DepthwiseConv.h

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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.
 */
 
#ifndef __NNFW_CKER_TRAIN_OPERATION_DEPTHWISECONV_H__
#define __NNFW_CKER_TRAIN_OPERATION_DEPTHWISECONV_H__
 
#include "cker/eigen/depthwise_conv_op.h"
#include "cker/Shape.h"
#include "cker/Types.h"
 
namespace nnfw
{
namespace cker
{
namespace train
{
 
template <typename T>
void backpropInput(const DepthwiseConvParams &params, const Shape &incoming_shape,
                   const T *incoming_data, const Shape &filter_shape, const T *filter_data,
                   T *padded_filter_data, const Shape &grad_shape, T *grad_data, bool pad_filter,
                   T *filter_buffers_data, T *filter_dim_buffers_data)
{
  if (params.stride_height != params.stride_width)
    throw std::runtime_error("Not support different length strides");
 
  if (params.dilation_height_factor != 1 || params.dilation_width_factor != 1)
    throw std::runtime_error{"Not support dilation other than 1."};
 
  const int batch = MatchingDim(incoming_shape, 0, grad_shape, 0);
  const int input_depth = grad_shape.Dims(3);
  const int output_depth = incoming_shape.Dims(3);
  const int incoming_height = incoming_shape.Dims(1);
  const int incoming_width = incoming_shape.Dims(2);
  const int grad_height = grad_shape.Dims(1);
  const int grad_width = grad_shape.Dims(2);
  const int stride = params.stride_height;
  const int depth_multiplier = params.depth_multiplier;
  const int filter_height = filter_shape.Dims(1);
  const int filter_width = filter_shape.Dims(2);
  const int pad_height = params.padding_values.height;
  const int pad_width = params.padding_values.width;
 
  depthwise_conv_op::LaunchDepthwiseConvBackpropInputOp<Eigen::ThreadPoolDevice, T>()(
    batch, grad_height, grad_width, input_depth, filter_height, filter_width, depth_multiplier,
    stride, pad_height, pad_width, incoming_height, incoming_width, output_depth, incoming_data,
    filter_data, padded_filter_data, grad_data, pad_filter, filter_buffers_data,
    filter_dim_buffers_data);
}
 
template <typename T>
void backpropFilter(const DepthwiseConvParams &params, const Shape &incoming_shape,
                    const T *incoming_data, const Shape &input_shape, const T *input_data,
                    const Shape &filter_grad_shape, T *filter_grad_data, T *padded_filter_data,
                    T *filter_buffers_data)
{
  if (params.stride_height != params.stride_width)
    throw std::runtime_error("Not support different length strides");
 
  if (params.dilation_height_factor != 1 || params.dilation_width_factor != 1)
    throw std::runtime_error{"Not support dilation other than 1."};
 
  const int batch = MatchingDim(incoming_shape, 0, input_shape, 0);
  const int input_depth = input_shape.Dims(3);
  const int output_depth = incoming_shape.Dims(3);
  const int incoming_height = incoming_shape.Dims(1);
  const int incoming_width = incoming_shape.Dims(2);
  const int input_height = input_shape.Dims(1);
  const int input_width = input_shape.Dims(2);
  const int stride = params.stride_height;
  const int depth_multiplier = params.depth_multiplier;
  const int filter_height = filter_grad_shape.Dims(1);
  const int filter_width = filter_grad_shape.Dims(2);
  const int pad_height = params.padding_values.height;
  const int pad_width = params.padding_values.width;
 
  depthwise_conv_op::LaunchDepthwiseConvBackpropFilterOp<Eigen::ThreadPoolDevice, T>()(
    batch, input_height, input_width, input_depth, filter_height, filter_width, depth_multiplier,
    stride, pad_height, pad_width, incoming_height, incoming_width, output_depth, incoming_data,
    input_data, filter_grad_data, padded_filter_data, filter_buffers_data);
}
 
} // namespace train
} // namespace cker
} // namespace nnfw
 
#endif // __NNFW_CKER_TRAIN_OPERATION_DEPTHWISECONV_H__