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import torch |
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from mmengine.structures import InstanceData |
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from mmdet.registry import TASK_UTILS |
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from ..assigners import AssignResult |
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from .base_sampler import BaseSampler |
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from .sampling_result import SamplingResult |
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@TASK_UTILS.register_module() |
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class PseudoSampler(BaseSampler): |
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"""A pseudo sampler that does not do sampling actually.""" |
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def __init__(self, **kwargs): |
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pass |
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def _sample_pos(self, **kwargs): |
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"""Sample positive samples.""" |
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raise NotImplementedError |
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def _sample_neg(self, **kwargs): |
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"""Sample negative samples.""" |
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raise NotImplementedError |
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def sample(self, assign_result: AssignResult, pred_instances: InstanceData, |
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gt_instances: InstanceData, *args, **kwargs): |
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"""Directly returns the positive and negative indices of samples. |
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Args: |
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assign_result (:obj:`AssignResult`): Bbox assigning results. |
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pred_instances (:obj:`InstanceData`): Instances of model |
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predictions. It includes ``priors``, and the priors can |
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be anchors, points, or bboxes predicted by the model, |
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shape(n, 4). |
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gt_instances (:obj:`InstanceData`): Ground truth of instance |
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annotations. It usually includes ``bboxes`` and ``labels`` |
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attributes. |
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Returns: |
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:obj:`SamplingResult`: sampler results |
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""" |
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gt_bboxes = gt_instances.bboxes |
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priors = pred_instances.priors |
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pos_inds = torch.nonzero( |
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assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique() |
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neg_inds = torch.nonzero( |
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assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique() |
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gt_flags = priors.new_zeros(priors.shape[0], dtype=torch.uint8) |
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sampling_result = SamplingResult( |
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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_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=False) |
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return sampling_result |
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