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import torch |
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from torchio import RandomGamma |
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from ...utils import TorchioTestCase |
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class TestRandomGamma(TorchioTestCase): |
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"""Tests for `RandomGamma`.""" |
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def get_random_tensor_zero_one(self): |
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return torch.rand(4, 5, 6, 7) |
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def test_with_zero_gamma(self): |
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transform = RandomGamma(log_gamma=0) |
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tensor = self.get_random_tensor_zero_one() |
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transformed = transform(tensor) |
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self.assertTensorAlmostEqual(tensor, transformed) |
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def test_with_non_zero_gamma(self): |
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transform = RandomGamma(log_gamma=(0.1, 0.3)) |
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tensor = self.get_random_tensor_zero_one() |
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transformed = transform(tensor) |
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self.assertTensorNotEqual(tensor, transformed) |
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def test_with_high_gamma(self): |
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transform = RandomGamma(log_gamma=(100, 100)) |
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tensor = self.get_random_tensor_zero_one() |
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transformed = transform(tensor) |
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self.assertTensorAlmostEqual( |
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tensor == 1, transformed |
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) |
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def test_with_low_gamma(self): |
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transform = RandomGamma(log_gamma=(-100, -100)) |
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tensor = self.get_random_tensor_zero_one() |
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transformed = transform(tensor) |
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self.assertTensorAlmostEqual( |
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tensor > 0, transformed |
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) |
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def test_wrong_gamma_type(self): |
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with self.assertRaises(ValueError): |
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RandomGamma(log_gamma='wrong') |
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fepegar /
torchio
