From 809f924db2823b9e1eaf3efb9370380edc1f76ed Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 23 Jan 2015 00:38:24 +0000
Subject: [PATCH] CUDA so fast
---
src/cnn.c | 49 +++++++++++++++++++++++++++++++++++++------------
1 files changed, 37 insertions(+), 12 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index fed69d0..c3b7b2c 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -7,7 +7,7 @@
#include "data.h"
#include "matrix.h"
#include "utils.h"
-#include "mini_blas.h"
+#include "blas.h"
#include "matrix.h"
#include "server.h"
@@ -165,6 +165,7 @@
free_data(val);
}
}
+/*
void train_imagenet_distributed(char *address)
{
@@ -203,6 +204,7 @@
free_data(train);
}
}
+*/
void train_imagenet(char *cfgfile)
{
@@ -210,10 +212,10 @@
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg(cfgfile);
- set_learning_network(&net, net.learning_rate*100., net.momentum, net.decay);
+ set_learning_network(&net, net.learning_rate, net.momentum, net.decay);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1024;
- int i = 6600;
+ int imgs = 3072;
+ 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");
char **paths = (char **)list_to_array(plist);
@@ -224,19 +226,20 @@
data buffer;
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
while(1){
- i += 1;
+ ++i;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- normalize_data_rows(train);
+ //normalize_data_rows(train);
//translate_data_rows(train, -128);
//scale_data_rows(train, 1./128);
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
+ net.seen += imgs;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
free_data(train);
if(i%100==0){
char buff[256];
@@ -272,7 +275,7 @@
pthread_join(load_thread, 0);
val = buffer;
- normalize_data_rows(val);
+ //normalize_data_rows(val);
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
@@ -466,6 +469,7 @@
save_network(net, buff);
}
+/*
void train_nist_distributed(char *address)
{
srand(time(0));
@@ -487,6 +491,7 @@
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
}
+*/
void test_ensemble()
{
@@ -537,10 +542,27 @@
cvWaitKey(0);
}
+void test_convolutional_layer()
+{
+ network net = parse_network_cfg("cfg/nist_conv.cfg");
+ int size = get_network_input_size(net);
+ float *in = calloc(size, sizeof(float));
+ int i;
+ for(i = 0; i < size; ++i) in[i] = rand_normal();
+ float *in_gpu = cuda_make_array(in, size);
+ convolutional_layer layer = *(convolutional_layer *)net.layers[0];
+ int out_size = convolutional_out_height(layer)*convolutional_out_width(layer)*layer.batch;
+ cuda_compare(layer.output_gpu, layer.output, out_size, "nothing");
+ cuda_compare(layer.biases_gpu, layer.biases, layer.n, "biases");
+ cuda_compare(layer.filters_gpu, layer.filters, layer.n*layer.size*layer.size*layer.c, "filters");
+ bias_output(layer);
+ bias_output_gpu(layer);
+ cuda_compare(layer.output_gpu, layer.output, out_size, "biased output");
+}
+
void test_correct_nist()
{
network net = parse_network_cfg("cfg/nist_conv.cfg");
- test_learn_bias(*(convolutional_layer *)net.layers[0]);
srand(222222);
net = parse_network_cfg("cfg/nist_conv.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
@@ -616,6 +638,7 @@
}
}
+/*
void run_server()
{
srand(time(0));
@@ -636,6 +659,7 @@
printf("3\n");
printf("Transfered: %lf seconds\n", sec(clock()-time));
}
+*/
void del_arg(int argc, char **argv, int index)
{
@@ -669,6 +693,7 @@
int main(int argc, char **argv)
{
+ //test_convolutional_layer();
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
@@ -680,7 +705,7 @@
gpu_index = -1;
#else
if(gpu_index >= 0){
- cl_setup();
+ cudaSetDevice(gpu_index);
}
#endif
@@ -688,7 +713,7 @@
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
else if(0==strcmp(argv[1], "test")) test_imagenet();
- else if(0==strcmp(argv[1], "server")) run_server();
+ //else if(0==strcmp(argv[1], "server")) run_server();
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
@@ -701,7 +726,7 @@
else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2]);
- else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
+ //else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
else if(0==strcmp(argv[1], "detect")) test_detection(argv[2]);
else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
--
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