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more detection stuff

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This commit is contained in:
Joseph Redmon 2015-05-31 13:49:50 -07:00
parent c521f87c9e
commit 28d5a4a913
6 changed files with 169 additions and 16 deletions
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4
src/data.c
View File

@ -527,11 +527,11 @@ pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, in
data load_data_writing(char **paths, int n, int m, int w, int h)
{
if(m) paths = get_random_paths(paths, n, m);
char **replace_paths = find_replace_paths(paths, n, ".png", "label.png");
char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, w, h);
d.y = load_image_paths_gray(replace_paths, n, w/4, h/4);
d.y = load_image_paths_gray(replace_paths, n, w/8, h/8);
if(m) free(paths);
int i;
for(i = 0; i < n; ++i) free(replace_paths[i]);

97
src/detection.c
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@ -21,7 +21,7 @@ void draw_detection(image im, float *box, int side, char *label)
//printf("%d\n", j);
//printf("Prob: %f\n", box[j]);
int class = max_index(box+j, classes);
if(box[j+class] > .4){
if(box[j+class] > .05){
//int z;
//for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
printf("%f %s\n", box[j+class], class_names[class]);
@ -257,8 +257,8 @@ void train_detection(char *cfgfile, char *weightfile)
if (imgnet){
plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
}else{
plist = get_paths("/home/pjreddie/data/voc/no_2012_val.txt");
//plist = get_paths("/home/pjreddie/data/voc/no_2007_test.txt");
//plist = get_paths("/home/pjreddie/data/voc/no_2012_val.txt");
plist = get_paths("/home/pjreddie/data/voc/no_2007_test.txt");
//plist = get_paths("/home/pjreddie/data/coco/trainval.txt");
//plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
}
@ -289,7 +289,7 @@ void train_detection(char *cfgfile, char *weightfile)
if(i == 100){
net.learning_rate *= 10;
}
if(i%100==0){
if(i%1000==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff);
@ -336,8 +336,8 @@ void validate_detection(char *cfgfile, char *weightfile)
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
//list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
@ -388,6 +388,89 @@ void validate_detection(char *cfgfile, char *weightfile)
}
}
void do_mask(network net, data d, int offset, int classes, int nuisance, int background, int num_boxes, int per_box)
{
matrix pred = network_predict_data(net, d);
int j, k, class;
for(j = 0; j < pred.rows; ++j){
printf("%d ", offset + j);
for(k = 0; k < pred.cols; k += per_box){
float scale = 1.;
if (nuisance) scale = 1.-pred.vals[j][k];
float max_prob = 0;
for (class = 0; class < classes; ++class){
float prob = scale*pred.vals[j][k+class+background+nuisance];
if(prob > max_prob) max_prob = prob;
}
printf("%f ", max_prob);
}
printf("\n");
}
free_matrix(pred);
}
void mask_detection(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer layer = get_network_detection_layer(net);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
char **paths = (char **)list_to_array(plist);
int classes = layer.classes;
int nuisance = layer.nuisance;
int background = (layer.background && !nuisance);
int num_boxes = sqrt(get_detection_layer_locations(layer));
int per_box = 4+classes+background+nuisance;
int num_output = num_boxes*num_boxes*per_box;
int m = plist->size;
int i = 0;
int splits = 100;
int nthreads = 4;
int t;
data *val = calloc(nthreads, sizeof(data));
data *buf = calloc(nthreads, sizeof(data));
pthread_t *thr = calloc(nthreads, sizeof(data));
for(t = 0; t < nthreads; ++t){
int num = (i+1+t)*m/splits - (i+t)*m/splits;
char **part = paths+((i+t)*m/splits);
thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
}
clock_t time;
for(i = nthreads; i <= splits; i += nthreads){
time=clock();
for(t = 0; t < nthreads; ++t){
pthread_join(thr[t], 0);
val[t] = buf[t];
}
for(t = 0; t < nthreads && i < splits; ++t){
int num = (i+1+t)*m/splits - (i+t)*m/splits;
char **part = paths+((i+t)*m/splits);
thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
}
fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
for(t = 0; t < nthreads; ++t){
do_mask(net, val[t], (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
free_data(val[t]);
}
time=clock();
}
}
void validate_detection_post(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
@ -534,6 +617,7 @@ void test_detection(char *cfgfile, char *weightfile)
printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
draw_detection(im, predictions, 7, "detections");
free_image(im);
cvWaitKey(0);
}
}
@ -551,5 +635,6 @@ void run_detection(int argc, char **argv)
else if(0==strcmp(argv[2], "teststuff")) train_detection_teststuff(cfg, weights);
else if(0==strcmp(argv[2], "trainloc")) train_localization(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
else if(0==strcmp(argv[2], "mask")) mask_detection(cfg, weights);
else if(0==strcmp(argv[2], "validpost")) validate_detection_post(cfg, weights);
}

5
src/detection_layer.c
View File

@ -372,15 +372,12 @@ void forward_detection_layer(const detection_layer l, network_state state)
l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
if(1){
if(0){
for (j = offset; j < offset+classes; ++j) {
if(state.truth[j]) state.truth[j] = iou;
l.delta[j] = state.truth[j] - l.output[j];
}
}
/*
*/
}
printf("Avg IOU: %f\n", avg_iou/count);
}

2
src/imagenet.c
View File

@ -32,7 +32,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
pthread_join(load_thread, 0);
train = buffer;
/*
/*
image im = float_to_image(256, 256, 3, train.X.vals[114]);
show_image(im, "training");
cvWaitKey(0);

4
src/network_kernels.cu
View File

@ -133,20 +133,18 @@ float train_network_datum_gpu(network net, float *x, float *y)
float *get_network_output_layer_gpu(network net, int i)
{
layer l = net.layers[i];
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
if(l.type == CONVOLUTIONAL){
return l.output;
} else if(l.type == DECONVOLUTIONAL){
return l.output;
} else if(l.type == CONNECTED){
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
return l.output;
} else if(l.type == DETECTION){
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
return l.output;
} else if(l.type == MAXPOOL){
return l.output;
} else if(l.type == SOFTMAX){
pull_softmax_layer_output(l);
return l.output;
}
return 0;

73
src/writing.c Normal file
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@ -0,0 +1,73 @@
#include "network.h"
#include "utils.h"
#include "parser.h"
void train_writing(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
int i = net.seen/imgs;
list *plist = get_paths("figures.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
while(1){
++i;
time=clock();
data train = load_data_writing(paths, imgs, plist->size, 512, 512);
float loss = train_network(net, train);
#ifdef GPU
float *out = get_network_output_gpu(net);
#else
float *out = get_network_output(net);
#endif
image pred = float_to_image(64, 64, 1, out);
print_image(pred);
/*
image im = float_to_image(256, 256, 3, train.X.vals[0]);
image lab = float_to_image(64, 64, 1, train.y.vals[0]);
image pred = float_to_image(64, 64, 1, out);
show_image(im, "image");
show_image(lab, "label");
print_image(lab);
show_image(pred, "pred");
cvWaitKey(0);
*/
net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
free_data(train);
if((i % 20000) == 0) net.learning_rate *= .1;
//if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
if(i%1000==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff);
}
}
}
void run_writing(int argc, char **argv)
{
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
}