mirror of
https://github.com/pjreddie/darknet.git
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using caffe's im2col, it's so much better\!
This commit is contained in:
Joseph Redmon
parent
4af116e996
commit
9d418102f4
2
Makefile
2
Makefile
@ -8,7 +8,7 @@ OBJDIR=./obj/
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CC=gcc
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NVCC=nvcc
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OPTS=-O0
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OPTS=-O3
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LDFLAGS=`pkg-config --libs opencv` -lm -pthread -lstdc++
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COMMON=`pkg-config --cflags opencv` -I/usr/local/cuda/include/
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CFLAGS=-Wall -Wfatal-errors
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@ -3,60 +3,112 @@ extern "C" {
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#include "cuda.h"
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}
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__global__ void col2im_kernel(float *data_col,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_im)
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{
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// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
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// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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if (pad){
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height_col = 1 + (height-1) / stride;
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width_col = 1 + (width-1) / stride;
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pad = ksize/2;
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}
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(id >= channels*height*width) return;
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int index = id;
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int w = id%width + pad;
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id /= width;
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int h = id%height + pad;
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id /= height;
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int c = id%channels;
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int w_start = (w-ksize+stride)/stride;
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int w_end = w/stride + 1;
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int h_start = (h-ksize+stride)/stride;
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int h_end = h/stride + 1;
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// int rows = channels * ksize * ksize;
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// int cols = height_col*width_col;
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int col_offset = (c*ksize*ksize + h * ksize + w)*height_col*width_col;
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int h_coeff = (1-stride*ksize*height_col)*width_col;
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int w_coeff = 1-stride*height_col*width_col;
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float val = 0;
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int h_col, w_col;
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for(h_col = h_start; h_col < h_end; ++h_col){
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for(w_col = w_start; w_col < w_end; ++w_col){
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int col_index = col_offset +h_col*h_coeff + w_col*w_coeff;
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float part = (w_col < 0 || h_col < 0 || h_col >= height_col || w_col >= width_col) ? 0 : data_col[col_index];
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val += part;
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__global__ void col2im_gpu_kernel(const int n, const float* data_col,
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const int height, const int width, const int ksize,
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const int pad,
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const int stride,
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const int height_col, const int width_col,
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float *data_im) {
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int index = blockIdx.x*blockDim.x+threadIdx.x;
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for(; index < n; index += blockDim.x*gridDim.x){
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float val = 0;
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int w = index % width + pad;
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int h = (index / width) % height + pad;
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int c = index / (width * height);
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// compute the start and end of the output
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int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
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int w_col_end = min(w / stride + 1, width_col);
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int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
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int h_col_end = min(h / stride + 1, height_col);
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// equivalent implementation
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int offset =
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(c * ksize * ksize + h * ksize + w) * height_col * width_col;
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int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
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int coeff_w_col = (1 - stride * height_col * width_col);
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for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
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for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
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val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
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}
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}
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data_im[index] = val;
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}
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data_im[index] = val;
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}
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void col2im_ongpu(float *im,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_col){
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// We are going to launch channels * height_col * width_col kernels, each
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// kernel responsible for copying a single-channel grid.
