mmdet.core.bbox.samplers.base_sampler 源代码

from abc import ABCMeta, abstractmethod

import torch

from .sampling_result import SamplingResult

[文档]class BaseSampler(metaclass=ABCMeta): """Base class of samplers.""" def __init__(self, num, pos_fraction, neg_pos_ub=-1, add_gt_as_proposals=True, **kwargs): self.num = num self.pos_fraction = pos_fraction self.neg_pos_ub = neg_pos_ub self.add_gt_as_proposals = add_gt_as_proposals self.pos_sampler = self self.neg_sampler = self @abstractmethod def _sample_pos(self, assign_result, num_expected, **kwargs): """Sample positive samples.""" pass @abstractmethod def _sample_neg(self, assign_result, num_expected, **kwargs): """Sample negative samples.""" pass
[文档] def sample(self, assign_result, bboxes, gt_bboxes, gt_labels=None, **kwargs): """Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground truth bboxes. Args: assign_result (:obj:`AssignResult`): Bbox assigning results. bboxes (Tensor): Boxes to be sampled from. gt_bboxes (Tensor): Ground truth bboxes. gt_labels (Tensor, optional): Class labels of ground truth bboxes. Returns: :obj:`SamplingResult`: Sampling result. Example: >>> from mmdet.core.bbox import RandomSampler >>> from mmdet.core.bbox import AssignResult >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes >>> rng = ensure_rng(None) >>> assign_result = AssignResult.random(rng=rng) >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng) >>> gt_labels = None >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1, >>> add_gt_as_proposals=False) >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels) """ if len(bboxes.shape) < 2: bboxes = bboxes[None, :] bboxes = bboxes[:, :4] gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) if self.add_gt_as_proposals and len(gt_bboxes) > 0: if gt_labels is None: raise ValueError( 'gt_labels must be given when add_gt_as_proposals is True') bboxes =[gt_bboxes, bboxes], dim=0) assign_result.add_gt_(gt_labels) gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) gt_flags =[gt_ones, gt_flags]) num_expected_pos = int(self.num * self.pos_fraction) pos_inds = self.pos_sampler._sample_pos( assign_result, num_expected_pos, bboxes=bboxes, **kwargs) # We found that sampled indices have duplicated items occasionally. # (may be a bug of PyTorch) pos_inds = pos_inds.unique() num_sampled_pos = pos_inds.numel() num_expected_neg = self.num - num_sampled_pos if self.neg_pos_ub >= 0: _pos = max(1, num_sampled_pos) neg_upper_bound = int(self.neg_pos_ub * _pos) if num_expected_neg > neg_upper_bound: num_expected_neg = neg_upper_bound neg_inds = self.neg_sampler._sample_neg( assign_result, num_expected_neg, bboxes=bboxes, **kwargs) neg_inds = neg_inds.unique() sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags) return sampling_result