Shortcuts

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.info 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(self.info) """ 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 = torch.cat([self_inds, self.gt_inds]) self.max_overlaps = torch.cat( [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps]) if self.labels is not None: self.labels = torch.cat([gt_labels, self.labels])
Read the Docs v: latest
Versions
latest
stable
v2.19.1
v2.19.0
v2.18.1
v2.18.0
v2.17.0
v2.16.0
v2.15.1
v2.15.0
v2.14.0
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.