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pad = pad ? ksize/2 : 0;
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int height_col = (height + 2 * pad - ksize) / stride + 1;
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int width_col = (width + 2 * pad - ksize) / stride + 1;
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int num_kernels = channels * height * width;
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col2im_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
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BLOCK>>>(
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num_kernels, data_col, height, width, ksize, pad,
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stride, height_col,
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width_col, im);
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}
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/*
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__global__ void col2im_kernel(float *data_col,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_im)
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{
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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if (pad){
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height_col = 1 + (height-1) / stride;
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width_col = 1 + (width-1) / stride;
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pad = ksize/2;
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}
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(id >= channels*height*width) return;
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int index = id;
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int w = id%width + pad;
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id /= width;
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int h = id%height + pad;
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id /= height;
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int c = id%channels;
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int w_start = (w-ksize+stride)/stride;
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int w_end = w/stride + 1;
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int h_start = (h-ksize+stride)/stride;
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int h_end = h/stride + 1;
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// int rows = channels * ksize * ksize;
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// int cols = height_col*width_col;
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int col_offset = (c*ksize*ksize + h * ksize + w)*height_col*width_col;
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int h_coeff = (1-stride*ksize*height_col)*width_col;
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int w_coeff = 1-stride*height_col*width_col;
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float val = 0;
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int h_col, w_col;
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for(h_col = h_start; h_col < h_end; ++h_col){
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for(w_col = w_start; w_col < w_end; ++w_col){
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int col_index = col_offset +h_col*h_coeff + w_col*w_coeff;
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float part = (w_col < 0 || h_col < 0 || h_col >= height_col || w_col >= width_col) ? 0 : data_col[col_index];
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val += part;
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}
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}
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data_im[index] = val;
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}
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extern "C" void col2im_ongpu(float *data_col,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_im)
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_im)
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{
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size_t n = channels*height*width;
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size_t n = channels*height*width;
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col2im_kernel<<<cuda_gridsize(n), BLOCK>>>(data_col, channels, height, width, ksize, stride, pad, data_im);
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check_error(cudaPeekAtLastError());
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col2im_kernel<<<cuda_gridsize(n), BLOCK>>>(data_col, channels, height, width, ksize, stride, pad, data_im);
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check_error(cudaPeekAtLastError());
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}
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*/
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@ -56,7 +56,7 @@ extern "C" void backward_bias_gpu(float *bias_updates, float *delta, int batch,
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extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
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{
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clock_t time = clock();
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//clock_t time = clock();
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int i;
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int m = layer.n;
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int k = layer.size*layer.size*layer.c;
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@ -64,31 +64,31 @@ clock_t time = clock();
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convolutional_out_width(layer);
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bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
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cudaDeviceSynchronize();
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printf("bias %f\n", sec(clock() - time));
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time = clock();
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//cudaDeviceSynchronize();
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//printf("bias %f\n", sec(clock() - time));
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//time = clock();
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float imt=0;
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float gemt = 0;
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//float imt=0;
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//float gemt = 0;
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for(i = 0; i < layer.batch; ++i){
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time = clock();
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//time = clock();
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im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
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cudaDeviceSynchronize();
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imt += sec(clock()-time);
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time = clock();
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//cudaDeviceSynchronize();
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//imt += sec(clock()-time);
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//time = clock();
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float * a = layer.filters_gpu;
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float * b = layer.col_image_gpu;
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float * c = layer.output_gpu;
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
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cudaDeviceSynchronize();
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gemt += sec(clock()-time);
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time = clock();
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//cudaDeviceSynchronize();
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//gemt += sec(clock()-time);
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//time = clock();
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}
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activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
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cudaDeviceSynchronize();
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printf("activate %f\n", sec(clock() - time));
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printf("im2col %f\n", imt);
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printf("gemm %f\n", gemt);
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//cudaDeviceSynchronize();
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//printf("activate %f\n", sec(clock() - time));
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//printf("im2col %f\n", imt);
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//printf("gemm %f\n", gemt);
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}
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extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
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@ -3,77 +3,127 @@ extern "C" {
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#include "cuda.h"
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}
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__global__ void im2col_pad_kernel(float *im,
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int channels, int height, int width,
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int ksize, int stride, float *data_col)
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{
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int c,h,w;
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int height_col = 1 + (height-1) / stride;
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int width_col = 1 + (width-1) / stride;
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int channels_col = channels * ksize * ksize;
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// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
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// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE
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int pad = ksize/2;
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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int col_size = height_col*width_col*channels_col;
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if (id >= col_size) return;
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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id /= height_col;
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c = id % channels_col;
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id /= channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride - pad;
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int im_col = w_offset + w * stride - pad;
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int im_index = im_col + width*(im_row + height*im_channel);
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float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
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data_col[col_index] = val;
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__global__ void im2col_gpu_kernel(const int n, const float* data_im,
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const int height, const int width, const int ksize,
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const int pad,
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const int stride,
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const int height_col, const int width_col,
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float *data_col) {
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int index = blockIdx.x*blockDim.x+threadIdx.x;
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for(; index < n; index += blockDim.x*gridDim.x){
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int w_out = index % width_col;
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int h_index = index / width_col;
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int h_out = h_index % height_col;
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int channel_in = h_index / height_col;
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int channel_out = channel_in * ksize * ksize;
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int h_in = h_out * stride - pad;
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int w_in = w_out * stride - pad;
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float* data_col_ptr = data_col;
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data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
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const float* data_im_ptr = data_im;
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data_im_ptr += (channel_in * height + h_in) * width + w_in;
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for (int i = 0; i < ksize; ++i) {
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for (int j = 0; j < ksize; ++j) {
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int h = h_in + i;
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int w = w_in + j;
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*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
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data_im_ptr[i * width + j] : 0;
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data_col_ptr += height_col * width_col;
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}
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}
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}
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}
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__global__ void im2col_nopad_kernel(float *im,
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int channels, int height, int width,
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int ksize, int stride, float *data_col)
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{
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int c,h,w;
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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int channels_col = channels * ksize * ksize;
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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int col_size = height_col*width_col*channels_col;
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if (id >= col_size) return;
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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id /= height_col;
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c = id % channels_col;
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id /= channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride;
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int im_col = w_offset + w * stride;
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int im_index = im_col + width*(im_row + height*im_channel);
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float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
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data_col[col_index] = val;
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void im2col_ongpu(float *im,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_col){
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// We are going to launch channels * height_col * width_col kernels, each
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// kernel responsible for copying a single-channel grid.
