basicsr/ops/dcn/src/deform_conv_cuda.cpp

// modify from
// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c

#include <torch/extension.h>
#include <ATen/DeviceGuard.h>

#include <cmath>
#include <vector>

void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset,

                       const int channels, const int height, const int width,

                       const int ksize_h, const int ksize_w, const int pad_h,

                       const int pad_w, const int stride_h, const int stride_w,

                       const int dilation_h, const int dilation_w,

                       const int parallel_imgs, const int deformable_group,

                       at::Tensor data_col);

void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset,

                       const int channels, const int height, const int width,

                       const int ksize_h, const int ksize_w, const int pad_h,

                       const int pad_w, const int stride_h, const int stride_w,

                       const int dilation_h, const int dilation_w,

                       const int parallel_imgs, const int deformable_group,

                       at::Tensor grad_im);

void deformable_col2im_coord(

    const at::Tensor data_col, const at::Tensor data_im,

    const at::Tensor data_offset, const int channels, const int height,

    const int width, const int ksize_h, const int ksize_w, const int pad_h,

    const int pad_w, const int stride_h, const int stride_w,

    const int dilation_h, const int dilation_w, const int parallel_imgs,

    const int deformable_group, at::Tensor grad_offset);

void modulated_deformable_im2col_cuda(

    const at::Tensor data_im, const at::Tensor data_offset,

    const at::Tensor data_mask, const int batch_size, const int channels,

    const int height_im, const int width_im, const int height_col,

    const int width_col, const int kernel_h, const int kenerl_w,

    const int pad_h, const int pad_w, const int stride_h, const int stride_w,

    const int dilation_h, const int dilation_w, const int deformable_group,

    at::Tensor data_col);

void modulated_deformable_col2im_cuda(

    const at::Tensor data_col, const at::Tensor data_offset,

    const at::Tensor data_mask, const int batch_size, const int channels,

    const int height_im, const int width_im, const int height_col,

    const int width_col, const int kernel_h, const int kenerl_w,

    const int pad_h, const int pad_w, const int stride_h, const int stride_w,

    const int dilation_h, const int dilation_w, const int deformable_group,

    at::Tensor grad_im);

void modulated_deformable_col2im_coord_cuda(

    const at::Tensor data_col, const at::Tensor data_im,

    const at::Tensor data_offset, const at::Tensor data_mask,

    const int batch_size, const int channels, const int height_im,

    const int width_im, const int height_col, const int width_col,

    const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,

    const int stride_h, const int stride_w, const int dilation_h,

    const int dilation_w, const int deformable_group, at::Tensor grad_offset,

    at::Tensor grad_mask);

void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput,

                 at::Tensor weight, int kH, int kW, int dH, int dW, int padH,

                 int padW, int dilationH, int dilationW, int group,

                 int deformable_group) {
  TORCH_CHECK(weight.ndimension() == 4,
           "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
           "but got: %s",
           weight.ndimension());

  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");

  TORCH_CHECK(kW > 0 && kH > 0,
           "kernel size should be greater than zero, but got kH: %d kW: %d", kH,
           kW);

  TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
           "kernel size should be consistent with weight, ",
           "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
           kW, weight.size(2), weight.size(3));

  TORCH_CHECK(dW > 0 && dH > 0,
           "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);

  TORCH_CHECK(
      dilationW > 0 && dilationH > 0,
      "dilation should be greater than 0, but got dilationH: %d dilationW: %d",
      dilationH, dilationW);

  int ndim = input.ndimension();
  int dimf = 0;
  int dimh = 1;
  int dimw = 2;

  if (ndim == 4) {
    dimf++;
    dimh++;
    dimw++;
  }

  TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
           ndim);

  long nInputPlane = weight.size(1) * group;
  long inputHeight = input.size(dimh);
  long inputWidth = input.size(dimw);
  long nOutputPlane = weight.size(0);
  long outputHeight =
      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
  long outputWidth =
      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;

  TORCH_CHECK(nInputPlane % deformable_group == 0,
           "input channels must divide deformable group size");

  if (outputWidth < 1 || outputHeight < 1)
    AT_ERROR(
        "Given input size: (%ld x %ld x %ld). "
        "Calculated output size: (%ld x %ld x %ld). Output size is too small",
        nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
        outputWidth);

  TORCH_CHECK(input.size(1) == nInputPlane,
           "invalid number of input planes, expected: %d, but got: %d",
           nInputPlane, input.size(1));

  TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
           "input image is smaller than kernel");

  TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
           "invalid spatial size of offset, expected height: %d width: %d, but "
           "got height: %d width: %d",
           outputHeight, outputWidth, offset.size(2), offset.size(3));

  TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
           "invalid number of channels of offset");

  if (gradOutput != NULL) {
    TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane,
             "invalid number of gradOutput planes, expected: %d, but got: %d",
             nOutputPlane, gradOutput->size(dimf));

    TORCH_CHECK((gradOutput->size(dimh) == outputHeight &&
              gradOutput->size(dimw) == outputWidth),
             "invalid size of gradOutput, expected height: %d width: %d , but "
             "got height: %d width: %d",
             outputHeight, outputWidth, gradOutput->size(dimh),
             gradOutput->size(dimw));
  }
}

int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,

                             at::Tensor offset, at::Tensor output,

                             at::Tensor columns, at::Tensor ones, int kW,

                             int kH, int dW, int dH, int padW, int padH,

                             int dilationW, int dilationH, int group,

                             int deformable_group, int im2col_step) {
  // todo: resize columns to include im2col: done
  // todo: add im2col_step as input
  // todo: add new output buffer and transpose it to output (or directly
  // transpose output) todo: possibly change data indexing because of
  // parallel_imgs

  shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW,
              dilationH, dilationW, group, deformable_group);
  at::DeviceGuard guard(input.device());

  input = input.contiguous();
  offset = offset.contiguous();
  weight = weight.contiguous();

  int batch = 1;
  if (input.ndimension() == 3) {
    // Force batch
    batch = 0;
    input.unsqueeze_(0);
    offset.unsqueeze_(0);
  }

  // todo: assert batchsize dividable by im2col_step

  long batchSize = input.size(0);
  long nInputPlane = input.size(1);
  long inputHeight = input.size(2);
  long inputWidth = input.size(3);

  long nOutputPlane = weight.size(0);

  long outputWidth =
      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
  long outputHeight =
      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");

  output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
                        outputHeight, outputWidth});
  columns = at::zeros(
      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
      input.options());

  if (ones.ndimension() != 2 ||
      ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
    ones = at::ones({outputHeight, outputWidth}, input.options());
  }

  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
                      inputHeight, inputWidth});
  offset =
      offset.view({batchSize / im2col_step, im2col_step,
                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  at::Tensor output_buffer =
      at::zeros({batchSize / im2col_step, nOutputPlane,
                 im2col_step * outputHeight, outputWidth},
                output.options());

  output_buffer = output_buffer.view(
      {output_buffer.size(0), group, output_buffer.size(1) / group,
       output_buffer.size(2), output_buffer.size(3)});

  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
                      dilationW, im2col_step, deformable_group, columns);

    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
    weight = weight.view({group, weight.size(0) / group, weight.size(1),
                          weight.size(2), weight.size(3)});

    for (int g = 0; g < group; g++) {
      output_buffer[elt][g] = output_buffer[elt][g]
                                  .flatten(1)
                                  .addmm_(weight[g].flatten(1), columns[g])
                                  .view_as(output_buffer[elt][g]);
    }
  }

  output_buffer = output_buffer.view(
      {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
       output_buffer.size(3), output_buffer.size(4)});

  output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
                                      im2col_step, outputHeight, outputWidth});
  output_buffer.transpose_(1, 2);
  output.copy_(output_buffer);
  output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});

  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
  offset = offset.view(
      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  if (batch == 0) {
    output = output.view({nOutputPlane, outputHeight, outputWidth});
    input = input.view({nInputPlane, inputHeight, inputWidth});
    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
  }

  return 1;
}

int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,

                                    at::Tensor gradOutput, at::Tensor gradInput,

                                    at::Tensor gradOffset, at::Tensor weight,

                                    at::Tensor columns, int kW, int kH, int dW,

                                    int dH, int padW, int padH, int dilationW,

                                    int dilationH, int group,

                                    int deformable_group, int im2col_step) {
  shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW,
              dilationH, dilationW, group, deformable_group);
  at::DeviceGuard guard(input.device());

  input = input.contiguous();
  offset = offset.contiguous();
  gradOutput = gradOutput.contiguous();
  weight = weight.contiguous();

  int batch = 1;

  if (input.ndimension() == 3) {
    // Force batch
    batch = 0;
    input = input.view({1, input.size(0), input.size(1), input.size(2)});
    offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
    gradOutput = gradOutput.view(
        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
  }

  long batchSize = input.size(0);
  long nInputPlane = input.size(1);
  long inputHeight = input.size(2);
  long inputWidth = input.size(3);

  long nOutputPlane = weight.size(0);

  long outputWidth =
      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
  long outputHeight =
      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

  TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
  columns = at::zeros(
      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
      input.options());

