def test_readers(self):
inst = ITKReader()
self.assertIsInstance(inst, ITKReader)
inst = NibabelReader()
self.assertIsInstance(inst, NibabelReader)
inst = NibabelReader(as_closest_canonical=True)
self.assertIsInstance(inst, NibabelReader)
inst = NumpyReader()
self.assertIsInstance(inst, NumpyReader)
inst = NumpyReader(npz_keys="test")
self.assertIsInstance(inst, NumpyReader)
inst = PILReader()
self.assertIsInstance(inst, PILReader)
def test_kwargs(self):
test_data = {"test": np.random.randint(0, 256, size=[3, 4, 4])}
with tempfile.TemporaryDirectory() as tempdir:
filepath = os.path.join(tempdir, "test_data.npy")
np.save(filepath, test_data, allow_pickle=True)
reader = NumpyReader(mmap_mode="r")
result = reader.get_data(reader.read(filepath, mmap_mode=None))[0].item()
np.testing.assert_allclose(result["test"].shape, test_data["test"].shape)
def __init__(self,
keys: KeysCollection,
mask_key='seg',
allow_missing_keys=False) -> None:
self.readers = [NumpyReader(), NrrdReader()]
super().__init__(keys,
mask_data=None,
mask_key=mask_key,
allow_missing_keys=allow_missing_keys)
def test_channel_dim(self):
test_data = np.random.randint(0, 256, size=[3, 4, 5, 2])
with tempfile.TemporaryDirectory() as tempdir:
filepath = os.path.join(tempdir, "test_data.npy")
np.save(filepath, test_data)
reader = NumpyReader(channel_dim=-1)
result = reader.get_data(reader.read(filepath))
np.testing.assert_allclose(result[1]["spatial_shape"], test_data.shape[:-1])
self.assertEqual(result[1]["original_channel_dim"], -1)
def test_npy_pickle(self):
test_data = {"test": np.random.randint(0, 256, size=[3, 4, 4])}
with tempfile.TemporaryDirectory() as tempdir:
filepath = os.path.join(tempdir, "test_data.npy")
np.save(filepath, test_data, allow_pickle=True)
reader = NumpyReader()
result = reader.get_data(reader.read(filepath))[0].item()
self.assertTupleEqual(result["test"].shape, test_data["test"].shape)
np.testing.assert_allclose(result["test"], test_data["test"])
def test_npz1(self):
test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
with tempfile.TemporaryDirectory() as tempdir:
filepath = os.path.join(tempdir, "test_data.npy")
np.save(filepath, test_data1)
reader = NumpyReader()
result = reader.get_data(reader.read(filepath))
np.testing.assert_allclose(result[1]["spatial_shape"], test_data1.shape)
np.testing.assert_allclose(result[0].shape, test_data1.shape)
np.testing.assert_allclose(result[0], test_data1)
def test_npz3(self):
test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
test_data2 = np.random.randint(0, 256, size=[3, 4, 4])
with tempfile.TemporaryDirectory() as tempdir:
filepath = os.path.join(tempdir, "test_data.npz")
np.savez(filepath, test1=test_data1, test2=test_data2)
reader = NumpyReader(npz_keys=["test1", "test2"])
result = reader.get_data(reader.read(filepath))
np.testing.assert_allclose(result[1]["spatial_shape"], test_data1.shape)
np.testing.assert_allclose(result[0].shape, (2, 3, 4, 4))
np.testing.assert_allclose(result[0], np.stack([test_data1, test_data2]))
def test_dataloader(self):
test_data = np.random.randint(0, 256, size=[3, 4, 5])
datalist = []
with tempfile.TemporaryDirectory() as tempdir:
for i in range(4):
filepath = os.path.join(tempdir, f"test_data{i}.npz")
np.savez(filepath, test_data)
datalist.append({"image": filepath})
num_workers = 2 if sys.platform == "linux" else 0
loader = DataLoader(
Dataset(data=datalist, transform=LoadImaged(keys="image", reader=NumpyReader())),
batch_size=2,
num_workers=num_workers,
)
for d in loader:
for s in d[PostFix.meta("image")]["spatial_shape"]:
torch.testing.assert_allclose(s, torch.as_tensor([3, 4, 5]))
for c in d["image"]:
torch.testing.assert_allclose(c, test_data)