mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
Added -mjpeg_port 8090 for Training. Loss-chart saved to chart.png even when used -dont_show.
mAP is calculated during training even without OpenCV.
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@ -26,8 +26,9 @@
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#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
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#endif
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IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
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void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision);
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IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show);
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void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches,
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float precision, int draw_precision, int dont_show, int mjpeg_port);
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#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 )
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#endif // OPENCV
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@ -38,7 +39,7 @@ int check_mistakes;
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static 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_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map)
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void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port)
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{
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list *options = read_data_cfg(datacfg);
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char *train_images = option_find_str(options, "train", "data/train.txt");
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@ -158,8 +159,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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float max_img_loss = 5;
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int number_of_lines = 100;
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int img_size = 1000;
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if (!dont_show)
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img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
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img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show);
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#endif //OPENCV
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pthread_t load_thread = load_data(args);
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@ -238,42 +238,42 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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avg_loss = avg_loss*.9 + loss*.1;
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i = get_current_batch(net);
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if (mean_average_precision > 0) {
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printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100);
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}
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if (net.cudnn_half) {
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if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
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else printf("\n Tensor Cores are used.");
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}
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printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs);
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#ifdef OPENCV
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if (!dont_show) {
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int draw_precision = 0;
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int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs
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if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {
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if (l.random) {
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printf("Resizing to initial size: %d x %d \n", init_w, init_h);
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args.w = init_w;
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args.h = init_h;
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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int k;
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for (k = 0; k < ngpus; ++k) {
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resize_network(nets + k, init_w, init_h);
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}
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net = nets[0];
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int draw_precision = 0;
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int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs
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if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {
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if (l.random) {
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printf("Resizing to initial size: %d x %d \n", init_w, init_h);
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args.w = init_w;
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args.h = init_h;
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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int k;
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for (k = 0; k < ngpus; ++k) {
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resize_network(nets + k, init_w, init_h);
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}
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// combine Training and Validation networks
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network net_combined = combine_train_valid_networks(net, net_map);
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iter_map = i;
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mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined);
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printf("\n mean_average_precision = %f \n", mean_average_precision);
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draw_precision = 1;
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net = nets[0];
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}
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draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision);
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// combine Training and Validation networks
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network net_combined = combine_train_valid_networks(net, net_map);
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iter_map = i;
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mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined);
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printf("\n mean_average_precision = %f \n", mean_average_precision);
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draw_precision = 1;
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}
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#ifdef OPENCV
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draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, dont_show, mjpeg_port);
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#endif // OPENCV
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//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
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@ -1378,7 +1378,7 @@ void run_detector(int argc, char **argv)
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int show = find_arg(argc, argv, "-show");
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int calc_map = find_arg(argc, argv, "-map");
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check_mistakes = find_arg(argc, argv, "-check_mistakes");
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int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
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int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
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int json_port = find_int_arg(argc, argv, "-json_port", -1);
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char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
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char *outfile = find_char_arg(argc, argv, "-out", 0);
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@ -1433,7 +1433,7 @@ void run_detector(int argc, char **argv)
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if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
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char *filename = (argc > 6) ? argv[6] : 0;
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if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile);
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else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map);
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else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port);
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else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
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else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
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else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL);
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@ -1447,7 +1447,7 @@ void run_detector(int argc, char **argv)
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if (strlen(filename) > 0)
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if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
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demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
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http_stream_port, json_port, dont_show, ext_output);
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mjpeg_port, json_port, dont_show, ext_output);
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free_list_contents_kvp(options);
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free_list(options);
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