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dataset.resize_batch()
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for i in range(num_images):
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data = next(data_iter)
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im_data.data.resize_(data[0].size()).copy_(data[0])
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im_info.data.resize_(data[1].size()).copy_(data[1])
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gt_boxes.data.resize_(data[2].size()).copy_(data[2])
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num_boxes.data.resize_(data[3].size()).copy_(data[3])
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det_tic = time.time()
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rois, cls_prob, bbox_pred, \
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rpn_loss_cls, rpn_loss_box, \
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RCNN_loss_cls, RCNN_loss_bbox, \
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rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)
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scores = cls_prob.data
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boxes = rois.data[:, :, 1:5]
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if cfg.TEST.BBOX_REG:
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# Apply bounding-box regression deltas
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box_deltas = bbox_pred.data
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if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
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# Optionally normalize targets by a precomputed mean and stdev
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if args.class_agnostic:
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box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
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+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
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box_deltas = box_deltas.view(args.batch_size, -1, 4)
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else:
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box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
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+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
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box_deltas = box_deltas.view(args.batch_size, -1, 4 * len(imdb.classes))
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pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
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pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
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else:
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# Simply repeat the boxes, once for each class
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pred_boxes = np.tile(boxes, (1, scores.shape[1]))
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pred_boxes /= data[1][0][2]
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scores = scores.squeeze()
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pred_boxes = pred_boxes.squeeze()
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det_toc = time.time()
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detect_time = det_toc - det_tic
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misc_tic = time.time()
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if vis:
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im = cv2.imread(imdb.image_path_at(i))
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im2show = np.copy(im)
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for j in xrange(1, imdb.num_classes):
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inds = torch.nonzero(scores[:,j]>thresh).view(-1)
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# if there is det
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if inds.numel() > 0:
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cls_scores = scores[:,j][inds]
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_, order = torch.sort(cls_scores, 0, True)
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if args.class_agnostic:
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cls_boxes = pred_boxes[inds, :]
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else:
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cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
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cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
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# cls_dets = torch.cat((cls_boxes, cls_scores), 1)
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cls_dets = cls_dets[order]
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keep = nms(cls_dets, cfg.TEST.NMS)
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cls_dets = cls_dets[keep.view(-1).long()]
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if vis:
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im2show = vis_detections(im2show, imdb.classes[j], cls_dets.cpu().numpy(), 0.3)
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all_boxes[j][i] = cls_dets.cpu().numpy()
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else:
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all_boxes[j][i] = empty_array
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# Limit to max_per_image detections *over all classes*
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if max_per_image > 0:
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image_scores = np.hstack([all_boxes[j][i][:, -1]
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for j in xrange(1, imdb.num_classes)])
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if len(image_scores) > max_per_image:
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image_thresh = np.sort(image_scores)[-max_per_image]
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for j in xrange(1, imdb.num_classes):
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keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
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all_boxes[j][i] = all_boxes[j][i][keep, :]
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misc_toc = time.time()
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nms_time = misc_toc - misc_tic
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sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r' \
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.format(i + 1, num_images, detect_time, nms_time))
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sys.stdout.flush()
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if vis:
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cv2.imwrite('result.png', im2show)
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pdb.set_trace()
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#cv2.imshow('test', im2show)
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#cv2.waitKey(0)
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with open(det_file, 'wb') as f:
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pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
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print('Evaluating detections')
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imdb.evaluate_detections(all_boxes, output_dir)
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end = time.time()
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