Conv2D.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 "import/OMKernelConfigureBuilder.h"
 
#include "execute/OMRuntimeKernel.h"
#include "execute/OMUtils.h"
 
using namespace onert_micro;
using namespace onert_micro::core;
 
namespace
{
 
constexpr uint32_t inputTensorIdx = 0;
constexpr uint32_t weightTensorIdx = 1;
constexpr uint32_t biasTensorIdx = 2;
 
constexpr uint32_t outputTensorIdx = 0;
 
} // namespace
 
OMStatus onert_micro::import::configure_kernel_CircleConv2D(const OMConfigureArgs &config_args)
{
  OMRuntimeContext &runtime_context = config_args.runtime_context;
  uint16_t op_index = config_args.kernel_index;
 
  execute::OMRuntimeKernel runtime_kernel;
  runtime_kernel.readKernel(op_index, runtime_context);
 
  const circle::Tensor *input = runtime_kernel.inputs[inputTensorIdx];
  const circle::Tensor *weight = runtime_kernel.inputs[weightTensorIdx];
  const circle::Tensor *bias = runtime_kernel.inputs[biasTensorIdx];
 
  const circle::Tensor *output = runtime_kernel.outputs[outputTensorIdx];
 
  assert(input != nullptr);
  assert(weight != nullptr);
  // Bias can be nullptr
  assert(output != nullptr);
 
  OMStatus status = Ok;
 
  if ((input->type() == circle::TensorType_FLOAT32 &&
       weight->type() != circle::TensorType_FLOAT32) or
      (input->type() == circle::TensorType_INT8 && weight->type() != circle::TensorType_INT8) or
      (input->type() == circle::TensorType_INT16 && weight->type() != circle::TensorType_INT16))
  {
    return UnsupportedType;
  }
 
  core::OMRuntimeShape input_shape(input);
  core::OMRuntimeShape weight_shape(weight);
  core::OMRuntimeShape bias_shape(bias);
  core::OMRuntimeShape output_shape(output);
 
  status = utils::checkCondition(input_shape.dimensionsCount() == 4);
  if (status != Ok)
    return status;
 
  status = utils::checkCondition(input_shape.dimensionsCount() == output_shape.dimensionsCount());
  if (status != Ok)
    return status;
 
  status = utils::checkCondition(input_shape.dimensionsCount() == weight_shape.dimensionsCount());
  if (status != Ok)
    return status;
 
  status = utils::checkCondition(bias == nullptr or weight_shape.dims(0) == bias_shape.flatSize());
 
  if (input->type() == circle::TensorType_FLOAT32)
    return status;
 
  auto input_quant = input->quantization();
  auto filter_quant = weight->quantization();
  auto output_quant = output->quantization();
 
  status = utils::checkCondition(input_quant != nullptr and filter_quant != nullptr and
                                 output_quant != nullptr);
  if (status != Ok)
    return status;
 
  auto input_scales = input_quant->scale();
  auto filter_scales = filter_quant->scale();
  auto output_scales = output_quant->scale();
 
  status = utils::checkCondition(input_scales != nullptr and filter_scales != nullptr and
                                 output_scales != nullptr);
  if (status != Ok)
    return status;
 
  // Support only per channel
  status = utils::checkCondition(filter_scales->size() > 1);
  if (status != Ok)
    return status;
 
  return status;
}