From 9d418102f4a44b2e2c437dec945189a646cbf3a4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 21 Mar 2015 21:17:39 +0000
Subject: [PATCH] using caffe's im2col, it's so much better\!
---
src/col2im_kernels.cu | 144 ++++++++++++++------
src/imagenet.c | 2
Makefile | 2
src/convolutional_kernels.cu | 34 ++--
src/im2col_kernels.cu | 183 ++++++++++++++++---------
5 files changed, 234 insertions(+), 131 deletions(-)
diff --git a/Makefile b/Makefile
index eee3c96..bdbc73d 100644
--- a/Makefile
+++ b/Makefile
@@ -8,7 +8,7 @@
CC=gcc
NVCC=nvcc
-OPTS=-O0
+OPTS=-O3
LDFLAGS=`pkg-config --libs opencv` -lm -pthread -lstdc++
COMMON=`pkg-config --cflags opencv` -I/usr/local/cuda/include/
CFLAGS=-Wall -Wfatal-errors
diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu
index 2fa2030..76a86e6 100644
--- a/src/col2im_kernels.cu
+++ b/src/col2im_kernels.cu
@@ -3,60 +3,112 @@
#include "cuda.h"
}
-__global__ void col2im_kernel(float *data_col,
- int channels, int height, int width,
- int ksize, int stride, int pad, float *data_im)
-{
+// 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
- int height_col = (height - ksize) / stride + 1;
- int width_col = (width - ksize) / stride + 1;
- if (pad){
- height_col = 1 + (height-1) / stride;
- width_col = 1 + (width-1) / stride;
- pad = ksize/2;
- }
-
- int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(id >= channels*height*width) return;
-
- int index = id;
- int w = id%width + pad;
- id /= width;
- int h = id%height + pad;
- id /= height;
- int c = id%channels;
-
- int w_start = (w-ksize+stride)/stride;
- int w_end = w/stride + 1;
-
- int h_start = (h-ksize+stride)/stride;
- int h_end = h/stride + 1;
-
- // int rows = channels * ksize * ksize;
- // int cols = height_col*width_col;
- int col_offset = (c*ksize*ksize + h * ksize + w)*height_col*width_col;
- int h_coeff = (1-stride*ksize*height_col)*width_col;
- int w_coeff = 1-stride*height_col*width_col;
- float val = 0;
- int h_col, w_col;
- for(h_col = h_start; h_col < h_end; ++h_col){
- for(w_col = w_start; w_col < w_end; ++w_col){
- int col_index = col_offset +h_col*h_coeff + w_col*w_coeff;
- float part = (w_col < 0 || h_col < 0 || h_col >= height_col || w_col >= width_col) ? 0 : data_col[col_index];
- val += part;
+__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;
}
- data_im[index] = val;
+}
+
+void col2im_ongpu(float *im,
+ int channels, int height, int width,
+ int ksize, int stride, int pad, float *data_col){
+ // We are going to launch channels * height_col * width_col kernels, each
+ // kernel responsible for copying a single-channel grid.
