TransposeConv.cpp

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/*
 * Copyright (c) 2024 Samsung Electronics Co., Ltd. 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 "OMStatus.h"
 
#include "core/OMUtils.h"
#include "core/OMKernelData.h"
 
#include "execute/OMKernelExecutionBuilder.h"
#include "execute/OMUtils.h"
#include "execute/OMRuntimeKernel.h"
 
#include "PALTransposeConv.h"
 
using namespace onert_micro;
using namespace onert_micro::core;
using namespace onert_micro::execute;
 
namespace
{
 
// For the TfLite transpose_conv implementation, input tensor 0 corresponds to
// the OutputShapeTensor. However, since TFLM does not support dynamic tensors,
// the TFLM implementation ignores input tensor 0 and the only inputs we care
// about are kFilterTensor, kInputTensor and kBiasTensor.
 
constexpr int kWeightTensorIdx = 1;
constexpr int kInputTensorIdx = 2;
constexpr int kBiasTensorIdx = 3;
constexpr int kOutputTensorIdx = 0;
 
} // namespace
 
// NOTE: doesn't currently support dynamic shapes
namespace onert_micro
{
namespace execute
{
 
OMStatus execute_kernel_CircleTransposeConv(const OMExecuteArgs &execute_args)
{
  core::OMRuntimeContext &runtime_context = execute_args.runtime_context;
  core::OMRuntimeStorage &runtime_storage = execute_args.runtime_storage;
  uint16_t op_index = execute_args.kernel_index;
 
  const circle::Tensor *input;
  const circle::Tensor *weight;
  const circle::Tensor *output;
 
  uint8_t *input_data;
  uint8_t *weight_data;
  uint8_t *bias_data;
  uint8_t *output_data;
 
  const circle::TransposeConvOptions *options;
  // Read kernel
  {
    execute::OMRuntimeKernel runtime_kernel;
    OMStatus status = runtime_kernel.readKernel(op_index, runtime_context);
    if (status != Ok)
      return status;
 
    input = runtime_kernel.inputs[kInputTensorIdx];
    weight = runtime_kernel.inputs[kWeightTensorIdx];
    output = runtime_kernel.outputs[kOutputTensorIdx];
    assert(input != nullptr);
    assert(weight != nullptr);
    // Bias can be nullptr
    assert(output != nullptr);
 
    status = runtime_kernel.getDataFromStorage(op_index, runtime_storage, runtime_context);
    if (status != Ok)
      return status;
 
    input_data = runtime_kernel.inputs_data[kInputTensorIdx];
    weight_data = runtime_kernel.inputs_data[kWeightTensorIdx];
    bias_data = runtime_kernel.inputs_data[kBiasTensorIdx];
    output_data = runtime_kernel.outputs_data[kOutputTensorIdx];
    assert(input_data != nullptr);
    assert(weight_data != nullptr);
    // Bias can be nullptr
    assert(output_data != nullptr);
 
    options = runtime_kernel.first_operator->builtin_options_as_TransposeConvOptions();
  }
 
  OMStatus status;
 
  int32_t padding_h = 0;
  int32_t padding_w = 0;
 
  OMRuntimeShape weight_shape(weight);
  OMRuntimeShape input_shape(input);
 
  const int input_width = input_shape.dims(2);
  const int input_height = input_shape.dims(1);
  const int weight_width = weight_shape.dims(2);
  const int weight_height = weight_shape.dims(1);
 
  // Note: Dilation height and width are always 1 for transpose_conv
  execute::computePaddingHeightWidth(options->stride_h(), options->stride_w(), 1, 1, input_height,
                                     input_width, weight_height, weight_width, options->padding(),
                                     &padding_h, &padding_w);
 
  switch (input->type())
  {
#ifndef DIS_FLOAT
    case circle::TensorType_FLOAT32:
    {
 
      FloatConv2D params{};
      status = calculateActivationRange(options->fused_activation_function(),
                                        &params.activation_min, &params.activation_max);
      params.stride_w = options->stride_w();
      params.stride_h = options->stride_h();
      params.dilation_width_factor = 1;
      params.dilation_height_factor = 1;
      params.pad_h = padding_h;
      params.pad_w = padding_w;
 
      if (status != Ok)
        return status;
 
      status = pal::TransposeConv<float>(
        &params, input_shape, core::utils::castInputData<float>(input_data), weight_shape,
        core::utils::castInputData<float>(weight_data),
        core::utils::castInputData<float>(bias_data), OMRuntimeShape(output),
        core::utils::castOutputData<float>(output_data));
      assert(status == Ok);
    }
    break;
#endif // DIS_FLOAT
    default:
    {
      status = UnsupportedActivation;
      assert(false && "Unsupported type.");
    }
  }
 
  return status;
}
 
} // namespace execute
} // namespace onert_micro