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pad = pad ? ksize/2 : 0;
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int height_col = (height + 2 * pad - ksize) / stride + 1;
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int width_col = (width + 2 * pad - ksize) / stride + 1;
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int num_kernels = channels * height_col * width_col;
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im2col_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
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BLOCK>>>(
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num_kernels, im, height, width, ksize, pad,
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stride, height_col,
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width_col, data_col);
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}
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/*
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__global__ void im2col_pad_kernel(float *im,
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int channels, int height, int width,
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int ksize, int stride, float *data_col)
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{
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int c,h,w;
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int height_col = 1 + (height-1) / stride;
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int width_col = 1 + (width-1) / stride;
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int channels_col = channels * ksize * ksize;
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extern "C" void im2col_ongpu(float *im,
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int channels, int height, int width,
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int ksize, int stride, int pad, float *data_col)
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int pad = ksize/2;
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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int col_size = height_col*width_col*channels_col;
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if (id >= col_size) return;
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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id /= height_col;
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c = id % channels_col;
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id /= channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride - pad;
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int im_col = w_offset + w * stride - pad;
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int im_index = im_col + width*(im_row + height*im_channel);
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float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
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data_col[col_index] = val;
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}
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__global__ void im2col_nopad_kernel(float *im,
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int channels, int height, int width,
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int ksize, int stride, float *data_col)
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{
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int c,h,w;
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int height_col = (height - ksize) / stride + 1;
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int width_col = (width - ksize) / stride + 1;
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int channels_col = channels * ksize * ksize;
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int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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int col_size = height_col*width_col*channels_col;
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if (id >= col_size) return;
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int col_index = id;
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w = id % width_col;
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id /= width_col;
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h = id % height_col;
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id /= height_col;
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c = id % channels_col;
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id /= channels_col;
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int w_offset = c % ksize;
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int h_offset = (c / ksize) % ksize;
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int im_channel = c / ksize / ksize;
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int im_row = h_offset + h * stride;
|
||||
int im_col = w_offset + w * stride;
|
||||
|
||||
int im_index = im_col + width*(im_row + height*im_channel);
|
||||
float val = (im_row < 0 || im_col < 0 || im_row >= height || im_col >= width) ? 0 : im[im_index];
|
||||
|
||||
data_col[col_index] = val;
|
||||
}
|
||||
|
||||
extern "C" void im2col_ongpu(float *im,
|
||||
int channels, int height, int width,
|
||||
int ksize, int stride, int pad, float *data_col)
|
||||
{
|
||||
|
||||
int height_col = (height - ksize) / stride + 1;
|
||||
@ -91,3 +141,4 @@ extern "C" void im2col_ongpu(float *im,
|
||||
else im2col_nopad_kernel<<<cuda_gridsize(n),BLOCK>>>(im, channels, height, width, ksize, stride, data_col);
|
||||
check_error(cudaPeekAtLastError());
|
||||
}
|
||||
*/
|
||||
|
||||
@ -13,7 +13,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
int imgs = 128;
|
||||
int imgs = 1024;
|
||||
int i = net.seen/imgs;
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
|
||||
list *plist = get_paths("/data/imagenet/cls.train.list");
|
||||
|
||||
Loading…
Reference in New Issue
Block a user