conv_kernel.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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#include "operators/kernel/conv_kernel.h"

namespace paddle_mobile {
namespace operators {

bool IsExpand(const std::vector<int64_t> &filter_dim,
              const std::vector<int> &strides, const std::vector<int> &paddings,
              const std::vector<int> &dilations) {
  bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
  for (size_t j = 0; j < strides.size(); ++j) {
    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
    strides_1 = strides_1 && (strides[j] == 1);
    padding_0 = padding_0 && (paddings[j] == 0);
    dilation_1 = dilation_1 && (dilations[j] == 1);
  }
  return !(filter_1 && strides_1 && padding_0 && dilation_1);
}

template <> void ConvKernel<CPU, float>::Compute(const ConvParam &param) const {
  LOG(kLOG_DEBUG) << param;

  const Tensor *input = param.Input();

  // The filter will be reshaped in the calculations,
  // so here use an assignment operation,
  // that avoids modifying the variable in the Scope.
  Tensor filter = *param.Filter();

  Tensor *output = param.Output();
  //            output->mutable_data<T>(context.GetPlace());

  int groups = param.Groups();
  std::vector<int> strides = param.Strides();
  std::vector<int> paddings = param.Paddings();
  std::vector<int> dilations = param.Dilations();

  DLOG << " compute end get Attrs " << strides[0];

  const int batch_size = static_cast<int>(input->dims()[0]);

  // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
  // k_w}
  std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
  // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
  // o_w}
  std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));

  // use col_shape in the im2col calculation
  // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
  // k_w, o_d,
  // o_h, o_w}
  size_t data_dim = filter_shape_vec.size() - 2;
  std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
  col_shape_vec[0] = input->dims()[1] / groups;
  for (size_t j = 0; j < data_dim; ++j) {
    col_shape_vec[j + 1] = filter_shape_vec[j + 2];
    col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
  }
  framework::DDim col_shape(framework::make_ddim(col_shape_vec));

  // use col_matrix_shape in the gemm calculation
  // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
  // o_d *
  // o_h * o_w)
  framework::DDim col_matrix_shape =
      framework::flatten_to_2d(col_shape, data_dim + 1);

  bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
  Tensor col;
  // col_matrix shares the same piece of data with col,
  // but will be reshaped into a two-dimensional matrix shape
  // to call the matrix multiplication interface.
  Tensor col_matrix;
  if (is_expand) {
    col.mutable_data<float>(col_shape);
    col_matrix.ShareDataWith(col);
    col_matrix.Resize(col_matrix_shape);
  }

  framework::DDim input_shape = framework::slice_ddim(
      input->dims(), 1, static_cast<int>(input->dims().size()));

  framework::DDim filter_matrix_shape = {filter.dims()[0],
                                         filter.numel() / filter.dims()[0]};
  filter.Resize(filter_matrix_shape);

  framework::DDim output_matrix_shape = {
      output->dims()[1],
      output->numel() / (output->dims()[0] * output->dims()[1])};

  // convolution operator: im2col(or vol2col) + gemm
  int in_step = static_cast<int>(input->dims()[1]) / groups;
  int out_step = static_cast<int>(output->dims()[1]) / groups;

  math::Vol2ColFunctor<CPU, float> vol2col;
  math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;

  //            auto& dev_ctx = context.template
  //            device_context<DeviceContext>();
  for (int i = 0; i < batch_size; i++) {
    Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
    Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);

    for (int g = 0; g < groups; g++) {
      Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);

      if (!is_expand) {
        col.ShareDataWith(in_slice);
        col_matrix.ShareDataWith(col);
        col_matrix.Resize(col_matrix_shape);
      } else if (data_dim == 2U) {
        // im2col
        im2col(in_slice, dilations, strides,
               std::vector<int>{paddings[0], paddings[1], paddings[0],
                                paddings[1]},
               &col);
      } else if (data_dim == 3U) {
        // vol2col
        vol2col(in_slice, dilations, strides, paddings, &col);
      }

      // gemm
      Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
      Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
      math::matmul<float>(filter_slice, false, col_matrix, false, float(1.0),
                          &out_slice, float(0.0));
    }
  }
}

template class ConvKernel<CPU, float>;

} // namespace operators
} // namespace paddle_mobile