apps_mlearning_tflite-micro/tensorflow/lite/micro/kernels/dequantize.cc

/* Copyright 2021 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/dequantize.h"

#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/quantize.h"
#include "tensorflow/lite/kernels/internal/reference/requantize.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/dequantize.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/micro_log.h"

namespace tflite {

void* DequantizeInit(TfLiteContext* context, const char* buffer,
                     size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  return context->AllocatePersistentBuffer(context, sizeof(DequantizeOpData));
}

TfLiteStatus DequantizeEval(TfLiteContext* context, TfLiteNode* node) {
  TFLITE_DCHECK(node->user_data != nullptr);
  DequantizeOpData* data = static_cast<DequantizeOpData*>(node->user_data);

  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);

  // Output type ensured to be kTfLiteFloat32 at the Prepare stage
  TFLITE_DCHECK(output->type == kTfLiteFloat32);

  switch (input->type) {
    case kTfLiteInt8:
      reference_ops::Dequantize(data->quantization_params,
                                tflite::micro::GetTensorShape(input),
                                tflite::micro::GetTensorData<int8_t>(input),
                                tflite::micro::GetTensorShape(output),
                                tflite::micro::GetTensorData<float>(output));
      break;
    case kTfLiteInt16:
      reference_ops::Dequantize(data->quantization_params,
                                tflite::micro::GetTensorShape(input),
                                tflite::micro::GetTensorData<int16_t>(input),
                                tflite::micro::GetTensorShape(output),
                                tflite::micro::GetTensorData<float>(output));
      break;
    case kTfLiteUInt8:
      reference_ops::Dequantize(data->quantization_params,
                                tflite::micro::GetTensorShape(input),
                                tflite::micro::GetTensorData<uint8_t>(input),
                                tflite::micro::GetTensorShape(output),
                                tflite::micro::GetTensorData<float>(output));
      break;
    default:
      MicroPrintf("Input %s, output %s not supported.",
                  TfLiteTypeGetName(input->type),
                  TfLiteTypeGetName(output->type));
      return kTfLiteError;
  }

  return kTfLiteOk;
}

TfLiteStatus DequantizeEvalInt8(TfLiteContext* context, TfLiteNode* node) {
  TFLITE_DCHECK(node->user_data != nullptr);
  DequantizeOpData* data = static_cast<DequantizeOpData*>(node->user_data);

  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);

  TFLITE_DCHECK(input->type == kTfLiteInt8 && output->type == kTfLiteFloat32);
  reference_ops::Dequantize(data->quantization_params,
                            tflite::micro::GetTensorShape(input),
                            tflite::micro::GetTensorData<int8_t>(input),
                            tflite::micro::GetTensorShape(output),
                            tflite::micro::GetTensorData<float>(output));
  return kTfLiteOk;
}

#ifdef TFLITE_MODEL_COMPILER
TfLiteStatus DequantizeCompileInt8(TfLiteContext* context, TfLiteNode* node,
                                   TfLiteCompileStep step, std::ofstream& ofs) {
  switch (step) {
    case kTfLiteCompileStepInclude:
      ofs << "#include \"tensorflow/lite/micro/kernels/dequantize.h\"" << std::endl
          << "#include \"tensorflow/lite/kernels/internal/reference/dequantize.h\""
          << std::endl;
      break;

    case kTfLiteCompileStepEval: {
      TFLITE_DCHECK(node->user_data != nullptr);
      DequantizeOpData* data = static_cast<DequantizeOpData*>(node->user_data);

      const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
      TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
      TFLITE_DCHECK(input->type == kTfLiteInt8 && output->type == kTfLiteFloat32);

      ofs << "{ // dequantize int8 to float" << std::endl;

      tflite::micro::CompileAddress(ofs, "input_data", input->data.data);
      tflite::micro::CompileAddress(ofs, "output_data", output->data.data);

      ofs << "static const tflite::DequantizationParams op_params = {"
          << ".scale = " << data->quantization_params.scale
          << ", .zero_point = " << data->quantization_params.zero_point
          << "};"
          << std::endl;

      tflite::micro::CompileArray(ofs, "const int32_t", "input_dims_data",
                                  input->dims->data, input->dims->size);

      tflite::micro::CompileArray(ofs, "const int32_t", "output_dims_data",
                                  output->dims->data, output->dims->size);

      ofs << "tflite::reference_ops::Dequantize(op_params, "
             "tflite::RuntimeShape("
          << input->dims->size
          << ", input_dims_data), "
             "reinterpret_cast<int8_t*>(input_data), "
             "tflite::RuntimeShape("
          << output->dims->size
          << ", output_dims_data), "
             "reinterpret_cast<float*>(output_data));"
          << std::endl;

      ofs << "}" << std::endl;
    } break;

    default:
      return kTfLiteError;
  }

  return kTfLiteOk;
}
#endif

TFLMRegistration Register_DEQUANTIZE() {
  return tflite::micro::RegisterOp(DequantizeInit, DequantizePrepare,
                                   DequantizeEval);
}

TFLMRegistration Register_DEQUANTIZE_INT8() {
#ifdef TFLITE_MODEL_COMPILER
  return tflite::micro::CompileOp(DequantizeInit, DequantizePrepare,
                                  DequantizeEvalInt8, DequantizeCompileInt8);
#else
  return tflite::micro::RegisterOp(DequantizeInit, DequantizePrepare,
                                   DequantizeEvalInt8);
#endif
}

}  // namespace tflite