_NegOptions(); -} - -template<> inline const tflite::PadV2Options *Operator::builtin_options_as() const { - return builtin_options_as_PadV2Options(); -} - -template<> inline const tflite::GreaterOptions *Operator::builtin_options_as() const { - return builtin_options_as_GreaterOptions(); -} - -template<> inline const tflite::GreaterEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_GreaterEqualOptions(); -} - -template<> inline const tflite::LessEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_LessEqualOptions(); -} - -template<> inline const tflite::SelectOptions *Operator::builtin_options_as() const { - return builtin_options_as_SelectOptions(); -} - -template<> inline const tflite::SliceOptions *Operator::builtin_options_as() const { - return builtin_options_as_SliceOptions(); -} - -template<> inline const tflite::TransposeConvOptions *Operator::builtin_options_as() const { - return builtin_options_as_TransposeConvOptions(); -} - -template<> inline const tflite::SparseToDenseOptions *Operator::builtin_options_as() const { - return builtin_options_as_SparseToDenseOptions(); -} - -template<> inline const tflite::TileOptions *Operator::builtin_options_as() const { - return builtin_options_as_TileOptions(); -} - -template<> inline const tflite::ExpandDimsOptions *Operator::builtin_options_as() const { - return builtin_options_as_ExpandDimsOptions(); -} - -template<> inline const tflite::EqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_EqualOptions(); -} - -template<> inline const tflite::NotEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_NotEqualOptions(); -} - -template<> inline const tflite::ShapeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ShapeOptions(); -} - -template<> inline const tflite::PowOptions *Operator::builtin_options_as() const { - return builtin_options_as_PowOptions(); -} - -template<> inline const tflite::ArgMinOptions *Operator::builtin_options_as() const { - return builtin_options_as_ArgMinOptions(); -} - -template<> inline const tflite::FakeQuantOptions *Operator::builtin_options_as() const { - return builtin_options_as_FakeQuantOptions(); -} - -template<> inline const tflite::PackOptions *Operator::builtin_options_as() const { - return builtin_options_as_PackOptions(); -} - -template<> inline const tflite::LogicalOrOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalOrOptions(); -} - -template<> inline const tflite::OneHotOptions *Operator::builtin_options_as() const { - return builtin_options_as_OneHotOptions(); -} - -template<> inline const tflite::LogicalAndOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalAndOptions(); -} - -template<> inline const tflite::LogicalNotOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalNotOptions(); -} - -template<> inline const tflite::UnpackOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnpackOptions(); -} - -template<> inline const tflite::FloorDivOptions *Operator::builtin_options_as() const { - return builtin_options_as_FloorDivOptions(); -} - -template<> inline const tflite::SquareOptions *Operator::builtin_options_as() const { - return builtin_options_as_SquareOptions(); -} - -template<> inline const tflite::ZerosLikeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ZerosLikeOptions(); -} - -template<> inline const tflite::FillOptions *Operator::builtin_options_as() const { - return builtin_options_as_FillOptions(); -} - -template<> inline const tflite::BidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_BidirectionalSequenceLSTMOptions(); -} - -template<> inline const tflite::BidirectionalSequenceRNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_BidirectionalSequenceRNNOptions(); -} - -template<> inline const tflite::UnidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnidirectionalSequenceLSTMOptions(); -} - -template<> inline const tflite::FloorModOptions *Operator::builtin_options_as() const { - return builtin_options_as_FloorModOptions(); -} - -template<> inline const tflite::RangeOptions *Operator::builtin_options_as() const { - return builtin_options_as_RangeOptions(); -} - -template<> inline const tflite::ResizeNearestNeighborOptions *Operator::builtin_options_as() const { - return builtin_options_as_ResizeNearestNeighborOptions(); -} - -template<> inline const tflite::LeakyReluOptions *Operator::builtin_options_as() const { - return builtin_options_as_LeakyReluOptions(); -} - -template<> inline const tflite::SquaredDifferenceOptions *Operator::builtin_options_as() const { - return builtin_options_as_SquaredDifferenceOptions(); -} - -template<> inline const tflite::MirrorPadOptions *Operator::builtin_options_as() const { - return builtin_options_as_MirrorPadOptions(); -} - -template<> inline const tflite::AbsOptions *Operator::builtin_options_as() const { - return builtin_options_as_AbsOptions(); -} - -template<> inline const tflite::SplitVOptions *Operator::builtin_options_as() const { - return builtin_options_as_SplitVOptions(); -} - -template<> inline const tflite::UniqueOptions *Operator::builtin_options_as() const { - return builtin_options_as_UniqueOptions(); -} - -template<> inline const tflite::ReverseV2Options *Operator::builtin_options_as() const { - return builtin_options_as_ReverseV2Options(); -} - -template<> inline const tflite::AddNOptions *Operator::builtin_options_as() const { - return builtin_options_as_AddNOptions(); -} - -template<> inline const tflite::GatherNdOptions *Operator::builtin_options_as() const { - return builtin_options_as_GatherNdOptions(); -} - -template<> inline const tflite::CosOptions *Operator::builtin_options_as() const { - return builtin_options_as_CosOptions(); -} - -template<> inline const tflite::WhereOptions *Operator::builtin_options_as() const { - return builtin_options_as_WhereOptions(); -} - -template<> inline const tflite::RankOptions *Operator::builtin_options_as() const { - return builtin_options_as_RankOptions(); -} - -template<> inline const tflite::ReverseSequenceOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReverseSequenceOptions(); -} - -template<> inline const tflite::MatrixDiagOptions *Operator::builtin_options_as() const { - return builtin_options_as_MatrixDiagOptions(); -} - -template<> inline const tflite::QuantizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_QuantizeOptions(); -} - -template<> inline const tflite::MatrixSetDiagOptions *Operator::builtin_options_as() const { - return builtin_options_as_MatrixSetDiagOptions(); -} - -template<> inline const tflite::HardSwishOptions *Operator::builtin_options_as() const { - return builtin_options_as_HardSwishOptions(); -} - -template<> inline const tflite::IfOptions *Operator::builtin_options_as() const { - return builtin_options_as_IfOptions(); -} - -template<> inline const tflite::WhileOptions *Operator::builtin_options_as() const { - return builtin_options_as_WhileOptions(); -} - -template<> inline const tflite::DepthToSpaceOptions *Operator::builtin_options_as() const { - return builtin_options_as_DepthToSpaceOptions(); -} - -template<> inline const tflite::NonMaxSuppressionV4Options *Operator::builtin_options_as() const { - return builtin_options_as_NonMaxSuppressionV4Options(); -} - -template<> inline const tflite::NonMaxSuppressionV5Options *Operator::builtin_options_as() const { - return builtin_options_as_NonMaxSuppressionV5Options(); -} - -template<> inline const tflite::ScatterNdOptions *Operator::builtin_options_as() const { - return builtin_options_as_ScatterNdOptions(); -} - -template<> inline const tflite::SelectV2Options *Operator::builtin_options_as() const { - return builtin_options_as_SelectV2Options(); -} - -template<> inline const tflite::DensifyOptions *Operator::builtin_options_as() const { - return builtin_options_as_DensifyOptions(); -} - -template<> inline const tflite::SegmentSumOptions *Operator::builtin_options_as() const { - return builtin_options_as_SegmentSumOptions(); -} - -template<> inline const tflite::BatchMatMulOptions *Operator::builtin_options_as() const { - return builtin_options_as_BatchMatMulOptions(); -} - -struct OperatorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_opcode_index(uint32_t opcode_index) { - fbb_.AddElement(Operator::VT_OPCODE_INDEX, opcode_index, 0); - } - void add_inputs(flatbuffers::Offset> inputs) { - fbb_.AddOffset(Operator::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset> outputs) { - fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); - } - void add_builtin_options_type(tflite::BuiltinOptions builtin_options_type) { - fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast(builtin_options_type), 0); - } - void add_builtin_options(flatbuffers::Offset builtin_options) { - fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); - } - void add_custom_options(flatbuffers::Offset> custom_options) { - fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); - } - void add_custom_options_format(tflite::CustomOptionsFormat custom_options_format) { - fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); - } - void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { - fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); - } - void add_intermediates(flatbuffers::Offset> intermediates) { - fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); - } - explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - OperatorBuilder &operator=(const OperatorBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOperator( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t opcode_index = 0, - flatbuffers::Offset> inputs = 0, - flatbuffers::Offset> outputs = 0, - tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE, - flatbuffers::Offset builtin_options = 0, - flatbuffers::Offset> custom_options = 0, - tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS, - flatbuffers::Offset> mutating_variable_inputs = 0, - flatbuffers::Offset> intermediates = 0) { - OperatorBuilder builder_(_fbb); - builder_.add_intermediates(intermediates); - builder_.add_mutating_variable_inputs(mutating_variable_inputs); - builder_.add_custom_options(custom_options); - builder_.add_builtin_options(builtin_options); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_opcode_index(opcode_index); - builder_.add_custom_options_format(custom_options_format); - builder_.add_builtin_options_type(builtin_options_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateOperatorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t opcode_index = 0, - const std::vector *inputs = nullptr, - const std::vector *outputs = nullptr, - tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE, - flatbuffers::Offset builtin_options = 0, - const std::vector *custom_options = nullptr, - tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS, - const std::vector *mutating_variable_inputs = nullptr, - const std::vector *intermediates = nullptr) { - auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; - auto custom_options__ = custom_options ? _fbb.CreateVector(*custom_options) : 0; - auto mutating_variable_inputs__ = mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0; - auto intermediates__ = intermediates ? _fbb.CreateVector(*intermediates) : 0; - return tflite::CreateOperator( - _fbb, - opcode_index, - inputs__, - outputs__, - builtin_options_type, - builtin_options, - custom_options__, - custom_options_format, - mutating_variable_inputs__, - intermediates__); -} - -flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SubGraphT : public flatbuffers::NativeTable { - typedef SubGraph TableType; - std::vector> tensors; - std::vector