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import torch |
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from ..builder import BBOX_SAMPLERS |
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from .base_sampler import BaseSampler |
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from .sampling_result import SamplingResult |
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@BBOX_SAMPLERS.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, bboxes, gt_bboxes, **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`): Assigned results |
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bboxes (torch.Tensor): Bounding boxes |
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gt_bboxes (torch.Tensor): Ground truth boxes |
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Returns: |
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:obj:`SamplingResult`: sampler results |
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""" |
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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 = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8) |
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sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, |
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assign_result, gt_flags) |
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return sampling_result |
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