# Copyright (c) OpenMMLab. All rights reserved. import torch from ..builder import BBOX_ASSIGNERS from ..iou_calculators import build_iou_calculator from .max_iou_assigner import MaxIoUAssigner @BBOX_ASSIGNERS.register_module() class ApproxMaxIoUAssigner(MaxIoUAssigner): """Assign a corresponding gt bbox or background to each bbox. Each proposals will be assigned with an integer indicating the ground truth index. (semi-positive index: gt label (0-based), -1: background) - -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 quality matches. This is usually allowed for RPN and single stage detectors, but not allowed in the second stage. 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) def assign(self, approxs, squares, approxs_per_octave, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): """Assign gt to approxs. This method assign a gt bbox to each group of approxs (bboxes), each group of approxs is represent by a base approx (bbox) and will be assigned with -1, or a semi-positive number. background_label (-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 background_label (-1) 2. use the max IoU of each group of approxs to assign 2. assign proposals whose iou with all gts < neg_iou_thr to background 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: approxs (Tensor): Bounding boxes to be assigned, shape(approxs_per_octave*n, 4). squares (Tensor): Base Bounding boxes to be assigned, shape(n, 4). approxs_per_octave (int): number of approxs per octave 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. """ num_squares = squares.size(0) num_gts = gt_bboxes.size(0) if num_squares == 0 or num_gts == 0: # No predictions and/or truth, return empty assignment overlaps = approxs.new(num_gts, num_squares) assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) return assign_result # re-organize anchors by approxs_per_octave x num_squares approxs = torch.transpose( approxs.view(num_squares, approxs_per_octave, 4), 0, 1).contiguous().view(-1, 4) assign_on_cpu = True if (self.gpu_assign_thr > 0) and ( num_gts > self.gpu_assign_thr) else False # compute overlap and assign gt on CPU when number of GT is large if assign_on_cpu: device = approxs.device approxs = approxs.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() all_overlaps = self.iou_calculator(approxs, gt_bboxes) overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares, num_gts).max(dim=0) overlaps = torch.transpose(overlaps, 0, 1) if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0): if self.ignore_wrt_candidates: ignore_overlaps = self.iou_calculator( squares, gt_bboxes_ignore, mode='iof') ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) else: ignore_overlaps = self.iou_calculator( gt_bboxes_ignore, squares, 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.gt_inds.to(device) assign_result.max_overlaps = assign_result.max_overlaps.to(device) if assign_result.labels is not None: assign_result.labels = assign_result.labels.to(device) return assign_result