+ pad = pad ? ksize/2 : 0;
+ 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>>>(
+ num_kernels, data_col, height, width, ksize, pad,
+ stride, height_col,
+ width_col, im);
+}
+
+/*
+ __global__ void col2im_kernel(float *data_col,
+ int channels, int height, int width,
+ int ksize, int stride, int pad, float *data_im)
+ {
+
+ int height_col = (height - ksize) / stride + 1;
+ int width_col = (width - ksize) / stride + 1;
+ if (pad){
+ height_col = 1 + (height-1) / stride;
+ width_col = 1 + (width-1) / stride;
+ pad = ksize/2;
+ }
+
+ int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(id >= channels*height*width) return;
+
+ int index = id;
+ int w = id%width + pad;
+ id /= width;
+ int h = id%height + pad;
+ id /= height;
+ int c = id%channels;
+
+ int w_start = (w-ksize+stride)/stride;
+ int w_end = w/stride + 1;
+
+ int h_start = (h-ksize+stride)/stride;
+ int h_end = h/stride + 1;
+
+// int rows = channels * ksize * ksize;
+// int cols = height_col*width_col;
+int col_offset = (c*ksize*ksize + h * ksize + w)*height_col*width_col;
+int h_coeff = (1-stride*ksize*height_col)*width_col;
+int w_coeff = 1-stride*height_col*width_col;
+float val = 0;
+int h_col, w_col;
+for(h_col = h_start; h_col < h_end; ++h_col){
+for(w_col = w_start; w_col < w_end; ++w_col){
+int col_index = col_offset +h_col*h_coeff + w_col*w_coeff;
+float part = (w_col < 0 || h_col < 0 || h_col >= height_col || w_col >= width_col) ? 0 : data_col[col_index];
+val += part;
+}
+}
+data_im[index] = val;
}
extern "C" void col2im_ongpu(float *data_col,
- int channels, int height, int width,
- int ksize, int stride, int pad, float *data_im)
+int channels, int height, int width,
+int ksize, int stride, int pad, float *data_im)
{
- size_t n = channels*height*width;
+size_t n = channels*height*width;
- col2im_kernel<<<cuda_gridsize(n), BLOCK>>>(data_col, channels, height, width, ksize, stride, pad, data_im);
- check_error(cudaPeekAtLastError());
+col2im_kernel<<<cuda_gridsize(n), BLOCK>>>(data_col, channels, height, width, ksize, stride, pad, data_im);
+check_error(cudaPeekAtLastError());
}
+ */
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 864d7fa..18a3b7d 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -56,7 +56,7 @@
extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
-clock_t time = clock();
+//clock_t time = clock();
int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
@@ -64,31 +64,31 @@
convolutional_out_width(layer);
bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
-cudaDeviceSynchronize();
-printf("bias %f\n", sec(clock() - time));
-time = clock();
+//cudaDeviceSynchronize();
+//printf("bias %f\n", sec(clock() - time));
+//time = clock();
-float imt=0;
-float gemt = 0;
+//float imt=0;
+//float gemt = 0;
for(i = 0; i < layer.batch; ++i){
-time = clock();
+//time = clock();
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);
-cudaDeviceSynchronize();
-imt += sec(clock()-time);
-time = clock();
+//cudaDeviceSynchronize();
+//imt += sec(clock()-time);
+//time = clock();
float * a = layer.filters_gpu;
float * b = layer.col_image_gpu;
float * c = layer.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
-cudaDeviceSynchronize();
-gemt += sec(clock()-time);
-time = clock();
+//cudaDeviceSynchronize();
+//gemt += sec(clock()-time);
+//time = clock();
}
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
-cudaDeviceSynchronize();
-printf("activate %f\n", sec(clock() - time));
-printf("im2col %f\n", imt);
-printf("gemm %f\n", gemt);
+//cudaDeviceSynchronize();
+//printf("activate %f\n", sec(clock() - time));
+//printf("im2col %f\n", imt);
+//printf("gemm %f\n", gemt);
}
extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu
index a82c2dc..d122748 100644
--- a/src/im2col_kernels.cu
+++ b/src/im2col_kernels.cu
@@ -3,77 +3,127 @@
#include "cuda.h"
}
-__global__ void im2col_pad_kernel(float *im,
- int channels, int height, int width,
- int ksize, int stride, float *data_col)
-{
- int c,h,w;
- int height_col = 1 + (height-1) / stride;
- int width_col = 1 + (width-1) / stride;
- int channels_col = channels * ksize * ksize;
+// 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
- int pad = ksize/2;
-
- int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- int col_size = height_col*width_col*channels_col;
- if (id >= col_size) return;
-
- int col_index = id;
- w = id % width_col;
- id /= width_col;
- h = id % height_col;
- id /= height_col;
- c = id % channels_col;
- id /= channels_col;
-
- int w_offset = c % ksize;
- int h_offset = (c / ksize) % ksize;
- int im_channel = c / ksize / ksize;