  // change order of grad output
  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
                                nOutputPlane, outputHeight, outputWidth});
  gradOutput.transpose_(1, 2);

  gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
                              inputHeight, inputWidth});
  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
                      inputHeight, inputWidth});
  gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
                                deformable_group * 2 * kH * kW, outputHeight,
                                outputWidth});
  offset =
      offset.view({batchSize / im2col_step, im2col_step,
                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
    // divide into groups
    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
    weight = weight.view({group, weight.size(0) / group, weight.size(1),
                          weight.size(2), weight.size(3)});
    gradOutput = gradOutput.view(
        {gradOutput.size(0), group, gradOutput.size(1) / group,
         gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});

    for (int g = 0; g < group; g++) {
      columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
                                     gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
    }

    columns =
        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
    gradOutput = gradOutput.view(
        {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
         gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});

    deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
                            inputHeight, inputWidth, kH, kW, padH, padW, dH, dW,
                            dilationH, dilationW, im2col_step, deformable_group,
                            gradOffset[elt]);

    deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
                      dilationW, im2col_step, deformable_group, gradInput[elt]);
  }

  gradOutput.transpose_(1, 2);
  gradOutput =
      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});

  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
  gradOffset = gradOffset.view(
      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
  offset = offset.view(
      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  if (batch == 0) {
    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
    input = input.view({nInputPlane, inputHeight, inputWidth});
    gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
    gradOffset =
        gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
  }

  return 1;
}

int deform_conv_backward_parameters_cuda(

    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,

    at::Tensor gradWeight,  // at::Tensor gradBias,

    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,

    int padW, int padH, int dilationW, int dilationH, int group,

    int deformable_group, float scale, int im2col_step) {
  // todo: transpose and reshape outGrad
  // todo: reshape columns
  // todo: add im2col_step as input

  shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH,
              padW, dilationH, dilationW, group, deformable_group);
  at::DeviceGuard guard(input.device());

  input = input.contiguous();
  offset = offset.contiguous();
  gradOutput = gradOutput.contiguous();

  int batch = 1;

  if (input.ndimension() == 3) {
    // Force batch
    batch = 0;
    input = input.view(
        at::IntList({1, input.size(0), input.size(1), input.size(2)}));
    gradOutput = gradOutput.view(
        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
  }

  long batchSize = input.size(0);
  long nInputPlane = input.size(1);
  long inputHeight = input.size(2);
  long inputWidth = input.size(3);

  long nOutputPlane = gradWeight.size(0);

  long outputWidth =
      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
  long outputHeight =
      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");

  columns = at::zeros(
      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
      input.options());

  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
                                nOutputPlane, outputHeight, outputWidth});
  gradOutput.transpose_(1, 2);

  at::Tensor gradOutputBuffer = at::zeros_like(gradOutput);
  gradOutputBuffer =
      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
                             outputHeight, outputWidth});
  gradOutputBuffer.copy_(gradOutput);
  gradOutputBuffer =
      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
                             im2col_step * outputHeight, outputWidth});

  gradOutput.transpose_(1, 2);
  gradOutput =
      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});

  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
                      inputHeight, inputWidth});
  offset =
      offset.view({batchSize / im2col_step, im2col_step,
                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
                      dilationW, im2col_step, deformable_group, columns);

    // divide into group
    gradOutputBuffer = gradOutputBuffer.view(
        {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
         gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
    gradWeight =
        gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
                         gradWeight.size(2), gradWeight.size(3)});

    for (int g = 0; g < group; g++) {
      gradWeight[g] = gradWeight[g]
                          .flatten(1)
                          .addmm_(gradOutputBuffer[elt][g].flatten(1),
                                  columns[g].transpose(1, 0), 1.0, scale)
                          .view_as(gradWeight[g]);
    }
    gradOutputBuffer = gradOutputBuffer.view(
        {gradOutputBuffer.size(0),
         gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
         gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
    columns =
        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
    gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
                                  gradWeight.size(2), gradWeight.size(3),
                                  gradWeight.size(4)});
  }

  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
  offset = offset.view(
      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});

  if (batch == 0) {
    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
    input = input.view({nInputPlane, inputHeight, inputWidth});
  }

  return 1;
}

void modulated_deform_conv_cuda_forward(

    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,

    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,

    int kernel_h, int kernel_w, const int stride_h, const int stride_w,

    const int pad_h, const int pad_w, const int dilation_h,

    const int dilation_w, const int group, const int deformable_group,

    const bool with_bias) {
  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
  at::DeviceGuard guard(input.device());

  const int batch = input.size(0);
  const int channels = input.size(1);
  const int height = input.size(2);
  const int width = input.size(3);

  const int channels_out = weight.size(0);
  const int channels_kernel = weight.size(1);
  const int kernel_h_ = weight.size(2);
  const int kernel_w_ = weight.size(3);

