def test_loading_dict(self):
set_determinism(seed=1234)
# test sequence input data with dict
data = [
{
"image": np.arange(16, dtype=float).reshape(1, 4, 4),
"label": np.arange(16, dtype=float).reshape(1, 4, 4),
"metadata": "test string",
},
{
"image": np.arange(16, dtype=float).reshape(1, 4, 4),
"label": np.arange(16, dtype=float).reshape(1, 4, 4),
"metadata": "test string",
},
]
# image level
patch_intensity = RandShiftIntensityd(keys="image",
offsets=1.0,
prob=1.0)
patch_iter = PatchIterd(keys=["image", "label"],
patch_size=(2, 2),
start_pos=(0, 0))
ds = GridPatchDataset(data=data,
patch_iter=patch_iter,
transform=patch_intensity,
with_coordinates=True)
# use the grid patch dataset
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):
np.testing.assert_equal(item[0]["image"].shape, (2, 1, 2, 2))
np.testing.assert_equal(item[0]["label"].shape, (2, 1, 2, 2))
self.assertListEqual(item[0]["metadata"],
["test string", "test string"])
np.testing.assert_allclose(
item[0]["image"],
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(item[0]["image"].shape, (2, 1, 2, 2))
np.testing.assert_allclose(
item[0]["image"],
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)
def test_shape(self):
test_dataset = ["vwxyz", "helloworld", "worldfoobar"]
result = GridPatchDataset(dataset=test_dataset,
patch_iter=identity_generator,
with_coordinates=False)
output = []
n_workers = 0 if sys.platform == "win32" else 2
for item in DataLoader(result, batch_size=3, num_workers=n_workers):
output.append("".join(item))
expected = [
"vwx", "wor", "yzh", "ldf", "ell", "oob", "owo", "ar", "rld"
]
self.assertEqual(sorted(output), sorted(expected))
self.assertEqual(len("".join(expected)), len("".join(test_dataset)))
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)