modulated_deformable_im2col.cuh

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 *
 * Copyright (c) 2018 Microsoft
 * Licensed under The MIT License [see LICENSE for details]
 * \file modulated_deformable_im2col.cuh
 * \brief Function definitions of converting an image to
 * column matrix based on kernel, padding, dilation, and offset.
 * These functions are mainly used in modulated deformable convolution operators.
 * \ref: https://arxiv.org/abs/1811.11168
 * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
 */

#ifndef MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_CUH_
#define MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_CUH_

#include <mxnet/base.h>
#include <mxnet/operator.h>
#include <algorithm>
#include <cstring>
#include <vector>
#include "../../mxnet_op.h"
#include "../../../common/cuda_utils.h"



namespace mxnet {
namespace op {

template <typename DType>
__device__ DType dmcn_im2col_bilinear(const DType* bottom_data, const int data_width,
  const int height, const int width, DType h, DType w) {

  int h_low = floor(h);
  int w_low = floor(w);
  int h_high = h_low + 1;
  int w_high = w_low + 1;

  DType lh = h - h_low;
  DType lw = w - w_low;
  DType hh = 1 - lh, hw = 1 - lw;

  DType v1 = 0;
  if (h_low >= 0 && w_low >= 0)
    v1 = bottom_data[h_low * data_width + w_low];
  DType v2 = 0;
  if (h_low >=0 && w_high <= width - 1)
    v2 = bottom_data[h_low * data_width + w_high];
  DType v3 = 0;
  if (h_high <= height - 1 && w_low >= 0)
    v3 = bottom_data[h_high * data_width + w_low];
  DType v4 = 0;
  if (h_high <= height - 1 && w_high <= width - 1)
    v4 = bottom_data[h_high * data_width + w_high];

  DType w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;

  DType val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
  return val;
}


template <typename DType>
__device__ DType dmcn_get_gradient_weight(DType argmax_h, DType argmax_w,
  const int h, const int w, const int height, const int width) {

  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) {
    //empty
    return 0;
  }

  int argmax_h_low = floor(argmax_h);
  int argmax_w_low = floor(argmax_w);
  int argmax_h_high = argmax_h_low + 1;
  int argmax_w_high = argmax_w_low + 1;

  DType weight = 0;
  if (h == argmax_h_low && w == argmax_w_low)
      weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
  if (h == argmax_h_low && w == argmax_w_high)
      weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
  if (h == argmax_h_high && w == argmax_w_low)
      weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
  if (h == argmax_h_high && w == argmax_w_high)
      weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
  return weight;
}


template <typename DType>
__device__ DType dmcn_get_coordinate_weight(DType argmax_h, DType argmax_w,
  const int height, const int width, const DType* im_data,
  const int data_width, const int bp_dir) {

  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
  {
    //empty
    return 0;
  }

  int argmax_h_low = floor(argmax_h);
  int argmax_w_low = floor(argmax_w);
  int argmax_h_high = argmax_h_low + 1;
  int argmax_w_high = argmax_w_low + 1;

  DType weight = 0;

  if (bp_dir == 0) {
    if (argmax_h_low >= 0 && argmax_w_low >= 0)
        weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
        weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
        weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
        weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
  } else if (bp_dir == 1) {
    if (argmax_h_low >= 0 && argmax_w_low >= 0)
        weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
        weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
        weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
        weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
  }

  return weight;
}


/*!
 * \brief deformable_im2col gpu kernel.
 * DO NOT call this directly. Use wrapper function im2col() instead;
 */
template <typename DType>
__global__ void modulated_deformable_im2col_gpu_kernel(const int n,
  const DType* data_im, const DType* data_offset, const DType* data_mask,
  const int height, const int width, const int kernel_h, const int kernel_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 channel_per_deformable_group,
  const int batch_size, const int num_channels, const int deformable_group,
  const int height_col, const int width_col,
  DType* data_col) {
  CUDA_KERNEL_LOOP(index, n) {
    // index index of output matrix
    const int w_col = index % width_col;
    const int h_col = (index / width_col) % height_col;
    const int b_col = (index / width_col / height_col) % batch_size;
    const int c_im = (index / width_col / height_col) / batch_size;
    const int c_col = c_im * kernel_h * kernel_w;

