cpu_adaptive_avg_pool3d_backward Class — pytorch Architecture

Source Code

aten/src/ATen/native/cpu/AdaptiveAvgPoolKernel.cpp lines 681–738

template <typename scalar_t>
void cpu_adaptive_avg_pool3d_backward(
    Tensor& grad_input_,
    const Tensor& grad_output_) {
  auto grad_output = grad_output_.contiguous();
  auto grad_input = grad_input_.contiguous();

  auto grad_output_data = grad_output.data_ptr<scalar_t>();
  auto grad_input_data = grad_input.mutable_data_ptr<scalar_t>();

  int64_t ndim = grad_output.ndimension();
  // treat batch size and channels as one dimension
  int64_t channels = ndim == 4 ? grad_output.size(0) : grad_output.size(0) * grad_output.size(1);
  int64_t input_depth = grad_input.size(-3);
  int64_t input_height = grad_input.size(-2);
  int64_t input_width = grad_input.size(-1);
  int64_t output_depth = grad_output.size(-3);
  int64_t output_height = grad_output.size(-2);
  int64_t output_width = grad_output.size(-1);

  // parallel on dim of N, C
  at::parallel_for(0, channels, 0, [&](int64_t begin, int64_t end) {
    for (const auto c : c10::irange(begin, end)) {
      scalar_t* grad_input_ptr = grad_input_data + c * input_depth * input_height * input_width;
      scalar_t* grad_output_ptr = grad_output_data + c * output_depth * output_height * output_width;

      for (const auto od : c10::irange(output_depth)) {
        int64_t id0 = start_index(od, output_depth, input_depth);
        int64_t id1 = end_index(od, output_depth, input_depth);
        int64_t kd = id1 - id0;
        for (const auto oh : c10::irange(output_height)) {
          int64_t ih0 = start_index(oh, output_height, input_height);
          int64_t ih1 = end_index(oh, output_height, input_height);
          int64_t kh = ih1 - ih0;

          for (const auto ow : c10::irange(output_width)) {
            int64_t iw0 = start_index(ow, output_width, input_width);
            int64_t iw1 = end_index(ow, output_width, input_width);
            int64_t kw = iw1 - iw0;

            scalar_t grad_delta = grad_output_ptr[od * output_width * output_height + oh * output_width + ow] / kd / kh / kw;
            for (const auto id : c10::irange(id0, id1)) {
              for (const auto ih : c10::irange(ih0, ih1)) {
                for (const auto iw : c10::irange(iw0, iw1)) {
                  grad_input_ptr[id * input_height * input_width + ih * input_width + iw] += grad_delta;
                }
              }
            }
          }
        }
      }
    }
  });

  if (!grad_input_.is_contiguous()) {
    grad_input_.copy_(grad_input);
  }
}