def test_get_set_meta_fns(self):
set_track_meta(False)
self.assertEqual(get_track_meta(), False)
set_track_meta(True)
self.assertEqual(get_track_meta(), True)
set_track_transforms(False)
self.assertEqual(get_track_transforms(), False)
set_track_transforms(True)
self.assertEqual(get_track_transforms(), True)
def test_correct(self, input_param, expected_shape, track_meta):
set_track_meta(track_meta)
r = LoadImage(image_only=True, **input_param)(self.test_data)
self.assertTupleEqual(r.shape, expected_shape)
if track_meta:
self.assertIsInstance(r, MetaTensor)
self.assertTrue(hasattr(r, "affine"))
self.assertIsInstance(r.affine, torch.Tensor)
else:
self.assertIsInstance(r, torch.Tensor)
self.assertNotIsInstance(r, MetaTensor)
self.assertFalse(hasattr(r, "affine"))
def test_torch(self, init_param, img: torch.Tensor, track_meta: bool, device):
set_track_meta(track_meta)
img = img.to(device)
xform = Flipd("image", init_param)
res = xform({"image": img})
self.assertEqual(img.shape, res["image"].shape)
if track_meta:
self.assertIsInstance(res["image"], MetaTensor)
else:
self.assertNotIsInstance(res["image"], MetaTensor)
self.assertIsInstance(res["image"], torch.Tensor)
with self.assertRaisesRegex(ValueError, "MetaTensor"):
xform.inverse(res)
def test_spacing_torch(self, pixdim, img, track_meta: bool):
set_track_meta(track_meta)
tr = Spacing(pixdim=pixdim)
res = tr(img)
if track_meta:
self.assertIsInstance(res, MetaTensor)
new_spacing = affine_to_spacing(res.affine, 3)
assert_allclose(new_spacing, pixdim, type_test=False)
self.assertNotEqual(img.shape, res.shape)
else:
self.assertIsInstance(res, torch.Tensor)
self.assertNotIsInstance(res, MetaTensor)
self.assertNotEqual(img.shape, res.shape)
set_track_meta(True)
def test_orntd_torch(self, init_param, img: torch.Tensor, track_meta: bool,
device):
set_track_meta(track_meta)
tr = Spacingd(**init_param)
data = {"seg": img.to(device)}
res = tr(data)["seg"]
if track_meta:
self.assertIsInstance(res, MetaTensor)
new_spacing = affine_to_spacing(res.affine, 3)
assert_allclose(new_spacing, init_param["pixdim"], type_test=False)
self.assertNotEqual(img.shape, res.shape)
else:
self.assertIsInstance(res, torch.Tensor)
self.assertNotIsInstance(res, MetaTensor)
self.assertNotEqual(img.shape, res.shape)
def test_correct(self, input_param, expected_shape, track_meta):
set_track_meta(track_meta)
result = LoadImaged(image_only=True, **input_param)(self.test_data)
# shouldn't have any extra meta data keys
self.assertEqual(len(result), len(KEYS))
for key in KEYS:
r = result[key]
self.assertTupleEqual(r.shape, expected_shape)
if track_meta:
self.assertIsInstance(r, MetaTensor)
self.assertTrue(hasattr(r, "affine"))
self.assertIsInstance(r.affine, torch.Tensor)
else:
self.assertIsInstance(r, torch.Tensor)
self.assertNotIsInstance(r, MetaTensor)
self.assertFalse(hasattr(r, "affine"))
def test_ornt_torch(self, init_param, img: torch.Tensor, track_meta: bool, device):
set_track_meta(track_meta)
ornt = Orientation(**init_param)
img = img.to(device)
expected_data = img.clone()
expected_code = ornt.axcodes
res = ornt(img)
assert_allclose(res, expected_data)
if track_meta:
self.assertIsInstance(res, MetaTensor)
new_code = nib.orientations.aff2axcodes(res.affine.cpu(), labels=ornt.labels)
self.assertEqual("".join(new_code), expected_code)
else:
self.assertIsInstance(res, torch.Tensor)
self.assertNotIsInstance(res, MetaTensor)
def test_orntd_torch(self, init_param, img: torch.Tensor, track_meta: bool,
device):
set_track_meta(track_meta)
ornt = Orientationd(**init_param)
img = img.to(device)
expected_shape = img.shape
expected_code = ornt.ornt_transform.axcodes
data = {k: img.clone() for k in ornt.keys}
res = ornt(data)
for k in ornt.keys:
_im = res[k]
np.testing.assert_allclose(_im.shape, expected_shape)
if track_meta:
self.assertIsInstance(_im, MetaTensor)
code = nib.aff2axcodes(_im.affine.cpu(),
ornt.ornt_transform.labels)
self.assertEqual("".join(code), expected_code)
else:
self.assertIsInstance(_im, torch.Tensor)
self.assertNotIsInstance(_im, MetaTensor)
