def test_construct_with_pre_applied_transforms(self):
key = "im"
_, im = self.get_im()
tr = Compose([BorderPadd(key, 1), DivisiblePadd(key, 16)])
data = tr({key: im})
m = MetaTensor(im, applied_operations=data["im"].applied_operations)
self.assertEqual(len(m.applied_operations), len(tr.transforms))
def test_transforms(self):
key = "im"
_, im = self.get_im()
tr = Compose([ToMetaTensord(key), BorderPadd(key, 1), DivisiblePadd(key, 16), FromMetaTensord(key)])
num_tr = len(tr.transforms)
data = {key: im, PostFix.meta(key): {"affine": torch.eye(4)}}
# apply one at a time
for i, _tr in enumerate(tr.transforms):
data = _tr(data)
is_meta = isinstance(_tr, (ToMetaTensord, BorderPadd, DivisiblePadd))
if is_meta:
self.assertEqual(len(data), 1 if not config.USE_META_DICT else 2) # im, im_transforms, compatibility
self.assertIsInstance(data[key], MetaTensor)
n_applied = len(data[key].applied_operations)
else:
self.assertEqual(len(data), 3) # im, im_meta_dict, im_transforms
self.assertIsInstance(data[key], torch.Tensor)
self.assertNotIsInstance(data[key], MetaTensor)
n_applied = len(data[PostFix.transforms(key)])
self.assertEqual(n_applied, i + 1)
# inverse one at a time
for i, _tr in enumerate(tr.transforms[::-1]):
data = _tr.inverse(data)
is_meta = isinstance(_tr, (FromMetaTensord, BorderPadd, DivisiblePadd))
if is_meta:
self.assertEqual(len(data), 1) # im
self.assertIsInstance(data[key], MetaTensor)
n_applied = len(data[key].applied_operations)
else:
self.assertEqual(len(data), 3) # im, im_meta_dict, im_transforms
self.assertIsInstance(data[key], torch.Tensor)
self.assertNotIsInstance(data[key], MetaTensor)
n_applied = len(data[PostFix.transforms(key)])
self.assertEqual(n_applied, num_tr - i - 1)
# apply all in one go
data = tr({key: im, PostFix.meta(key): {"affine": torch.eye(4)}})
self.assertEqual(len(data), 3) # im, im_meta_dict, im_transforms
self.assertIsInstance(data[key], torch.Tensor)
self.assertNotIsInstance(data[key], MetaTensor)
n_applied = len(data[PostFix.transforms(key)])
self.assertEqual(n_applied, num_tr)
# inverse all in one go
data = tr.inverse(data)
self.assertEqual(len(data), 3) # im, im_meta_dict, im_transforms
self.assertIsInstance(data[key], torch.Tensor)
self.assertNotIsInstance(data[key], MetaTensor)
n_applied = len(data[PostFix.transforms(key)])
self.assertEqual(n_applied, 0)
"3D",
0,
SpatialCropd(KEYS, [49, 51, 44], [90, 89, 93]),
))
TESTS.append(("RandSpatialCropd 2d", "2D", 0,
RandSpatialCropd(KEYS, [96, 93], True, False)))
TESTS.append(("RandSpatialCropd 3d", "3D", 0,
RandSpatialCropd(KEYS, [96, 93, 92], False, False)))
TESTS.append((
"BorderPadd 2d",
"2D",
0,
BorderPadd(KEYS, [3, 7, 2, 5]),
))
TESTS.append((
"BorderPadd 2d",
"2D",
0,
BorderPadd(KEYS, [3, 7]),
))
TESTS.append((
"BorderPadd 3d",
"3D",
0,
BorderPadd(KEYS, [4]),
))
def test_pad_shape(self, input_param, input_data, expected_val):
padder = BorderPadd(**input_param)
result = padder(input_data)
self.assertAlmostEqual(result["img"].shape, expected_val.shape)
))
TESTS.append(
("SpatialCropd 2d", "2D", 0, True, SpatialCropd(KEYS, [49, 51],
[390, 89])))
TESTS.append(("SpatialCropd 3d", "3D", 0, True,
SpatialCropd(KEYS, [49, 51, 44], [90, 89, 93])))
TESTS.append(("RandSpatialCropd 2d", "2D", 0, True,
RandSpatialCropd(KEYS, [96, 93], None, True, False)))
TESTS.append(("RandSpatialCropd 3d", "3D", 0, True,
RandSpatialCropd(KEYS, [96, 93, 92], None, False, False)))
TESTS.append(("BorderPadd 2d", "2D", 0, True, BorderPadd(KEYS, [3, 7, 2, 5])))
TESTS.append(("BorderPadd 2d", "2D", 0, True, BorderPadd(KEYS, [3, 7])))
TESTS.append(("BorderPadd 3d", "3D", 0, True, BorderPadd(KEYS, [4])))
TESTS.append(("DivisiblePadd 2d", "2D", 0, True, DivisiblePadd(KEYS, k=4)))
TESTS.append(
("DivisiblePadd 3d", "3D", 0, True, DivisiblePadd(KEYS, k=[4, 8, 11])))
TESTS.append(("CenterSpatialCropd 2d", "2D", 0, True,
CenterSpatialCropd(KEYS, roi_size=95)))
TESTS.append(("CenterSpatialCropd 3d", "3D", 0, True,
CenterSpatialCropd(KEYS, roi_size=[95, 97, 98])))