main.cpp

#include <iostream>
#include <torch/torch.h>
#include <torchvision/vision.h>
#include <torchvision/ops/nms.h>
#include <torch/script.h>

#include <ATen/core/dispatch/Dispatcher.h>
#include <torch/library.h>
#include <torch/types.h>

#include <ATen/ATen.h>
#include <torchvision/ops/cpu/roi_align_common.h>

namespace vision {
namespace ops {
namespace {

template <typename T>
void roi_align_forward_kernel_impl(
    int n_rois,
    const T* input,
    const T& spatial_scale,
    int channels,
    int height,
    int width,
    int pooled_height,
    int pooled_width,
    int sampling_ratio,
    bool aligned,
    const T* rois,
    T* output) {a
#pragma omp parallel for num_threads(8)
  for (int n = 0; n < n_rois; n++) {
    int index_n = n * channels * pooled_width * pooled_height;

    const T* offset_rois = rois + n * 5;
    int roi_batch_ind = offset_rois[0];

    T offset = aligned ? (T)0.5 : (T)0.0;
    T roi_start_w = offset_rois[1] * spatial_scale - offset;
    T roi_start_h = offset_rois[2] * spatial_scale - offset;
    T roi_end_w = offset_rois[3] * spatial_scale - offset;
    T roi_end_h = offset_rois[4] * spatial_scale - offset;

    T roi_width = roi_end_w - roi_start_w;
    T roi_height = roi_end_h - roi_start_h;
    if (!aligned) {
      // Force malformed ROIs to be 1x1
      roi_width = std::max(roi_width, (T)1.);
      roi_height = std::max(roi_height, (T)1.);
    }

    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);

    int roi_bin_grid_h = (sampling_ratio > 0)
        ? sampling_ratio
        : ceil(roi_height / pooled_height); // e.g., = 2
    int roi_bin_grid_w =
        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);

    const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4

    std::vector<detail::PreCalc<T>> pre_calc(
        roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
    detail::pre_calc_for_bilinear_interpolate(
        height,
        width,
        pooled_height,
        pooled_width,
        roi_start_h,
        roi_start_w,
        bin_size_h,
        bin_size_w,
        roi_bin_grid_h,
        roi_bin_grid_w,
        pre_calc);

    for (int c = 0; c < channels; c++) {
      int index_n_c = index_n + c * pooled_width * pooled_height;
      const T* offset_input =
          input + (roi_batch_ind * channels + c) * height * width;
      int pre_calc_index = 0;

      for (int ph = 0; ph < pooled_height; ph++) {
        for (int pw = 0; pw < pooled_width; pw++) {
          int index = index_n_c + ph * pooled_width + pw;

          T output_val = 0.;
          for (int iy = 0; iy < roi_bin_grid_h; iy++) {
            for (int ix = 0; ix < roi_bin_grid_w; ix++) {
              detail::PreCalc<T> pc = pre_calc[pre_calc_index];
              output_val += pc.w1 * offset_input[pc.pos1] +
                  pc.w2 * offset_input[pc.pos2] +
                  pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4];

              pre_calc_index += 1;
            }
          }
          output_val /= count;
          output[index] = output_val;
        } 
      } 
    } 
  } 
}


template <class T>
inline void add(T* address, const T& val) {
  *address += val;
}


at::Tensor roi_align_forward_kernel(
    const at::Tensor& input,
    const at::Tensor& rois,
    double spatial_scale,
    int64_t pooled_height,
    int64_t pooled_width,
    int64_t sampling_ratio,
    bool aligned) {
  TORCH_CHECK(input.device().is_cpu(), "input must be a CPU tensor");
  TORCH_CHECK(rois.device().is_cpu(), "rois must be a CPU tensor");
  TORCH_CHECK(rois.size(1) == 5, "rois must have shape as Tensor[K, 5]");

  at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2};

  at::CheckedFrom c = "roi_align_forward_kernel";
  at::checkAllSameType(c, {input_t, rois_t});

  auto num_rois = rois.size(0);
  auto channels = input.size(1);
  auto height = input.size(2);
  auto width = input.size(3);

  at::Tensor output = at::zeros(
      {num_rois, channels, pooled_height, pooled_width}, input.options());

  if (output.numel() == 0)
    return output;

  auto input_ = input.contiguous(), rois_ = rois.contiguous();
  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
      input.scalar_type(), "roi_align_forward_kernel", [&] {
        roi_align_forward_kernel_impl<scalar_t>(
            num_rois,
            input_.data_ptr<scalar_t>(),
            spatial_scale,
            channels,
            height,
            width,
            pooled_height,
            pooled_width,
            sampling_ratio,
            aligned,
            rois_.data_ptr<scalar_t>(),
            output.data_ptr<scalar_t>());
      });
  return output;
}

} // namespace
} // namespace ops
} // namespace vision

int main(){
  torch::manual_seed(0);
  
  torch::jit::script::Module tensors = torch::jit::load("/home/edmc/tensors.pth");
  c10::IValue feats = tensors.attr("cr_features");
  torch::Tensor feat_ts = feats.toTensor();
  
  c10::IValue boxes = tensors.attr("cr_proposal");
  torch::Tensor boxes_ts = boxes.toTensor();
  
  std::cout << boxes_ts << std::endl;

  double spatial_scale = 1.0;
  int64_t pooled_height = 2, pooled_width = 2, sampling_ratio = -1;
  bool aligned = false;
  
  at::Tensor out = vision::ops::roi_align_forward_kernel(feat_ts, boxes_ts, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned);
  std::cout << out;
}