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#!/usr/bin/env python |
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import torch |
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from torchio import DATA, SubjectsDataset |
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from ..utils import TorchioTestCase |
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class TestSubjectsDataset(TorchioTestCase): |
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def test_indexing_nonint(self): |
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dset = SubjectsDataset(self.subjects_list) |
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dset[torch.tensor(0)] |
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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_wrong_transform_init(self): |
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with self.assertRaises(ValueError): |
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SubjectsDataset( |
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self.subjects_list, |
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transform={}, |
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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 = SubjectsDataset(subjects_list) |
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for _ in dataset: |
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pass |
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def test_data_loader(self): |
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from torch.utils.data import DataLoader |
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subj_list = [self.sample_subject] |
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dataset = SubjectsDataset(subj_list) |
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loader = DataLoader(dataset, batch_size=1, shuffle=True) |
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for batch in loader: |
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batch['t1'][DATA] |
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batch['label'][DATA] |
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fepegar /
torchio
