def test_rand_affined(self, input_param, input_data, expected_val, track_meta):
set_track_meta(track_meta)
g = RandAffined(**input_param).set_random_state(123)
res = g(input_data)
if input_param.get("cache_grid", False):
self.assertTrue(g.rand_affine._cached_grid is not None)
for key in res:
if isinstance(key, str) and key.endswith("_transforms"):
continue
result = res[key]
if track_meta:
self.assertIsInstance(result, MetaTensor)
self.assertEqual(len(result.applied_operations), 1)
expected = expected_val[key] if isinstance(expected_val, dict) else expected_val
assert_allclose(result, expected, rtol=_rtol, atol=1e-3, type_test=False)
g.set_random_state(4)
res = g(input_data)
if not track_meta:
return
# affine should be tensor because the resampler only supports pytorch backend
if isinstance(res["img"], MetaTensor) and "extra_info" in res["img"].applied_operations[0]:
if not res["img"].applied_operations[-1]["extra_info"]["do_resampling"]:
return
affine_img = res["img"].applied_operations[0]["extra_info"]["rand_affine_info"]["extra_info"]["affine"]
affine_seg = res["seg"].applied_operations[0]["extra_info"]["rand_affine_info"]["extra_info"]["affine"]
assert_allclose(affine_img, affine_seg, rtol=_rtol, atol=1e-3)
res_inv = g.inverse(res)
for k, v in res_inv.items():
self.assertIsInstance(v, MetaTensor)
self.assertEqual(len(v.applied_operations), 0)
self.assertTupleEqual(v.shape, input_data[k].shape)
def test_affine_grid(self, input_param, input_data, expected_val):
g = AffineGrid(**input_param)
set_track_meta(False)
result, _ = g(**input_data)
self.assertNotIsInstance(result, MetaTensor)
self.assertIsInstance(result, torch.Tensor)
set_track_meta(True)
if "device" in input_data:
self.assertEqual(result.device, input_data[device])
assert_allclose(result, expected_val, type_test=False, rtol=_rtol)
def test_rand_2d_elastic(self, input_param, input_data, expected_val):
g = Rand2DElastic(**input_param)
set_track_meta(False)
result = g(**input_data)
self.assertNotIsInstance(result, MetaTensor)
self.assertIsInstance(result, torch.Tensor)
set_track_meta(True)
g.set_random_state(123)
result = g(**input_data)
assert_allclose(result, expected_val, type_test=False, rtol=_rtol, atol=1e-4)
def test_longest_shape(self, input_param, expected_shape):
input_data = np.random.randint(0, 2, size=[3, 4, 7, 10])
input_param["size_mode"] = "longest"
result = Resize(**input_param)(input_data)
np.testing.assert_allclose(result.shape[1:], expected_shape)
set_track_meta(False)
result = Resize(**input_param)(input_data)
self.assertNotIsInstance(result, MetaTensor)
np.testing.assert_allclose(result.shape[1:], expected_shape)
set_track_meta(True)
def test_correct_shape(self, im_type, angle, mode, padding_mode, align_corners):
rotate_fn = Rotate(angle, True, align_corners=align_corners, dtype=np.float64)
im = im_type(self.imt[0])
set_track_meta(False)
rotated = rotate_fn(im, mode=mode, padding_mode=padding_mode)
self.assertNotIsInstance(rotated, MetaTensor)
np.testing.assert_allclose(self.imt[0].shape, rotated.shape)
set_track_meta(True)
rotated = rotate_fn(im, mode=mode, padding_mode=padding_mode)
np.testing.assert_allclose(self.imt[0].shape, rotated.shape)
test_local_inversion(rotate_fn, rotated, im)
def test_longest_shape(self, input_param, expected_shape):
input_data = {
"img": np.random.randint(0, 2, size=[3, 4, 7, 10]),
"label": np.random.randint(0, 2, size=[3, 4, 7, 10]),
}
input_param["size_mode"] = "longest"
rescaler = Resized(**input_param)
result = rescaler(input_data)
for k in rescaler.keys:
np.testing.assert_allclose(result[k].shape[1:], expected_shape)
set_track_meta(False)
result = Resized(**input_param)(input_data)
self.assertNotIsInstance(result["img"], MetaTensor)
np.testing.assert_allclose(result["img"].shape[1:], expected_shape)
