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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- from torch import Tensor
- from ..assigners import AssignResult
- from .sampling_result import SamplingResult
- class MultiInstanceSamplingResult(SamplingResult):
- """Bbox sampling result. Further encapsulation of SamplingResult. Three
- attributes neg_assigned_gt_inds, neg_gt_labels, and neg_gt_bboxes have been
- added for SamplingResult.
- Args:
- pos_inds (Tensor): Indices of positive samples.
- neg_inds (Tensor): Indices of negative samples.
- priors (Tensor): The priors can be anchors or points,
- or the bboxes predicted by the previous stage.
- gt_and_ignore_bboxes (Tensor): Ground truth and ignore bboxes.
- assign_result (:obj:`AssignResult`): Assigning results.
- gt_flags (Tensor): The Ground truth flags.
- avg_factor_with_neg (bool): If True, ``avg_factor`` equal to
- the number of total priors; Otherwise, it is the number of
- positive priors. Defaults to True.
- """
- def __init__(self,
- pos_inds: Tensor,
- neg_inds: Tensor,
- priors: Tensor,
- gt_and_ignore_bboxes: Tensor,
- assign_result: AssignResult,
- gt_flags: Tensor,
- avg_factor_with_neg: bool = True) -> None:
- self.neg_assigned_gt_inds = assign_result.gt_inds[neg_inds]
- self.neg_gt_labels = assign_result.labels[neg_inds]
- if gt_and_ignore_bboxes.numel() == 0:
- self.neg_gt_bboxes = torch.empty_like(gt_and_ignore_bboxes).view(
- -1, 4)
- else:
- if len(gt_and_ignore_bboxes.shape) < 2:
- gt_and_ignore_bboxes = gt_and_ignore_bboxes.view(-1, 4)
- self.neg_gt_bboxes = gt_and_ignore_bboxes[
- self.neg_assigned_gt_inds.long(), :]
- # To resist the minus 1 operation in `SamplingResult.init()`.
- assign_result.gt_inds += 1
- super().__init__(
- pos_inds=pos_inds,
- neg_inds=neg_inds,
- priors=priors,
- gt_bboxes=gt_and_ignore_bboxes,
- assign_result=assign_result,
- gt_flags=gt_flags,
- avg_factor_with_neg=avg_factor_with_neg)
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