inputs; - std::vector outputs; - std::vector> operators; - std::string name; - SubGraphT() { - } -}; - -struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SubGraphT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TENSORS = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_OPERATORS = 10, - VT_NAME = 12 - }; - const flatbuffers::Vector> *tensors() const { - return GetPointer> *>(VT_TENSORS); - } - const flatbuffers::Vector *inputs() const { - return GetPointer *>(VT_INPUTS); - } - const flatbuffers::Vector *outputs() const { - return GetPointer *>(VT_OUTPUTS); - } - const flatbuffers::Vector> *operators() const { - return GetPointer> *>(VT_OPERATORS); - } - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_TENSORS) && - verifier.VerifyVector(tensors()) && - verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - VerifyOffset(verifier, VT_OPERATORS) && - verifier.VerifyVector(operators()) && - verifier.VerifyVectorOfTables(operators()) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - verifier.EndTable(); - } - SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SubGraphBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_tensors(flatbuffers::Offset>> tensors) { - fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); - } - void add_inputs(flatbuffers::Offset> inputs) { - fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset> outputs) { - fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); - } - void add_operators(flatbuffers::Offset>> operators) { - fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); - } - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(SubGraph::VT_NAME, name); - } - explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SubGraphBuilder &operator=(const SubGraphBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset>> tensors = 0, - flatbuffers::Offset> inputs = 0, - flatbuffers::Offset> outputs = 0, - flatbuffers::Offset>> operators = 0, - flatbuffers::Offset name = 0) { - SubGraphBuilder builder_(_fbb); - builder_.add_name(name); - builder_.add_operators(operators); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_tensors(tensors); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSubGraphDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector> *tensors = nullptr, - const std::vector *inputs = nullptr, - const std::vector *outputs = nullptr, - const std::vector> *operators = nullptr, - const char *name = nullptr) { - auto tensors__ = tensors ? _fbb.CreateVector>(*tensors) : 0; - auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; - auto operators__ = operators ? _fbb.CreateVector>(*operators) : 0; - auto name__ = name ? _fbb.CreateString(name) : 0; - return tflite::CreateSubGraph( - _fbb, - tensors__, - inputs__, - outputs__, - operators__, - name__); -} - -flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BufferT : public flatbuffers::NativeTable { - typedef Buffer TableType; - std::vector data; - BufferT() { - } -}; - -struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BufferT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DATA = 4 - }; - const flatbuffers::Vector *data() const { - return GetPointer *>(VT_DATA); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_DATA) && - verifier.VerifyVector(data()) && - verifier.EndTable(); - } - BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BufferBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_data(flatbuffers::Offset> data) { - fbb_.AddOffset(Buffer::VT_DATA, data); - } - explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - BufferBuilder &operator=(const BufferBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBuffer( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> data = 0) { - BufferBuilder builder_(_fbb); - builder_.add_data(data); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateBufferDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *data = nullptr) { - if (data) { _fbb.ForceVectorAlignment(data->size(), sizeof(uint8_t), 16); } - auto data__ = data ? _fbb.CreateVector(*data) : 0; - return tflite::CreateBuffer( - _fbb, - data__); -} - -flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MetadataT : public flatbuffers::NativeTable { - typedef Metadata TableType; - std::string name; - uint32_t buffer; - MetadataT() - : buffer(0) { - } -}; - -struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MetadataT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_BUFFER = 6 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - uint32_t buffer() const { - return GetField(VT_BUFFER, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyField(verifier, VT_BUFFER) && - verifier.EndTable(); - } - MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MetadataBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Metadata::VT_NAME, name); - } - void add_buffer(uint32_t buffer) { - fbb_.AddElement(Metadata::VT_BUFFER, buffer, 0); - } - explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MetadataBuilder &operator=(const MetadataBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMetadata( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - uint32_t buffer = 0) { - MetadataBuilder builder_(_fbb); - builder_.add_buffer(buffer); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateMetadataDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - uint32_t buffer = 0) { - auto name__ = name ? _fbb.CreateString(name) : 0; - return tflite::CreateMetadata( - _fbb, - name__, - buffer); -} - -flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ModelT : public flatbuffers::NativeTable { - typedef Model TableType; - uint32_t version; - std::vector> operator_codes; - std::vector> subgraphs; - std::string description; - std::vector> buffers; - std::vector metadata_buffer; - std::vector> metadata; - ModelT() - : version(0) { - } -}; - -struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ModelT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VERSION = 4, - VT_OPERATOR_CODES = 6, - VT_SUBGRAPHS = 8, - VT_DESCRIPTION = 10, - VT_BUFFERS = 12, - VT_METADATA_BUFFER = 14, - VT_METADATA = 16 - }; - uint32_t version() const { - return GetField(VT_VERSION, 0); - } - const flatbuffers::Vector> *operator_codes() const { - return GetPointer> *>(VT_OPERATOR_CODES); - } - const flatbuffers::Vector> *subgraphs() const { - return GetPointer> *>(VT_SUBGRAPHS); - } - const flatbuffers::String *description() const { - return GetPointer(VT_DESCRIPTION); - } - const flatbuffers::Vector> *buffers() const { - return GetPointer> *>(VT_BUFFERS); - } - const flatbuffers::Vector *metadata_buffer() const { - return GetPointer *>(VT_METADATA_BUFFER); - } - const flatbuffers::Vector> *metadata() const { - return GetPointer> *>(VT_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VERSION) && - VerifyOffset(verifier, VT_OPERATOR_CODES) && - verifier.VerifyVector(operator_codes()) && - verifier.VerifyVectorOfTables(operator_codes()) && - VerifyOffset(verifier, VT_SUBGRAPHS) && - verifier.VerifyVector(subgraphs()) && - verifier.VerifyVectorOfTables(subgraphs()) && - VerifyOffset(verifier, VT_DESCRIPTION) && - verifier.VerifyString(description()) && - VerifyOffset(verifier, VT_BUFFERS) && - verifier.VerifyVector(buffers()) && - verifier.VerifyVectorOfTables(buffers()) && - VerifyOffset(verifier, VT_METADATA_BUFFER) && - verifier.VerifyVector(metadata_buffer()) && - VerifyOffset(verifier, VT_METADATA) && - verifier.VerifyVector(metadata()) && - verifier.VerifyVectorOfTables(metadata()) && - verifier.EndTable(); - } - ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ModelBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_version(uint32_t version) { - fbb_.AddElement(Model::VT_VERSION, version, 0); - } - void add_operator_codes(flatbuffers::Offset>> operator_codes) { - fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); - } - void add_subgraphs(flatbuffers::Offset>> subgraphs) { - fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); - } - void add_description(flatbuffers::Offset description) { - fbb_.AddOffset(Model::VT_DESCRIPTION, description); - } - void add_buffers(flatbuffers::Offset>> buffers) { - fbb_.AddOffset(Model::VT_BUFFERS, buffers); - } - void add_metadata_buffer(flatbuffers::Offset> metadata_buffer) { - fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); - } - void add_metadata(flatbuffers::Offset>> metadata) { - fbb_.AddOffset(Model::VT_METADATA, metadata); - } - explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ModelBuilder &operator=(const ModelBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t version = 0, - flatbuffers::Offset>> operator_codes = 0, - flatbuffers::Offset>> subgraphs = 0, - flatbuffers::Offset description = 0, - flatbuffers::Offset>> buffers = 0, - flatbuffers::Offset> metadata_buffer = 0, - flatbuffers::Offset>> metadata = 0) { - ModelBuilder builder_(_fbb); - builder_.add_metadata(metadata); - builder_.add_metadata_buffer(metadata_buffer); - builder_.add_buffers(buffers); - builder_.add_description(description); - builder_.add_subgraphs(subgraphs); - builder_.add_operator_codes(operator_codes); - builder_.add_version(version); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateModelDirect( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t version = 0, - const std::vector> *operator_codes = nullptr, - const std::vector> *subgraphs = nullptr, - const char *description = nullptr, - const std::vector> *buffers = nullptr, - const std::vector *metadata_buffer = nullptr, - const std::vector> *metadata = nullptr) { - auto operator_codes__ = operator_codes ? _fbb.CreateVector>(*operator_codes) : 0; - auto subgraphs__ = subgraphs ? _fbb.CreateVector>(*subgraphs) : 0; - auto description__ = description ? _fbb.CreateString(description) : 0; - auto buffers__ = buffers ? _fbb.CreateVector>(*buffers) : 0; - auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector(*metadata_buffer) : 0; - auto metadata__ = metadata ? _fbb.CreateVector>(*metadata) : 0; - return tflite::CreateModel( - _fbb, - version, - operator_codes__, - subgraphs__, - description__, - buffers__, - metadata_buffer__, - metadata__); -} - -flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new CustomQuantizationT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->custom[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCustomQuantization(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16); - auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0; - return tflite::CreateCustomQuantization( - _fbb, - _custom); -} - -inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new QuantizationParametersT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } } - { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } } - { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } } - { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } } - { auto _e = details_type(); _o->details.type = _e; } - { auto _e = details(); if (_e) _o->details.value = tflite::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); } - { auto _e = quantized_dimension(); _o->quantized_dimension = _e; } -} - -inline flatbuffers::Offset QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateQuantizationParameters(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0; - auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0; - auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0; - auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; - auto _details_type = _o->details.type; - auto _details = _o->details.Pack(_fbb); - auto _quantized_dimension = _o->quantized_dimension; - return tflite::CreateQuantizationParameters( - _fbb, - _min, - _max, - _scale, - _zero_point, - _details_type, - _details, - _quantized_dimension); -} - -inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new Int32VectorT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateInt32Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite::CreateInt32Vector( - _fbb, - _values); -} - -inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new Uint16VectorT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUint16Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4); - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite::CreateUint16Vector( - _fbb, - _values); -} - -inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new Uint8VectorT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUint8Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4); - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite::CreateUint8Vector( - _fbb, - _values); -} - -inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DimensionMetadataT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = format(); _o->format = _e; } - { auto _e = dense_size(); _o->dense_size = _e; } - { auto _e = array_segments_type(); _o->array_segments.type = _e; } - { auto _e = array_segments(); if (_e) _o->array_segments.