| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960 |
- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- from mmengine.structures import InstanceData
- from mmdet.registry import TASK_UTILS
- from ..assigners import AssignResult
- from .base_sampler import BaseSampler
- from .sampling_result import SamplingResult
- @TASK_UTILS.register_module()
- class PseudoSampler(BaseSampler):
- """A pseudo sampler that does not do sampling actually."""
- def __init__(self, **kwargs):
- pass
- def _sample_pos(self, **kwargs):
- """Sample positive samples."""
- raise NotImplementedError
- def _sample_neg(self, **kwargs):
- """Sample negative samples."""
- raise NotImplementedError
- def sample(self, assign_result: AssignResult, pred_instances: InstanceData,
- gt_instances: InstanceData, *args, **kwargs):
- """Directly returns the positive and negative indices of samples.
- Args:
- assign_result (:obj:`AssignResult`): Bbox assigning results.
- pred_instances (:obj:`InstanceData`): Instances of model
- predictions. It includes ``priors``, and the priors can
- be anchors, points, or bboxes predicted by the model,
- shape(n, 4).
- gt_instances (:obj:`InstanceData`): Ground truth of instance
- annotations. It usually includes ``bboxes`` and ``labels``
- attributes.
- Returns:
- :obj:`SamplingResult`: sampler results
- """
- gt_bboxes = gt_instances.bboxes
- priors = pred_instances.priors
- pos_inds = torch.nonzero(
- assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
- neg_inds = torch.nonzero(
- assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
- gt_flags = priors.new_zeros(priors.shape[0], dtype=torch.uint8)
- sampling_result = SamplingResult(
- pos_inds=pos_inds,
- neg_inds=neg_inds,
- priors=priors,
- gt_bboxes=gt_bboxes,
- assign_result=assign_result,
- gt_flags=gt_flags,
- avg_factor_with_neg=False)
- return sampling_result
|