- int im_row = h_offset + h * stride - pad;
- int im_col = w_offset + w * stride - pad;
-
- 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;
+__global__ void im2col_gpu_kernel(const int n, const float* data_im,
+ 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_col) {
+ int index = blockIdx.x*blockDim.x+threadIdx.x;
+ for(; index < n; index += blockDim.x*gridDim.x){
+ int w_out = index % width_col;
+ int h_index = index / width_col;
+ int h_out = h_index % height_col;
+ int channel_in = h_index / height_col;
+ int channel_out = channel_in * ksize * ksize;
+ int h_in = h_out * stride - pad;
+ int w_in = w_out * stride - pad;
+ float* data_col_ptr = data_col;
+ data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+ const float* data_im_ptr = data_im;
+ data_im_ptr += (channel_in * height + h_in) * width + w_in;
+ for (int i = 0; i < ksize; ++i) {
+ for (int j = 0; j < ksize; ++j) {
+ int h = h_in + i;
+ int w = w_in + j;
+ *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
+ data_im_ptr[i * width + j] : 0;
+ data_col_ptr += height_col * width_col;
+ }
+ }
+ }
}
-__global__ void im2col_nopad_kernel(float *im,
- int channels, int height, int width,
- int ksize, int stride, float *data_col)
-{
- int c,h,w;
- int height_col = (height - ksize) / stride + 1;
- int width_col = (width - ksize) / stride + 1;
- int channels_col = channels * ksize * ksize;
-
- int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- int col_size = height_col*width_col*channels_col;
- if (id >= col_size) return;
-
- int col_index = id;
- w = id % width_col;
- id /= width_col;
- h = id % height_col;
- id /= height_col;
- c = id % channels_col;
- id /= channels_col;
-
- int w_offset = c % ksize;
- int h_offset = (c / ksize) % ksize;
- int im_channel = c / ksize / ksize;
- 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;
+void im2col_ongpu(float *im,
+ int channels, int height, int width,
+ int ksize, int stride, int pad, float *data_col){
+ // We are going to launch channels * height_col * width_col kernels, each
+ // kernel responsible for copying a single-channel grid.
+ pad = pad ? ksize/2 : 0;
+ int height_col = (height + 2 * pad - ksize) / stride + 1;
+ int width_col = (width + 2 * pad - ksize) / stride + 1;
+ int num_kernels = channels * height_col * width_col;
+ im2col_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
+ BLOCK>>>(
+ num_kernels, im, height, width, ksize, pad,
+ stride, height_col,
+ width_col, data_col);
}
+/*
+ __global__ void im2col_pad_kernel(float *im,
+ int channels, int height, int width,
+ int ksize, int stride, float *data_col)
+ {
+ int c,h,w;
+ int height_col = 1 + (height-1) / stride;
+ int width_col = 1 + (width-1) / stride;
+ int channels_col = channels * ksize * ksize;
-extern "C" void im2col_ongpu(float *im,
- int channels, int height, int width,
- int ksize, int stride, int pad, float *data_col)
+ int pad = ksize/2;
+
+ int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ int col_size = height_col*width_col*channels_col;
+ if (id >= col_size) return;
+
+ int col_index = id;
+ w = id % width_col;
+ id /= width_col;
+ h = id % height_col;
+ id /= height_col;
+ c = id % channels_col;
+ id /= channels_col;
+
+ int w_offset = c % ksize;
+ int h_offset = (c / ksize) % ksize;
+ int im_channel = c / ksize / ksize;
+ int im_row = h_offset + h * stride - pad;
+ int im_col = w_offset + w * stride - pad;
+
+ 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;
+ }
+
+ __global__ void im2col_nopad_kernel(float *im,
+ int channels, int height, int width,
+ int ksize, int stride, float *data_col)
+ {
+ int c,h,w;
+ int height_col = (height - ksize) / stride + 1;
+ int width_col = (width - ksize) / stride + 1;
+ int channels_col = channels * ksize * ksize;
+
+ int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ int col_size = height_col*width_col*channels_col;
+ if (id >= col_size) return;
+
+ int col_index = id;
+ w = id % width_col;
+ id /= width_col;
+ h = id % height_col;
+ id /= height_col;
+ c = id % channels_col;
+ id /= channels_col;
+
+ int w_offset = c % ksize;
+ int h_offset = (c / ksize) % ksize;
+ int im_channel = c / ksize / ksize;
+ 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 @@
else im2col_nopad_kernel<<<cuda_gridsize(n),BLOCK>>>(im, channels, height, width, ksize, stride, data_col);
check_error(cudaPeekAtLastError());
}
+*/
diff --git a/src/imagenet.c b/src/imagenet.c
index 7da73a0..9118c08 100644
--- a/src/imagenet.c
+++ b/src/imagenet.c
@@ -13,7 +13,7 @@
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");
--
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