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from torchio import RandomBlur |
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from ...utils import TorchioTestCase |
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4
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class TestRandomBlur(TorchioTestCase): |
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"""Tests for `RandomBlur`.""" |
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def test_no_blurring(self): |
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transform = RandomBlur(std=0) |
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transformed = transform(self.sample_subject) |
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self.assertTensorAlmostEqual(self.sample_subject.t1.data, transformed.t1.data) |
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12
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def test_with_blurring(self): |
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transform = RandomBlur(std=(1, 3)) |
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transformed = transform(self.sample_subject) |
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self.assertTensorNotEqual(self.sample_subject.t1.data, transformed.t1.data) |
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def test_negative_std(self): |
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with self.assertRaises(ValueError): |
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RandomBlur(std=-2) |
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def test_std_range_with_negative_min(self): |
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with self.assertRaises(ValueError): |
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RandomBlur(std=(-0.5, 4)) |
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def test_wrong_std_type(self): |
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with self.assertRaises(ValueError): |
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RandomBlur(std='wrong') |
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def test_parse_stds(self): |
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def do_assert(transform): |
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self.assertEqual(transform.std_ranges, 3 * (0, 1)) |
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do_assert(RandomBlur(std=1)) |
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do_assert(RandomBlur(std=(0, 1))) |
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do_assert(RandomBlur(std=3 * (1,))) |
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do_assert(RandomBlur(std=3 * [0, 1])) |
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fepegar /
torchio
