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// Copyright (c) Facebook, Inc. and its affiliates. | |
// modified from | |
// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu | |
// Original license: Apache 2.0 | |
// clang-format off | |
// modify from | |
// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu | |
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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 deformable convolution operators. | |
* \ref: https://arxiv.org/abs/1703.06211 | |
* \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng | |
*/ | |
#include <ATen/ATen.h> | |
#include <c10/cuda/CUDAGuard.h> | |
#include <float.h> | |
#include <math.h> | |
#include <stdio.h> | |
#include <THC/THCAtomics.cuh> | |
using namespace at; | |
#define CUDA_KERNEL_LOOP(i, n) \ | |
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ | |
i += blockDim.x * gridDim.x) | |
namespace { | |
const int CUDA_NUM_THREADS = 1024; | |
const int kMaxGridNum = 65535; | |
inline int GET_BLOCKS(const int N) { | |
return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); | |
} | |
} | |
template <typename scalar_t> | |
__device__ scalar_t deformable_im2col_bilinear( | |
const scalar_t* bottom_data, | |
const int data_width, | |
const int height, | |
const int width, | |
scalar_t h, | |
scalar_t w) { | |
int h_low = floor(h); | |
int w_low = floor(w); | |
int h_high = h_low + 1; | |
int w_high = w_low + 1; | |
scalar_t lh = h - h_low; | |
scalar_t lw = w - w_low; | |
scalar_t hh = 1 - lh, hw = 1 - lw; | |
scalar_t v1 = 0; | |
if (h_low >= 0 && w_low >= 0) | |
v1 = bottom_data[h_low * data_width + w_low]; | |
scalar_t v2 = 0; | |
if (h_low >= 0 && w_high <= width - 1) | |
v2 = bottom_data[h_low * data_width + w_high]; | |
scalar_t v3 = 0; | |
if (h_high <= height - 1 && w_low >= 0) | |
v3 = bottom_data[h_high * data_width + w_low]; | |
scalar_t v4 = 0; | |
if (h_high <= height - 1 && w_high <= width - 1) | |
v4 = bottom_data[h_high * data_width + w_high]; | |
scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; | |
scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); | |
return val; | |
} | |
template <typename scalar_t> | |
__device__ scalar_t get_gradient_weight( | |
scalar_t argmax_h, | |
scalar_t 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; | |
scalar_t 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 scalar_t> | |
__device__ scalar_t get_coordinate_weight( | |
scalar_t argmax_h, | |
scalar_t argmax_w, | |
const int height, | |
const int width, | |
const scalar_t* 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; | |
scalar_t 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; | |
} | |
template <typename scalar_t> | |
__global__ void deformable_im2col_gpu_kernel( | |
const int n, | |
const scalar_t* data_im, | |
const scalar_t* data_offset, | |
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, | |
scalar_t* 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; | |
scalar_t* data_col_ptr = data_col + | |
((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; | |
// const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * | |
// height + h_in) * width + w_in; | |
const scalar_t* data_im_ptr = | |
data_im + (b_col * num_channels + c_im) * height * width; | |
const scalar_t* data_offset_ptr = data_offset + | |
(b_col * deformable_group + deformable_group_index) * 2 * 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
scalar_t val = static_cast<scalar_t>(0); | |
const scalar_t h_im = h_in + i * dilation_h + offset_h; | |
const scalar_t w_im = w_in + j * dilation_w + offset_w; | |
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { | |
// const scalar_t map_h = i * dilation_h + offset_h; | |
// const scalar_t map_w = j * dilation_w + offset_w; | |
// const int cur_height = height - h_in; | |
// const int cur_width = width - w_in; | |
// val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, | |
// cur_width, map_h, map_w); | |
val = deformable_im2col_bilinear( | |
data_im_ptr, width, height, width, h_im, w_im); | |
} | |
*data_col_ptr = val; | |
data_col_ptr += batch_size * height_col * width_col; | |
} | |
} | |
} | |
} | |
template <typename scalar_t> | |
__global__ void deformable_col2im_gpu_kernel( | |
const int n, | |
const scalar_t* data_col, | |
const scalar_t* data_offset, | |
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, | |
scalar_t* grad_im) { | |
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 scalar_t* data_offset_ptr = data_offset + | |
(b * deformable_group + deformable_group_index) * 2 * 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; | |
const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; | |
