mmdet.core.bbox.assigners.assign_result 源代码

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

from mmdet.utils import util_mixins

[文档]class AssignResult(util_mixins.NiceRepr): """Stores assignments between predicted and truth boxes. Attributes: num_gts (int): the number of truth boxes considered when computing this assignment gt_inds (LongTensor): for each predicted box indicates the 1-based index of the assigned truth box. 0 means unassigned and -1 means ignore. max_overlaps (FloatTensor): the iou between the predicted box and its assigned truth box. labels (None | LongTensor): If specified, for each predicted box indicates the category label of the assigned truth box. Example: >>> # An assign result between 4 predicted boxes and 9 true boxes >>> # where only two boxes were assigned. >>> num_gts = 9 >>> max_overlaps = torch.LongTensor([0, .5, .9, 0]) >>> gt_inds = torch.LongTensor([-1, 1, 2, 0]) >>> labels = torch.LongTensor([0, 3, 4, 0]) >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels) >>> print(str(self)) # xdoctest: +IGNORE_WANT <AssignResult(num_gts=9, gt_inds.shape=(4,), max_overlaps.shape=(4,), labels.shape=(4,))> >>> # Force addition of gt labels (when adding gt as proposals) >>> new_labels = torch.LongTensor([3, 4, 5]) >>> self.add_gt_(new_labels) >>> print(str(self)) # xdoctest: +IGNORE_WANT <AssignResult(num_gts=9, gt_inds.shape=(7,), max_overlaps.shape=(7,), labels.shape=(7,))> """ def __init__(self, num_gts, gt_inds, max_overlaps, labels=None): self.num_gts = num_gts self.gt_inds = gt_inds self.max_overlaps = max_overlaps self.labels = labels # Interface for possible user-defined properties self._extra_properties = {} @property def num_preds(self): """int: the number of predictions in this assignment""" return len(self.gt_inds)
[文档] def set_extra_property(self, key, value): """Set user-defined new property.""" assert key not in self._extra_properties[key] = value
[文档] def get_extra_property(self, key): """Get user-defined property.""" return self._extra_properties.get(key, None)
@property def info(self): """dict: a dictionary of info about the object""" basic_info = { 'num_gts': self.num_gts, 'num_preds': self.num_preds, 'gt_inds': self.gt_inds, 'max_overlaps': self.max_overlaps, 'labels': self.labels, } basic_info.update(self._extra_properties) return basic_info def __nice__(self): """str: a "nice" summary string describing this assign result""" parts = [] parts.append(f'num_gts={self.num_gts!r}') if self.gt_inds is None: parts.append(f'gt_inds={self.gt_inds!r}') else: parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}') if self.max_overlaps is None: parts.append(f'max_overlaps={self.max_overlaps!r}') else: parts.append('max_overlaps.shape=' f'{tuple(self.max_overlaps.shape)!r}') if self.labels is None: parts.append(f'labels={self.labels!r}') else: parts.append(f'labels.shape={tuple(self.labels.shape)!r}') return ', '.join(parts)
[文档] @classmethod def random(cls, **kwargs): """Create random AssignResult for tests or debugging. Args: 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 rng (None | int | numpy.random.RandomState): seed or state Returns: :obj:`AssignResult`: Randomly generated assign results. Example: >>> from mmdet.core.bbox.assigners.assign_result import * # NOQA >>> self = AssignResult.random() >>> print( """ from mmdet.core.bbox import demodata rng = demodata.ensure_rng(kwargs.get('rng', None)) num_gts = kwargs.get('num_gts', None) num_preds = kwargs.get('num_preds', None) p_ignore = kwargs.get('p_ignore', 0.3) p_assigned = kwargs.get('p_assigned', 0.7) p_use_label = kwargs.get('p_use_label', 0.5) num_classes = kwargs.get('p_use_label', 3) if num_gts is None: num_gts = rng.randint(0, 8) if num_preds is None: num_preds = rng.randint(0, 16) if num_gts == 0: max_overlaps = torch.zeros(num_preds, dtype=torch.float32) gt_inds = torch.zeros(num_preds, dtype=torch.int64) if p_use_label is True or p_use_label < rng.rand(): labels = torch.zeros(num_preds, dtype=torch.int64) else: labels = None else: import numpy as np # Create an overlap for each predicted box max_overlaps = torch.from_numpy(rng.rand(num_preds)) # Construct gt_inds for each predicted box is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned) # maximum number of assignments constraints n_assigned = min(num_preds, min(num_gts, is_assigned.sum())) assigned_idxs = np.where(is_assigned)[0] rng.shuffle(assigned_idxs) assigned_idxs = assigned_idxs[0:n_assigned] assigned_idxs.sort() is_assigned[:] = 0 is_assigned[assigned_idxs] = True is_ignore = torch.from_numpy( rng.rand(num_preds) < p_ignore) & is_assigned gt_inds = torch.zeros(num_preds, dtype=torch.int64) true_idxs = np.arange(num_gts) rng.shuffle(true_idxs) true_idxs = torch.from_numpy(true_idxs) gt_inds[is_assigned] = true_idxs[:n_assigned] gt_inds = torch.from_numpy( rng.randint(1, num_gts + 1, size=num_preds)) gt_inds[is_ignore] = -1 gt_inds[~is_assigned] = 0 max_overlaps[~is_assigned] = 0 if p_use_label is True or p_use_label < rng.rand(): if num_classes == 0: labels = torch.zeros(num_preds, dtype=torch.int64) else: labels = torch.from_numpy( # remind that we set FG labels to [0, num_class-1] # since mmdet v2.0 # BG cat_id: num_class rng.randint(0, num_classes, size=num_preds)) labels[~is_assigned] = 0 else: labels = None self = cls(num_gts, gt_inds, max_overlaps, labels) return self
[文档] def add_gt_(self, gt_labels): """Add ground truth as assigned results. Args: gt_labels (torch.Tensor): Labels of gt boxes """ self_inds = torch.arange( 1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device) self.gt_inds =[self_inds, self.gt_inds]) self.max_overlaps = [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps]) if self.labels is not None: self.labels =[gt_labels, self.labels])
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