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#!/usr/bin/env python |
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"""Tests for ImagesDataset.""" |
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import nibabel as nib |
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from torchio import DATA, ImagesDataset |
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from ..utils import TorchioTestCase |
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class TestImagesDataset(TorchioTestCase): |
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"""Tests for `ImagesDataset`.""" |
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def test_images(self): |
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self.iterate_dataset(self.subjects_list) |
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def test_empty_subjects_list(self): |
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with self.assertRaises(ValueError): |
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self.iterate_dataset([]) |
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def test_empty_subjects_tuple(self): |
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with self.assertRaises(ValueError): |
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self.iterate_dataset(()) |
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def test_wrong_subjects_type(self): |
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with self.assertRaises(TypeError): |
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self.iterate_dataset(0) |
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def test_wrong_subject_type_int(self): |
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with self.assertRaises(TypeError): |
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self.iterate_dataset([0]) |
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def test_wrong_subject_type_dict(self): |
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with self.assertRaises(TypeError): |
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self.iterate_dataset([{}]) |
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def test_wrong_index(self): |
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with self.assertRaises(ValueError): |
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self.dataset[:3] |
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def test_save_sample(self): |
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dataset = ImagesDataset( |
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self.subjects_list, transform=lambda x: x) |
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_ = len(dataset) # for coverage |
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sample = dataset[0] |
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output_path = self.dir / 'test.nii.gz' |
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paths_dict = {'t1': output_path} |
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with self.assertWarns(DeprecationWarning): |
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dataset.save_sample(sample, paths_dict) |
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nii = nib.load(str(output_path)) |
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ndims_output = len(nii.shape) |
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ndims_sample = len(sample['t1'].shape) |
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assert ndims_sample == ndims_output + 1 |
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def test_wrong_transform_init(self): |
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with self.assertRaises(ValueError): |
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ImagesDataset( |
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self.subjects_list, |
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transform=dict(), |
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) |
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def test_wrong_transform_arg(self): |
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with self.assertRaises(ValueError): |
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self.dataset.set_transform(1) |
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@staticmethod |
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def iterate_dataset(subjects_list): |
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dataset = ImagesDataset(subjects_list) |
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for _ in dataset: |
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pass |
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fepegar /
torchio
