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- # Copyright (c) OpenMMLab. All rights reserved.
- import pytest
- import torch
-
- from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, DistancePointBBoxCoder,
- TBLRBBoxCoder, YOLOBBoxCoder)
-
-
- def test_yolo_bbox_coder():
- coder = YOLOBBoxCoder()
- bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.],
- [22., -29., 138., 61.], [54., -29., 170., 61.]])
- pred_bboxes = torch.Tensor([[0.4709, 0.6152, 0.1690, -0.4056],
- [0.5399, 0.6653, 0.1162, -0.4162],
- [0.4654, 0.6618, 0.1548, -0.4301],
- [0.4786, 0.6197, 0.1896, -0.4479]])
- grid_size = 32
- expected_decode_bboxes = torch.Tensor(
- [[-53.6102, -10.3096, 83.7478, 49.6824],
- [-15.8700, -8.3901, 114.4236, 50.9693],
- [11.1822, -8.0924, 146.6034, 50.4476],
- [41.2068, -8.9232, 181.4236, 48.5840]])
- assert expected_decode_bboxes.allclose(
- coder.decode(bboxes, pred_bboxes, grid_size))
-
-
- def test_delta_bbox_coder():
- coder = DeltaXYWHBBoxCoder()
-
- rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.],
- [5., 5., 5., 5.]])
- deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.],
- [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
- expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 1.0000, 1.0000],
- [0.1409, 0.1409, 2.8591, 2.8591],
- [0.0000, 0.3161, 4.1945, 0.6839],
- [5.0000, 5.0000, 5.0000, 5.0000]])
-
- out = coder.decode(rois, deltas, max_shape=(32, 32))
- assert expected_decode_bboxes.allclose(out, atol=1e-04)
- out = coder.decode(rois, deltas, max_shape=torch.Tensor((32, 32)))
- assert expected_decode_bboxes.allclose(out, atol=1e-04)
-
- batch_rois = rois.unsqueeze(0).repeat(2, 1, 1)
- batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1)
- batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(32, 32))[0]
- assert out.allclose(batch_out)
- batch_out = coder.decode(
- batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32)])[0]
- assert out.allclose(batch_out)
-
- # test max_shape is not equal to batch
- with pytest.raises(AssertionError):
- coder.decode(
- batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32), (32, 32)])
-
- rois = torch.zeros((0, 4))
- deltas = torch.zeros((0, 4))
- out = coder.decode(rois, deltas, max_shape=(32, 32))
- assert rois.shape == out.shape
-
- # test add_ctr_clamp
- coder = DeltaXYWHBBoxCoder(add_ctr_clamp=True, ctr_clamp=2)
-
- rois = torch.Tensor([[0., 0., 6., 6.], [0., 0., 1., 1.], [0., 0., 1., 1.],
- [5., 5., 5., 5.]])
- deltas = torch.Tensor([[1., 1., 2., 2.], [1., 1., 1., 1.],
- [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
- expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 27.1672, 27.1672],
- [0.1409, 0.1409, 2.8591, 2.8591],
- [0.0000, 0.3161, 4.1945, 0.6839],
- [5.0000, 5.0000, 5.0000, 5.0000]])
-
- out = coder.decode(rois, deltas, max_shape=(32, 32))
- assert expected_decode_bboxes.allclose(out, atol=1e-04)
-
-
- def test_tblr_bbox_coder():
- coder = TBLRBBoxCoder(normalizer=15.)
-
- rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.],
- [5., 5., 5., 5.]])
- deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.],
- [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
- expected_decode_bboxes = torch.Tensor([[0.5000, 0.5000, 0.5000, 0.5000],
- [0.0000, 0.0000, 12.0000, 13.0000],
- [0.0000, 0.5000, 0.0000, 0.5000],
- [5.0000, 5.0000, 5.0000, 5.0000]])
-
- out = coder.decode(rois, deltas, max_shape=(13, 12))
- assert expected_decode_bboxes.allclose(out)
- out = coder.decode(rois, deltas, max_shape=torch.Tensor((13, 12)))
- assert expected_decode_bboxes.allclose(out)
-
- batch_rois = rois.unsqueeze(0).repeat(2, 1, 1)
- batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1)
- batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(13, 12))[0]
- assert out.allclose(batch_out)
- batch_out = coder.decode(
- batch_rois, batch_deltas, max_shape=[(13, 12), (13, 12)])[0]
- assert out.allclose(batch_out)
-
- # test max_shape is not equal to batch
- with pytest.raises(AssertionError):
- coder.decode(batch_rois, batch_deltas, max_shape=[(13, 12)])
-
- rois = torch.zeros((0, 4))
- deltas = torch.zeros((0, 4))
- out = coder.decode(rois, deltas, max_shape=(32, 32))
- assert rois.shape == out.shape
-
-
- def test_distance_point_bbox_coder():
- coder = DistancePointBBoxCoder()
-
- points = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]])
- gt_bboxes = torch.Tensor([[74., 61., 75., 62.], [0., 104., 0., 112.],
- [100., 90., 100., 120.], [0., 120., 100., 120.]])
- expected_distance = torch.Tensor([[0., 0., 1., 1.], [0., 2., 29., 6.],
- [38., 0., 0., 50.], [29., 50., 50., 0.]])
- out_distance = coder.encode(points, gt_bboxes, max_dis=50, eps=0)
- assert expected_distance.allclose(out_distance)
-
- distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.],
- [22., -29., 138., 61.], [54., -29., 170., 61.]])
- out_bbox = coder.decode(points, distance, max_shape=(120, 100))
- assert gt_bboxes.allclose(out_bbox)
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