From cb1f33c6ae840e8dc0f43518daf76e6ed01034f0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 08 Dec 2014 19:48:57 +0000
Subject: [PATCH] Fixed race condition in server
---
src/cnn.c | 183 ++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 160 insertions(+), 23 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index a6627b1..f40e9a9 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -8,6 +8,7 @@
#include "matrix.h"
#include "utils.h"
#include "mini_blas.h"
+#include "server.h"
#include <time.h>
#include <stdlib.h>
@@ -36,6 +37,7 @@
void test_convolutional_layer()
{
+/*
int i;
image dog = load_image("data/dog.jpg",224,224);
network net = parse_network_cfg("cfg/convolutional.cfg");
@@ -72,6 +74,7 @@
float *gpu_del = calloc(del_size, sizeof(float));
memcpy(gpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
+ */
/*
start = clock();
@@ -97,6 +100,7 @@
*/
}
+/*
void test_col2im()
{
float col[] = {1,2,1,2,
@@ -116,13 +120,12 @@
int ksize = 3;
int stride = 1;
int pad = 0;
- col2im_gpu(col, batch,
- channels, height, width,
- ksize, stride, pad, im);
+ //col2im_gpu(col, batch,
+ // channels, height, width,
+ // ksize, stride, pad, im);
int i;
for(i = 0; i < 16; ++i)printf("%f,", im[i]);
printf("\n");
- /*
float data_im[] = {
1,2,3,4,
5,6,7,8,
@@ -134,8 +137,8 @@
ksize, stride, pad, data_col) ;
for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
printf("\n");
- */
}
+*/
#endif
@@ -158,7 +161,7 @@
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
- im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
+ //im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
}
end = clock();
@@ -175,6 +178,7 @@
void verify_convolutional_layer()
{
+/*
srand(0);
int i;
int n = 1;
@@ -225,6 +229,7 @@
printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
show_image(mj1, "forward jacobian");
show_image(mj2, "backward jacobian");
+ */
}
void test_load()
@@ -366,15 +371,52 @@
}
}
+void train_imagenet_distributed(char *address)
+{
+ float avg_loss = 1;
+ srand(time(0));
+ network net = parse_network_cfg("cfg/alexnet.client");
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = 1000/net.batch+1;
+ imgs = 1;
+ int i = 0;
+ 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);
+ printf("%d\n", plist->size);
+ clock_t time;
+ while(1){
+ i += 1;
+ time=clock();
+ data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+ //translate_data_rows(train, -144);
+ normalize_data_rows(train);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+#ifdef GPU
+ float loss = train_network_data_gpu(net, train, imgs);
+ client_update(net, address);
+ 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*net.batch);
+#endif
+ free_data(train);
+ if(i%10==0){
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
+ save_network(net, buff);
+ }
+ }
+}
void train_imagenet()
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/alexnet.part");
+ srand(time(0));
+ network net = parse_network_cfg("cfg/alexnet.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
- srand(time(0));
+ //imgs=1;
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@@ -446,7 +488,7 @@
for(c = 0; c < 8; ++c){
j = (r*8 + c) * 5;
printf("Prob: %f\n", box[j]);
- if(box[j] > .999){
+ if(box[j] > .01){
int d = 256/8;
int y = r*d+box[j+1]*d;
int x = c*d+box[j+2]*d;
@@ -465,7 +507,7 @@
void test_detection()
{
- network net = parse_network_cfg("cfg/detnet_test.cfg");
+ network net = parse_network_cfg("cfg/detnet.test");
srand(2222222);
clock_t time;
char filename[256];
@@ -609,19 +651,43 @@
network net = parse_network_cfg("cfg/nist.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
- translate_data_rows(train, -144);
- translate_data_rows(test, -144);
+ normalize_data_rows(train);
+ normalize_data_rows(test);
int count = 0;
int iters = 50000/net.batch;
+ iters = 1000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
- float loss = train_network_sgd(net, train, iters);
+ float loss = train_network_sgd_gpu(net, train, iters);
end = clock();
- float test_acc = network_accuracy(net, test);
+ float test_acc = network_accuracy_gpu(net, test);
+ //float test_acc = 0;
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
}
+void train_nist_distributed(char *address)
+{