  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
    AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
             kernel_h_, kernel_w, kernel_h_, kernel_w_);
  if (channels != channels_kernel * group)
    AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
             channels, channels_kernel * group);

  const int height_out =
      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
  const int width_out =
      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;

  if (ones.ndimension() != 2 ||
      ones.size(0) * ones.size(1) < height_out * width_out) {
    // Resize plane and fill with ones...
    ones = at::ones({height_out, width_out}, input.options());
  }

  // resize output
  output = output.view({batch, channels_out, height_out, width_out}).zero_();
  // resize temporary columns
  columns =
      at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
                input.options());

  output = output.view({output.size(0), group, output.size(1) / group,
                        output.size(2), output.size(3)});

  for (int b = 0; b < batch; b++) {
    modulated_deformable_im2col_cuda(
        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
        dilation_h, dilation_w, deformable_group, columns);

    // divide into group
    weight = weight.view({group, weight.size(0) / group, weight.size(1),
                          weight.size(2), weight.size(3)});
    columns = columns.view({group, columns.size(0) / group, columns.size(1)});

    for (int g = 0; g < group; g++) {
      output[b][g] = output[b][g]
                         .flatten(1)
                         .addmm_(weight[g].flatten(1), columns[g])
                         .view_as(output[b][g]);
    }

    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
                          weight.size(3), weight.size(4)});
    columns =
        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
  }

  output = output.view({output.size(0), output.size(1) * output.size(2),
                        output.size(3), output.size(4)});

  if (with_bias) {
    output += bias.view({1, bias.size(0), 1, 1});
  }
}

void modulated_deform_conv_cuda_backward(

    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,

    at::Tensor offset, at::Tensor mask, at::Tensor columns,

    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,

    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,

    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,

    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,

    const bool with_bias) {
  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
  at::DeviceGuard guard(input.device());

  const int batch = input.size(0);
  const int channels = input.size(1);
  const int height = input.size(2);
  const int width = input.size(3);

  const int channels_kernel = weight.size(1);
  const int kernel_h_ = weight.size(2);
  const int kernel_w_ = weight.size(3);
  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
    AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
             kernel_h_, kernel_w, kernel_h_, kernel_w_);
  if (channels != channels_kernel * group)
    AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
             channels, channels_kernel * group);

  const int height_out =
      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
  const int width_out =
      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;

  if (ones.ndimension() != 2 ||
      ones.size(0) * ones.size(1) < height_out * width_out) {
    // Resize plane and fill with ones...
    ones = at::ones({height_out, width_out}, input.options());
  }

  grad_input = grad_input.view({batch, channels, height, width});
  columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
                      input.options());

  grad_output =
      grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
                        grad_output.size(2), grad_output.size(3)});

  for (int b = 0; b < batch; b++) {
    // divide int group
    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
    weight = weight.view({group, weight.size(0) / group, weight.size(1),
                          weight.size(2), weight.size(3)});

    for (int g = 0; g < group; g++) {
      columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
                        grad_output[b][g].flatten(1), 0.0f, 1.0f);
    }

    columns =
        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
                          weight.size(3), weight.size(4)});

    // gradient w.r.t. input coordinate data
    modulated_deformable_col2im_coord_cuda(
        columns, input[b], offset[b], mask[b], 1, channels, height, width,
        height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
        stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
        grad_mask[b]);
    // gradient w.r.t. input data
    modulated_deformable_col2im_cuda(
        columns, offset[b], mask[b], 1, channels, height, width, height_out,
        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
        dilation_h, dilation_w, deformable_group, grad_input[b]);

    // gradient w.r.t. weight, dWeight should accumulate across the batch and
    // group
    modulated_deformable_im2col_cuda(
        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
        dilation_h, dilation_w, deformable_group, columns);

    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
    grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
                                    grad_weight.size(1), grad_weight.size(2),
                                    grad_weight.size(3)});
    if (with_bias)
      grad_bias = grad_bias.view({group, grad_bias.size(0) / group});

    for (int g = 0; g < group; g++) {
      grad_weight[g] =
          grad_weight[g]
              .flatten(1)
              .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
              .view_as(grad_weight[g]);
      if (with_bias) {
        grad_bias[g] =
            grad_bias[g]
                .view({-1, 1})
                .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
                .view(-1);
      }
    }

    columns =
        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
    grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
                                    grad_weight.size(2), grad_weight.size(3),
                                    grad_weight.size(4)});
    if (with_bias)
      grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
  }
  grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
                                  grad_output.size(2), grad_output.size(3),
                                  grad_output.size(4)});
}