set_track_meta(True)
def test_correct_results(self):
for p in TEST_NDARRAYS_ALL:
flip = RandAxisFlip(prob=1.0)
im = p(self.imt[0])
result = flip(im)
expected = [
np.flip(channel, flip._axis) for channel in self.imt[0]
]
assert_allclose(result, p(np.stack(expected)), type_test="tensor")
test_local_inversion(flip, result, im)
set_track_meta(False)
result = flip(im)
self.assertNotIsInstance(result, MetaTensor)
self.assertIsInstance(result, torch.Tensor)
set_track_meta(True)
def test_keep_size(self):
for p in TEST_NDARRAYS_ALL:
zoom_fn = Zoom(zoom=[0.6, 0.6], keep_size=True, align_corners=True)
im = p(self.imt[0])
zoomed = zoom_fn(im, mode="bilinear")
assert_allclose(zoomed.shape, self.imt.shape[1:], type_test=False)
test_local_inversion(zoom_fn, zoomed, im)
zoom_fn = Zoom(zoom=[1.3, 1.3], keep_size=True)
im = p(self.imt[0])
zoomed = zoom_fn(im)
assert_allclose(zoomed.shape, self.imt.shape[1:], type_test=False)
test_local_inversion(zoom_fn, zoomed, p(self.imt[0]))
set_track_meta(False)
rotated = zoom_fn(im)
self.assertNotIsInstance(rotated, MetaTensor)
np.testing.assert_allclose(zoomed.shape, self.imt.shape[1:])
set_track_meta(True)
def test_default(self):
key = None
rotate = RandRotate90d(keys=key)
for p in TEST_NDARRAYS_ALL:
rotate.set_random_state(1323)
im = {key: p(self.imt[0])}
rotated = rotate(im)
test_local_inversion(rotate, rotated, im, key)
expected = [
np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated[key], p(expected), type_test="tensor")
set_track_meta(False)
rotated = rotate(im)[key]
self.assertNotIsInstance(rotated, MetaTensor)
self.assertIsInstance(rotated, torch.Tensor)
set_track_meta(True)
def test_affine(self, input_param, input_data, expected_val):
input_copy = deepcopy(input_data["img"])
g = Affine(**input_param)
result = g(**input_data)
if isinstance(result, tuple):
result = result[0]
test_local_inversion(g, result, input_copy)
assert_allclose(result,
expected_val,
rtol=1e-4,
atol=1e-4,
type_test=False)
set_track_meta(False)
result = g(**input_data)
if isinstance(result, tuple):
result = result[0]
self.assertNotIsInstance(result, MetaTensor)
self.assertIsInstance(result, torch.Tensor)
set_track_meta(True)
def test_k(self):
rotate = RandRotate90(max_k=2)
for p in TEST_NDARRAYS_ALL:
im = p(self.imt[0])
set_track_meta(False)
rotated = rotate(im)
self.assertNotIsInstance(rotated, MetaTensor)
self.assertIsInstance(rotated, torch.Tensor)
set_track_meta(True)
rotate.set_random_state(123)
rotated = rotate(im)
test_local_inversion(rotate, rotated, im)
expected = [
np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated,
p(expected),
rtol=1.0e-5,
atol=1.0e-8,
type_test="tensor")
def test_correct_results(self, im_type, x, y, z, keep_size, mode, padding_mode, align_corners, expected):
rotate_fn = RandRotate(
range_x=x,
range_y=y,
range_z=z,
prob=1.0,
keep_size=keep_size,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
dtype=np.float64,
)
rotate_fn.set_random_state(243)
im = im_type(self.imt[0])
rotated = rotate_fn(im)
torch.testing.assert_allclose(rotated.shape, expected, rtol=1e-7, atol=0)
test_local_inversion(rotate_fn, rotated, im)
set_track_meta(False)
rotated = rotate_fn(im)
self.assertNotIsInstance(rotated, MetaTensor)
self.assertIsInstance(rotated, torch.Tensor)
set_track_meta(True)
def test_rotate90_default(self):
key = "test"
rotate = Rotate90d(keys=key)
for p in TEST_NDARRAYS_ALL:
im = p(self.imt[0])
set_track_meta(True)
rotated = rotate({key: im})
test_local_inversion(rotate, rotated, {key: im}, key)
expected = [
np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated[key], p(expected), type_test="tensor")
set_track_meta(False)
rotated = rotate({key: im})
self.assertNotIsInstance(rotated[key], MetaTensor)
set_track_meta(True)