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_correct_results(self, _, spatial_axis):
for p in TEST_NDARRAYS:
flip = Flipd(keys="img", spatial_axis=spatial_axis)
expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]
expected = np.stack(expected)
result = flip({"img": p(self.imt[0])})["img"]
assert_allclose(result, p(expected))
def test_tranform_dict(self, input):
transforms = Compose([
Range("random flip dict")(Flipd(keys="image")),
Range()(ToTensord("image"))
])
# Apply transforms
output = transforms(input)["image"]
# Decorate with NVTX Range
transforms1 = Range()(transforms)
transforms2 = Range("Transforms2")(transforms)
transforms3 = Range(name="Transforms3", methods="__call__")(transforms)
# Apply transforms with Range
output1 = transforms1(input)["image"]
output2 = transforms2(input)["image"]
output3 = transforms3(input)["image"]
# Check the outputs
self.assertIsInstance(output, torch.Tensor)
self.assertIsInstance(output1, torch.Tensor)
self.assertIsInstance(output2, torch.Tensor)
self.assertIsInstance(output3, torch.Tensor)
np.testing.assert_equal(output.numpy(), output1.numpy())
np.testing.assert_equal(output.numpy(), output2.numpy())
np.testing.assert_equal(output.numpy(), output3.numpy())
def test_correct_results(self, _, spatial_axis):
flip = Flipd(keys="img", spatial_axis=spatial_axis)
expected = list()
for channel in self.imt[0]:
expected.append(np.flip(channel, spatial_axis))
expected = np.stack(expected)
res = flip({"img": self.imt[0]})
assert np.allclose(expected, res["img"])
def test_correct_results(self, _, spatial_axis):
for p in TEST_NDARRAYS_ALL:
flip = Flipd(keys="img", spatial_axis=spatial_axis)
expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]
expected = np.stack(expected)
im = p(self.imt[0])
result = flip({"img": im})["img"]
assert_allclose(result, p(expected), type_test="tensor")
test_local_inversion(flip, {"img": result}, {"img": im}, "img")
))
TESTS.append(("CropForegroundd 2d", "2D", 0,
CropForegroundd(KEYS, source_key="label", margin=2)))
TESTS.append(
("CropForegroundd 3d", "3D", 0, CropForegroundd(KEYS, source_key="label")))
TESTS.append(("ResizeWithPadOrCropd 3d", "3D", 0,
ResizeWithPadOrCropd(KEYS, [201, 150, 105])))
TESTS.append((
"Flipd 3d",
"3D",
0,
Flipd(KEYS, [1, 2]),
))
TESTS.append((
"Flipd 3d",
"3D",
0,
Flipd(KEYS, [1, 2]),
))
TESTS.append((
"RandFlipd 3d",
"3D",
0,
RandFlipd(KEYS, 1, [1, 2]),
))
def test_invalid_cases(self, _, spatial_axis, raises):
with self.assertRaises(raises):
flip = Flipd(keys="img", spatial_axis=spatial_axis)
flip({"img": self.imt[0]})
def test_meta_dict(self):
xform = Flipd("image", [0, 1])
res = xform({"image": torch.zeros(1, 3, 4)})
self.assertTrue(
res["image"].applied_operations == res["image_transforms"])
TESTS.append(("CenterSpatialCropd 3d", "3D", 0, True,
CenterSpatialCropd(KEYS, roi_size=[95, 97, 98])))
TESTS.append(("CropForegroundd 2d", "2D", 0, True,
CropForegroundd(KEYS, source_key="label", margin=2)))
TESTS.append(("CropForegroundd 3d", "3D", 0, True,
CropForegroundd(KEYS,
source_key="label",
k_divisible=[5, 101, 2])))
TESTS.append(("ResizeWithPadOrCropd 3d", "3D", 0, True,
ResizeWithPadOrCropd(KEYS, [201, 150, 105])))
TESTS.append(("Flipd 3d", "3D", 0, True, Flipd(KEYS, [1, 2])))
TESTS.append(("Flipd 3d", "3D", 0, True, Flipd(KEYS, [1, 2])))
TESTS.append(("RandFlipd 3d", "3D", 0, True, RandFlipd(KEYS, 1, [1, 2])))
TESTS.append(("RandAxisFlipd 3d", "3D", 0, True, RandAxisFlipd(KEYS, 1)))
TESTS.append(("RandAxisFlipd 3d", "3D", 0, True, RandAxisFlipd(KEYS, 1)))
for acc in [True, False]:
TESTS.append(("Orientationd 3d", "3D", 0, True,
Orientationd(KEYS, "RAS", as_closest_canonical=acc)))
TESTS.append(("Rotate90d 2d", "2D", 0, True, Rotate90d(KEYS)))
TESTS.append(
("Rotate90d 3d", "3D", 0, True, Rotate90d(KEYS, k=2, spatial_axes=(1, 2))))