tflite_micro_person_detection_init

components/tflite_micro/tensorflow/lite/micro/kernels/tanh.cc

@@ -0,0 +1,154 @@
+/* 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.
+==============================================================================*/
+
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h"
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/tanh.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_utils.h"
+
+namespace tflite {
+namespace ops {
+namespace micro {
+namespace activations {
+namespace {
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+
+struct OpData {
+  int32_t input_zero_point;
+  int32_t input_range_radius;
+  int32_t input_multiplier;
+  int input_left_shift;
+};
+
+void* TanhInit(TfLiteContext* context, const char* buffer, size_t length) {
+  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+  return context->AllocatePersistentBuffer(context, sizeof(OpData));
+}
+
+TfLiteStatus CalculateArithmeticOpData(TfLiteContext* context, TfLiteNode* node,
+                                       OpData* data) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
+
+  if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) {
+    static constexpr int kInputIntegerBits = 4;
+    const double input_real_multiplier =
+        static_cast<double>(input->params.scale) *
+        static_cast<double>(1 << (31 - kInputIntegerBits));
+
+    const double q = std::frexp(input_real_multiplier, &data->input_left_shift);
+    data->input_multiplier = static_cast<int32_t>(TfLiteRound(q * (1ll << 31)));
+
+    data->input_range_radius =
+        CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31);
+  }
+  return kTfLiteOk;
+}
+
+TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) {
+  TFLITE_DCHECK(node->user_data != nullptr);
+
+  OpData* data = static_cast<OpData*>(node->user_data);
+
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  data->input_zero_point = input->params.zero_point;
+  return CalculateArithmeticOpData(context, node, data);
+}
+
+}  // namespace
+
+TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
+  const TfLiteEvalTensor* input =
+      tflite::micro::GetEvalInput(context, node, kInputTensor);
+  TfLiteEvalTensor* output =
+      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+  TFLITE_DCHECK(node->user_data != nullptr);
+  const OpData& data = *(static_cast<const OpData*>(node->user_data));
+
+  switch (input->type) {
+    case kTfLiteFloat32: {
+      reference_ops::Tanh(tflite::micro::GetTensorShape(input),
+                          tflite::micro::GetTensorData<float>(input),
+                          tflite::micro::GetTensorShape(output),
+                          tflite::micro::GetTensorData<float>(output));
+      return kTfLiteOk;
+    } break;
+    case kTfLiteInt16: {
+      TanhParams params;
+      params.input_left_shift = data.input_left_shift;
+      reference_ops::Tanh(params, tflite::micro::GetTensorShape(input),
+                          tflite::micro::GetTensorData<int16_t>(input),
+                          tflite::micro::GetTensorShape(output),
+                          tflite::micro::GetTensorData<int16_t>(output));
+      return kTfLiteOk;
+    } break;
+    case kTfLiteUInt8: {
+      TanhParams params;
+      params.input_zero_point = data.input_zero_point;
+      params.input_range_radius = data.input_range_radius;
+      params.input_multiplier = data.input_multiplier;
+      params.input_left_shift = data.input_left_shift;
+      reference_ops::Tanh(params, tflite::micro::GetTensorShape(input),
+                          tflite::micro::GetTensorData<uint8_t>(input),
+                          tflite::micro::GetTensorShape(output),
+                          tflite::micro::GetTensorData<uint8_t>(output));
+
+      return kTfLiteOk;
+    } break;
+    case kTfLiteInt8: {
+      reference_integer_ops::Tanh(
+          data.input_zero_point, data.input_range_radius, data.input_multiplier,
+          data.input_left_shift, NumElements(input->dims),
+          tflite::micro::GetTensorData<int8_t>(input),
+          tflite::micro::GetTensorData<int8_t>(output));
+      return kTfLiteOk;
+    } break;
+    default:
+      TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
+                         TfLiteTypeGetName(input->type),
+                         TfLiteTypeGetName(output->type));
+      return kTfLiteError;
+  }
+}
+
+}  // namespace activations
+
+TfLiteRegistration Register_TANH() {
+  return {/*init=*/activations::TanhInit,
+          /*free=*/nullptr,
+          /*prepare=*/activations::TanhPrepare,
+          /*invoke=*/activations::TanhEval,
+          /*profiling_string=*/nullptr,
+          /*builtin_code=*/0,
+          /*custom_name=*/nullptr,
+          /*version=*/0};
+}
+}  // namespace micro
+}  // namespace ops
+}  // namespace tflite