| | |
| | | variance[i] *= scale; |
| | | } |
| | | |
| | | __global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out) |
| | | { |
| | | int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; |
| | | if(i >= N) return; |
| | | int in_index = i; |
| | | int in_w = i%w; |
| | | i = i/w; |
| | | int in_h = i%h; |
| | | i = i/h; |
| | | int in_c = i%c; |
| | | i = i/c; |
| | | int b = i%batch; |
| | | |
| | | int out_c = c/(stride*stride); |
| | | |
| | | int c2 = in_c % out_c; |
| | | int offset = in_c / out_c; |
| | | int w2 = in_w*stride + offset % stride; |
| | | int h2 = in_h*stride + offset / stride; |
| | | //printf("%d\n", offset); |
| | | int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); |
| | | |
| | | // printf("%d %d %d\n", w2, h2, c2); |
| | | //printf("%d %d\n", in_index, out_index); |
| | | //if(out_index >= N || out_index < 0) printf("bad bad bad \n"); |
| | | |
| | | if(forward) out[out_index] = x[in_index]; |
| | | else out[in_index] = x[out_index]; |
| | | //if(forward) out[1] = x[1]; |
| | | //else out[0] = x[0]; |
| | | } |
| | | |
| | | __global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) |
| | | { |
| | | int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; |
| | |
| | | check_error(cudaPeekAtLastError()); |
| | | } |
| | | |
| | | extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) |
| | | { |
| | | int size = w*h*c*batch; |
| | | reorg_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, w, h, c, batch, stride, forward, out); |
| | | check_error(cudaPeekAtLastError()); |
| | | } |
| | | |
| | | extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) |
| | | { |
| | | mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask); |