From c53e03348c65462bcba33f6352087dd6b9268e8f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 16 Sep 2015 21:12:10 +0000
Subject: [PATCH] yolo working w/ regions
---
src/swag.c | 145 +++++++++++++++++++++---------------------------
1 files changed, 64 insertions(+), 81 deletions(-)
diff --git a/src/yoloplus.c b/src/swag.c
similarity index 64%
rename from src/yoloplus.c
rename to src/swag.c
index dcae7bc..4dcf36b 100644
--- a/src/yoloplus.c
+++ b/src/swag.c
@@ -11,7 +11,7 @@
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-void draw_yoloplus(image im, float *box, int side, int objectness, char *label, float thresh)
+void draw_swag(image im, float *box, int side, int objectness, char *label, float thresh)
{
int classes = 20;
int elems = 4+classes+objectness;
@@ -52,7 +52,7 @@
show_image(im, label);
}
-void train_yoloplus(char *cfgfile, char *weightfile)
+void train_swag(char *cfgfile, char *weightfile)
{
char *train_images = "/home/pjreddie/data/voc/test/train.txt";
char *backup_directory = "/home/pjreddie/backup/";
@@ -65,23 +65,20 @@
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer layer = get_network_detection_layer(net);
- int imgs = 128;
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = net.batch*net.subdivisions;
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*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));
+
+ layer l = net.layers[net.n - 1];
+
+ int side = l.side;
+ int classes = l.classes;
+
+ list *plist = get_paths(train_images);
+ int N = plist->size;
+ char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
@@ -91,12 +88,12 @@
args.m = plist->size;
args.classes = classes;
args.num_boxes = side;
- args.background = background;
args.d = &buffer;
- args.type = DETECTION_DATA;
+ args.type = REGION_DATA;
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
+ //while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
@@ -105,36 +102,21 @@
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+/*
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
+ image copy = copy_image(im);
+ draw_swag(copy, train.y.vals[113], 7, "truth");
+ cvWaitKey(0);
+ free_image(copy);
+ */
+
time=clock();
float loss = train_network(net, train);
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N);
-
- if((i-1)*imgs <= 80*N && i*imgs > N*80){
- fprintf(stderr, "Second stage done.\n");
- 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);
- args.background = background;
- load_thread = load_data_in_thread(args);
- }
-
- 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);
- }
-
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
@@ -143,36 +125,38 @@
free_data(train);
}
char buff[256];
- sprintf(buff, "%s/%s_rescore.weights", backup_directory, base);
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
-void convert_yoloplus_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
+void convert_swag_detections(float *predictions, int classes, int num, int square, int side, 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 i,j,n;
+ int per_cell = 5*num+classes;
+ for (i = 0; i < side*side; ++i){
+ int row = i / side;
+ int col = i % side;
+ for(n = 0; n < num; ++n){
+ int offset = i*per_cell + 5*n;
+ float scale = predictions[offset];
+ int index = i*num + n;
+ boxes[index].x = (predictions[offset + 1] + col) / side * w;
+ boxes[index].y = (predictions[offset + 2] + row) / side * h;
+ boxes[index].w = pow(predictions[offset + 3], (square?2:1)) * w;
+ boxes[index].h = pow(predictions[offset + 4], (square?2:1)) * h;
+ for(j = 0; j < classes; ++j){
+ offset = i*per_cell + 5*num;
+ float prob = scale*predictions[offset+j];
+ probs[index][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_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+void print_swag_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
int i, j;
- for(i = 0; i < num_boxes*num_boxes; ++i){
+ for(i = 0; i < total; ++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.;
@@ -190,14 +174,13 @@
}
}
-void validate_yoloplus(char *cfgfile, char *weightfile)
+void validate_swag(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));
@@ -205,10 +188,10 @@
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));
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ int square = l.sqrt;
+ int side = l.side;
int j;
FILE **fps = calloc(classes, sizeof(FILE *));
@@ -217,9 +200,9 @@
snprintf(buff, 1024, "%s%s.txt", base, voc_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 *));
+ box *boxes = calloc(side*side*l.n, sizeof(box));
+ float **probs = calloc(side*side*l.n, sizeof(float *));
+ for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -268,9 +251,9 @@
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_yoloplus_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
- if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh);
- print_yoloplus_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
+ convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes);
+ if (nms) do_nms(boxes, probs, side*side*l.n, classes, iou_thresh);
+ print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
@@ -279,7 +262,7 @@
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
-void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh)
+void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
{
network net = parse_network_cfg(cfgfile);
@@ -306,7 +289,7 @@
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- draw_yoloplus(im, predictions, 7, layer.objectness, "predictions", thresh);
+ draw_swag(im, predictions, 7, layer.objectness, "predictions", thresh);
free_image(im);
free_image(sized);
#ifdef OPENCV
@@ -317,7 +300,7 @@
}
}
-void run_yoloplus(int argc, char **argv)
+void run_swag(int argc, char **argv)
{
float thresh = find_float_arg(argc, argv, "-thresh", .2);
if(argc < 4){
@@ -328,7 +311,7 @@
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_yoloplus(cfg, weights, filename, thresh);
- else if(0==strcmp(argv[2], "train")) train_yoloplus(cfg, weights);
- else if(0==strcmp(argv[2], "valid")) validate_yoloplus(cfg, weights);
+ if(0==strcmp(argv[2], "test")) test_swag(cfg, weights, filename, thresh);
+ else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights);
+ else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights);
}
--
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