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import torch |
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import torchio as tio |
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from torchio.utils import ( |
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to_tuple, |
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5
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get_stem, |
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guess_type, |
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apply_transform_to_file, |
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) |
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from .utils import TorchioTestCase |
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class TestUtils(TorchioTestCase): |
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"""Tests for `utils` module.""" |
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def test_to_tuple(self): |
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assert to_tuple(1) == (1,) |
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assert to_tuple((1,)) == (1,) |
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assert to_tuple(1, length=3) == (1, 1, 1) |
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assert to_tuple((1, 2)) == (1, 2) |
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assert to_tuple((1, 2), length=3) == (1, 2) |
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assert to_tuple([1, 2], length=3) == (1, 2) |
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def test_get_stem(self): |
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assert get_stem('/home/image.nii.gz') == 'image' |
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assert get_stem('/home/image.nii') == 'image' |
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assert get_stem('/home/image.nrrd') == 'image' |
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def test_guess_type(self): |
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assert guess_type('None') is None |
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assert isinstance(guess_type('1'), int) |
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assert isinstance(guess_type('1.5'), float) |
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assert isinstance(guess_type('(1, 3, 5)'), tuple) |
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assert isinstance(guess_type('(1,3,5)'), tuple) |
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assert isinstance(guess_type('[1,3,5]'), list) |
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assert isinstance(guess_type('test'), str) |
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def test_apply_transform_to_file(self): |
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transform = tio.RandomFlip() |
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apply_transform_to_file( |
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self.get_image_path('input'), |
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transform, |
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self.get_image_path('output'), |
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verbose=True, |
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) |
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def test_subjects_from_batch(self): |
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dataset = tio.SubjectsDataset(4 * [self.sample_subject]) |
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loader = torch.utils.data.DataLoader(dataset, batch_size=4) |
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batch = tio.utils.get_first_item(loader) |
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subjects = tio.utils.get_subjects_from_batch(batch) |
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assert isinstance(subjects[0], tio.Subject) |
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53
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def test_empty_batch(self): |
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with self.assertRaises(RuntimeError): |
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tio.utils.get_batch_images_and_size({}) |
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fepegar /
torchio
