From cd8d53df21f3ad2810add2a8cff766c745f55a17 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 09 May 2014 22:14:52 +0000
Subject: [PATCH] So there WAS this huge bug. Gone now
---
src/tests.c | 63 ++++++++++++++-----------------
1 files changed, 28 insertions(+), 35 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 0319947..8105404 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -52,7 +52,7 @@
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
- im2col_cpu(dog.data, dog.c, dog.h, dog.w, size, stride, matrix);
+ im2col_cpu(dog.data, 1, dog.c, dog.h, dog.w, size, stride, matrix);
gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
}
end = clock();
@@ -168,7 +168,7 @@
float v = ((float)rand()/RAND_MAX);
float truth = v*v;
input[0] = v;
- forward_network(net, input);
+ forward_network(net, input, 1);
float *out = get_network_output(net);
float *delta = get_network_delta(net);
float err = pow((out[0]-truth),2.);
@@ -245,7 +245,7 @@
normalize_data_rows(test);
for(j = 0; j < test.X.rows; ++j){
float *x = test.X.vals[j];
- forward_network(net, x);
+ forward_network(net, x, 0);
int class = get_predicted_class_network(net);
fprintf(fp, "%d\n", class);
}
@@ -302,9 +302,9 @@
{
srand(444444);
srand(888888);
- network net = parse_network_cfg("cfg/nist_basic.cfg");
- data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
- data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
+ 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);
normalize_data_rows(train);
normalize_data_rows(test);
//randomize_data(train);
@@ -317,21 +317,13 @@
int batch = 10000;
while(++count <= 10000){
float loss = train_network_sgd(net, train, batch, lr, momentum, decay);
- printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+ float test_acc = network_accuracy(net, test);
+ printf("%3d %5f %5f\n",count, loss, test_acc);
//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
//end = clock();
//printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
//start=end;
- /*
- if(count%5 == 0){
- float train_acc = network_accuracy(net, train);
- fprintf(stderr, "\nTRAIN: %f\n", train_acc);
- float test_acc = network_accuracy(net, test);
- fprintf(stderr, "TEST: %f\n\n", test_acc);
- printf("%d, %f, %f\n", count, train_acc, test_acc);
//lr *= .5;
- }
- */
}
}
@@ -387,7 +379,7 @@
int index = rand()%m.rows;
//image p = float_to_image(1690,1,1,m.vals[index]);
//normalize_image(p);
- forward_network(net, m.vals[index]);
+ forward_network(net, m.vals[index], 1);
float *out = get_network_output(net);
float *delta = get_network_delta(net);
//printf("%f\n", out[0]);
@@ -408,7 +400,7 @@
matrix test = csv_to_matrix("test.csv");
truth = pop_column(&test, 0);
for(i = 0; i < test.rows; ++i){
- forward_network(net, test.vals[i]);
+ forward_network(net, test.vals[i], 0);
float *out = get_network_output(net);
if(fabs(out[0]) < .5) fprintf(fp, "0\n");
else fprintf(fp, "1\n");
@@ -439,7 +431,7 @@
float *matrix = calloc(msize, sizeof(float));
int i;
for(i = 0; i < 1000; ++i){
- im2col_cpu(test.data, c, h, w, size, stride, matrix);
+ im2col_cpu(test.data, 1, c, h, w, size, stride, matrix);
//image render = float_to_image(mh, mw, mc, matrix);
}
}
@@ -503,10 +495,11 @@
IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
cvResize(src, sized, CV_INTER_LINEAR);
image im = ipl_to_image(sized);
- normalize_array(im.data, im.h*im.w*im.c);
+ //normalize_array(im.data, im.h*im.w*im.c);
+ translate_image(im, -144);
resize_network(net, im.h, im.w, im.c);
- forward_network(net, im.data);
- image out = get_network_image_layer(net, 6);
+ forward_network(net, im.data, 0);
+ image out = get_network_image(net);
free_image(im);
cvReleaseImage(&sized);
return copy_image(out);
@@ -557,7 +550,7 @@
resize_network(net, im.h, im.w, im.c);
//scale_image(im, 1./255);
translate_image(im, -144);
- forward_network(net, im.data);
+ forward_network(net, im.data, 0);
image out = get_network_image(net);
int dh = (im.h - h)/(out.h-1);
@@ -619,7 +612,7 @@
image im = load_image(image_path, 0, 0);
printf("Processing %dx%d image\n", im.h, im.w);
resize_network(net, im.h, im.w, im.c);
- forward_network(net, im.data);
+ forward_network(net, im.data, 0);
image out = get_network_image(net);
int dh = (im.h - h)/h;
@@ -652,17 +645,14 @@
image im = load_image("data/cat.png", 0, 0);
printf("Processing %dx%d image\n", im.h, im.w);
resize_network(net, im.h, im.w, im.c);
- forward_network(net, im.data);
+ forward_network(net, im.data, 0);
- image out = get_network_image(net);
visualize_network(net);
- cvWaitKey(1000);
cvWaitKey(0);
}
-void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
+void features_VOC_image(char *image_file, char *image_dir, char *out_dir, int flip)
{
- int flip = 1;
int interval = 4;
int i,j;
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
@@ -672,8 +662,6 @@
if (flip)sprintf(out_path, "%s%d/%s_r.txt",out_dir, interval, image_file);
else sprintf(out_path, "%s%d/%s.txt",out_dir, interval, image_file);
printf("%s\n", image_file);
- FILE *fp = fopen(out_path, "w");
- if(fp == 0) file_error(out_path);
IplImage* src = 0;
if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
@@ -709,12 +697,16 @@
ims[j+interval] = features_output_size(net, src, ex_h, ex_w);
}
}
+ FILE *fp = fopen(out_path, "w");
+ if(fp == 0) file_error(out_path);
for(i = 0; i < max_scale+interval; ++i){
image out = ims[i];
fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w);
for(j = 0; j < out.c*out.h*out.w; ++j){
if(j != 0)fprintf(fp, ",");
- fprintf(fp, "%g", out.data[j]);
+ float o = out.data[j];
+ if(o < 0) o = 0;
+ fprintf(fp, "%g", o);
}
fprintf(fp, "\n");
free_image(out);
@@ -782,15 +774,16 @@
// test_im2row();
//test_split();
//test_ensemble();
- //test_nist();
+ test_nist();
//test_cifar10();
//test_vince();
//test_full();
//train_VOC();
- //features_VOC_image(argv[1], argv[2], argv[3]);
+ //features_VOC_image(argv[1], argv[2], argv[3], 0);
+ //features_VOC_image(argv[1], argv[2], argv[3], 1);
//features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3]));
//visualize_imagenet_features("data/assira/train.list");
- visualize_imagenet_topk("data/VOC2012.list");
+ //visualize_imagenet_topk("data/VOC2012.list");
//visualize_cat();
//flip_network();
//test_visualize();
--
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