const scalar_t cur_top_grad = data_col[index]; | |
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; | |
scalar_t weight = 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); | |
} | |
} | |
} | |
} | |
} | |
template <typename scalar_t> | |
__global__ void deformable_col2im_coord_gpu_kernel( | |
const int n, | |
const scalar_t* data_col, | |
const scalar_t* data_im, | |
const scalar_t* data_offset, | |
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, | |
scalar_t* grad_offset) { | |
CUDA_KERNEL_LOOP(index, n) { | |
scalar_t val = 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 scalar_t* data_col_ptr = data_col + | |
deformable_group_index * channel_per_deformable_group * batch_size * | |
width_col * height_col; | |
const scalar_t* data_im_ptr = data_im + | |
(b * deformable_group + deformable_group_index) * | |
channel_per_deformable_group / kernel_h / kernel_w * height * width; | |
const scalar_t* data_offset_ptr = data_offset + | |
(b * deformable_group + deformable_group_index) * 2 * 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
scalar_t inv_h = h_in + i * dilation_h + offset_h; | |
scalar_t 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; | |
} | |
const scalar_t weight = 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]; | |
cnt += 1; | |
} | |
grad_offset[index] = val; | |
} | |
} | |
namespace detectron2 { | |
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) { | |
// num_axes should be smaller than block size | |
// todo: check parallel_imgs is correctly passed in | |
int height_col = | |
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; | |
int width_col = | |
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; | |
int num_kernels = channels * height_col * width_col * parallel_imgs; | |
int channel_per_deformable_group = channels / deformable_group; | |
at::cuda::CUDAGuard device_guard(data_im.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_im.scalar_type(), "deformable_im2col_gpu", ([&] { | |
const scalar_t* data_im_ = data_im.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
deformable_im2col_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_im_, | |
data_offset_, | |
height, | |
width, | |
ksize_h, | |
ksize_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
parallel_imgs, | |
channels, | |
deformable_group, | |
height_col, | |
width_col, | |
data_col_); | |
})); | |
cudaError_t err = cudaGetLastError(); | |
if (err != cudaSuccess) { | |
printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); | |
} | |
} | |
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) { | |
// todo: make sure parallel_imgs is passed in correctly | |
int height_col = | |
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; | |
int width_col = | |
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; | |
int num_kernels = | |
channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; | |
int channel_per_deformable_group = channels / deformable_group; | |
at::cuda::CUDAGuard device_guard(data_col.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_col.scalar_type(), "deformable_col2im_gpu", ([&] { | |
const scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
scalar_t* grad_im_ = grad_im.data_ptr<scalar_t>(); | |
deformable_col2im_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_col_, | |
data_offset_, | |
channels, | |
height, | |
width, | |
ksize_h, | |
ksize_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
parallel_imgs, | |
deformable_group, | |
height_col, | |
width_col, | |
grad_im_); | |
})); | |
cudaError_t err = cudaGetLastError(); | |
if (err != cudaSuccess) { | |
printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); | |
} | |
} | |
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) { | |
int height_col = | |
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; | |
int width_col = | |
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; | |
int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * | |
deformable_group * parallel_imgs; | |
int channel_per_deformable_group = | |
channels * ksize_h * ksize_w / deformable_group; | |
at::cuda::CUDAGuard device_guard(data_col.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { | |
const scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
const scalar_t* data_im_ = data_im.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
scalar_t* grad_offset_ = grad_offset.data_ptr<scalar_t>(); | |
deformable_col2im_coord_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_col_, | |
data_im_, | |
data_offset_, | |
channels, | |
height, | |
width, | |
ksize_h, | |
ksize_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
parallel_imgs, | |
2 * ksize_h * ksize_w * deformable_group, | |
