mask_pseudo_sampler.py
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# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from mmdet.core.bbox.builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
from .mask_sampling_result import MaskSamplingResult
@BBOX_SAMPLERS.register_module()
class MaskPseudoSampler(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, masks, gt_masks, **kwargs):
"""Directly returns the positive and negative indices of samples.
Args:
assign_result (:obj:`AssignResult`): Assigned results
masks (torch.Tensor): Bounding boxes
gt_masks (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 = masks.new_zeros(masks.shape[0], dtype=torch.uint8)
sampling_result = MaskSamplingResult(pos_inds, neg_inds, masks,
gt_masks, assign_result, gt_flags)
return sampling_result