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single shot yolo training, separate file

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This commit is contained in:
Joseph Redmon 2015-08-14 11:45:11 -07:00
parent e625b32e06
commit 2afa376bb3
4 changed files with 329 additions and 2 deletions
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2
Makefile
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@ -34,7 +34,7 @@ CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o
ifeq ($(GPU), 1)
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
endif

2
cfg/yolo.cfg
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@ -1,6 +1,6 @@
[net]
batch=64
subdivisions=2
subdivisions=64
height=448
width=448
channels=3

3
src/darknet.c
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@ -12,6 +12,7 @@
extern void run_imagenet(int argc, char **argv);
extern void run_detection(int argc, char **argv);
extern void run_yolo(int argc, char **argv);
extern void run_coco(int argc, char **argv);
extern void run_writing(int argc, char **argv);
extern void run_captcha(int argc, char **argv);
@ -115,6 +116,8 @@ int main(int argc, char **argv)
run_imagenet(argc, argv);
} else if (0 == strcmp(argv[1], "detection")){
run_detection(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){

324
src/yolo.c Normal file
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@ -0,0 +1,324 @@
#include "network.h"
#include "detection_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
char *voc_class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
void draw_yolo(image im, float *box, int side, int objectness, char *label, float thresh)
{
int classes = 20;
int elems = 4+classes+objectness;
int j;
int r, c;
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
j = (r*side + c) * elems;
float scale = 1;
if(objectness) scale = 1 - box[j++];
int class = max_index(box+j, classes);
if(scale * box[j+class] > thresh){
int width = sqrt(scale*box[j+class])*5 + 1;
printf("%f %s\n", scale * box[j+class], voc_class_names[class]);
float red = get_color(0,class,classes);
float green = get_color(1,class,classes);
float blue = get_color(2,class,classes);
j += classes;
float x = box[j+0];
float y = box[j+1];
x = (x+c)/side;
y = (y+r)/side;
float w = box[j+2]; //*maxwidth;
float h = box[j+3]; //*maxheight;
h = h*h;
w = w*w;
int left = (x-w/2)*im.w;
int right = (x+w/2)*im.w;
int top = (y-h/2)*im.h;
int bot = (y+h/2)*im.h;
draw_box_width(im, left, top, right, bot, width, red, green, blue);
}
}
}
show_image(im, label);
}
void train_yolo(char *cfgfile, char *weightfile)
{
char *train_images = "/home/pjreddie/data/voc/test/train.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer layer = get_network_detection_layer(net);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
int i = net.seen/imgs;
char **paths;
list *plist = get_paths(train_images);
int N = plist->size;
paths = (char **)list_to_array(plist);
if(i*imgs > N*80){
net.layers[net.n-1].joint = 1;
net.layers[net.n-1].objectness = 0;
}
if(i*imgs > N*120){
net.layers[net.n-1].rescore = 1;
}
data train, buffer;
int classes = layer.classes;
int background = layer.objectness;
int side = sqrt(get_detection_layer_locations(layer));
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
clock_t time;
while(i*imgs < N*130){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
net.seen += imgs;
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images, epoch: %f\n", i, loss, avg_loss, sec(clock()-time), i*imgs, ((float)i)*imgs/N);
if((i-1)*imgs <= N && i*imgs > N){
fprintf(stderr, "First stage done\n");
net.learning_rate *= 10;
char buff[256];
sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
save_weights(net, buff);
}
if((i-1)*imgs <= 80*N && i*imgs > N*80){
fprintf(stderr, "Second stage done.\n");
net.learning_rate *= .1;
char buff[256];
sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
save_weights(net, buff);
net.layers[net.n-1].joint = 1;
net.layers[net.n-1].objectness = 0;
background = 0;
pthread_join(load_thread, 0);
free_data(buffer);
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
}
if((i-1)*imgs <= 120*N && i*imgs > N*120){
fprintf(stderr, "Third stage done.\n");
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
net.layers[net.n-1].rescore = 1;
save_weights(net, buff);
}
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
char buff[256];
sprintf(buff, "%s/%s_rescore.weights", backup_directory, base);
save_weights(net, buff);
}
void convert_yolo_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
{
int i,j;
int per_box = 4+classes+(background || objectness);
for (i = 0; i < num_boxes*num_boxes; ++i){
float scale = 1;
if(objectness) scale = 1-predictions[i*per_box];
int offset = i*per_box+(background||objectness);
for(j = 0; j < classes; ++j){
float prob = scale*predictions[offset+j];
probs[i][j] = (prob > thresh) ? prob : 0;
}
int row = i / num_boxes;
int col = i % num_boxes;
offset += classes;
boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
boxes[i].w = pow(predictions[offset + 2], 2) * w;
boxes[i].h = pow(predictions[offset + 3], 2) * h;
}
}
void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{
int i, j;
for(i = 0; i < num_boxes*num_boxes; ++i){
float xmin = boxes[i].x - boxes[i].w/2.;
float xmax = boxes[i].x + boxes[i].w/2.;
float ymin = boxes[i].y - boxes[i].h/2.;
float ymax = boxes[i].y + boxes[i].h/2.;
if (xmin < 0) xmin = 0;
if (ymin < 0) ymin = 0;
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
for(j = 0; j < classes; ++j){
if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
xmin, ymin, xmax, ymax);
}
}
}
void validate_yolo(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
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));
char *base = "results/comp4_det_test_";
list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
char **paths = (char **)list_to_array(plist);
int classes = layer.classes;
int objectness = layer.objectness;
int background = layer.background;
int num_boxes = sqrt(get_detection_layer_locations(layer));
int j;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_class_names[j]);
fps[j] = fopen(buff, "w");
}
box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
int t;
float thresh = .001;
int nms = 1;
float iou_thresh = .5;
int nthreads = 8;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
for(t = 0; t < nthreads; ++t){
thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
}
time_t start = time(0);
for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
pthread_join(thr[t], 0);
val[t] = buf[t];
val_resized[t] = buf_resized[t];
}
for(t = 0; t < nthreads && i+t < m; ++t){
thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
}
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
convert_yolo_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
print_yolo_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer layer = get_network_detection_layer(net);
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char input[256];
while(1){
if(filename){
strncpy(input, filename, 256);
} else {
printf("Enter Image Path: ");
fflush(stdout);
fgets(input, 256, stdin);
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
image sized = resize_image(im, net.w, net.h);
float *X = sized.data;
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
draw_yolo(im, predictions, 7, layer.objectness, "predictions", thresh);
free_image(im);
free_image(sized);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
void run_yolo(int argc, char **argv)
{
float thresh = find_float_arg(argc, argv, "-thresh", .2);
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;
char *filename = (argc > 5) ? argv[5]: 0;
if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
}