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import pytest |
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import torch |
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from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder, |
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YOLOBBoxCoder) |
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def test_yolo_bbox_coder(): |
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coder = YOLOBBoxCoder() |
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bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.], |
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[22., -29., 138., 61.], [54., -29., 170., 61.]]) |
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pred_bboxes = torch.Tensor([[0.4709, 0.6152, 0.1690, -0.4056], |
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[0.5399, 0.6653, 0.1162, -0.4162], |
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[0.4654, 0.6618, 0.1548, -0.4301], |
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[0.4786, 0.6197, 0.1896, -0.4479]]) |
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grid_size = 32 |
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expected_decode_bboxes = torch.Tensor( |
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[[-53.6102, -10.3096, 83.7478, 49.6824], |
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[-15.8700, -8.3901, 114.4236, 50.9693], |
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[11.1822, -8.0924, 146.6034, 50.4476], |
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[41.2068, -8.9232, 181.4236, 48.5840]]) |
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assert expected_decode_bboxes.allclose( |
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coder.decode(bboxes, pred_bboxes, grid_size)) |
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def test_delta_bbox_coder(): |
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coder = DeltaXYWHBBoxCoder() |
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rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], |
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[5., 5., 5., 5.]]) |
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deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], |
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[0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) |
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expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 1.0000, 1.0000], |
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[0.1409, 0.1409, 2.8591, 2.8591], |
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[0.0000, 0.3161, 4.1945, 0.6839], |
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[5.0000, 5.0000, 5.0000, 5.0000]]) |
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out = coder.decode(rois, deltas, max_shape=(32, 32)) |
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assert expected_decode_bboxes.allclose(out, atol=1e-04) |
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out = coder.decode(rois, deltas, max_shape=torch.Tensor((32, 32))) |
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assert expected_decode_bboxes.allclose(out, atol=1e-04) |
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batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) |
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batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) |
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batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(32, 32))[0] |
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assert out.allclose(batch_out) |
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batch_out = coder.decode( |
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batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32)])[0] |
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assert out.allclose(batch_out) |
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with pytest.raises(AssertionError): |
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coder.decode( |
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batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32), (32, 32)]) |
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rois = torch.zeros((0, 4)) |
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deltas = torch.zeros((0, 4)) |
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out = coder.decode(rois, deltas, max_shape=(32, 32)) |
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assert rois.shape == out.shape |
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def test_tblr_bbox_coder(): |
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coder = TBLRBBoxCoder(normalizer=15.) |
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rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], |
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[5., 5., 5., 5.]]) |
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deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], |
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[0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) |
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expected_decode_bboxes = torch.Tensor([[0.5000, 0.5000, 0.5000, 0.5000], |
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[0.0000, 0.0000, 12.0000, 13.0000], |
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[0.0000, 0.5000, 0.0000, 0.5000], |
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[5.0000, 5.0000, 5.0000, 5.0000]]) |
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out = coder.decode(rois, deltas, max_shape=(13, 12)) |
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assert expected_decode_bboxes.allclose(out) |
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out = coder.decode(rois, deltas, max_shape=torch.Tensor((13, 12))) |
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assert expected_decode_bboxes.allclose(out) |
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batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) |
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batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) |
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batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(13, 12))[0] |
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assert out.allclose(batch_out) |
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batch_out = coder.decode( |
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batch_rois, batch_deltas, max_shape=[(13, 12), (13, 12)])[0] |
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assert out.allclose(batch_out) |
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with pytest.raises(AssertionError): |
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coder.decode(batch_rois, batch_deltas, max_shape=[(13, 12)]) |
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rois = torch.zeros((0, 4)) |
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deltas = torch.zeros((0, 4)) |
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out = coder.decode(rois, deltas, max_shape=(32, 32)) |
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assert rois.shape == out.shape |
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