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@ -133,7 +133,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, |
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float topk = 0; |
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int count = 0; |
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double start, end, tmp, time_remaining[1000] = {0}; |
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double start, end, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; |
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start = what_time_is_it_now(); |
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ |
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@ -187,16 +187,15 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, |
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printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); |
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#ifdef OPENCV |
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end = what_time_is_it_now(); |
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time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; |
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tmp = 0.0; |
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int j, count = 0; |
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for (j = 0; j < 1000; ++j){ |
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if (time_remaining[j] != 0){ |
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tmp += time_remaining[j]; |
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count++; |
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time_remaining = (net.max_batches - i)*(end - start) / 60 / 60; |
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if (i > 1){ // ignore the first iteration
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if (i == 2){ |
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avg_t_minus_1 = time_remaining; |
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} |
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avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; |
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avg_t_minus_1 = avg_t; |
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} |
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if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, tmp/count); |
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if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_t); |
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start = what_time_is_it_now(); |
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#endif // OPENCV
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