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_segments_type(), _resolver); } - { auto _e = array_indices_type(); _o->array_indices.type = _e; } - { auto _e = array_indices(); if (_e) _o->array_indices.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); } -} - -inline flatbuffers::Offset DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDimensionMetadata(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _format = _o->format; - auto _dense_size = _o->dense_size; - auto _array_segments_type = _o->array_segments.type; - auto _array_segments = _o->array_segments.Pack(_fbb); - auto _array_indices_type = _o->array_indices.type; - auto _array_indices = _o->array_indices.Pack(_fbb); - return tflite::CreateDimensionMetadata( - _fbb, - _format, - _dense_size, - _array_segments_type, - _array_segments, - _array_indices_type, - _array_indices); -} - -inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SparsityParametersT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } } - { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } } - { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dim_metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } -} - -inline flatbuffers::Offset SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSparsityParameters(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0; - auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0; - auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0; - return tflite::CreateSparsityParameters( - _fbb, - _traversal_order, - _block_map, - _dim_metadata); -} - -inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new TensorT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } - { auto _e = type(); _o->type = _e; } - { auto _e = buffer(); _o->buffer = _e; } - { auto _e = name(); if (_e) _o->name = _e->str(); } - { auto _e = quantization(); if (_e) _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); } - { auto _e = is_variable(); _o->is_variable = _e; } - { auto _e = sparsity(); if (_e) _o->sparsity = std::unique_ptr(_e->UnPack(_resolver)); } - { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTensor(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; - auto _type = _o->type; - auto _buffer = _o->buffer; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; - auto _is_variable = _o->is_variable; - auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0; - auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0; - return tflite::CreateTensor( - _fbb, - _shape, - _type, - _buffer, - _name, - _quantization, - _is_variable, - _sparsity, - _shape_signature); -} - -inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new Conv2DOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } - { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } -} - -inline flatbuffers::Offset Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConv2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _fused_activation_function = _o->fused_activation_function; - auto _dilation_w_factor = _o->dilation_w_factor; - auto _dilation_h_factor = _o->dilation_h_factor; - return tflite::CreateConv2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _fused_activation_function, - _dilation_w_factor, - _dilation_h_factor); -} - -inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new Pool2DOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = filter_width(); _o->filter_width = _e; } - { auto _e = filter_height(); _o->filter_height = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePool2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _filter_width = _o->filter_width; - auto _filter_height = _o->filter_height; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreatePool2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _filter_width, - _filter_height, - _fused_activation_function); -} - -inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DepthwiseConv2DOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = depth_multiplier(); _o->depth_multiplier = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } - { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } -} - -inline flatbuffers::Offset DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _depth_multiplier = _o->depth_multiplier; - auto _fused_activation_function = _o->fused_activation_function; - auto _dilation_w_factor = _o->dilation_w_factor; - auto _dilation_h_factor = _o->dilation_h_factor; - return tflite::CreateDepthwiseConv2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _depth_multiplier, - _fused_activation_function, - _dilation_w_factor, - _dilation_h_factor); -} - -inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ConcatEmbeddingsOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_channels(); _o->num_channels = _e; } - { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } } - { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_channels = _o->num_channels; - auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0; - auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0; - return tflite::CreateConcatEmbeddingsOptions( - _fbb, - _num_channels, - _num_columns_per_channel, - _embedding_dim_per_channel); -} - -inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LSHProjectionOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = type(); _o->type = _e; } -} - -inline flatbuffers::Offset LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLSHProjectionOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _type = _o->type; - return tflite::CreateLSHProjectionOptions( - _fbb, - _type); -} - -inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SVDFOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = rank(); _o->rank = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSVDFOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _rank = _o->rank; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateSVDFOptions( - _fbb, - _rank, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new RNNOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateRNNOptions( - _fbb, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SequenceRNNOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSequenceRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _time_major = _o->time_major; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateSequenceRNNOptions( - _fbb, - _time_major, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new BidirectionalSequenceRNNOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = merge_outputs(); _o->merge_outputs = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _time_major = _o->time_major; - auto _fused_activation_function = _o->fused_activation_function; - auto _merge_outputs = _o->merge_outputs; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateBidirectionalSequenceRNNOptions( - _fbb, - _time_major, - _fused_activation_function, - _merge_outputs, - _asymmetric_quantize_inputs); -} - -inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new FullyConnectedOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = weights_format(); _o->weights_format = _e; } - { auto _e = keep_num_dims(); _o->keep_num_dims = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFullyConnectedOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _weights_format = _o->weights_format; - auto _keep_num_dims = _o->keep_num_dims; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateFullyConnectedOptions( - _fbb, - _fused_activation_function, - _weights_format, - _keep_num_dims, - _asymmetric_quantize_inputs); -} - -inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SoftmaxOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = beta(); _o->beta = _e; } -} - -inline flatbuffers::Offset SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSoftmaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _beta = _o->beta; - return tflite::CreateSoftmaxOptions( - _fbb, - _beta); -} - -inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ConcatenationOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConcatenationOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateConcatenationOptions( - _fbb, - _axis, - _fused_activation_function); -} - -inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new AddOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAddOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateAddOptions( - _fbb, - _fused_activation_function); -} - -inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MulOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMulOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateMulOptions( - _fbb, - _fused_activation_function); -} - -inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new L2NormOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateL2NormOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateL2NormOptions( - _fbb, - _fused_activation_function); -} - -inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LocalResponseNormalizationOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = radius(); _o->radius = _e; } - { auto _e = bias(); _o->bias = _e; } - { auto _e = alpha(); _o->alpha = _e; } - { auto _e = beta(); _o->beta = _e; } -} - -inline flatbuffers::Offset LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _radius = _o->radius; - auto _bias = _o->bias; - auto _alpha = _o->alpha; - auto _beta = _o->beta; - return tflite::CreateLocalResponseNormalizationOptions( - _fbb, - _radius, - _bias, - _alpha, - _beta); -} - -inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LSTMOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = kernel_type(); _o->kernel_type = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _kernel_type = _o->kernel_type; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _kernel_type, - _asymmetric_quantize_inputs); -} - -inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new UnidirectionalSequenceLSTMOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _time_major = _o->time_major; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateUnidirectionalSequenceLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _time_major, - _asymmetric_quantize_inputs); -} - -inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new BidirectionalSequenceLSTMOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = merge_outputs(); _o->merge_outputs = _e; } - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _merge_outputs = _o->merge_outputs; - auto _time_major = _o->time_major; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite::CreateBidirectionalSequenceLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _merge_outputs, - _time_major, - _asymmetric_quantize_inputs); -} - -inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ResizeBilinearOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = align_corners(); _o->align_corners = _e; } - { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } -} - -inline flatbuffers::Offset ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateResizeBilinearOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _align_corners = _o->align_corners; - auto _half_pixel_centers = _o->half_pixel_centers; - return tflite::CreateResizeBilinearOptions( - _fbb, - _align_corners, - _half_pixel_centers); -} - -inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ResizeNearestNeighborOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = align_corners(); _o->align_corners = _e; } - { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } -} - -inline flatbuffers::Offset ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _align_corners = _o->align_corners; - auto _half_pixel_centers = _o->half_pixel_centers; - return tflite::CreateResizeNearestNeighborOptions( - _fbb, - _align_corners, - _half_pixel_centers); -} - -inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new CallOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = subgraph(); _o->subgraph = _e; } -} - -inline flatbuffers::Offset CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCallOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _subgraph = _o->subgraph; - return tflite::CreateCallOptions( - _fbb, - _subgraph); -} - -inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new PadOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePadOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreatePadOptions( - _fbb); -} - -inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new PadV2OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePadV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreatePadV2Options( - _fbb); -} - -inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ReshapeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReshapeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0; - return tflite::CreateReshapeOptions( - _fbb, - _new_shape); -} - -inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SpaceToBatchNDOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSpaceToBatchNDOptions( - _fbb); -} - -inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new BatchToSpaceNDOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateBatchToSpaceNDOptions( - _fbb); -} - -inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SkipGramOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = ngram_size(); _o->ngram_size = _e; } - { auto _e = max_skip_size(); _o->max_skip_size = _e; } - { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; } -} - -inline flatbuffers::Offset SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSkipGramOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _ngram_size = _o->ngram_size; - auto _max_skip_size = _o->max_skip_size; - auto _include_all_ngrams = _o->include_all_ngrams; - return tflite::CreateSkipGramOptions( - _fbb, - _ngram_size, - _max_skip_size, - _include_all_ngrams); -} - -inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SpaceToDepthOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = block_size(); _o->block_size = _e; } -} - -inline flatbuffers::Offset SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSpaceToDepthOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _block_size = _o->block_size; - return tflite::CreateSpaceToDepthOptions( - _fbb, - _block_size); -} - -inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DepthToSpaceOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = block_size(); _o->block_size = _e; } -} - -inline flatbuffers::Offset DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDepthToSpaceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _block_size = _o->block_size; - return tflite::CreateDepthToSpaceOptions( - _fbb, - _block_size); -} - -inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SubOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSubOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateSubOptions( - _fbb, - _fused_activation_function); -} - -inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DivOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDivOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateDivOptions( - _fbb, - _fused_activation_function); -} - -inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new TopKV2OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTopKV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateTopKV2Options( - _fbb); -} - -inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new EmbeddingLookupSparseOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = combiner(); _o->combiner = _e; } -} - -inline flatbuffers::Offset EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _combiner = _o->combiner; - return tflite::CreateEmbeddingLookupSparseOptions( - _fbb, - _combiner); -} - -inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new GatherOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGatherOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - return tflite::CreateGatherOptions( - _fbb, - _axis); -} - -inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new TransposeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTransposeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateTransposeOptions( - _fbb); -} - -inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ExpOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateExpOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateExpOptions( - _fbb); -} - -inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new CosOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCosOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateCosOptions( - _fbb); -} - -inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ReducerOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = keep_dims(); _o->keep_dims = _e; } -} - -inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReducerOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _keep_dims = _o->keep_dims; - return tflite::CreateReducerOptions( - _fbb, - _keep_dims); -} - -inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SqueezeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSqueezeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; - return tflite::CreateSqueezeOptions( - _fbb, - _squeeze_dims); -} - -inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SplitOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_splits(); _o->num_splits = _e; } -} - -inline flatbuffers::Offset SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSplitOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_splits = _o->num_splits; - return tflite::CreateSplitOptions( - _fbb, - _num_splits); -} - -inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SplitVOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_splits(); _o->num_splits = _e; } -} - -inline flatbuffers::Offset SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSplitVOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_splits = _o->num_splits; - return tflite::CreateSplitVOptions( - _fbb, - _num_splits); -} - -inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new StridedSliceOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = begin_mask(); _o->begin_mask = _e; } - { auto _e = end_mask(); _o->end_mask = _e; } - { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; } - { auto _e = new_axis_mask(); _o->new_axis_mask = _e; } - { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; } -} - -inline flatbuffers::Offset StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStridedSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _begin_mask = _o->begin_mask; - auto _end_mask = _o->end_mask; - auto _ellipsis_mask = _o->ellipsis_mask; - auto _new_axis_mask = _o->new_axis_mask; - auto _shrink_axis_mask = _o->shrink_axis_mask; - return tflite::CreateStridedSliceOptions( - _fbb, - _begin_mask, - _end_mask, - _ellipsis_mask, - _new_axis_mask, - _shrink_axis_mask); -} - -inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LogSoftmaxOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogSoftmaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLogSoftmaxOptions( - _fbb); -} - -inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new CastOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = in_data_type(); _o->in_data_type = _e; } - { auto _e = out_data_type(); _o->out_data_type = _e; } -} - -inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCastOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _in_data_type = _o->in_data_type; - auto _out_data_type = _o->out_data_type; - return tflite::CreateCastOptions( - _fbb, - _in_data_type, - _out_data_type); -} - -inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DequantizeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDequantizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateDequantizeOptions( - _fbb); -} - -inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MaximumMinimumOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMaximumMinimumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateMaximumMinimumOptions( - _fbb); -} - -inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new TileOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTileOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateTileOptions( - _fbb); -} - -inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ArgMaxOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = output_type(); _o->output_type = _e; } -} - -inline flatbuffers::Offset ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateArgMaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _output_type = _o->output_type; - return tflite::CreateArgMaxOptions( - _fbb, - _output_type); -} - -inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ArgMinOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = output_type(); _o->output_type = _e; } -} - -inline flatbuffers::Offset ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateArgMinOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _output_type = _o->output_type; - return tflite::CreateArgMinOptions( - _fbb, - _output_type); -} - -inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new GreaterOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGreaterOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateGreaterOptions( - _fbb); -} - -inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new GreaterEqualOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGreaterEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateGreaterEqualOptions( - _fbb); -} - -inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LessOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLessOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLessOptions( - _fbb); -} - -inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LessEqualOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLessEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLessEqualOptions( - _fbb); -} - -inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new NegOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNegOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateNegOptions( - _fbb); -} - -inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SelectOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSelectOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSelectOptions( - _fbb); -} - -inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SliceOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSliceOptions( - _fbb); -} - -inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new TransposeConvOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } -} - -inline flatbuffers::Offset TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTransposeConvOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - return tflite::CreateTransposeConvOptions( - _fbb, - _padding, - _stride_w, - _stride_h); -} - -inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ExpandDimsOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateExpandDimsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateExpandDimsOptions( - _fbb); -} - -inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SparseToDenseOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = validate_indices(); _o->validate_indices = _e; } -} - -inline flatbuffers::Offset SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSparseToDenseOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _validate_indices = _o->validate_indices; - return tflite::CreateSparseToDenseOptions( - _fbb, - _validate_indices); -} - -inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new EqualOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateEqualOptions( - _fbb); -} - -inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new NotEqualOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNotEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateNotEqualOptions( - _fbb); -} - -inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ShapeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = out_type(); _o->out_type = _e; } -} - -inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateShapeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _out_type = _o->out_type; - return tflite::CreateShapeOptions( - _fbb, - _out_type); -} - -inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new RankOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRankOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateRankOptions( - _fbb); -} - -inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new PowOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePowOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreatePowOptions( - _fbb); -} - -inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new FakeQuantOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = min(); _o->min = _e; } - { auto _e = max(); _o->max = _e; } - { auto _e = num_bits(); _o->num_bits = _e; } - { auto _e = narrow_range(); _o->narrow_range = _e; } -} - -inline flatbuffers::Offset FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFakeQuantOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _min = _o->min; - auto _max = _o->max; - auto _num_bits = _o->num_bits; - auto _narrow_range = _o->narrow_range; - return tflite::CreateFakeQuantOptions( - _fbb, - _min, - _max, - _num_bits, - _narrow_range); -} - -inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new PackOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values_count(); _o->values_count = _e; } - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePackOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _values_count = _o->values_count; - auto _axis = _o->axis; - return tflite::CreatePackOptions( - _fbb, - _values_count, - _axis); -} - -inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LogicalOrOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalOrOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLogicalOrOptions( - _fbb); -} - -inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new OneHotOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOneHotOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - return tflite::CreateOneHotOptions( - _fbb, - _axis); -} - -inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new AbsOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAbsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateAbsOptions( - _fbb); -} - -inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new HardSwishOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHardSwishOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateHardSwishOptions( - _fbb); -} - -inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LogicalAndOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalAndOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLogicalAndOptions( - _fbb); -} - -inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LogicalNotOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalNotOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateLogicalNotOptions( - _fbb); -} - -inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new UnpackOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num(); _o->num = _e; } - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnpackOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num = _o->num; - auto _axis = _o->axis; - return tflite::CreateUnpackOptions( - _fbb, - _num, - _axis); -} - -inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new FloorDivOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFloorDivOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateFloorDivOptions( - _fbb); -} - -inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SquareOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSquareOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSquareOptions( - _fbb); -} - -inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ZerosLikeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateZerosLikeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateZerosLikeOptions( - _fbb); -} - -inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new FillOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFillOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateFillOptions( - _fbb); -} - -inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new FloorModOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFloorModOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateFloorModOptions( - _fbb); -} - -inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new RangeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRangeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateRangeOptions( - _fbb); -} - -inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new LeakyReluOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = alpha(); _o->alpha = _e; } -} - -inline flatbuffers::Offset LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLeakyReluOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _alpha = _o->alpha; - return tflite::CreateLeakyReluOptions( - _fbb, - _alpha); -} - -inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SquaredDifferenceOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSquaredDifferenceOptions( - _fbb); -} - -inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MirrorPadOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = mode(); _o->mode = _e; } -} - -inline flatbuffers::Offset MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMirrorPadOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _mode = _o->mode; - return tflite::CreateMirrorPadOptions( - _fbb, - _mode); -} - -inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new UniqueOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = idx_out_type(); _o->idx_out_type = _e; } -} - -inline flatbuffers::Offset UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUniqueOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _idx_out_type = _o->idx_out_type; - return tflite::CreateUniqueOptions( - _fbb, - _idx_out_type); -} - -inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ReverseV2OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReverseV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateReverseV2Options( - _fbb); -} - -inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new AddNOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAddNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateAddNOptions( - _fbb); -} - -inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new GatherNdOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGatherNdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateGatherNdOptions( - _fbb); -} - -inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new WhereOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateWhereOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateWhereOptions( - _fbb); -} - -inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ReverseSequenceOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = seq_dim(); _o->seq_dim = _e; } - { auto _e = batch_dim(); _o->batch_dim = _e; } -} - -inline flatbuffers::Offset ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReverseSequenceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _seq_dim = _o->seq_dim; - auto _batch_dim = _o->batch_dim; - return tflite::CreateReverseSequenceOptions( - _fbb, - _seq_dim, - _batch_dim); -} - -inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MatrixDiagOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMatrixDiagOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateMatrixDiagOptions( - _fbb); -} - -inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new QuantizeOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateQuantizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateQuantizeOptions( - _fbb); -} - -inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MatrixSetDiagOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateMatrixSetDiagOptions( - _fbb); -} - -inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new IfOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; } - { auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; } -} - -inline flatbuffers::Offset IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateIfOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _then_subgraph_index = _o->then_subgraph_index; - auto _else_subgraph_index = _o->else_subgraph_index; - return tflite::CreateIfOptions( - _fbb, - _then_subgraph_index, - _else_subgraph_index); -} - -inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new WhileOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } - { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } -} - -inline flatbuffers::Offset WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateWhileOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _cond_subgraph_index = _o->cond_subgraph_index; - auto _body_subgraph_index = _o->body_subgraph_index; - return tflite::CreateWhileOptions( - _fbb, - _cond_subgraph_index, - _body_subgraph_index); -} - -inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new NonMaxSuppressionV4OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateNonMaxSuppressionV4Options( - _fbb); -} - -inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new