pseudo_sampler.py
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@BBOX_SAMPLERS.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, bboxes, gt_bboxes, *args, **kwargs):
"""Directly returns the positive and negative indices of samples.
Args:
assign_result (:obj:`AssignResult`): Assigned results
bboxes (torch.Tensor): Bounding boxes
gt_bboxes (torch.Tensor): Ground truth boxes
Returns:
:obj:`SamplingResult`: sampler results
"""
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 = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8)
sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
assign_result, gt_flags)
return sampling_result