mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
358 lines
12 KiB
C
358 lines
12 KiB
C
#include "darknet.h"
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#include <stdio.h>
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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"};
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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};
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void train_coco(char *cfgfile, char *weightfile)
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{
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//char *train_images = "/home/pjreddie/data/voc/test/train.txt";
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//char *train_images = "/home/pjreddie/data/coco/train.txt";
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char *train_images = "data/coco.trainval.txt";
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//char *train_images = "data/bags.train.list";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network *net = load_network(cfgfile, weightfile, 0);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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int imgs = net->batch*net->subdivisions;
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int i = *net->seen/imgs;
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data train, buffer;
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layer l = net->layers[net->n - 1];
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int side = l.side;
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int classes = l.classes;
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float jitter = l.jitter;
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list *plist = get_paths(train_images);
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//int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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load_args args = {0};
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.classes = classes;
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args.jitter = jitter;
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args.num_boxes = side;
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args.d = &buffer;
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args.type = REGION_DATA;
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args.angle = net->angle;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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//while(i*imgs < N*120){
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while(get_current_batch(net) < net->max_batches){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_in_thread(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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/*
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image im = float_to_image(net->w, net->h, 3, train.X.vals[113]);
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image copy = copy_image(im);
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draw_coco(copy, train.y.vals[113], 7, "truth");
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cvWaitKey(0);
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free_image(copy);
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*/
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time=clock();
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float loss = train_network(net, train);
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
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if(i%1000==0 || (i < 1000 && i%100 == 0)){
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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save_weights(net, buff);
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}
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if(i%100==0){
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char buff[256];
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sprintf(buff, "%s/%s.backup", backup_directory, base);
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save_weights(net, buff);
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}
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free_data(train);
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}
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char buff[256];
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sprintf(buff, "%s/%s_final.weights", backup_directory, base);
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save_weights(net, buff);
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}
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static void print_cocos(FILE *fp, int image_id, detection *dets, int num_boxes, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < num_boxes; ++i){
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float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
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float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
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float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
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float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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for(j = 0; j < classes; ++j){
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if (dets[i].prob[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, dets[i].prob[j]);
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}
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}
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}
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int get_coco_image_id(char *filename)
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{
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char *p = strrchr(filename, '_');
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return atoi(p+1);
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}
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void validate_coco(char *cfg, char *weights)
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{
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network *net = load_network(cfg, weights, 0);
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set_batch_network(net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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srand(time(0));
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char *base = "results/";
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list *plist = get_paths("data/coco_val_5k.list");
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//list *plist = get_paths("/home/pjreddie/data/people-art/test.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
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char **paths = (char **)list_to_array(plist);
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layer l = net->layers[net->n-1];
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int classes = l.classes;
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char buff[1024];
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snprintf(buff, 1024, "%s/coco_results.json", base);
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FILE *fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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int m = plist->size;
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int i=0;
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int t;
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float thresh = .01;
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int nms = 1;
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float iou_thresh = .5;
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int nthreads = 8;
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image *val = calloc(nthreads, sizeof(image));
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image *val_resized = calloc(nthreads, sizeof(image));
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image *buf = calloc(nthreads, sizeof(image));
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image *buf_resized = calloc(nthreads, sizeof(image));
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pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
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load_args args = {0};
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args.w = net->w;
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args.h = net->h;
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args.type = IMAGE_DATA;
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for(t = 0; t < nthreads; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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time_t start = time(0);
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for(i = nthreads; i < m+nthreads; i += nthreads){
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fprintf(stderr, "%d\n", i);
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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val_resized[t] = buf_resized[t];
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}
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for(t = 0; t < nthreads && i+t < m; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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char *path = paths[i+t-nthreads];
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int image_id = get_coco_image_id(path);
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float *X = val_resized[t].data;
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network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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int nboxes = 0;
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detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes);
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if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh);
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print_cocos(fp, image_id, dets, l.side*l.side*l.n, classes, w, h);
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free_detections(dets, nboxes);
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free_image(val[t]);
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free_image(val_resized[t]);
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}
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}
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fseek(fp, -2, SEEK_CUR);
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fprintf(fp, "\n]\n");
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fclose(fp);
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fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
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}
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void validate_coco_recall(char *cfgfile, char *weightfile)
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{
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network *net = load_network(cfgfile, weightfile, 0);
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set_batch_network(net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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srand(time(0));
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char *base = "results/comp4_det_test_";
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list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
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char **paths = (char **)list_to_array(plist);
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layer l = net->layers[net->n-1];
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int classes = l.classes;
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int side = l.side;
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int j, k;
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FILE **fps = calloc(classes, sizeof(FILE *));
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for(j = 0; j < classes; ++j){
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char buff[1024];
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snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]);
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fps[j] = fopen(buff, "w");
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}
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int m = plist->size;
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int i=0;
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float thresh = .001;
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int nms = 0;
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float iou_thresh = .5;
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int total = 0;
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int correct = 0;
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int proposals = 0;
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float avg_iou = 0;
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for(i = 0; i < m; ++i){
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char *path = paths[i];
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image orig = load_image_color(path, 0, 0);
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image sized = resize_image(orig, net->w, net->h);
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char *id = basecfg(path);
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network_predict(net, sized.data);
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int nboxes = 0;
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detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
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if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
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char labelpath[4096];
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find_replace(path, "images", "labels", labelpath);
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find_replace(labelpath, "JPEGImages", "labels", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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int num_labels = 0;
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box_label *truth = read_boxes(labelpath, &num_labels);
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for(k = 0; k < side*side*l.n; ++k){
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if(dets[k].objectness > thresh){
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++proposals;
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}
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}
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for (j = 0; j < num_labels; ++j) {
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++total;
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box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
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float best_iou = 0;
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for(k = 0; k < side*side*l.n; ++k){
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float iou = box_iou(dets[k].bbox, t);
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if(dets[k].objectness > thresh && iou > best_iou){
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best_iou = iou;
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}
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}
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avg_iou += best_iou;
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if(best_iou > iou_thresh){
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++correct;
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}
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}
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free_detections(dets, nboxes);
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fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
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free(id);
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free_image(orig);
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free_image(sized);
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}
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}
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void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
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{
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image **alphabet = load_alphabet();
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network *net = load_network(cfgfile, weightfile, 0);
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layer l = net->layers[net->n-1];
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set_batch_network(net, 1);
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srand(2222222);
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float nms = .4;
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clock_t time;
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char buff[256];
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char *input = buff;
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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} else {
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printf("Enter Image Path: ");
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fflush(stdout);
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input = fgets(input, 256, stdin);
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if(!input) return;
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strtok(input, "\n");
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}
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image im = load_image_color(input,0,0);
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image sized = resize_image(im, net->w, net->h);
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float *X = sized.data;
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time=clock();
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network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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int nboxes = 0;
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detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
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if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
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draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
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save_image(im, "prediction");
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show_image(im, "predictions", 0);
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free_detections(dets, nboxes);
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free_image(im);
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free_image(sized);
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if (filename) break;
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}
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}
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void run_coco(int argc, char **argv)
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{
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char *prefix = find_char_arg(argc, argv, "-prefix", 0);
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float thresh = find_float_arg(argc, argv, "-thresh", .2);
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int cam_index = find_int_arg(argc, argv, "-c", 0);
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int frame_skip = find_int_arg(argc, argv, "-s", 0);
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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char *filename = (argc > 5) ? argv[5]: 0;
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int avg = find_int_arg(argc, argv, "-avg", 1);
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if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh);
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else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
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else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
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else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
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else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix, avg, .5, 0,0,0,0);
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}
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