265 lines
9.5 KiB
C++
265 lines
9.5 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/kernels/internal/reference/concatenation.h"
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#include <cstdint>
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace concatenation {
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constexpr int kMaxInputNum = 10; // Maximum number of input tensors
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constexpr int kOutputTensor = 0;
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struct OpData {
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ConcatenationParams params;
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};
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// Handles negative axis index, coerces to positive index value.
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inline int CalculatePositiveAxis(int axis, const TfLiteTensor* output_tensor) {
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if (axis >= 0) {
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return axis;
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} else {
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return NumDimensions(output_tensor) + axis;
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}
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}
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// The following functions are helpers to get tensor data in the format that the
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// reference op implementation expects. They provide the same functionality as
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// class VectorOfTensors and class VectorOfQuantizedTensors in TFLite.
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// Gets shapes from a list of tensors.
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inline void GetAllInputTensorShapes(const TfLiteContext* context,
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const TfLiteNode* node,
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RuntimeShape all_shapes[kMaxInputNum]) {
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TFLITE_DCHECK(context != nullptr);
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TFLITE_DCHECK(node != nullptr);
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for (int i = 0; i < node->inputs->size; ++i) {
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const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
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RuntimeShape shape = tflite::micro::GetTensorShape(t);
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all_shapes[i].ReplaceWith(shape.DimensionsCount(), shape.DimsData());
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}
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}
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// Get shape pointers from a list of shapes.
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inline void GetShapesPointers(const RuntimeShape* shapes, size_t num,
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const RuntimeShape* pointers[]) {
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for (size_t i = 0; i < num; ++i) {
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pointers[i] = &shapes[i];
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}
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}
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// Gets data pointers from a list of tensors.
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template <typename T>
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inline void GetAllInputTensorData(const TfLiteContext* context,
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const TfLiteNode* node,
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T* all_data[kMaxInputNum]) {
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TFLITE_DCHECK(context != nullptr);
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TFLITE_DCHECK(node != nullptr);
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for (int i = 0; i < node->inputs->size; ++i) {
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const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
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all_data[i] = tflite::micro::GetTensorData<T>(t);
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}
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}
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template <typename data_type>
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void EvalUnquantized(TfLiteContext* context, TfLiteNode* node) {
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// Collect the shapes and data pointer of input tensors
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RuntimeShape inputs_shape[kMaxInputNum];
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const RuntimeShape* inputs_shape_ptr[kMaxInputNum];
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const data_type* inputs_data[kMaxInputNum];
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GetAllInputTensorShapes(context, node, inputs_shape);
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GetShapesPointers(inputs_shape, node->inputs->size, inputs_shape_ptr);
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GetAllInputTensorData(context, node, inputs_data);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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reference_ops::Concatenation(data->params, inputs_shape_ptr, inputs_data,
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<data_type>(output));
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}
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void EvalQuantizedUInt8(TfLiteContext* context, TfLiteNode* node) {
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// Collect the shapes and data pointer of input tensors
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RuntimeShape inputs_shape[kMaxInputNum];
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const RuntimeShape* inputs_shape_ptr[kMaxInputNum];
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const uint8_t* inputs_data[kMaxInputNum];
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GetAllInputTensorShapes(context, node, inputs_shape);
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GetShapesPointers(inputs_shape, node->inputs->size, inputs_shape_ptr);
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GetAllInputTensorData(context, node, inputs_data);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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reference_ops::ConcatenationWithScaling(
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data->params, inputs_shape_ptr, inputs_data,
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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}
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpData));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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// This function only checks the types. Additional shape validations are
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// performed in the reference implementation called during Eval().
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const TfLiteConcatenationParams* params =
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reinterpret_cast<TfLiteConcatenationParams*>(node->builtin_data);
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TfLiteType input_type = GetInput(context, node, 0)->type;
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TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type;
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// Check activation and input type
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TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
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TF_LITE_ENSURE(context,
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input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 ||
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input_type == kTfLiteInt8 || input_type == kTfLiteInt32 ||
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input_type == kTfLiteInt64);
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// Output type must match input type
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TF_LITE_ENSURE_EQ(context, output_type, input_type);
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// This implementation does not support large number of input tensors
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const int num_inputs = NumInputs(node);
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TF_LITE_ENSURE(context, num_inputs <= kMaxInputNum);
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// Shapes with dimensions >4 are not yet supported with static allocation.
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for (int i = 0; i < num_inputs; ++i) {
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const TfLiteTensor* input = GetInput(context, node, i);
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int num_dimensions = NumDimensions(input);
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if (num_dimensions > 4) {
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TF_LITE_KERNEL_LOG(context,
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"Op Concatenation does not currently support num dimensions >4 "
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"Tensor has %d dimensions.",
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num_dimensions);
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return kTfLiteError;
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}
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}
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// Calculate OpData.
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TFLITE_DCHECK(node->user_data != nullptr);
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OpData* data = static_cast<OpData*>(node->user_data);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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switch (output_type) { // Already know in/outtypes are same.
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case kTfLiteFloat32:
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case kTfLiteInt32:
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case kTfLiteInt64: {
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data->params.axis = CalculatePositiveAxis(params->axis, output);
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data->params.inputs_count = node->inputs->size;
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break;
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}
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case kTfLiteUInt8:
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case kTfLiteInt8: {
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data->params.axis = CalculatePositiveAxis(params->axis, output);
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data->params.inputs_count = node->inputs->size;
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float* input_scales =
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reinterpret_cast<float*>(context->AllocatePersistentBuffer(
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context, node->inputs->size * sizeof(float)));
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int32_t* input_zero_points =
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reinterpret_cast<int32_t*>(context->AllocatePersistentBuffer(
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context, node->inputs->size * sizeof(int32_t)));
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// Allocate persistent scale and zeropoint buffers.
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// Store input scale and zero point values in OpParams:
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for (int i = 0; i < node->inputs->size; ++i) {
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const TfLiteTensor* t = GetInput(context, node, i);
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input_scales[i] = t->params.scale;
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input_zero_points[i] = t->params.zero_point;
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}
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data->params.input_scale = input_scales;
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data->params.input_zeropoint = input_zero_points;
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data->params.output_zeropoint = output->params.zero_point;
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data->params.output_scale = output->params.scale;
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break;
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}
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default:
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TF_LITE_KERNEL_LOG(context, "Op Concatenation does not currently support Type '%s'.",
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TfLiteTypeGetName(output_type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type;
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switch (output_type) { // Already know in/outtypes are same.
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case kTfLiteFloat32:
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EvalUnquantized<float>(context, node);
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break;
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case kTfLiteInt32:
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EvalUnquantized<int32_t>(context, node);
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break;
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case kTfLiteUInt8:
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EvalQuantizedUInt8(context, node);
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break;
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case kTfLiteInt8:
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EvalUnquantized<int8_t>(context, node);
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break;
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case kTfLiteInt64:
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EvalUnquantized<int64_t>(context, node);
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Op Concatenation does not currently support Type '%s'.",
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TfLiteTypeGetName(output_type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace concatenation
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TfLiteRegistration Register_CONCATENATION() {
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return {/*init=*/concatenation::Init,
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/*free=*/nullptr,
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/*prepare=*/concatenation::Prepare,
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/*invoke=*/concatenation::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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