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mmdet.core.bbox.samplers.score_hlr_sampler 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops import nms_match

from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult


[文档]@BBOX_SAMPLERS.register_module() class ScoreHLRSampler(BaseSampler): r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample Attention in Object Detection <https://arxiv.org/abs/1904.04821>`_. Score hierarchical local rank (HLR) differentiates with RandomSampler in negative part. It firstly computes Score-HLR in a two-step way, then linearly maps score hlr to the loss weights. Args: num (int): Total number of sampled RoIs. pos_fraction (float): Fraction of positive samples. context (:class:`BaseRoIHead`): RoI head that the sampler belongs to. neg_pos_ub (int): Upper bound of the ratio of num negative to num positive, -1 means no upper bound. add_gt_as_proposals (bool): Whether to add ground truth as proposals. k (float): Power of the non-linear mapping. bias (float): Shift of the non-linear mapping. score_thr (float): Minimum score that a negative sample is to be considered as valid bbox. """ def __init__(self, num, pos_fraction, context, neg_pos_ub=-1, add_gt_as_proposals=True, k=0.5, bias=0, score_thr=0.05, iou_thr=0.5, **kwargs): super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals) self.k = k self.bias = bias self.score_thr = score_thr self.iou_thr = iou_thr self.context = context # context of cascade detectors is a list, so distinguish them here. if not hasattr(context, 'num_stages'): self.bbox_roi_extractor = context.bbox_roi_extractor self.bbox_head = context.bbox_head self.with_shared_head = context.with_shared_head if self.with_shared_head: self.shared_head = context.shared_head else: self.bbox_roi_extractor = context.bbox_roi_extractor[ context.current_stage] self.bbox_head = context.bbox_head[context.current_stage]
[文档] @staticmethod def random_choice(gallery, num): """Randomly select some elements from the gallery. If `gallery` is a Tensor, the returned indices will be a Tensor; If `gallery` is a ndarray or list, the returned indices will be a ndarray. Args: gallery (Tensor | ndarray | list): indices pool. num (int): expected sample num. Returns: Tensor or ndarray: sampled indices. """ assert len(gallery) >= num is_tensor = isinstance(gallery, torch.Tensor) if not is_tensor: if torch.cuda.is_available(): device = torch.cuda.current_device() else: device = 'cpu' gallery = torch.tensor(gallery, dtype=torch.long, device=device) perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] rand_inds = gallery[perm] if not is_tensor: rand_inds = rand_inds.cpu().numpy() return rand_inds
def _sample_pos(self, assign_result, num_expected, **kwargs): """Randomly sample some positive samples.""" pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten() if pos_inds.numel() <= num_expected: return pos_inds else: return self.random_choice(pos_inds, num_expected) def _sample_neg(self, assign_result, num_expected, bboxes, feats=None, img_meta=None, **kwargs): """Sample negative samples. Score-HLR sampler is done in the following steps: 1. Take the maximum positive score prediction of each negative samples as s_i. 2. Filter out negative samples whose s_i <= score_thr, the left samples are called valid samples. 3. Use NMS-Match to divide valid samples into different groups, samples in the same group will greatly overlap with each other 4. Rank the matched samples in two-steps to get Score-HLR. (1) In the same group, rank samples with their scores. (2) In the same score rank across different groups, rank samples with their scores again. 