deformable_group, | |
height_col, | |
width_col, | |
grad_offset_); | |
})); | |
} | |
} // namespace detectron2 | |
template <typename scalar_t> | |
__device__ scalar_t dmcn_im2col_bilinear( | |
const scalar_t* bottom_data, | |
const int data_width, | |
const int height, | |
const int width, | |
scalar_t h, | |
scalar_t w) { | |
int h_low = floor(h); | |
int w_low = floor(w); | |
int h_high = h_low + 1; | |
int w_high = w_low + 1; | |
scalar_t lh = h - h_low; | |
scalar_t lw = w - w_low; | |
scalar_t hh = 1 - lh, hw = 1 - lw; | |
scalar_t v1 = 0; | |
if (h_low >= 0 && w_low >= 0) | |
v1 = bottom_data[h_low * data_width + w_low]; | |
scalar_t v2 = 0; | |
if (h_low >= 0 && w_high <= width - 1) | |
v2 = bottom_data[h_low * data_width + w_high]; | |
scalar_t v3 = 0; | |
if (h_high <= height - 1 && w_low >= 0) | |
v3 = bottom_data[h_high * data_width + w_low]; | |
scalar_t v4 = 0; | |
if (h_high <= height - 1 && w_high <= width - 1) | |
v4 = bottom_data[h_high * data_width + w_high]; | |
scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; | |
scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); | |
return val; | |
} | |
template <typename scalar_t> | |
__device__ scalar_t dmcn_get_gradient_weight( | |
scalar_t argmax_h, | |
scalar_t 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; | |
scalar_t 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 scalar_t> | |
__device__ scalar_t dmcn_get_coordinate_weight( | |
scalar_t argmax_h, | |
scalar_t argmax_w, | |
const int height, | |
const int width, | |
const scalar_t* 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; | |
scalar_t 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; | |
} | |
template <typename scalar_t> | |
__global__ void modulated_deformable_im2col_gpu_kernel( | |
const int n, | |
const scalar_t* data_im, | |
const scalar_t* data_offset, | |
const scalar_t* 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, | |
scalar_t* 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; | |
scalar_t* data_col_ptr = data_col + | |
((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; | |
// const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * | |
// height + h_in) * width + w_in; | |
const scalar_t* data_im_ptr = | |
data_im + (b_col * num_channels + c_im) * height * width; | |
const scalar_t* data_offset_ptr = data_offset + | |
(b_col * deformable_group + deformable_group_index) * 2 * kernel_h * | |
kernel_w * height_col * width_col; | |
const scalar_t* 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; | |
scalar_t val = static_cast<scalar_t>(0); | |
const scalar_t h_im = h_in + i * dilation_h + offset_h; | |
const scalar_t 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 float map_h = i * dilation_h + offset_h; | |
// const float 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; | |
} | |
} | |
} | |
} | |
template <typename scalar_t> | |
__global__ void modulated_deformable_col2im_gpu_kernel( | |
const int n, | |
const scalar_t* data_col, | |
const scalar_t* data_offset, | |
const scalar_t* 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, | |
scalar_t* grad_im) { | |
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 scalar_t* data_offset_ptr = data_offset + | |
(b * deformable_group + deformable_group_index) * 2 * kernel_h * | |
kernel_w * height_col * width_col; | |
const scalar_t* 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; | |
const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; | |
const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; | |
const scalar_t 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; | |
scalar_t 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); | |
} | |
} | |
} | |
} | |
} | |
template <typename scalar_t> | |
__global__ void modulated_deformable_col2im_coord_gpu_kernel( | |
const int n, | |
const scalar_t* data_col, | |
const scalar_t* data_im, | |
const scalar_t* data_offset, | |
const scalar_t* 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, | |
scalar_t* grad_offset, | |
scalar_t* grad_mask) { | |
CUDA_KERNEL_LOOP(index, n) { | |
scalar_t 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 scalar_t* data_col_ptr = data_col + | |
deformable_group_index * channel_per_deformable_group * batch_size * | |
width_col * height_col; | |
const scalar_t* data_im_ptr = data_im + | |
(b * deformable_group + deformable_group_index) * | |
channel_per_deformable_group / kernel_h / kernel_w * height * width; | |
const scalar_t* data_offset_ptr = data_offset + | |
(b * deformable_group + deformable_group_index) * 2 * kernel_h * | |
kernel_w * height_col * width_col; | |
const scalar_t* 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 scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; | |
