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import numpy as np
from torchio.transforms import RandomDownsample
from ...utils import TorchioTestCase
class TestRandomDownsample(TorchioTestCase):
"""Tests for `RandomDownsample`."""
def test_downsample(self):
transform = RandomDownsample(
axes=1,
downsampling=(2., 2.)
)
transformed = transform(self.sample)
self.assertEqual(self.sample.spacing[1] * 2, transformed.spacing[1])
def test_out_of_range_axis(self):
with self.assertRaises(ValueError):
RandomDownsample(axes=3)
def test_out_of_range_axis_in_tuple(self):
RandomDownsample(axes=(0, -1, 2))
def test_wrong_axes_type(self):
RandomDownsample(axes='wrong')
def test_wrong_downsampling_type(self):
RandomDownsample(downsampling='wrong')
def test_below_one_downsampling(self):
RandomDownsample(downsampling=0.2)
fepegar /
torchio
