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from typing import List, Union
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import mmengine
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import torch
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import torch.nn.functional as F
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def bbox_target(pos_bboxes_list: List[torch.Tensor],
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neg_bboxes_list: List[torch.Tensor],
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gt_labels: List[torch.Tensor],
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cfg: Union[dict, mmengine.ConfigDict]) -> tuple:
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"""Generate classification targets for bboxes.
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Args:
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pos_bboxes_list (List[torch.Tensor]): Positive bboxes list.
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neg_bboxes_list (List[torch.Tensor]): Negative bboxes list.
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gt_labels (List[torch.Tensor]): Groundtruth classification label list.
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cfg (dict | mmengine.ConfigDict): RCNN config.
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Returns:
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tuple: Label and label_weight for bboxes.
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"""
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labels, label_weights = [], []
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pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
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assert len(pos_bboxes_list) == len(neg_bboxes_list) == len(gt_labels)
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length = len(pos_bboxes_list)
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for i in range(length):
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pos_bboxes = pos_bboxes_list[i]
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neg_bboxes = neg_bboxes_list[i]
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gt_label = gt_labels[i]
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num_pos = pos_bboxes.size(0)
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num_neg = neg_bboxes.size(0)
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num_samples = num_pos + num_neg
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label = F.pad(gt_label, (0, 0, 0, num_neg))
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label_weight = pos_bboxes.new_zeros(num_samples)
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label_weight[:num_pos] = pos_weight
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label_weight[-num_neg:] = 1.
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labels.append(label)
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label_weights.append(label_weight)
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labels = torch.cat(labels, 0)
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label_weights = torch.cat(label_weights, 0)
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return labels, label_weights
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