const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; | |
const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; | |
scalar_t inv_h = h_in + i * dilation_h + offset_h; | |
scalar_t 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 scalar_t 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; | |
} | |
// KERNEL_ASSIGN(grad_offset[index], offset_req, val); | |
grad_offset[index] = 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); | |
grad_mask | |
[(((b * deformable_group + deformable_group_index) * kernel_h * | |
kernel_w + | |
offset_c / 2) * | |
height_col + | |
h) * | |
width_col + | |
w] = mval; | |
} | |
} | |
namespace detectron2 { | |
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) { | |
// num_axes should be smaller than block size | |
const int channel_per_deformable_group = channels / deformable_group; | |
const int num_kernels = channels * batch_size * height_col * width_col; | |
at::cuda::CUDAGuard device_guard(data_im.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { | |
const scalar_t* data_im_ = data_im.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
const scalar_t* data_mask_ = data_mask.data_ptr<scalar_t>(); | |
scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
modulated_deformable_im2col_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_im_, | |
data_offset_, | |
data_mask_, | |
height_im, | |
width_im, | |
kernel_h, | |
kenerl_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
batch_size, | |
channels, | |
deformable_group, | |
height_col, | |
width_col, | |
data_col_); | |
})); | |
cudaError_t err = cudaGetLastError(); | |
if (err != cudaSuccess) { | |
printf( | |
"error in modulated_deformable_im2col_cuda: %s\n", | |
cudaGetErrorString(err)); | |
} | |
} | |
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 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 deformable_group, | |
at::Tensor grad_im) { | |
const int channel_per_deformable_group = channels / deformable_group; | |
const int num_kernels = | |
channels * kernel_h * kernel_w * batch_size * height_col * width_col; | |
at::cuda::CUDAGuard device_guard(data_col.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { | |
const scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
const scalar_t* data_mask_ = data_mask.data_ptr<scalar_t>(); | |
scalar_t* grad_im_ = grad_im.data_ptr<scalar_t>(); | |
modulated_deformable_col2im_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_col_, | |
data_offset_, | |
data_mask_, | |
channels, | |
height_im, | |
width_im, | |
kernel_h, | |
kernel_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
batch_size, | |
deformable_group, | |
height_col, | |
width_col, | |
grad_im_); | |
})); | |
cudaError_t err = cudaGetLastError(); | |
if (err != cudaSuccess) { | |
printf( | |
"error in modulated_deformable_col2im_cuda: %s\n", | |
cudaGetErrorString(err)); | |
} | |
} | |
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 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 deformable_group, | |
at::Tensor grad_offset, | |
at::Tensor grad_mask) { | |
const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * | |
kernel_w * deformable_group; | |
const int channel_per_deformable_group = | |
channels * kernel_h * kernel_w / deformable_group; | |
at::cuda::CUDAGuard device_guard(data_col.device()); | |
cudaStream_t stream = at::cuda::getCurrentCUDAStream(); | |
AT_DISPATCH_FLOATING_TYPES_AND_HALF( | |
data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { | |
const scalar_t* data_col_ = data_col.data_ptr<scalar_t>(); | |
const scalar_t* data_im_ = data_im.data_ptr<scalar_t>(); | |
const scalar_t* data_offset_ = data_offset.data_ptr<scalar_t>(); | |
const scalar_t* data_mask_ = data_mask.data_ptr<scalar_t>(); | |
scalar_t* grad_offset_ = grad_offset.data_ptr<scalar_t>(); | |
scalar_t* grad_mask_ = grad_mask.data_ptr<scalar_t>(); | |
modulated_deformable_col2im_coord_gpu_kernel<<< | |
GET_BLOCKS(num_kernels), | |
CUDA_NUM_THREADS, | |
0, | |
stream>>>( | |
num_kernels, | |
data_col_, | |
data_im_, | |
data_offset_, | |
data_mask_, | |
channels, | |
height_im, | |
width_im, | |
kernel_h, | |
kernel_w, | |
pad_h, | |
pad_w, | |
stride_h, | |
stride_w, | |
dilation_h, | |
dilation_w, | |
channel_per_deformable_group, | |
batch_size, | |
2 * kernel_h * kernel_w * deformable_group, | |
deformable_group, | |
height_col, | |
width_col, | |
grad_offset_, | |
grad_mask_); | |
})); | |
cudaError_t err = cudaGetLastError(); | |
if (err != cudaSuccess) { | |
printf( | |
"error in modulated_deformable_col2im_coord_cuda: %s\n", | |
cudaGetErrorString(err)); | |
} | |
} | |
} // namespace detectron2 | |