def test_inconsistent_shape(self):
# https://github.com/fepegar/torchio/issues/234#issuecomment-675029767
subject = tio.Subject(
im1=tio.ScalarImage(tensor=torch.rand(2, 4, 5, 6)),
im2=tio.LabelMap(tensor=torch.rand(1, 4, 5, 6)),
)
patch_size = 2
sampler = tio.LabelSampler(patch_size, 'im2')
next(sampler(subject))
def test_empty_map(self):
# https://github.com/fepegar/torchio/issues/392
im = tio.ScalarImage(tensor=torch.rand(1, 6, 6, 6))
label = torch.zeros(1, 6, 6, 6)
label[..., 0] = 1 # voxels far from center
label_im = tio.LabelMap(tensor=label)
subject = tio.Subject(image=im, label=label_im)
sampler = tio.LabelSampler(4)
with self.assertRaises(RuntimeError):
next(sampler(subject))
def test_label_probabilities(self):
labels = torch.Tensor((0, 0, 1, 1, 2, 1, 0)).reshape(1, 1, 1, -1)
subject = tio.Subject(label=tio.Image(tensor=labels, type=tio.LABEL), )
subject = tio.SubjectsDataset([subject])[0]
probs_dict = {0: 0, 1: 50, 2: 25, 3: 25}
patch_size = (1, 1, 5)
sampler = tio.LabelSampler(patch_size, label_probabilities=probs_dict)
probabilities = sampler.get_probability_map(subject)
fixture = torch.Tensor((0, 0, 1 / 4, 1 / 4, 1 / 4, 0, 0))
assert torch.all(probabilities.squeeze().eq(fixture))
def test_multichannel_label_sampler(self):
subject = tio.Subject(label=tio.LabelMap(
tensor=torch.tensor([[[[1, 1]]], [[[0, 1]]]])))
patch_size = 1
sampler = tio.LabelSampler(patch_size,
'label',
label_probabilities={
0: 1,
1: 1
})
# There are 2 voxels in the image, channels have same probabilities,
# 1st voxel has probability 0.5 * 0.5 + 0 * 0.5 of being chosen while
# 2nd voxel has probability 0.5 * 0.5 + 1 * 0.5 of being chosen.
probabilities = sampler.get_probability_map(subject)
fixture = torch.Tensor((1 / 4, 3 / 4))
assert torch.all(probabilities.squeeze().eq(fixture))
def test_label_sampler(self):
sampler = tio.LabelSampler(5)
for patch in sampler(self.sample_subject, num_patches=10):
patch_center = patch['label'][tio.DATA][0, 2, 2, 2]
self.assertEqual(patch_center, 1)
def test_no_labelmap(self):
im = tio.ScalarImage(tensor=torch.rand(1, 1, 1, 1))
subject = tio.Subject(image=im, no_label=im)
sampler = tio.LabelSampler(1)
with self.assertRaises(RuntimeError):
next(sampler(subject))