mmdet.core.bbox.assigners.max_iou_assigner 源代码

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

from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner

[文档]@BBOX_ASSIGNERS.register_module() class MaxIoUAssigner(BaseAssigner): """Assign a corresponding gt bbox or background to each bbox. Each proposals will be assigned with `-1`, or a semi-positive integer indicating the ground truth index. - -1: negative sample, no assigned gt - semi-positive integer: positive sample, index (0-based) of assigned gt Args: pos_iou_thr (float): IoU threshold for positive bboxes. neg_iou_thr (float or tuple): IoU threshold for negative bboxes. min_pos_iou (float): Minimum iou for a bbox to be considered as a positive bbox. Positive samples can have smaller IoU than pos_iou_thr due to the 4th step (assign max IoU sample to each gt). gt_max_assign_all (bool): Whether to assign all bboxes with the same highest overlap with some gt to that gt. ignore_iof_thr (float): IoF threshold for ignoring bboxes (if `gt_bboxes_ignore` is specified). Negative values mean not ignoring any bboxes. ignore_wrt_candidates (bool): Whether to compute the iof between `bboxes` and `gt_bboxes_ignore`, or the contrary. match_low_quality (bool): Whether to allow low quality matches. This is usually allowed for RPN and single stage detectors, but not allowed in the second stage. Details are demonstrated in Step 4. gpu_assign_thr (int): The upper bound of the number of GT for GPU assign. When the number of gt is above this threshold, will assign on CPU device. Negative values mean not assign on CPU. """ def __init__(self, pos_iou_thr, neg_iou_thr, min_pos_iou=.0, gt_max_assign_all=True, ignore_iof_thr=-1, ignore_wrt_candidates=True, match_low_quality=True, gpu_assign_thr=-1, iou_calculator=dict(type='BboxOverlaps2D')): self.pos_iou_thr = pos_iou_thr self.neg_iou_thr = neg_iou_thr self.min_pos_iou = min_pos_iou self.gt_max_assign_all = gt_max_assign_all self.ignore_iof_thr = ignore_iof_thr self.ignore_wrt_candidates = ignore_wrt_candidates self.gpu_assign_thr = gpu_assign_thr self.match_low_quality = match_low_quality self.iou_calculator = build_iou_calculator(iou_calculator)
[文档] def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): """Assign gt to bboxes. This method assign a gt bbox to every bbox (proposal/anchor), each bbox will be assigned with -1, or a semi-positive number. -1 means negative sample, semi-positive number is the index (0-based) of assigned gt. The assignment is done in following steps, the order matters. 1. assign every bbox to the background 2. assign proposals whose iou with all gts < neg_iou_thr to 0 3. for each bbox, if the iou with its nearest gt >= pos_iou_thr, assign it to that bbox 4. for each gt bbox, assign its nearest proposals (may be more than one) to itself Args: bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are labelled as `ignored`, e.g., crowd boxes in COCO. gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). Returns: :obj:`AssignResult`: The assign result. Example: >>> self = MaxIoUAssigner(0.5, 0.5) >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]]) >>> assign_result = self.assign(bboxes, gt_bboxes) >>> expected_gt_inds = torch.LongTensor([1, 0]) >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) """ assign_on_cpu = True if (self.gpu_assign_thr > 0) and ( gt_bboxes.shape[0] > self.gpu_assign_thr) else False # compute overlap and assign gt on CPU when number of GT is large if assign_on_cpu: device = bboxes.device bboxes = bboxes.cpu() gt_bboxes = gt_bboxes.cpu() if gt_bboxes_ignore is not None: gt_bboxes_ignore = gt_bboxes_ignore.cpu() if gt_labels is not None: gt_labels = gt_labels.cpu() overlaps = self.iou_calculator(gt_bboxes, bboxes) if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0): if self.ignore_wrt_candidates: ignore_overlaps = self.iou_calculator( bboxes, gt_bboxes_ignore, mode='iof') ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) else: ignore_overlaps = self.iou_calculator( gt_bboxes_ignore, bboxes, mode='iof') ignore_max_overlaps, _ = ignore_overlaps.max(dim=0) overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1 assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) if assign_on_cpu: assign_result.gt_inds = assign_result.max_overlaps = if assign_result.labels is not None: assign_result.labels = return assign_result
[文档] def assign_wrt_overlaps(self, overlaps, gt_labels=None): """Assign w.r.t. the overlaps of bboxes with gts. Args: overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes, shape(k, n). gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ). Returns: :obj:`AssignResult`: The assign result. """ num_gts, num_bboxes = overlaps.size(0), overlaps.size(1) # 1. assign -1 by default assigned_gt_inds = overlaps.new_full((num_bboxes, ), -1, dtype=torch.long) if num_gts == 0 or num_bboxes == 0: # No ground truth or boxes, return empty assignment max_overlaps = overlaps.new_zeros((num_bboxes, )) if num_gts == 0: # No truth, assign everything to background assigned_gt_inds[:] = 0 if gt_labels is None: assigned_labels = None else: assigned_labels = overlaps.new_full((num_bboxes, ), -1, dtype=torch.long) return AssignResult( num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) # for each anchor, which gt best overlaps with it # for each anchor, the max iou of all gts max_overlaps, argmax_overlaps = overlaps.max(dim=0) # for each gt, which anchor best overlaps with it # for each gt, the max iou of all proposals gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) # 2. assign negative: below # the negative inds are set to be 0 if isinstance(self.neg_iou_thr, float): assigned_gt_inds[(max_overlaps >= 0) & (max_overlaps < self.neg_iou_thr)] = 0 elif isinstance(self.neg_iou_thr, tuple): assert len(self.neg_iou_thr) == 2 assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0]) & (max_overlaps < self.neg_iou_thr[1])] = 0 # 3. assign positive: above positive IoU threshold pos_inds = max_overlaps >= self.pos_iou_thr assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 if self.match_low_quality: # Low-quality matching will overwrite the assigned_gt_inds assigned # in Step 3. Thus, the assigned gt might not be the best one for # prediction. # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2, # bbox 1 will be assigned as the best target for bbox A in step 3. # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's # assigned_gt_inds will be overwritten to be bbox 2. # This might be the reason that it is not used in ROI Heads. for i in range(num_gts): if gt_max_overlaps[i] >= self.min_pos_iou: if self.gt_max_assign_all: max_iou_inds = overlaps[i, :] == gt_max_overlaps[i] assigned_gt_inds[max_iou_inds] = i + 1 else: assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1 if gt_labels is not None: assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) pos_inds = torch.nonzero( assigned_gt_inds > 0, as_tuple=False).squeeze() if pos_inds.numel() > 0: assigned_labels[pos_inds] = gt_labels[ assigned_gt_inds[pos_inds] - 1] else: assigned_labels = None return AssignResult( num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
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