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#!/usr/bin/env python |
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"""Tests for Subject.""" |
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import tempfile |
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import torch |
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import numpy as np |
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import torchio as tio |
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from ..utils import TorchioTestCase |
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class TestSubject(TorchioTestCase): |
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"""Tests for `Subject`.""" |
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def test_positional_args(self): |
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with self.assertRaises(ValueError): |
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with tempfile.NamedTemporaryFile() as f: |
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tio.Subject(tio.ScalarImage(f.name)) |
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def test_input_dict(self): |
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with tempfile.NamedTemporaryFile() as f: |
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input_dict = {'image': tio.ScalarImage(f.name)} |
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tio.Subject(input_dict) |
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tio.Subject(**input_dict) |
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def test_no_sample(self): |
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with tempfile.NamedTemporaryFile() as f: |
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input_dict = {'image': tio.ScalarImage(f.name)} |
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subject = tio.Subject(input_dict) |
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with self.assertRaises(RuntimeError): |
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tio.RandomFlip()(subject) |
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def test_history(self): |
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transformed = tio.RandomFlip()(self.sample_subject) |
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self.assertIs(len(transformed.history), 1) |
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def test_inconsistent_shape(self): |
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subject = tio.Subject( |
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a=tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)), |
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b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)), |
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) |
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subject.spatial_shape |
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with self.assertRaises(RuntimeError): |
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subject.shape |
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def test_inconsistent_spatial_shape(self): |
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subject = tio.Subject( |
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a=tio.ScalarImage(tensor=torch.rand(1, 3, 3, 4)), |
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b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)), |
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) |
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with self.assertRaises(RuntimeError): |
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subject.spatial_shape |
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def test_plot(self): |
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self.sample_subject.plot( |
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show=False, |
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output_path=self.dir / 'figure.png', |
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cmap_dict=dict( |
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t2='viridis', |
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label={0: 'yellow', 1: 'blue'}, |
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), |
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) |
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def test_plot_one_image(self): |
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subject = tio.Subject(t1=tio.ScalarImage(self.get_image_path('t1_plot'))) |
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subject.plot(show=False) |
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# flake8: noqa: E203, E241 |
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def test_different_space(self): |
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affine1 = np.array([ |
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[ -0.69921875, 0. , 0. , 169.11578369], |
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[ 0. , -0.69921875, 0. , 37.26315689], |
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[ 0. , 0. , 0.69999993, 15.30004883], |
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[ 0. , 0. , 0. , 1. ], |
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]) |
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affine2 = np.array([ |
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[ -0.69921881, 0. , 0. , 169.11578369], |
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[ 0. , -0.69921881, 0. , 37.26315689], |
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[ 0. , 0. , 0.69999993, 15.30003738], |
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[ 0. , 0. , 0. , 1. ], |
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]) |
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t = torch.rand(1, 2, 3, 4) |
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subject = tio.Subject( |
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im1=tio.ScalarImage(tensor=t, affine=affine1), |
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im2=tio.ScalarImage(tensor=t, affine=affine2), |
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) |
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with self.assertRaises(RuntimeError): |
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subject.check_consistent_space() |
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fepegar /
torchio