+ srand(time(0));
+ network net = parse_network_cfg("cfg/nist.client");
+ data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
+ //data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+ normalize_data_rows(train);
+ //normalize_data_rows(test);
+ int count = 0;
+ int iters = 50000/net.batch;
+ iters = 1000/net.batch + 1;
+ while(++count <= 2000){
+ clock_t start = clock(), end;
+ float loss = train_network_sgd_gpu(net, train, iters);
+ client_update(net, address);
+ end = clock();
+ //float test_acc = network_accuracy_gpu(net, test);
+ //float test_acc = 0;
+ printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
+ }
+}
+
void test_ensemble()
{
int i;
@@ -711,6 +777,7 @@
printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
}
+/*
void test_im2row()
{
int h = 20;
@@ -726,10 +793,11 @@
float *matrix = calloc(msize, sizeof(float));
int i;
for(i = 0; i < 1000; ++i){
- im2col_cpu(test.data,1, c, h, w, size, stride, 0, matrix);
+ //im2col_cpu(test.data,1, c, h, w, size, stride, 0, matrix);
//image render = float_to_image(mh, mw, mc, matrix);
}
}
+*/
void flip_network()
{
@@ -782,13 +850,74 @@
#endif
}
-void test_server()
+void test_correct_alexnet()
{
- server_update();
+ 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);
+ printf("%d\n", plist->size);
+ clock_t time;
+ int count = 0;
+
+ srand(222222);
+ network net = parse_network_cfg("cfg/alexnet.test");
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = 1000/net.batch+1;
+ imgs = 1;
+
+ while(++count <= 5){
+ time=clock();
+ data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+ //translate_data_rows(train, -144);
+ normalize_data_rows(train);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+ float loss = train_network_data_cpu(net, train, imgs);
+ printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
+ free_data(train);
+ }
+#ifdef GPU
+ count = 0;
+ srand(222222);
+ net = parse_network_cfg("cfg/alexnet.test");
+ while(++count <= 5){
+ time=clock();
+ data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+ //translate_data_rows(train, -144);
+ normalize_data_rows(train);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+ float loss = train_network_data_gpu(net, train, imgs);
+ printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
+ free_data(train);
+ }
+#endif
+}
+
+void run_server()
+{
+ srand(time(0));
+ network net = parse_network_cfg("cfg/nist.server");
+ server_update(net);
}
void test_client()
{
- client_update();
+ network net = parse_network_cfg("cfg/alexnet.client");
+ clock_t time=clock();
+ client_update(net, "localhost");
+ printf("1\n");
+ client_update(net, "localhost");
+ printf("2\n");
+ client_update(net, "localhost");
+ printf("3\n");
+ printf("Transfered: %lf seconds\n", sec(clock()-time));
+}
+
+int find_int_arg(int argc, char* argv[], char *arg)
+{
+ int i;
+ for(i = 0; i < argc-1; ++i) if(0==strcmp(argv[i], arg)) return atoi(argv[i+1]);
+ return 0;
}
int main(int argc, char *argv[])
@@ -797,20 +926,28 @@
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
+ int index = find_int_arg(argc, argv, "-i");
+ #ifdef GPU
+ cl_setup(index);
+ #endif
if(0==strcmp(argv[1], "train")) train_imagenet();
else if(0==strcmp(argv[1], "detection")) train_detection_net();
else if(0==strcmp(argv[1], "asirra")) train_asirra();
else if(0==strcmp(argv[1], "nist")) train_nist();
- else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
+ else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test")) test_imagenet();
- else if(0==strcmp(argv[1], "server")) test_server();
- else if(0==strcmp(argv[1], "client")) test_client();
+ else if(0==strcmp(argv[1], "server")) run_server();
else if(0==strcmp(argv[1], "detect")) test_detection();
- else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
- else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
#endif
+ else if(argc < 3){
+ fprintf(stderr, "usage: %s <function>\n", argv[0]);
+ return 0;
+ }
+ else if(0==strcmp(argv[1], "client")) train_nist_distributed(argv[2]);
+ else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
+ else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
fprintf(stderr, "Success!\n");
return 0;
}
--
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