| | |
| | | |
| | | int ti = index + class_id; |
| | | float pt = output[ti] + 0.000000000000001F; |
| | | //float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 |
| | | float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss |
| | | // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d |
| | | float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 |
| | | //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss |
| | | |
| | | for (n = 0; n < classes; ++n) { |
| | | delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); |
| | |
| | | int best_class_id = -1; |
| | | for(t = 0; t < l.max_boxes; ++t){ |
| | | box truth = float_to_box(state.truth + t*5 + b*l.truths); |
| | | int class_id = state.truth[t * 5 + b*l.truths + 4]; |
| | | if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file |
| | | if(!truth.x) break; |
| | | float iou = box_iou(pred, truth); |
| | | if (iou > best_iou) { |
| | |
| | | } |
| | | for(t = 0; t < l.max_boxes; ++t){ |
| | | box truth = float_to_box(state.truth + t*5 + b*l.truths); |
| | | int class_id = state.truth[t * 5 + b*l.truths + 4]; |
| | | if (class_id >= l.classes) { |
| | | printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); |
| | | getchar(); |
| | | continue; // if label contains class_id more than number of classes in the cfg-file |
| | | } |
| | | |
| | | if(!truth.x) break; |
| | | float best_iou = 0; |
| | |
| | | l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); |
| | | } |
| | | |
| | | |
| | | int class_id = state.truth[t*5 + b*l.truths + 4]; |
| | | if (l.map) class_id = l.map[class_id]; |
| | | delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); |
| | | ++count; |
| | |
| | | } |
| | | } |
| | | |
| | | |
| | | void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) |
| | | { |
| | | int i, j, n, z; |
| | |
| | | int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); |
| | | int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); |
| | | float scale = l.background ? 1 : predictions[obj_index]; |
| | | dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); |
| | | dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h); |
| | | dets[index].objectness = scale > thresh ? scale : 0; |
| | | if (dets[index].mask) { |
| | | for (j = 0; j < l.coords - 4; ++j) { |
| | |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); |
| | | if (l.softmax_tree) { |
| | | |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h); |
| | | if (map) { |
| | | for (j = 0; j < 200; ++j) { |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); |
| | |
| | | } |
| | | } |
| | | correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); |
| | | } |
| | | } |