From 1edcf73a73d2007afc61289245763f5cf0c29e10 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 04 Dec 2014 07:20:29 +0000
Subject: [PATCH] Detection good, split up col images
---
src/cnn.c | 306 ++++++++++++++++++++++++++++++++++++--------------
1 files changed, 220 insertions(+), 86 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index 29f9565..46248ed 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -36,6 +36,7 @@
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 +73,7 @@
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 +99,7 @@
*/
}
+/*
void test_col2im()
{
float col[] = {1,2,1,2,
@@ -116,13 +119,12 @@
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 +136,8 @@
ksize, stride, pad, data_col) ;
for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
printf("\n");
- */
}
+*/
#endif
@@ -158,7 +160,7 @@
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 +177,7 @@
void verify_convolutional_layer()
{
+/*
srand(0);
int i;
int n = 1;
@@ -225,6 +228,7 @@
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()
@@ -312,18 +316,43 @@
network net = parse_network_cfg("cfg/detnet.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));
+ //srand(time(0));
+ srand(23410);
int i = 0;
- char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
- list *plist = get_paths("/data/imagenet/cls.train.list");
+ list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
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);
+ data train = load_data_detection_random(imgs*net.batch, paths, plist->size, 256, 256, 8, 8, 256);
//translate_data_rows(train, -144);
+ /*
+ image im = float_to_image(256, 256, 3, train.X.vals[0]);
+ float *truth = train.y.vals[0];
+ int j;
+ int r, c;
+ for(r = 0; r < 8; ++r){
+ for(c = 0; c < 8; ++c){
+ j = (r*8 + c) * 5;
+ if(truth[j]){
+ int d = 256/8;
+ int y = r*d+truth[j+1]*d;
+ int x = c*d+truth[j+2]*d;
+ int h = truth[j+3]*256;
+ int w = truth[j+4]*256;
+ printf("%f %f %f %f\n", truth[j+1], truth[j+2], truth[j+3], truth[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);
+ }
+ }
+ }
+ show_image(im, "box");
+ cvWaitKey(0);
+ */
+
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -335,7 +364,7 @@
free_data(train);
if(i%10==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
save_network(net, buff);
}
}
@@ -346,7 +375,7 @@
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/alexnet.cfg");
+ network net = parse_network_cfg("cfg/alexnet.part");
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));
@@ -372,7 +401,7 @@
free_data(train);
if(i%10==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
save_network(net, buff);
}
}
@@ -400,6 +429,7 @@
char **part = paths+(i*m/splits);
int num = (i+1)*m/splits - i*m/splits;
data val = load_data(part, num, labels, 1000, 256, 256);
+
normalize_data_rows(val);
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
@@ -412,6 +442,52 @@
}
}
+void draw_detection(image im, float *box)
+{
+ int j;
+ int r, c;
+ for(r = 0; r < 8; ++r){
+ for(c = 0; c < 8; ++c){
+ j = (r*8 + c) * 5;
+ printf("Prob: %f\n", box[j]);
+ if(box[j] > .05){
+ int d = 256/8;
+ int y = r*d+box[j+1]*d;
+ 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);
+ draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
+ }
+ }
+ }
+ show_image(im, "box");
+ cvWaitKey(0);
+}
+
+void test_detection()
+{
+ network net = parse_network_cfg("cfg/detnet.test");
+ srand(2222222);
+ clock_t time;
+ char filename[256];
+ while(1){
+ fgets(filename, 256, stdin);
+ strtok(filename, "\n");
+ image im = load_image_color(filename, 256, 256);
+ z_normalize_image(im);
+ printf("%d %d %d\n", im.h, im.w, im.c);
+ float *X = im.data;
+ time=clock();
+ float *predictions = network_predict(net, X);
+ printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
+ draw_detection(im, predictions);
+ free_image(im);
+ }
+}
+
void test_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
@@ -424,6 +500,7 @@
int indexes[10];
while(1){
fgets(filename, 256, stdin);
+ strtok(filename, "\n");
image im = load_image_color(filename, 256, 256);
z_normalize_image(im);
printf("%d %d %d\n", im.h, im.w, im.c);
@@ -653,7 +730,7 @@
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);
}
}
@@ -709,6 +786,60 @@
#endif
}