    // compute deformable group index
    const int deformable_group_index = c_im / channel_per_deformable_group;

    const int h_in = h_col * stride_h - pad_h;
    const int w_in = w_col * stride_w - pad_w;

    DType* data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
    //const DType* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
    const DType* data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
    const DType* data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;

    const DType* data_mask_ptr = data_mask + (b_col *  deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;

    for (int i = 0; i < kernel_h; ++i) {
      for (int j = 0; j < kernel_w; ++j) {
        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
        const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
        const DType offset_h = data_offset_ptr[data_offset_h_ptr];
        const DType offset_w = data_offset_ptr[data_offset_w_ptr];
        const DType mask = data_mask_ptr[data_mask_hw_ptr];
        DType val = static_cast<DType>(0);
        const DType h_im = h_in + i * dilation_h + offset_h;
        const DType w_im = w_in + j * dilation_w + offset_w;
        //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) {
          //const DType map_h = i * dilation_h + offset_h;
          //const DType map_w = j * dilation_w + offset_w;
          //const int cur_height = height - h_in;
          //const int cur_width = width - w_in;
          //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
          val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
        }
        *data_col_ptr = val * mask;
        data_col_ptr += batch_size * height_col * width_col;
        //data_col_ptr += height_col * width_col;
      }
    }
  }
}



/*!\brief
 * cpu function of deformable_im2col algorithm
 * \param s device stream
 * \param data_im pointer of an image (N, C, H, W, ...) in the image batch
 * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
 * \param im_shape input image shape in dimensions (N, C, H, W,)
 * \param col_shape column buffer shape (#channels, N, output_im_height, output_im_width, ...)
 * \param kernel_shape kernel filter shape
 * \param pad pad shape
 * \param stride stride shape
 * \param dilation dilation shape
 * \param deformable_group #offset group that deformable convolution use
 * \param data_col column buffer pointer
 */
template <typename DType>
inline void modulated_deformable_im2col(mshadow::Stream<gpu>* s,
  const DType* data_im, const DType* data_offset, const DType* data_mask,
  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
  const TShape& pad, const TShape& stride, const TShape& dilation,
  const uint32_t deformable_group, DType* data_col) {
  // num_axes should be smaller than block size
  index_t num_spatial_axes = kernel_shape.ndim();
  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
  index_t channel_per_deformable_group = im_shape[1] / deformable_group;
  index_t num_kernels = im_shape[1] * col_shape.ProdShape(1, col_shape.ndim());
  using namespace mxnet_op;
  switch (num_spatial_axes) {
  case 2:
    modulated_deformable_im2col_gpu_kernel<DType> // NOLINT_NEXT_LINE(whitespace/operators)
        <<<cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
           0, mshadow::Stream<gpu>::GetStream(s)>>>(
        num_kernels, data_im, data_offset, data_mask, im_shape[2], im_shape[3], kernel_shape[0], kernel_shape[1],
        pad[0], pad[1], stride[0], stride[1], dilation[0], dilation[1], channel_per_deformable_group,
        col_shape[1], im_shape[1], deformable_group, col_shape[2], col_shape[3], data_col);
    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_im2col_gpu_kernel);
    break;
  default:
    LOG(FATAL) << "im2col_nd_gpu does not support computation with "
               << num_spatial_axes << " spatial axes";
  }
}


/*!
* \brief deformable_col2im gpu kernel.
* \brief DO NOT call this directly. Use wrapper function deformable_col2im() instead;
*/
template <typename DType>
__global__ void modulated_deformable_col2im_gpu_kernel(const int n,
  const DType* data_col, const DType* data_offset, const DType* data_mask,
  const int channels, const int height, const int width,
  const int kernel_h, const int kernel_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 channel_per_deformable_group,
  const int batch_size, const int deformable_group,
  const int height_col, const int width_col,
  DType* grad_im, OpReqType req) {
  CUDA_KERNEL_LOOP(index, n) {
    const int j = (index / width_col / height_col / batch_size) % kernel_w;
    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
    // compute the start and end of the output

    const int deformable_group_index = c / channel_per_deformable_group;

