// Copyright (c) 2019 PaddlePaddle 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 "lite/operators/conv_op.h"
#include <algorithm>
#include <vector>
#include "lite/core/op_registry.h"

namespace paddle {
namespace lite {
namespace operators {

bool ConvOpLite::CheckShape() const {
  CHECK_OR_FALSE(param_.x);
  CHECK_OR_FALSE(param_.output);
  CHECK_OR_FALSE(param_.filter);
  // bias is optional.

  const auto in_dims = param_.x->dims();
  const auto filter_dims = param_.filter->dims();

  CHECK_OR_FALSE(in_dims.size() == 4 || in_dims.size() == 5);

  CHECK_EQ_OR_FALSE(in_dims.size(), filter_dims.size());
  CHECK_OR_FALSE(in_dims.size() - param_.strides.size() == 2U);
  CHECK_EQ_OR_FALSE(filter_dims.size(), 4UL);

  return true;
}

inline int ConvOutputSize(
    int input_size, int filter_size, int dilation, int padding, int stride) {
  const int dkernel = dilation * (filter_size - 1) + 1;
  int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
  // CHECK_GT_OR_FALSE(output_size, 0);

  return output_size;
}

inline void UpdatePaddingAndDilation(std::vector<int>* paddings,
                                     std::vector<int>* dilations,
                                     const std::vector<int>& strides,
                                     const std::string padding_algorithm,
                                     const lite::DDim data_dims,
                                     const lite::DDim& ksize) {
  // when padding_desc is "VALID" or "SAME"
  if (padding_algorithm == "SAME") {
    for (size_t i = 0; i < strides.size(); ++i) {
      int out_size = (data_dims[i + 2] + strides[i] - 1) / strides[i];
      int pad_sum =
          std::max((out_size - 1) * strides[i] + ksize[i] - data_dims[i + 2],
                   (int64_t)0);
      // pad
      *(paddings->begin() + i) = pad_sum / 2;
      // dilation
      *(dilations->begin() + i) = 1;
    }
  } else if (padding_algorithm == "VALID") {
    for (auto& it : *paddings) {
      it = 0;
    }
  }
}

bool ConvOpLite::InferShape() const {
  const auto in_dims = param_.x->dims();
  const auto filter_dims = param_.filter->dims();

  UpdatePaddingAndDilation(&param_.paddings,
                           &param_.dilations,
                           param_.strides,
                           padding_algorithm_,
                           in_dims,
                           filter_dims);
  std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
  for (size_t i = 0; i < param_.strides.size(); ++i) {
    output_shape.push_back(ConvOutputSize(in_dims[i + 2],
                                          filter_dims[i + 2],
                                          param_.dilations[i],
                                          param_.paddings[i],
                                          param_.strides[i]));
  }

  // Set output dims
  param_.output->Resize(lite::DDim(output_shape));

  // share LoD
  // param_.output->set_lod(param_.x->lod());
  return true;
}

}  // namespace operators
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_OP(conv2d, paddle::lite::operators::ConvOpLite);
REGISTER_LITE_OP(depthwise_conv2d, paddle::lite::operators::ConvOpLite);