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# 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
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