    int w_out = index % width_col;
    int h_out = (index / width_col) % height_col;
    int b = (index / width_col / height_col) % batch_size;
    int w_in = w_out * stride_w - pad_w;
    int h_in = h_out * stride_h - pad_h;

    const DType* data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
    const DType* data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
    const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
    const DType offset_h = data_offset_ptr[data_offset_h_ptr];
    const DType offset_w = data_offset_ptr[data_offset_w_ptr];
    const DType mask = data_mask_ptr[data_mask_hw_ptr];
    const DType cur_inv_h_data = h_in + i * dilation_h + offset_h;
    const DType cur_inv_w_data = w_in + j * dilation_w + offset_w;

    const DType cur_top_grad = data_col[index] * mask;
    const int cur_h = (int)cur_inv_h_data;
    const int cur_w = (int)cur_inv_w_data;
    for (int dy = -2; dy <= 2; dy++) {
      for (int dx = -2; dx <= 2; dx++) {
        if (cur_h + dy >= 0 && cur_h + dy < height &&
          cur_w + dx >= 0 && cur_w + dx < width &&
          abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
          abs(cur_inv_w_data - (cur_w + dx)) < 1
          ) {
          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
          DType weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
        }
      }
    }
  }
}


/*!\brief
 * gpu function of deformable_col2im algorithm
 * \param s device stream
 * \param data_col start pointer of the column buffer to be filled
 * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
 * \param im_shape input image shape in dimensions (N, C, H, W,)
 * \param col_shape column buffer shape
 * \param kernel_shape kernel filter shape
 * \param pad pad shape
 * \param stride stride shape
 * \param dilation dilation shape
 * \param deformable_group #offset group that deformable convolution use
 * \param grad_im pointer of images (N, C, H, W,...) in the image batch
 */
template <typename DType>
inline void modulated_deformable_col2im(mshadow::Stream<gpu>* s,
  const DType* data_col, const DType* data_offset, const DType* data_mask,
  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
  const TShape& pad, const TShape& stride,
  const TShape& dilation, const uint32_t deformable_group,
  DType* grad_im, OpReqType req) {
  index_t num_spatial_axes = kernel_shape.ndim();
  index_t im_size = im_shape.ProdShape(1, im_shape.ndim());
  index_t channel_per_deformable_group = im_shape[1] / deformable_group;
  index_t num_kernels = col_shape.ProdShape(0, col_shape.ndim());
  // num_axes should be smaller than block size
  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
  using namespace mxnet_op;
  switch (num_spatial_axes) {
  case 2:
    // To avoid involving atomic operations, we will launch one kernel per
    // bottom dimension, and then in the kernel add up the top dimensions.
    // NOLINT_NEXT_LINE(whitespace/operators)
    modulated_deformable_col2im_gpu_kernel<DType><<<cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
                               0, mshadow::Stream<gpu>::GetStream(s)>>>(
        num_kernels, data_col, data_offset, data_mask, im_shape[1], im_shape[2], im_shape[3],
        kernel_shape[0], kernel_shape[1], pad[0], pad[1], stride[0], stride[1],
        dilation[0], dilation[1], channel_per_deformable_group,
        col_shape[1], deformable_group, col_shape[2], col_shape[3], grad_im, req);
    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_col2im_gpu_kernel);
    break;
  default:
    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
               << num_spatial_axes << " spatial axes";
  }
}


/*!
 * \brief deformable_col2im_coord gpu kernel.
 * \brief DO NOT call this directly. Use wrapper function deformable_col2im_coord() instead;
 */
template <typename DType>
__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n,
  const DType* data_col, const DType* data_im,
  const DType* data_offset, const DType* data_mask,
  const int channels, const int height, const int width,
  const int kernel_h, const int kernel_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 channel_per_deformable_group,
  const int batch_size, const int offset_channels, const int deformable_group,
  const int height_col, const int width_col,
  DType* grad_offset, DType* grad_mask, OpReqType offset_req, OpReqType mask_req) {
  CUDA_KERNEL_LOOP(index, n) {
    DType val = 0, mval = 0;
    int w = index % width_col;
    int h = (index / width_col) % height_col;
    int c = (index / width_col / height_col) % offset_channels;
    int b = (index / width_col / height_col) / offset_channels;
    // compute the start and end of the output

