Correct usage of torch::nn::functional::pad and torch::nn::functional::conv1d

21fa417e3fb06e56040c · December 4, 2019, 7:27am

Hi
The function exists below link , is still before release.
If you need the following function, either code it yourself or use the nightly build version.

//usage sample
torch ::nn::functional::detail ::pad( 
input_data.unsqueeze(1), {2,2,0,0},torch::nn::functional::PadFuncOptions().mode(torch::kReplicate));

github.com

pytorch/pytorch/blob/433baf1b9067774aecdeef8e32664827779ddb86/torch/csrc/api/include/torch/nn/functional/padding.h#L31




  if (padding.size() > 4) {
    input = torch::cat({input, _narrow_with_range(input, 4, 0, padding[-5 + padding.size()])}, /*dim=*/4);
    input = torch::cat({_narrow_with_range(input, 4, -(padding[-5 + padding.size()] + padding[-6 + padding.size()]), -padding[-5 + padding.size()]), input}, /*dim=*/4);
  }


  return input;
}


namespace detail {
inline Tensor pad(const Tensor& input,
                  IntArrayRef pad,
                  PadFuncOptions::mode_t mode,
                  double value) {
  TORCH_CHECK(pad.size() % 2 == 0, "Padding length must be divisible by 2");
  TORCH_CHECK(((int64_t)(pad.size() / 2)) <= input.dim(), "Padding length too large");
  if (c10::get_if<enumtype::kConstant>(&mode)) {
    return torch::constant_pad_nd(input, pad, value);
  } else {
    TORCH_CHECK(
      value == 0,