+void test_correct_alexnet()
+{
+ 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 test_server()
+{
+ network net = parse_network_cfg("cfg/alexnet.test");
+ server_update(net);
+}
+void test_client()
+{
+ network net = parse_network_cfg("cfg/alexnet.test");
+ client_update(net);
+}
int main(int argc, char *argv[])
{
@@ -717,94 +848,97 @@
return 0;
}
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], "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
- test_parser();
fprintf(stderr, "Success!\n");
return 0;
}
/*
-void visualize_imagenet_topk(char *filename)
-{
- int i,j,k,l;
- int topk = 10;
- network net = parse_network_cfg("cfg/voc_imagenet.cfg");
- list *plist = get_paths(filename);
- node *n = plist->front;
- int h = voc_size(1), w = voc_size(1);
- int num = get_network_image(net).c;
- image **vizs = calloc(num, sizeof(image*));
- float **score = calloc(num, sizeof(float *));
- for(i = 0; i < num; ++i){
- vizs[i] = calloc(topk, sizeof(image));
- for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3);
- score[i] = calloc(topk, sizeof(float));
- }
+ void visualize_imagenet_topk(char *filename)
+ {
+ int i,j,k,l;
+ int topk = 10;
+ network net = parse_network_cfg("cfg/voc_imagenet.cfg");
+ list *plist = get_paths(filename);
+ node *n = plist->front;
+ int h = voc_size(1), w = voc_size(1);
+ int num = get_network_image(net).c;
+ image **vizs = calloc(num, sizeof(image*));
+ float **score = calloc(num, sizeof(float *));
+ for(i = 0; i < num; ++i){
+ vizs[i] = calloc(topk, sizeof(image));
+ for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3);
+ score[i] = calloc(topk, sizeof(float));
+ }
- int count = 0;
- while(n){
- ++count;
- char *image_path = (char *)n->val;
- image im = load_image(image_path, 0, 0);
- n = n->next;
- if(im.h < 200 || im.w < 200) continue;
- printf("Processing %dx%d image\n", im.h, im.w);
- resize_network(net, im.h, im.w, im.c);
- //scale_image(im, 1./255);
- translate_image(im, -144);
- forward_network(net, im.data, 0, 0);
- image out = get_network_image(net);
+ int count = 0;
+ while(n){
+ ++count;
+ char *image_path = (char *)n->val;
+ image im = load_image(image_path, 0, 0);
+ n = n->next;
+ if(im.h < 200 || im.w < 200) continue;
+ printf("Processing %dx%d image\n", im.h, im.w);
+ resize_network(net, im.h, im.w, im.c);
+//scale_image(im, 1./255);
+translate_image(im, -144);
+forward_network(net, im.data, 0, 0);
+image out = get_network_image(net);
- int dh = (im.h - h)/(out.h-1);
- int dw = (im.w - w)/(out.w-1);
- //printf("%d %d\n", dh, dw);
- for(k = 0; k < out.c; ++k){
- float topv = 0;
- int topi = -1;
- int topj = -1;
- for(i = 0; i < out.h; ++i){
- for(j = 0; j < out.w; ++j){
- float val = get_pixel(out, i, j, k);
- if(val > topv){
- topv = val;
- topi = i;
- topj = j;
- }
- }
- }
- if(topv){
- image sub = get_sub_image(im, dh*topi, dw*topj, h, w);
- for(l = 0; l < topk; ++l){
- if(topv > score[k][l]){
- float swap = score[k][l];
- score[k][l] = topv;
- topv = swap;
+int dh = (im.h - h)/(out.h-1);
+int dw = (im.w - w)/(out.w-1);
+//printf("%d %d\n", dh, dw);
+for(k = 0; k < out.c; ++k){
+float topv = 0;
+int topi = -1;
+int topj = -1;
+for(i = 0; i < out.h; ++i){
+for(j = 0; j < out.w; ++j){
+float val = get_pixel(out, i, j, k);
+if(val > topv){
+topv = val;
+topi = i;
+topj = j;
+}
+}
+}
+if(topv){
+image sub = get_sub_image(im, dh*topi, dw*topj, h, w);
+for(l = 0; l < topk; ++l){
+if(topv > score[k][l]){
+float swap = score[k][l];
+score[k][l] = topv;
+topv = swap;
- image swapi = vizs[k][l];
- vizs[k][l] = sub;
- sub = swapi;
- }
- }
- free_image(sub);
- }
- }
- free_image(im);
- if(count%50 == 0){
- image grid = grid_images(vizs, num, topk);
- //show_image(grid, "IMAGENET Visualization");
- save_image(grid, "IMAGENET Grid Single Nonorm");
- free_image(grid);
- }
- }
- //cvWaitKey(0);
+image swapi = vizs[k][l];
+vizs[k][l] = sub;
+sub = swapi;
+}
+}
+free_image(sub);
+}
+}
+free_image(im);
+if(count%50 == 0){
+image grid = grid_images(vizs, num, topk);
+//show_image(grid, "IMAGENET Visualization");
+save_image(grid, "IMAGENET Grid Single Nonorm");
+free_image(grid);
+}
+}
+//cvWaitKey(0);
}
void visualize_imagenet_features(char *filename)
--
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