    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
    const int col_step = kernel_h * kernel_w;
    int cnt = 0;
    const DType* data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col;
    const DType* data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width;
    const DType* data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
    const DType* data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;

    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;

    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) {
      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
      const int bp_dir = offset_c % 2;

      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
      int w_out = col_pos % width_col;
      int h_out = (col_pos / width_col) % height_col;
      int w_in = w_out * stride_w - pad_w;
      int h_in = h_out * stride_h - pad_h;
      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
      const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
      const DType offset_h = data_offset_ptr[data_offset_h_ptr];
      const DType offset_w = data_offset_ptr[data_offset_w_ptr];
      const DType mask = data_mask_ptr[data_mask_hw_ptr];
      DType inv_h = h_in + i * dilation_h + offset_h;
      DType inv_w = w_in + j * dilation_w + offset_w;
      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) {
        inv_h = inv_w = -2;
      } else {
        mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w);
      }
      const DType weight = dmcn_get_coordinate_weight(
        inv_h, inv_w,
        height, width, data_im_ptr + cnt * height * width, width, bp_dir);
      val  += weight * data_col_ptr[col_pos] * mask;
      cnt  += 1;
    }

    //grad_offset[index] = val;
    KERNEL_ASSIGN(grad_offset[index], offset_req, val);
    if (offset_c % 2 == 0)
        KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval);
  }
}

/*!\brief
 * gpu function of deformable_col2im_coord algorithm
 * \param s device stream
 * \param data_col start pointer of the column buffer to be filled
 * \param data_im pointer of an image (N, C, H, W, ...) in the image batch
 * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
 * \param im_shape input image shape in dimensions (N, C, H, W,)
 * \param col_shape column buffer shape
 * \param kernel_shape kernel filter shape
 * \param pad pad shape
 * \param stride stride shape
 * \param dilation dilation shape
 * \param deformable_group #offset group that deformable convolution use
 * \param grad_offset pointer of the offset (N, deformable_group*kernel_h*kernel_w*2, H, W,...) in the offset batch
 */
template <typename DType>
inline void modulated_deformable_col2im_coord(mshadow::Stream<gpu>* s,
  const DType* data_col, const DType* data_im, const DType* data_offset, const DType* data_mask,
  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
  const TShape& pad, const TShape& stride,
  const TShape& dilation, const uint32_t deformable_group,
  DType* grad_offset, DType* grad_mask, OpReqType offset_req, OpReqType mask_req) {
  index_t num_spatial_axes = kernel_shape.ndim();
  index_t num_kernels = col_shape[1] * col_shape[2] * col_shape[3] * 2 * kernel_shape[0] * kernel_shape[1] * deformable_group;
  index_t channel_per_deformable_group = col_shape[0] / deformable_group;
  // num_axes should be smaller than block size
  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
  using namespace mxnet_op;
  switch (num_spatial_axes) {
  case 2:
    // To avoid involving atomic operations, we will launch one kernel per
    // bottom dimension, and then in the kernel add up the top dimensions.
    // NOLINT_NEXT_LINE(whitespace/operators)

    modulated_deformable_col2im_coord_gpu_kernel<DType> << <cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
      0, mshadow::Stream<gpu>::GetStream(s) >> >(
        num_kernels, data_col, data_im, data_offset, data_mask, im_shape[1], im_shape[2], im_shape[3],
        kernel_shape[0], kernel_shape[1], pad[0], pad[1], stride[0], stride[1],
        dilation[0], dilation[1], channel_per_deformable_group,
        col_shape[1], 2 * kernel_shape[0] * kernel_shape[1] * deformable_group, deformable_group, col_shape[2], col_shape[3],
        grad_offset, grad_mask, offset_req, mask_req);
    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_col2im_coord_gpu_kernel);
    break;
  default:
    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
      << num_spatial_axes << " spatial axes";
  }
}


}  // namespace op
}  // namespace mxnet

#endif  // MXNET_OPERATOR_CONTRIB_NN_DEFORMABLE_MASKED_IM2COL_CUH_