def test_value(self):
shifter = RandShiftIntensity(offsets=1.0, prob=1.0)
shifter.set_random_state(seed=0)
result = shifter(self.imt)
np.random.seed(0)
expected = self.imt + np.random.uniform(low=-1.0, high=1.0)
np.testing.assert_allclose(result, expected)
def test_value(self):
shifter = RandShiftIntensity(offsets=1.0, prob=1.0)
shifter.set_random_state(seed=0)
result = shifter(self.imt, factor=1.0)
np.random.seed(0)
# simulate the randomize() of transform
np.random.random()
expected = self.imt + np.random.uniform(low=-1.0, high=1.0)
np.testing.assert_allclose(result, expected)
def test_loading_array(self):
set_determinism(seed=1234)
# image dataset
images = [
np.arange(16, dtype=float).reshape(1, 4, 4),
np.arange(16, dtype=float).reshape(1, 4, 4)
]
# image patch sampler
n_samples = 8
sampler = RandSpatialCropSamples(roi_size=(3, 3),
num_samples=n_samples,
random_center=True,
random_size=False)
# image level
patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
image_ds = Dataset(images, transform=patch_intensity)
# patch level
ds = PatchDataset(dataset=image_ds,
patch_func=sampler,
samples_per_image=n_samples,
transform=patch_intensity)
np.testing.assert_equal(len(ds), n_samples * len(images))
# use the patch dataset, length: len(images) x samplers_per_image
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):
np.testing.assert_equal(tuple(item.shape), (2, 1, 3, 3))
np.testing.assert_allclose(
item[0],
np.array([[[1.338681, 2.338681, 3.338681],
[5.338681, 6.338681, 7.338681],
[9.338681, 10.338681, 11.338681]]]),
rtol=1e-5,
)
if sys.platform != "win32":
for item in DataLoader(ds,
batch_size=2,
shuffle=False,
num_workers=2):
np.testing.assert_equal(tuple(item.shape), (2, 1, 3, 3))
np.testing.assert_allclose(
item[0],
np.array([[
[4.957847, 5.957847, 6.957847],
[8.957847, 9.957847, 10.957847],
[12.957847, 13.957847, 14.957847],
]]),
rtol=1e-5,
)
set_determinism(seed=None)
def test_loading_array(self):
set_determinism(seed=1234)
# image dataset
images = [
np.arange(16, dtype=float).reshape(1, 4, 4),
np.arange(16, dtype=float).reshape(1, 4, 4)
]
# image level
patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
patch_iter = PatchIter(patch_size=(2, 2), start_pos=(0, 0))
ds = GridPatchDataset(dataset=images,
patch_iter=patch_iter,
transform=patch_intensity)
# use the grid patch dataset
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[1.7413, 2.7413], [5.7413, 6.7413]]],
[[[9.1419, 10.1419], [13.1419, 14.1419]]]]),
rtol=1e-5,
)
np.testing.assert_allclose(
item[1],
np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]),
rtol=1e-5,
)
if sys.platform != "win32":
for item in DataLoader(ds,
batch_size=2,
shuffle=False,
num_workers=2):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[2.3944, 3.3944], [6.3944, 7.3944]]],
[[[10.6551, 11.6551], [14.6551, 15.6551]]]]),
rtol=1e-3,
)
np.testing.assert_allclose(
item[1],
np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]),
rtol=1e-5,
)
def test_loading_array(self):
set_determinism(seed=1234)
# test sequence input data with images
images = [
np.arange(16, dtype=float).reshape(1, 4, 4),
np.arange(16, dtype=float).reshape(1, 4, 4)
]
# image level
patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
patch_iter = PatchIter(patch_size=(2, 2), start_pos=(0, 0))
ds = GridPatchDataset(data=images,
patch_iter=patch_iter,
transform=patch_intensity)
# use the grid patch dataset
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[1.4965, 2.4965], [5.4965, 6.4965]]],
[[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
rtol=1e-4,
)
np.testing.assert_allclose(item[1],
np.array([[[0, 1], [0, 2], [2, 4]],
[[0, 1], [2, 4], [2, 4]]]),
rtol=1e-5)
if sys.platform != "win32":
for item in DataLoader(ds,
batch_size=2,
shuffle=False,
num_workers=2):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[1.2548, 2.2548], [5.2548, 6.2548]]],
[[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
rtol=1e-3,
)
np.testing.assert_allclose(item[1],
np.array([[[0, 1], [0, 2], [2, 4]],
[[0, 1], [2, 4], [2, 4]]]),
rtol=1e-5)