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