From f26da0ad5c679936274917c3d1e53821250414f6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 28 Dec 2014 17:42:35 +0000
Subject: [PATCH] Need to fix line reads
---
src/cnn.c | 80 +++++++++++++++++++++++++++-------------
1 files changed, 54 insertions(+), 26 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index cf9cd24..1c74e5c 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -31,21 +31,23 @@
save_network(net, "cfg/trained_imagenet_smaller.cfg");
}
+#define AMNT 3
void draw_detection(image im, float *box, int side)
{
int j;
int r, c;
- float amount[5] = {0,0,0,0,0};
+ float amount[AMNT] = {0};
for(r = 0; r < side*side; ++r){
- for(j = 0; j < 5; ++j){
- if(box[r*5] > amount[j]) {
- amount[j] = box[r*5];
- break;
+ float val = box[r*5];
+ for(j = 0; j < AMNT; ++j){
+ if(val > amount[j]) {
+ float swap = val;
+ val = amount[j];
+ amount[j] = swap;
}
}
}
- float smallest = amount[0];
- for(j = 1; j < 5; ++j) if(amount[j] < smallest) smallest = amount[j];
+ float smallest = amount[AMNT-1];
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
@@ -57,9 +59,9 @@
int x = c*d+box[j+2]*d;
int h = box[j+3]*256;
int w = box[j+4]*256;
- printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
- printf("%d %d %d %d\n", x, y, w, h);
- printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
+ //printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
+ //printf("%d %d %d %d\n", x, y, w, h);
+ //printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
}
}
@@ -82,14 +84,20 @@
list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
+ data train, buffer;
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
clock_t time;
while(1){
i += 1;
time=clock();
- data train = load_data_detection_jitter_random(imgs, paths, plist->size, 256, 256, 7, 7, 256);
- /*
- image im = float_to_image(224, 224, 3, train.X.vals[0]);
- draw_detection(im, train.y.vals[0], 7);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
+ //data train = load_data_detection_random(imgs, paths, plist->size, 224, 224, 7, 7, 256);
+
+/*
+ image im = float_to_image(224, 224, 3, train.X.vals[923]);
+ draw_detection(im, train.y.vals[923], 7);
*/
normalize_data_rows(train);
@@ -98,7 +106,7 @@
float loss = train_network(net, train);
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);
- if(i%10==0){
+ if(i%100==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
save_network(net, buff);
@@ -151,10 +159,10 @@
//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, .5, .0005);
+ //set_learning_network(&net, net.learning_rate, 0, .0005);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
- int i = 23030;
+ int i = 47900;
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);
@@ -355,6 +363,16 @@
free_data(train);
}
+void compare_nist(char *p1,char *p2)
+{
+ srand(222222);
+ network n1 = parse_network_cfg(p1);
+ network n2 = parse_network_cfg(p2);
+ data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+ normalize_data_rows(test);
+ compare_networks(n1, n2, test);
+}
+
void test_nist(char *path)
{
srand(222222);
@@ -367,25 +385,30 @@
printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
-void train_nist()
+void train_nist(char *cfgfile)
{
srand(222222);
- network net = parse_network_cfg("cfg/nist.cfg.old");
+ // srand(time(0));
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);
+ network net = parse_network_cfg(cfgfile);
int count = 0;
- int iters = 60000/net.batch + 1;
- while(++count <= 200){
+ int iters = 6000/net.batch + 1;
+ while(++count <= 100){
clock_t start = clock(), end;
+ normalize_data_rows(train);
+ normalize_data_rows(test);
float loss = train_network_sgd(net, train, iters);
- end = clock();
float test_acc = 0;
if(count%1 == 0) test_acc = network_accuracy(net, test);
+ end = clock();
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
- save_network(net, "~/nist_conv.cfg");
+ free_data(train);
+ free_data(test);
+ char buff[256];
+ sprintf(buff, "%s.trained", cfgfile);
+ save_network(net, buff);
}
void train_nist_distributed(char *address)
@@ -602,7 +625,6 @@
#endif
if(0==strcmp(argv[1], "detection")) train_detection_net();
- else if(0==strcmp(argv[1], "nist")) train_nist();
else if(0==strcmp(argv[1], "cifar")) train_cifar10();
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test")) test_imagenet();
@@ -616,6 +638,7 @@
fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
return 0;
}
+ 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], "detect")) test_detection(argv[2]);
@@ -623,6 +646,11 @@
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
else if(0==strcmp(argv[1], "testnist")) test_nist(argv[2]);
+ else if(argc < 4){
+ fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
+ return 0;
+ }
+ else if(0==strcmp(argv[1], "compare")) compare_nist(argv[2], argv[3]);
fprintf(stderr, "Success!\n");
return 0;
}
--
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