NonMaxSuppressionV5OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateNonMaxSuppressionV5Options( - _fbb); -} - -inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ScatterNdOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateScatterNdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateScatterNdOptions( - _fbb); -} - -inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SelectV2OptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSelectV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSelectV2Options( - _fbb); -} - -inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new DensifyOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDensifyOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateDensifyOptions( - _fbb); -} - -inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SegmentSumOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSegmentSumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite::CreateSegmentSumOptions( - _fbb); -} - -inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new BatchMatMulOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = adj_x(); _o->adj_x = _e; } - { auto _e = adj_y(); _o->adj_y = _e; } -} - -inline flatbuffers::Offset BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBatchMatMulOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _adj_x = _o->adj_x; - auto _adj_y = _o->adj_y; - return tflite::CreateBatchMatMulOptions( - _fbb, - _adj_x, - _adj_y); -} - -inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new OperatorCodeT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = builtin_code(); _o->builtin_code = _e; } - { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); } - { auto _e = version(); _o->version = _e; } -} - -inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOperatorCode(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _builtin_code = _o->builtin_code; - auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); - auto _version = _o->version; - return tflite::CreateOperatorCode( - _fbb, - _builtin_code, - _custom_code, - _version); -} - -inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new OperatorT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = opcode_index(); _o->opcode_index = _e; } - { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } - { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } - { auto _e = builtin_options_type(); _o->builtin_options.type = _e; } - { auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); } - { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->custom_options[_i] = _e->Get(_i); } } } - { auto _e = custom_options_format(); _o->custom_options_format = _e; } - { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } } - { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOperator(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _opcode_index = _o->opcode_index; - auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; - auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; - auto _builtin_options_type = _o->builtin_options.type; - auto _builtin_options = _o->builtin_options.Pack(_fbb); - auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; - auto _custom_options_format = _o->custom_options_format; - auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; - auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0; - return tflite::CreateOperator( - _fbb, - _opcode_index, - _inputs, - _outputs, - _builtin_options_type, - _builtin_options, - _custom_options, - _custom_options_format, - _mutating_variable_inputs, - _intermediates); -} - -inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new SubGraphT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } - { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } - { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } - { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operators[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } - { auto _e = name(); if (_e) _o->name = _e->str(); } -} - -inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSubGraph(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _tensors = _o->tensors.size() ? _fbb.CreateVector> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; - auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; - auto _operators = _o->operators.size() ? _fbb.CreateVector> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - return tflite::CreateSubGraph( - _fbb, - _tensors, - _inputs, - _outputs, - _operators, - _name); -} - -inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new BufferT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = data(); if (_e) { _o->data.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->data[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBuffer(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16); - auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; - return tflite::CreateBuffer( - _fbb, - _data); -} - -inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MetadataT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = name(); if (_e) _o->name = _e->str(); } - { auto _e = buffer(); _o->buffer = _e; } -} - -inline flatbuffers::Offset Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMetadata(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _buffer = _o->buffer; - return tflite::CreateMetadata( - _fbb, - _name, - _buffer); -} - -inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new ModelT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = version(); _o->version = _e; } - { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operator_codes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } - { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } - { auto _e = description(); if (_e) _o->description = _e->str(); } - { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } - { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } } - { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } } -} - -inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateModel(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _version = _o->version; - auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); - auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0; - auto _metadata = _o->metadata.size() ? _fbb.CreateVector> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0; - return tflite::CreateModel( - _fbb, - _version, - _operator_codes, - _subgraphs, - _description, - _buffers, - _metadata_buffer, - _metadata); -} - -inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) { - switch (type) { - case QuantizationDetails_NONE: { - return true; - } - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyQuantizationDetails( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) { - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(value); - return CreateCustomQuantization(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline QuantizationDetailsUnion::QuantizationDetailsUnion(const QuantizationDetailsUnion &u) FLATBUFFERS_NOEXCEPT : type(u.type), value(nullptr) { - switch (type) { - case QuantizationDetails_CustomQuantization: { - value = new tflite::CustomQuantizationT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void QuantizationDetailsUnion::Reset() { - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = QuantizationDetails_NONE; -} - -inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) { - switch (type) { - case SparseIndexVector_NONE: { - return true; - } - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifySparseIndexVector( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) { - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(value); - return CreateInt32Vector(_fbb, ptr, _rehasher).Union(); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(value); - return CreateUint16Vector(_fbb, ptr, _rehasher).Union(); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(value); - return CreateUint8Vector(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline SparseIndexVectorUnion::SparseIndexVectorUnion(const SparseIndexVectorUnion &u) FLATBUFFERS_NOEXCEPT : type(u.type), value(nullptr) { - switch (type) { - case SparseIndexVector_Int32Vector: { - value = new tflite::Int32VectorT(*reinterpret_cast(u.value)); - break; - } - case SparseIndexVector_Uint16Vector: { - value = new tflite::Uint16VectorT(*reinterpret_cast(u.value)); - break; - } - case SparseIndexVector_Uint8Vector: { - value = new tflite::Uint8VectorT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void SparseIndexVectorUnion::Reset() { - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = SparseIndexVector_NONE; -} - -inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { - switch (type) { - case BuiltinOptions_NONE: { - return true; - } - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyBuiltinOptions( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(value); - return CreateConv2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(value); - return CreateDepthwiseConv2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(value); - return CreateConcatEmbeddingsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(value); - return CreateLSHProjectionOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(value); - return CreatePool2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(value); - return CreateSVDFOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(value); - return CreateFullyConnectedOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateSoftmaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(value); - return CreateConcatenationOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(value); - return CreateAddOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(value); - return CreateL2NormOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(value); - return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(value); - return CreateResizeBilinearOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(value); - return CreateCallOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(value); - return CreateReshapeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(value); - return CreateSkipGramOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(value); - return CreateSpaceToDepthOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(value); - return CreateEmbeddingLookupSparseOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(value); - return CreateMulOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(value); - return CreatePadOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(value); - return CreateGatherOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(value); - return CreateBatchToSpaceNDOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(value); - return CreateSpaceToBatchNDOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(value); - return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(value); - return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(value); - return CreateSubOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(value); - return CreateDivOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(value); - return CreateSqueezeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(value); - return CreateExpOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(value); - return CreateTopKV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(value); - return CreateSplitOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(value); - return CreateCastOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(value); - return CreateMaximumMinimumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(value); - return CreateLessOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(value); - return CreateNegOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(value); - return CreatePadV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(value); - return CreateGreaterOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(value); - return CreateSelectOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(value); - return CreateTransposeConvOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(value); - return CreateSparseToDenseOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(value); - return CreateTileOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(value); - return CreateExpandDimsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(value); - return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(value); - return CreatePowOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(value); - return CreateArgMinOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(value); - return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(value); - return CreatePackOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(value); - return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalAndOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalNotOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnpackOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(value); - return CreateFloorDivOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(value); - return CreateSquareOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(value); - return CreateZerosLikeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(value); - return CreateFillOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateBidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateBidirectionalSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(value); - return CreateFloorModOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(value); - return CreateRangeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(value); - return CreateResizeNearestNeighborOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(value); - return CreateLeakyReluOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(value); - return CreateSquaredDifferenceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(value); - return CreateMirrorPadOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(value); - return CreateAbsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(value); - return CreateSplitVOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(value); - return CreateUniqueOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(value); - return CreateReverseV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(value); - return CreateAddNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(value); - return CreateGatherNdOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(value); - return CreateCosOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(value); - return CreateWhereOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(value); - return CreateRankOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(value); - return CreateReverseSequenceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(value); - return CreateMatrixDiagOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateQuantizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(value); - return CreateMatrixSetDiagOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(value); - return CreateHardSwishOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(value); - return CreateIfOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(value); - return CreateWhileOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(value); - return CreateDepthToSpaceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(value); - return CreateNonMaxSuppressionV4Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(value); - return CreateNonMaxSuppressionV5Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(value); - return CreateScatterNdOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(value); - return CreateSelectV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(value); - return CreateDensifyOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(value); - return CreateSegmentSumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(value); - return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FLATBUFFERS_NOEXCEPT : type(u.type), value(nullptr) { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - value = new tflite::Conv2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DepthwiseConv2DOptions: { - value = new tflite::DepthwiseConv2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - value = new tflite::ConcatEmbeddingsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LSHProjectionOptions: { - value = new tflite::LSHProjectionOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_Pool2DOptions: { - value = new tflite::Pool2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SVDFOptions: { - value = new tflite::SVDFOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RNNOptions: { - value = new tflite::RNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FullyConnectedOptions: { - value = new tflite::FullyConnectedOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SoftmaxOptions: { - value = new tflite::SoftmaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ConcatenationOptions: { - value = new tflite::ConcatenationOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AddOptions: { - value = new tflite::AddOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_L2NormOptions: { - value = new tflite::L2NormOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - value = new tflite::LocalResponseNormalizationOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LSTMOptions: { - value = new tflite::LSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ResizeBilinearOptions: { - value = new tflite::ResizeBilinearOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CallOptions: { - value = new tflite::CallOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReshapeOptions: { - value = new tflite::ReshapeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SkipGramOptions: { - value = new tflite::SkipGramOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SpaceToDepthOptions: { - value = new tflite::SpaceToDepthOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - value = new tflite::EmbeddingLookupSparseOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MulOptions: { - value = new tflite::MulOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PadOptions: { - value = new tflite::PadOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GatherOptions: { - value = new tflite::GatherOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BatchToSpaceNDOptions: { - value = new tflite::BatchToSpaceNDOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SpaceToBatchNDOptions: { - value = new tflite::SpaceToBatchNDOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TransposeOptions: { - value = new tflite::TransposeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReducerOptions: { - value = new tflite::ReducerOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SubOptions: { - value = new tflite::SubOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DivOptions: { - value = new tflite::DivOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SqueezeOptions: { - value = new tflite::SqueezeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SequenceRNNOptions: { - value = new tflite::SequenceRNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_StridedSliceOptions: { - value = new tflite::StridedSliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ExpOptions: { - value = new tflite::ExpOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TopKV2Options: { - value = new tflite::TopKV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SplitOptions: { - value = new tflite::SplitOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogSoftmaxOptions: { - value = new tflite::LogSoftmaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CastOptions: { - value = new tflite::CastOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DequantizeOptions: { - value = new tflite::DequantizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MaximumMinimumOptions: { - value = new tflite::MaximumMinimumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ArgMaxOptions: { - value = new tflite::ArgMaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LessOptions: { - value = new tflite::LessOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NegOptions: { - value = new tflite::NegOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PadV2Options: { - value = new tflite::PadV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GreaterOptions: { - value = new tflite::GreaterOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GreaterEqualOptions: { - value = new tflite::GreaterEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LessEqualOptions: { - value = new tflite::LessEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SelectOptions: { - value = new tflite::SelectOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SliceOptions: { - value = new tflite::SliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TransposeConvOptions: { - value = new tflite::TransposeConvOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SparseToDenseOptions: { - value = new tflite::SparseToDenseOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TileOptions: { - value = new tflite::TileOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ExpandDimsOptions: { - value = new tflite::ExpandDimsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_EqualOptions: { - value = new tflite::EqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NotEqualOptions: { - value = new tflite::NotEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ShapeOptions: { - value = new tflite::ShapeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PowOptions: { - value = new tflite::PowOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ArgMinOptions: { - value = new tflite::ArgMinOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FakeQuantOptions: { - value = new tflite::FakeQuantOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PackOptions: { - value = new tflite::PackOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalOrOptions: { - value = new tflite::LogicalOrOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_OneHotOptions: { - value = new tflite::OneHotOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalAndOptions: { - value = new tflite::LogicalAndOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalNotOptions: { - value = new tflite::LogicalNotOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnpackOptions: { - value = new tflite::UnpackOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FloorDivOptions: { - value = new tflite::FloorDivOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SquareOptions: { - value = new tflite::SquareOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ZerosLikeOptions: { - value = new tflite::ZerosLikeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FillOptions: { - value = new tflite::FillOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - value = new tflite::BidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - value = new tflite::BidirectionalSequenceRNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - value = new tflite::UnidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FloorModOptions: { - value = new tflite::FloorModOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RangeOptions: { - value = new tflite::RangeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - value = new tflite::ResizeNearestNeighborOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LeakyReluOptions: { - value = new tflite::LeakyReluOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SquaredDifferenceOptions: { - value = new tflite::SquaredDifferenceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MirrorPadOptions: { - value = new tflite::MirrorPadOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AbsOptions: { - value = new tflite::AbsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SplitVOptions: { - value = new tflite::SplitVOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UniqueOptions: { - value = new tflite::UniqueOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReverseV2Options: { - value = new tflite::ReverseV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AddNOptions: { - value = new tflite::AddNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GatherNdOptions: { - value = new tflite::GatherNdOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CosOptions: { - value = new tflite::CosOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_WhereOptions: { - value = new tflite::WhereOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RankOptions: { - value = new tflite::RankOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReverseSequenceOptions: { - value = new tflite::ReverseSequenceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MatrixDiagOptions: { - value = new tflite::MatrixDiagOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_QuantizeOptions: { - value = new tflite::QuantizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MatrixSetDiagOptions: { - value = new tflite::MatrixSetDiagOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HardSwishOptions: { - value = new tflite::HardSwishOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_IfOptions: { - value = new tflite::IfOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_WhileOptions: { - value = new tflite::WhileOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DepthToSpaceOptions: { - value = new tflite::DepthToSpaceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - value = new tflite::NonMaxSuppressionV4OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - value = new tflite::NonMaxSuppressionV5OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ScatterNdOptions: { - value = new tflite::ScatterNdOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SelectV2Options: { - value = new tflite::SelectV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DensifyOptions: { - value = new tflite::DensifyOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SegmentSumOptions: { - value = new tflite::SegmentSumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BatchMatMulOptions: { - value = new tflite::BatchMatMulOptionsT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void BuiltinOptionsUnion::Reset() { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = BuiltinOptions_NONE; -} - -inline const tflite::Model *GetModel(const void *buf) { - return flatbuffers::GetRoot(buf); -} - -inline const tflite::Model *GetSizePrefixedModel(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); -} - -inline const char *ModelIdentifier() { - return "TFL3"; -} - -inline bool ModelBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, ModelIdentifier()); -} - -inline bool VerifyModelBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(ModelIdentifier()); -} - -inline bool VerifySizePrefixedModelBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(ModelIdentifier()); -} - -inline const char *ModelExtension() { - return "tflite"; -} - -inline void FinishModelBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.Finish(root, ModelIdentifier()); -} - -inline void FinishSizePrefixedModelBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.FinishSizePrefixed(root, ModelIdentifier()); -} - -inline std::unique_ptr UnPackModel( - const void *buf, - const flatbuffers::resolver_function_t *res = nullptr) { - return std::unique_ptr(GetModel(buf)->UnPack(res)); -} - -inline std::unique_ptr UnPackSizePrefixedModel( - const void *buf, - const flatbuffers::resolver_function_t *res = nullptr) { - return std::unique_ptr(GetSizePrefixedModel(buf)->UnPack(res)); -} - -} // namespace tflite - -#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_type.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_type.h deleted file mode 100644 index f5a7f833..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_type.h +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -// Abstract string. We don't want even absl at this level. -#ifndef TENSORFLOW_LITE_STRING_TYPE_H_ -#define TENSORFLOW_LITE_STRING_TYPE_H_ - -#include - -namespace tflite { - -using std::string; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_STRING_TYPE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_util.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_util.h deleted file mode 100644 index 2086f9ba..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/string_util.h +++ /dev/null @@ -1,109 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Util methods to read and write String tensors. -// String tensors are considered to be char tensor with protocol. -// [0, 3] 4 bytes: N, num of strings in the tensor in little endian. -// [(i+1)*4, (i+1)*4+3] 4 bytes: offset of i-th string in little endian. -// [(N+2)*4, (N+2)*4+3] 4 bytes: length of the whole char buffer. -// [offset(i), offset(i+1) - 1] : content of i-th string. -// Example of a string tensor: -// [ -// 2, 0, 0, 0, # 2 strings. -// 16, 0, 0, 0, # 0-th string starts from index 16. -// 18, 0, 0, 0, # 1-st string starts from index 18. -// 18, 0, 0, 0, # total length of array. -// 'A', 'B', # 0-th string [16..17]: "AB" -// ] # 1-th string, empty -// -// A typical usage: -// In op.Eval(context, node): -// DynamicBuffer buf; -// # Add string "AB" to tensor, string is stored in dynamic buffer. -// buf.AddString("AB", 2); -// # Write content of DynamicBuffer to tensor in format of string tensor -// # described above. -// buf.WriteToTensor(tensor, nullptr) - -#ifndef TENSORFLOW_LITE_STRING_UTIL_H_ -#define TENSORFLOW_LITE_STRING_UTIL_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/string_type.h" - -namespace tflite { - -// Convenient structure to store string pointer and length. -typedef struct { - const char* str; - int len; -} StringRef; - -// DynamicBuffer holds temporary buffer that will be used to create a dynamic -// tensor. A typical usage is to initialize a DynamicBuffer object, fill in -// content and call CreateStringTensor in op.Eval(). -class DynamicBuffer { - public: - DynamicBuffer() : offset_({0}) {} - - // Add string to dynamic buffer by resizing the buffer and copying the data. - void AddString(const StringRef& string); - - // Add string to dynamic buffer by resizing the buffer and copying the data. - void AddString(const char* str, size_t len); - - // Join a list of string with separator, and add as a single string to the - // buffer. - void AddJoinedString(const std::vector& strings, char separator); - void AddJoinedString(const std::vector& strings, - StringRef separator); - - // Fill content into a buffer and returns the number of bytes stored. - // The function allocates space for the buffer but does NOT take ownership. - int WriteToBuffer(char** buffer); - - // String tensors are not generally supported on platforms w/ static memory. - // TODO(b/156130024): Remove this guard after removing header from TFLM deps. -#ifndef TF_LITE_STATIC_MEMORY - // Fill content into a string tensor, with the given new_shape. The new shape - // must match the number of strings in this object. Caller relinquishes - // ownership of new_shape. If 'new_shape' is nullptr, keep the tensor's - // existing shape. - void WriteToTensor(TfLiteTensor* tensor, TfLiteIntArray* new_shape); - - // Fill content into a string tensor. Set shape to {num_strings}. - void WriteToTensorAsVector(TfLiteTensor* tensor); -#endif // TF_LITE_STATIC_MEMORY - - private: - // Data buffer to store contents of strings, not including headers. - std::vector data_; - // Offset of the starting index of each string in data buffer. - std::vector offset_; -}; - -// Return num of strings in a String tensor. -int GetStringCount(const void* raw_buffer); -int GetStringCount(const TfLiteTensor* tensor); - -// Get String pointer and length of index-th string in tensor. -// NOTE: This will not create a copy of string data. -StringRef GetString(const void* raw_buffer, int string_index); -StringRef GetString(const TfLiteTensor* tensor, int string_index); -} // namespace tflite - -#endif // TENSORFLOW_LITE_STRING_UTIL_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/type_to_tflitetype.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/type_to_tflitetype.h deleted file mode 100644 index a95b233c..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/type_to_tflitetype.h +++ /dev/null @@ -1,70 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_TYPE_TO_TFLITETYPE_H_ -#define TENSORFLOW_LITE_TYPE_TO_TFLITETYPE_H_ - -// Arduino build defines abs as a macro here. That is invalid C++, and breaks -// libc++'s header, undefine it. -#ifdef abs -#undef abs -#endif - -#include -#include - -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -// Map statically from a C++ type to a TfLiteType. Used in interpreter for -// safe casts. -// Example: -// typeToTfLiteType() -> kTfLiteBool -template -constexpr TfLiteType typeToTfLiteType() { - return kTfLiteNoType; -} -// Map from TfLiteType to the corresponding C++ type. -// Example: -// TfLiteTypeToType::Type -> bool -template -struct TfLiteTypeToType {}; // Specializations below - -// Template specialization for both typeToTfLiteType and TfLiteTypeToType. -#define MATCH_TYPE_AND_TFLITE_TYPE(CPP_TYPE, TFLITE_TYPE_ENUM) \ - template <> \ - constexpr TfLiteType typeToTfLiteType() { \ - return TFLITE_TYPE_ENUM; \ - } \ - template <> \ - struct TfLiteTypeToType { \ - using Type = CPP_TYPE; \ - } - -MATCH_TYPE_AND_TFLITE_TYPE(int, kTfLiteInt32); -MATCH_TYPE_AND_TFLITE_TYPE(int16_t, kTfLiteInt16); -MATCH_TYPE_AND_TFLITE_TYPE(int64_t, kTfLiteInt64); -MATCH_TYPE_AND_TFLITE_TYPE(float, kTfLiteFloat32); -MATCH_TYPE_AND_TFLITE_TYPE(unsigned char, kTfLiteUInt8); -MATCH_TYPE_AND_TFLITE_TYPE(int8_t, kTfLiteInt8); -MATCH_TYPE_AND_TFLITE_TYPE(bool, kTfLiteBool); -MATCH_TYPE_AND_TFLITE_TYPE(std::complex, kTfLiteComplex64); -MATCH_TYPE_AND_TFLITE_TYPE(std::complex, kTfLiteComplex128); -MATCH_TYPE_AND_TFLITE_TYPE(std::string, kTfLiteString); -MATCH_TYPE_AND_TFLITE_TYPE(TfLiteFloat16, kTfLiteFloat16); -MATCH_TYPE_AND_TFLITE_TYPE(double, kTfLiteFloat64); - -} // namespace tflite -#endif // TENSORFLOW_LITE_TYPE_TO_TFLITETYPE_H_ diff --git a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/version.h b/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/version.h deleted file mode 100644 index f667447b..00000000 --- a/components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/version.h +++ /dev/null @@ -1,29 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_VERSION_H_ -#define TENSORFLOW_LITE_VERSION_H_ - -#include "tensorflow/core/public/version.h" - -// The version number of the Schema. Ideally all changes will be backward -// compatible. 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