def test_random_sampler(): assigner = MaxIoUAssigner( pos_iou_thr=0.5, neg_iou_thr=0.5, ignore_iof_thr=0.5, ignore_wrt_candidates=False, ) bboxes = torch.FloatTensor([ [0, 0, 10, 10], [10, 10, 20, 20], [5, 5, 15, 15], [32, 32, 38, 42], ]) gt_bboxes = torch.FloatTensor([ [0, 0, 10, 9], [0, 10, 10, 19], ]) gt_labels = torch.LongTensor([1, 2]) gt_bboxes_ignore = torch.Tensor([ [30, 30, 40, 40], ]) assign_result = assigner.assign(bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore, gt_labels=gt_labels) sampler = RandomSampler(num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
def test_ohem_sampler(): assigner = MaxIoUAssigner( pos_iou_thr=0.5, neg_iou_thr=0.5, ignore_iof_thr=0.5, ignore_wrt_candidates=False, ) bboxes = torch.FloatTensor([ [0, 0, 10, 10], [10, 10, 20, 20], [5, 5, 15, 15], [32, 32, 38, 42], ]) gt_bboxes = torch.FloatTensor([ [0, 0, 10, 9], [0, 10, 10, 19], ]) gt_labels = torch.LongTensor([1, 2]) gt_bboxes_ignore = torch.Tensor([ [30, 30, 40, 40], ]) assign_result = assigner.assign(bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore, gt_labels=gt_labels) context = _context_for_ohem() sampler = OHEMSampler(num=10, pos_fraction=0.5, context=context, neg_pos_ub=-1, add_gt_as_proposals=True) feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)