/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 "Function.h"
#include "GemmConvOp.h"

namespace paddle {

/*
 * imData = [input_channels, input_height, input_width]
 * colData = [output_height, output_width,
 *            input_channels, filter_height, filter_width]
 */
template <class T>
class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
public:
  void operator()(const T* imData,
                  int inputChannels,
                  int inputHeight,
                  int inputWidth,
                  int filterHeight,
                  int filterWidth,
                  int strideHeight,
                  int strideWidth,
                  int paddingHeight,
                  int paddingWidth,
                  int outputHeight,
                  int outputWidth,
                  T* colData) {
    for (int outputH = 0; outputH < outputHeight; ++outputH) {
      for (int outputW = 0; outputW < outputWidth; ++outputW) {
        for (int channel = 0; channel < inputChannels; ++channel) {
          for (int filterH = 0; filterH < filterHeight; ++filterH) {
            for (int filterW = 0; filterW < filterWidth; ++filterW) {
              int imRowOffset =
                  outputH * strideHeight + filterH - paddingHeight;
              int imColOffset = outputW * strideWidth + filterW - paddingWidth;
              int colDataOffset =
                  (((outputH * outputWidth + outputW) * inputChannels +
                    channel) *
                       filterHeight +
                   filterH) *
                      filterWidth +
                  filterW;
              if (imRowOffset < 0 || imRowOffset >= inputHeight ||
                  imColOffset < 0 || imColOffset >= inputWidth) {
                colData[colDataOffset] = T(0);
              } else {
                int imDataOffset =
                    (channel * inputHeight + imRowOffset) * inputWidth +
                    imColOffset;
                colData[colDataOffset] = imData[imDataOffset];
              }
            }
          }
        }
      }
    }
  }
};

/*
 * \brief Converts the image data of four dimensions(NCHW) into
 *        a sequence data of three dimensions(NST). Where N is batch size,
 *        S is the length of the sequence after each image is expanded,
 *        T is the size of each time step in the sequence.
 *
 * \param inputs[0]  Image data of NCHW format.
 * \param outputs[0] Sequence data of NST format.
 */
template <DeviceType Device>
class ImageExpandFunction : public FunctionBase {
public:
  void init(const FuncConfig& config) override {
    // function arguments
    strides_ = config.get<std::vector<size_t>>("strides");
    paddings_ = config.get<std::vector<size_t>>("paddings");
    blocks_ = config.get<std::vector<size_t>>("blocks");

    // number of inputs and outputs
    numInputs_ = 1;
    numOutputs_ = 1;
  }

  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
    CHECK_EQ(numInputs_, inputs.size());
    CHECK_EQ(numOutputs_, outputs.size());
    const TensorShape& input = inputs[0].shape();
    const TensorShape& output = outputs[0].shape();
    // input argument should be 4-dimensional.
    CHECK_EQ(input.ndims(), (size_t)4);
    // output argument should be 3-dimensional.
    CHECK_EQ(output.ndims(), (size_t)3);
    // The batchSize of the input needs to be equal to
    // the batchSize of the output.
    CHECK_EQ(input[0], output[0]);

    size_t batchSize = input[0];
    size_t inputChannels = input[1];
    size_t inputHeight = input[2];
    size_t inputWidth = input[3];
    size_t seqLength = output[1];
    size_t stepSize = output[2];
    size_t outputHeight =
        1 +
        (inputHeight + 2 * paddingH() - blockH() + strideH() - 1) / strideH();
    size_t outputWidth =
        1 +
        (inputWidth + 2 * paddingW() - blockW() + strideW() - 1) / strideW();
    CHECK_EQ(seqLength, outputHeight * outputWidth);
    CHECK_EQ(stepSize, inputChannels * blockH() * blockW());

    real* inputData = inputs[0].data<real>();
    real* outputData = outputs[0].data<real>();
    Im2ColFunctor<kOCF, Device, real> im2col;
    for (size_t i = 0; i < batchSize; i++) {
      // The result of im2col is [output_height, output_width,
      // input_channels, filter_height, filter_width], and it is easy to
      // reshape into [seqLength, stepSize], where seqLength is equal
      // output_height * output_width, stepSize is equal
      // input_channels * filter_height * filter_width
      im2col(inputData,
             inputChannels,
             inputHeight,
             inputWidth,
             blockH(),
             blockW(),
             strideH(),
             strideW(),
             paddingH(),
             paddingW(),
             outputHeight,
             outputWidth,
             outputData);
      inputData += inputChannels * inputHeight * inputWidth;
      outputData += seqLength * stepSize;
    }
  }

protected:
  std::vector<size_t> strides_;
  std::vector<size_t> paddings_;
  std::vector<size_t> blocks_;

  inline int strideH() const { return strides_[0]; }

  inline int strideW() const { return strides_[1]; }

  inline int paddingH() const { return paddings_[0]; }

  inline int paddingW() const { return paddings_[1]; }

  inline int blockH() const { return blocks_[0]; }

  inline int blockW() const { return blocks_[1]; }
};

REGISTER_TYPED_FUNC(ImageExpand, CPU, ImageExpandFunction);

}  // namespace paddle