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85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import numpy as np
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import paddle
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def _recalls(all_ious, proposal_nums, thrs):
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img_num = all_ious.shape[0]
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total_gt_num = sum([ious.shape[0] for ious in all_ious])
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ious_ = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32)
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for k, proposal_num in enumerate(proposal_nums):
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tmp_ious = np.zeros(0)
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for i in range(img_num):
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ious = all_ious[i][:, :proposal_num].copy()
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gt_ious = np.zeros(ious.shape[0])
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if ious.size == 0:
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tmp_ious = np.hstack((tmp_ious, gt_ious))
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continue
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for j in range(ious.shape[0]):
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gt_max_overlaps = ious.argmax(axis=1)
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max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps]
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gt_idx = max_ious.argmax()
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gt_ious[j] = max_ious[gt_idx]
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box_idx = gt_max_overlaps[gt_idx]
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ious[gt_idx, :] = -1
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ious[:, box_idx] = -1
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tmp_ious = np.hstack((tmp_ious, gt_ious))
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ious_[k, :] = tmp_ious
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ious_ = np.fliplr(np.sort(ious_, axis=1))
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recalls = np.zeros((proposal_nums.size, thrs.size))
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for i, thr in enumerate(thrs):
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recalls[:, i] = (ious_ >= thr).sum(axis=1) / float(total_gt_num)
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return recalls
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def set_recall_param(proposal_nums, iou_thrs):
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if isinstance(proposal_nums, list):
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proposal_nums_ = np.array(proposal_nums)
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elif isinstance(proposal_nums, int):
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proposal_nums_ = np.array([proposal_nums])
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else:
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proposal_nums_ = proposal_nums
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if iou_thrs is None:
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_iou_thrs = np.array([0.5])
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elif isinstance(iou_thrs, list):
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_iou_thrs = np.array(iou_thrs)
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elif isinstance(iou_thrs, float):
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_iou_thrs = np.array([iou_thrs])
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else:
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_iou_thrs = iou_thrs
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return proposal_nums_, _iou_thrs
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def eval_recalls(gts, proposals, proposal_nums=None, iou_thrs=None):
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"""Calculate recalls. """
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img_num = len(gts)
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assert img_num == len(proposals)
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proposal_nums, iou_thrs = set_recall_param(proposal_nums, iou_thrs)
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all_ious = []
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for i in range(img_num):
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if proposals[i].ndim == 2 and proposals[i].shape[1] == 5:
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scores = proposals[i][:, 4]
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sort_idx = np.argsort(scores)[::-1]
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img_proposal = proposals[i][sort_idx, :]
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else:
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img_proposal = proposals[i]
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prop_num = min(img_proposal.shape[0], proposal_nums[-1])
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if gts[i] is None or gts[i].shape[0] == 0:
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ious = np.zeros((0, img_proposal.shape[0]), dtype=np.float32)
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else:
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ious = bbox_overlaps(
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torch.tensor(gts[i]),
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torch.tensor(img_proposal[:prop_num, :4]))
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ious = ious.data.numpy()
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all_ious.append(ious)
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all_ious = np.array(all_ious)
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recalls = _recalls(all_ious, proposal_nums, iou_thrs)
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return recalls
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