5. Linearly map Score-HLR to the final label weights. Args: assign_result (:obj:`AssignResult`): result of assigner. num_expected (int): Expected number of samples. bboxes (Tensor): bbox to be sampled. feats (Tensor): Features come from FPN. img_meta (dict): Meta information dictionary. """ neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten() num_neg = neg_inds.size(0) if num_neg == 0: return neg_inds, None with torch.no_grad(): neg_bboxes = bboxes[neg_inds] neg_rois = bbox2roi([neg_bboxes]) bbox_result = self.context._bbox_forward(feats, neg_rois) cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[ 'bbox_pred'] ori_loss = self.bbox_head.loss( cls_score=cls_score, bbox_pred=None, rois=None, labels=neg_inds.new_full((num_neg, ), self.bbox_head.num_classes), label_weights=cls_score.new_ones(num_neg), bbox_targets=None, bbox_weights=None, reduction_override='none')['loss_cls'] # filter out samples with the max score lower than score_thr max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1) valid_inds = (max_score > self.score_thr).nonzero().view(-1) invalid_inds = (max_score <= self.score_thr).nonzero().view(-1) num_valid = valid_inds.size(0) num_invalid = invalid_inds.size(0) num_expected = min(num_neg, num_expected) num_hlr = min(num_valid, num_expected) num_rand = num_expected - num_hlr if num_valid > 0: valid_rois = neg_rois[valid_inds] valid_max_score = max_score[valid_inds] valid_argmax_score = argmax_score[valid_inds] valid_bbox_pred = bbox_pred[valid_inds] # valid_bbox_pred shape: [num_valid, #num_classes, 4] valid_bbox_pred = valid_bbox_pred.view( valid_bbox_pred.size(0), -1, 4) selected_bbox_pred = valid_bbox_pred[range(num_valid), valid_argmax_score] pred_bboxes = self.bbox_head.bbox_coder.decode( valid_rois[:, 1:], selected_bbox_pred) pred_bboxes_with_score = torch.cat( [pred_bboxes, valid_max_score[:, None]], -1) group = nms_match(pred_bboxes_with_score, self.iou_thr) # imp: importance imp = cls_score.new_zeros(num_valid) for g in group: g_score = valid_max_score[g] # g_score has already sorted rank = g_score.new_tensor(range(g_score.size(0))) imp[g] = num_valid - rank + g_score _, imp_rank_inds = imp.sort(descending=True) _, imp_rank = imp_rank_inds.sort() hlr_inds = imp_rank_inds[:num_expected] if num_rand > 0: rand_inds = torch.randperm(num_invalid)[:num_rand] select_inds = torch.cat( [valid_inds[hlr_inds], invalid_inds[rand_inds]]) else: select_inds = valid_inds[hlr_inds] neg_label_weights = cls_score.new_ones(num_expected) up_bound = max(num_expected, num_valid) imp_weights = (up_bound - imp_rank[hlr_inds].float()) / up_bound neg_label_weights[:num_hlr] = imp_weights neg_label_weights[num_hlr:] = imp_weights.min() neg_label_weights = (self.bias + (1 - self.bias) * neg_label_weights).pow( self.k) ori_selected_loss = ori_loss[select_inds] new_loss = ori_selected_loss * neg_label_weights norm_ratio = ori_selected_loss.sum() / new_loss.sum() neg_label_weights *= norm_ratio else: neg_label_weights = cls_score.new_ones(num_expected) select_inds = torch.randperm(num_neg)[:num_expected] return neg_inds[select_inds], neg_label_weights
[文档] def sample(self, assign_result, bboxes, gt_bboxes, gt_labels=None, img_meta=None, **kwargs): """Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground truth bboxes. Args: assign_result (:obj:`AssignResult`): Bbox assigning results. bboxes (Tensor): Boxes to be sampled from. gt_bboxes (Tensor): Ground truth bboxes. gt_labels (Tensor, optional): Class labels of ground truth bboxes. Returns: tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negative label weights. """ bboxes = bboxes[:, :4] gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) if self.add_gt_as_proposals: bboxes = torch.cat([gt_bboxes, bboxes], dim=0) assign_result.add_gt_(gt_labels) gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) gt_flags = torch.cat([gt_ones, gt_flags]) num_expected_pos = int(self.num * self.pos_fraction) pos_inds = self.pos_sampler._sample_pos( assign_result, num_expected_pos, bboxes=bboxes, **kwargs) num_sampled_pos = pos_inds.numel() num_expected_neg = self.num - num_sampled_pos if self.neg_pos_ub >= 0: _pos = max(1, num_sampled_pos) neg_upper_bound = int(self.neg_pos_ub * _pos) if num_expected_neg > neg_upper_bound: num_expected_neg = neg_upper_bound neg_inds, neg_label_weights = self.neg_sampler._sample_neg( assign_result, num_expected_neg, bboxes, img_meta=img_meta, **kwargs) return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags), neg_label_weights
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