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import torch
import numpy as np
import torchio as tio
from ...utils import TorchioTestCase
class TestResize(TorchioTestCase):
"""Tests for `Resize`."""
def test_one_dim(self):
target_shape = 5
transform = tio.Resize(target_shape)
transformed = transform(self.sample_subject)
for image in transformed.get_images(intensity_only=False):
self.assertEqual(image.spatial_shape, 3 * (target_shape,))
def test_all_dims(self):
target_shape = 11, 6, 7
self.assertEqual(image.spatial_shape, target_shape)
def test_fix_shape(self):
# We use values that are known to need cropping
tensor = torch.rand(1, 8, 180, 320)
affine = np.diag((5, 1, 1, 1))
im = tio.ScalarImage(tensor=tensor, affine=affine)
target = 12
with self.assertWarns(UserWarning):
result = tio.Resize(target)(im)
self.assertEqual(result.spatial_shape, 3 * (target,))
fepegar /
torchio
