From 9d42f49a240136a8cd643cdc1f98230d4f22b05e Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 25 Aug 2015 01:27:42 +0000
Subject: [PATCH] changing data loading
---
src/coco.c | 139 +++++++++++++++++++++++++++------------------
1 files changed, 83 insertions(+), 56 deletions(-)
diff --git a/src/coco.c b/src/coco.c
index 3f74be7..d2a108a 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -1,3 +1,5 @@
+#include <stdio.h>
+
#include "network.h"
#include "detection_layer.h"
#include "cost_layer.h"
@@ -5,44 +7,40 @@
#include "parser.h"
#include "box.h"
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
-void draw_coco(image im, float *box, int side, int objectness, char *label)
+int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
+
+void draw_coco(image im, float *pred, int side, char *label)
{
- int classes = 80;
- int elems = 4+classes+objectness;
+ int classes = 81;
+ int elems = 4+classes;
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] > 0.2){
- int width = box[j+class]*5 + 1;
- printf("%f %s\n", scale * box[j+class], coco_classes[class]);
+ int class = max_index(pred+j, classes);
+ if (class == 0) continue;
+ if (pred[j+class] > 0.2){
+ int width = pred[j+class]*5 + 1;
+ printf("%f %s\n", pred[j+class], coco_classes[class-1]);
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);
+ box predict = {pred[j+0], pred[j+1], pred[j+2], pred[j+3]};
+ box anchor = {(c+.5)/side, (r+.5)/side, .5, .5};
+ box decode = decode_box(predict, anchor);
+
+ draw_bbox(im, decode, width, red, green, blue);
}
}
}
@@ -62,39 +60,47 @@
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;
data train, buffer;
- int classes = layer.classes;
- int background = layer.objectness;
- int side = sqrt(get_detection_layer_locations(layer));
+ int classes = 81;
+ int side = 7;
- char **paths;
list *plist = get_paths(train_images);
int N = plist->size;
+ char **paths = (char **)list_to_array(plist);
- paths = (char **)list_to_array(plist);
- pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.paths = paths;
+ args.n = imgs;
+ args.m = plist->size;
+ args.classes = classes;
+ args.num_boxes = side;
+ args.d = &buffer;
+ args.type = REGION_DATA;
+
+ pthread_t load_thread = load_data_in_thread(args);
clock_t time;
while(i*imgs < N*120){
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);
+ 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[114]);
- image copy = copy_image(im);
- draw_coco(copy, train.y.vals[114], 7, layer.objectness, "truth");
- cvWaitKey(0);
- free_image(copy);
- */
+/*
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
+ image copy = copy_image(im);
+ draw_coco(copy, train.y.vals[114], 7, "truth");
+ cvWaitKey(0);
+ free_image(copy);
+ */
time=clock();
float loss = train_network(net, train);
@@ -144,7 +150,7 @@
}
}
-void print_cocos(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+void print_cocos(FILE *fp, int image_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){
@@ -158,13 +164,23 @@
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
+ float bx = xmin;
+ float by = ymin;
+ float bw = xmax - xmin;
+ float bh = ymax - ymin;
+
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);
+ if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
}
}
}
+int get_coco_image_id(char *filename)
+{
+ char *p = strrchr(filename, '_');
+ return atoi(p+1);
+}
+
void validate_coco(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
@@ -176,8 +192,8 @@
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("data/voc.2012test.list");
+ char *base = "/home/pjreddie/backup/";
+ list *plist = get_paths("data/coco_val_5k.list");
char **paths = (char **)list_to_array(plist);
int classes = layer.classes;
@@ -186,12 +202,11 @@
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, coco_classes[j]);
- fps[j] = fopen(buff, "w");
- }
+ char buff[1024];
+ snprintf(buff, 1024, "%s/coco_results.json", base);
+ FILE *fp = fopen(buff, "w");
+ fprintf(fp, "[\n");
+
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 *));
@@ -200,10 +215,15 @@
int i=0;
int t;
- float thresh = .001;
+ float thresh = .01;
int nms = 1;
float iou_thresh = .5;
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.type = IMAGE_DATA;
+
int nthreads = 8;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
@@ -211,7 +231,10 @@
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);
+ args.path = paths[i+t];
+ args.im = &buf[t];
+ args.resized = &buf_resized[t];
+ thr[t] = load_data_in_thread(args);
}
time_t start = time(0);
for(i = nthreads; i < m+nthreads; i += nthreads){
@@ -222,23 +245,28 @@
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);
+ args.path = paths[i+t];
+ args.im = &buf[t];
+ args.resized = &buf_resized[t];
+ thr[t] = load_data_in_thread(args);
}
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
- char *id = basecfg(path);
+ int image_id = get_coco_image_id(path);
float *X = val_resized[t].data;
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
convert_cocos(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
- print_cocos(fps, id, boxes, probs, num_boxes, classes, w, h);
- free(id);
+ print_cocos(fp, image_id, boxes, probs, num_boxes, classes, w, h);
free_image(val[t]);
free_image(val_resized[t]);
}
}
+ fseek(fp, -2, SEEK_CUR);
+ fprintf(fp, "\n]\n");
+ fclose(fp);
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
@@ -249,7 +277,6 @@
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer layer = get_network_detection_layer(net);
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -269,7 +296,7 @@
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- draw_coco(im, predictions, 7, layer.objectness, "predictions");
+ draw_coco(im, predictions, 7, "predictions");
free_image(im);
free_image(sized);
#ifdef OPENCV
--
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