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