#include <cuda_runtime.h>
|
#include <curand.h>
|
#include <cublas_v2.h>
|
|
#include "col2im.h"
|
#include "dark_cuda.h"
|
|
// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
|
// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE
|
|
__global__ void col2im_gpu_kernel(const int n, const float* data_col,
|
const int height, const int width, const int ksize,
|
const int pad,
|
const int stride,
|
const int height_col, const int width_col,
|
float *data_im) {
|
int index = blockIdx.x*blockDim.x+threadIdx.x;
|
for(; index < n; index += blockDim.x*gridDim.x){
|
float val = 0;
|
int w = index % width + pad;
|
int h = (index / width) % height + pad;
|
int c = index / (width * height);
|
// compute the start and end of the output
|
int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
|
int w_col_end = min(w / stride + 1, width_col);
|
int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
|
int h_col_end = min(h / stride + 1, height_col);
|
// equivalent implementation
|
int offset =
|
(c * ksize * ksize + h * ksize + w) * height_col * width_col;
|
int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
|
int coeff_w_col = (1 - stride * height_col * width_col);
|
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
|
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
|
val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
|
}
|
}
|
data_im[index] += val;
|
}
|
}
|
|
void col2im_ongpu(float *data_col,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float *data_im){
|
// We are going to launch channels * height_col * width_col kernels, each
|
// kernel responsible for copying a single-channel grid.
|
int height_col = (height + 2 * pad - ksize) / stride + 1;
|
int width_col = (width + 2 * pad - ksize) / stride + 1;
|
int num_kernels = channels * height * width;
|
col2im_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
|
BLOCK, 0, get_cuda_stream() >>>(
|
num_kernels, data_col, height, width, ksize, pad,
|
stride, height_col,
|
width_col, data_im);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
// -----------------------------------------
|
|
// CUDA: use 512 threads per block
|
const int CAFFE_CUDA_NUM_THREADS = 512;
|
|
// CUDA: number of blocks for threads.
|
inline int CAFFE_GET_BLOCKS(const int N) {
|
return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
|
}
|
|
// CUDA: grid stride looping
|
#define CUDA_KERNEL_LOOP(i, n) \
|
for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
|
i < (n); \
|
i += blockDim.x * gridDim.x)
|
|
// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
|
__global__ void col2im_gpu_kernel_ext(const int n, const float* data_col,
|
const int height, const int width, const int channels,
|
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 height_col, const int width_col,
|
float* data_im) {
|
CUDA_KERNEL_LOOP(index, n) {
|
float val = 0;
|
const int w_im = index % width + pad_w;
|
const int h_im = (index / width) % height + pad_h;
|
const int c_im = index / (width * height);
|
int kernel_extent_w = (kernel_w - 1) * dilation_w + 1;
|
int kernel_extent_h = (kernel_h - 1) * dilation_h + 1;
|
// compute the start and end of the output
|
const int w_col_start =
|
(w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1;
|
const int w_col_end = min(w_im / stride_w + 1, width_col);
|
const int h_col_start =
|
(h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1;
|
const int h_col_end = min(h_im / stride_h + 1, height_col);
|
// TODO: use LCM of stride and dilation to avoid unnecessary loops
|
for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) {
|
for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {
|
int h_k = (h_im - h_col * stride_h);
|
int w_k = (w_im - w_col * stride_w);
|
if (h_k % dilation_h == 0 && w_k % dilation_w == 0) {
|
h_k /= dilation_h;
|
w_k /= dilation_w;
|
int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) *
|
height_col + h_col) * width_col + w_col;
|
val += data_col[data_col_index];
|
}
|
}
|
}
|
data_im[index] = val;
|
}
|
}
|
|
void col2im_gpu_ext(const float* data_col, 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,
|
float* data_im)
|
{
|
int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) /
|
stride_h + 1;
|
int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) /
|
stride_w + 1;
|
int num_kernels = channels * height * width;
|
// 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)
|
col2im_gpu_kernel_ext<< <CAFFE_GET_BLOCKS(num_kernels),
|
CAFFE_CUDA_NUM_THREADS >> >(
|
num_kernels, data_col, height, width, channels, kernel_h, kernel_w,
|
pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
|
height_col, width_col, data_im);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
