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mmdet.core.bbox.samplers.sampling_result 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch

from mmdet.utils import util_mixins


[文档]class SamplingResult(util_mixins.NiceRepr): """Bbox sampling result. Example: >>> # xdoctest: +IGNORE_WANT >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random(rng=10) >>> print(f'self = {self}') self = <SamplingResult({ 'neg_bboxes': torch.Size([12, 4]), 'neg_inds': tensor([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12]), 'num_gts': 4, 'pos_assigned_gt_inds': tensor([], dtype=torch.int64), 'pos_bboxes': torch.Size([0, 4]), 'pos_inds': tensor([], dtype=torch.int64), 'pos_is_gt': tensor([], dtype=torch.uint8) })> """ def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags): self.pos_inds = pos_inds self.neg_inds = neg_inds self.pos_bboxes = bboxes[pos_inds] self.neg_bboxes = bboxes[neg_inds] self.pos_is_gt = gt_flags[pos_inds] self.num_gts = gt_bboxes.shape[0] self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 if gt_bboxes.numel() == 0: # hack for index error case assert self.pos_assigned_gt_inds.numel() == 0 self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4) else: if len(gt_bboxes.shape) < 2: gt_bboxes = gt_bboxes.view(-1, 4) self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :] if assign_result.labels is not None: self.pos_gt_labels = assign_result.labels[pos_inds] else: self.pos_gt_labels = None @property def bboxes(self): """torch.Tensor: concatenated positive and negative boxes""" return torch.cat([self.pos_bboxes, self.neg_bboxes])
[文档] def to(self, device): """Change the device of the data inplace. Example: >>> self = SamplingResult.random() >>> print(f'self = {self.to(None)}') >>> # xdoctest: +REQUIRES(--gpu) >>> print(f'self = {self.to(0)}') """ _dict = self.__dict__ for key, value in _dict.items(): if isinstance(value, torch.Tensor): _dict[key] = value.to(device) return self
def __nice__(self): data = self.info.copy() data['pos_bboxes'] = data.pop('pos_bboxes').shape data['neg_bboxes'] = data.pop('neg_bboxes').shape parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] body = ' ' + ',\n '.join(parts) return '{\n' + body + '\n}' @property def info(self): """Returns a dictionary of info about the object.""" return { 'pos_inds': self.pos_inds, 'neg_inds': self.neg_inds, 'pos_bboxes': self.pos_bboxes, 'neg_bboxes': self.neg_bboxes, 'pos_is_gt': self.pos_is_gt, 'num_gts': self.num_gts, 'pos_assigned_gt_inds': self.pos_assigned_gt_inds, }
[文档] @classmethod def random(cls, rng=None, **kwargs): """ Args: rng (None | int | numpy.random.RandomState): seed or state. kwargs (keyword arguments): - num_preds: number of predicted boxes - num_gts: number of true boxes - p_ignore (float): probability of a predicted box assigned to \ an ignored truth. - p_assigned (float): probability of a predicted box not being \ assigned. - p_use_label (float | bool): with labels or not. Returns: :obj:`SamplingResult`: Randomly generated sampling result. Example: >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random() >>> print(self.__dict__) """ from mmdet.core.bbox.samplers.random_sampler import RandomSampler from mmdet.core.bbox.assigners.assign_result import AssignResult from mmdet.core.bbox import demodata rng = demodata.ensure_rng(rng) # make probabalistic? num = 32 pos_fraction = 0.5 neg_pos_ub = -1 assign_result = AssignResult.random(rng=rng, **kwargs) # Note we could just compute an assignment bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng) gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng) if rng.rand() > 0.2: # sometimes algorithms squeeze their data, be robust to that gt_bboxes = gt_bboxes.squeeze() bboxes = bboxes.squeeze() if assign_result.labels is None: gt_labels = None else: gt_labels = None # todo if gt_labels is None: add_gt_as_proposals = False else: add_gt_as_proposals = True # make probabalistic? sampler = RandomSampler( num, pos_fraction, neg_pos_ub=neg_pos_ub, add_gt_as_proposals=add_gt_as_proposals, rng=rng) self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) return self
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