def test_correct_results(self, degrees, keep_size, mode, padding_mode, align_corners):
rotate_fn = RandRotate(
range_x=degrees,
prob=1.0,
keep_size=keep_size,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
rotate_fn.set_random_state(243)
rotated = rotate_fn(self.imt[0])
_order = 0 if mode == "nearest" else 1
if mode == "border":
_mode = "nearest"
elif mode == "reflection":
_mode = "reflect"
else:
_mode = "constant"
angle = rotate_fn.x
expected = scipy.ndimage.rotate(
self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False
)
expected = np.stack(expected).astype(np.float32)
good = np.sum(np.isclose(expected, rotated[0], atol=1e-3))
self.assertLessEqual(np.abs(good - expected.size), 5, "diff at most 5 pixels")
def test_correct_results(self, degrees, keep_size, order, mode,
align_corners):
rotate_fn = RandRotate(range_x=degrees,
prob=1.0,
keep_size=keep_size,
interp_order=order,
mode=mode,
align_corners=align_corners)
rotate_fn.set_random_state(243)
rotated = rotate_fn(self.imt[0])
_order = 0 if order == "nearest" else 1
if mode == "border":
_mode = "nearest"
elif mode == "reflection":
_mode = "reflect"
else:
_mode = "constant"
angle = rotate_fn.x
expected = scipy.ndimage.rotate(self.imt[0, 0],
-angle, (0, 1),
not keep_size,
order=_order,
mode=_mode,
prefilter=False)
expected = np.stack(expected).astype(np.float32)
np.testing.assert_allclose(expected, rotated[0])
def test_correct_results(self, degrees, spatial_axes, reshape, order, mode,
cval, prefilter):
rotate_fn = RandRotate(
degrees,
prob=1.0,
spatial_axes=spatial_axes,
reshape=reshape,
interp_order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
)
rotate_fn.set_random_state(243)
rotated = rotate_fn(self.imt[0])
angle = rotate_fn.angle
expected = list()
for channel in self.imt[0]:
expected.append(
scipy.ndimage.rotate(channel,
angle,
spatial_axes,
reshape,
order=order,
mode=mode,
cval=cval,
prefilter=prefilter))
expected = np.stack(expected).astype(np.float32)
self.assertTrue(np.allclose(expected, rotated))
def test_correct_results(self, 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,
)
rotate_fn.set_random_state(243)
rotated = rotate_fn(self.imt[0])
np.testing.assert_allclose(rotated.shape, expected)
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)
rotated = rotate_fn(im_type(self.imt[0]))
torch.testing.assert_allclose(rotated.shape, expected, rtol=1e-7, atol=0)
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)
