def assert_allclose(
actual: NdarrayOrTensor,
desired: NdarrayOrTensor,
type_test: Union[bool, str] = True,
device_test: bool = False,
*args,
**kwargs,
):
"""
Assert that types and all values of two data objects are close.
Args:
actual: Pytorch Tensor or numpy array for comparison.
desired: Pytorch Tensor or numpy array to compare against.
type_test: whether to test that `actual` and `desired` are both numpy arrays or torch tensors.
if type_test == "tensor", it checks whether the `actual` is a torch.tensor or metatensor according to
`get_track_meta`.
device_test: whether to test the device property.
args: extra arguments to pass on to `np.testing.assert_allclose`.
kwargs: extra arguments to pass on to `np.testing.assert_allclose`.
"""
if isinstance(type_test, str) and type_test == "tensor":
if get_track_meta():
np.testing.assert_equal(isinstance(actual, MetaTensor), True,
"must be a MetaTensor")
else:
np.testing.assert_equal(
isinstance(actual, torch.Tensor)
and not isinstance(actual, MetaTensor), True,
"must be a torch.Tensor")
elif type_test:
# check both actual and desired are of the same type
np.testing.assert_equal(isinstance(actual, np.ndarray),
isinstance(desired, np.ndarray), "numpy type")
np.testing.assert_equal(isinstance(actual, torch.Tensor),
isinstance(desired, torch.Tensor),
"torch type")
if isinstance(desired, torch.Tensor) or isinstance(actual, torch.Tensor):
if device_test:
np.testing.assert_equal(str(actual.device), str(desired.device),
"torch device check") # type: ignore
actual = actual.detach().cpu().numpy() if isinstance(
actual, torch.Tensor) else actual
desired = desired.detach().cpu().numpy() if isinstance(
desired, torch.Tensor) else desired
np.testing.assert_allclose(actual, desired, *args, **kwargs)
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Filter the image on the `applied_labels`.
Args:
img: Pytorch tensor or numpy array of any shape.
Raises:
NotImplementedError: The provided image was not a Pytorch Tensor or numpy array.
Returns:
Pytorch tensor or numpy array of the same shape as the input.
"""
if not isinstance(img, (np.ndarray, torch.Tensor)):
raise NotImplementedError(
f"{self.__class__} can not handle data of type {type(img)}.")
if isinstance(img, torch.Tensor):
if hasattr(torch, "isin"): # `isin` is new in torch 1.10.0
appl_lbls = torch.as_tensor(self.applied_labels,
device=img.device)
return torch.where(torch.isin(img, appl_lbls), img,
torch.tensor(0.0).to(img))
else:
out = self(img.detach().cpu().numpy())
out, *_ = convert_to_dst_type(out, img)
return out
return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0))
def assert_allclose(
actual: NdarrayOrTensor,
desired: NdarrayOrTensor,
type_test: bool = True,
device_test: bool = False,
*args,
**kwargs,
):
"""
Assert that types and all values of two data objects are close.
Args:
actual: Pytorch Tensor or numpy array for comparison.
desired: Pytorch Tensor or numpy array to compare against.
type_test: whether to test that `actual` and `desired` are both numpy arrays or torch tensors.
device_test: whether to test the device property.
args: extra arguments to pass on to `np.testing.assert_allclose`.
kwargs: extra arguments to pass on to `np.testing.assert_allclose`.
"""
if type_test:
# check both actual and desired are of the same type
np.testing.assert_equal(isinstance(actual, np.ndarray),
isinstance(desired, np.ndarray), "numpy type")
np.testing.assert_equal(isinstance(actual, torch.Tensor),
isinstance(desired, torch.Tensor),
"torch type")
if isinstance(desired, torch.Tensor) or isinstance(actual, torch.Tensor):
if device_test:
np.testing.assert_equal(str(actual.device), str(desired.device),
"torch device check") # type: ignore
actual = actual.detach().cpu().numpy() if isinstance(
actual, torch.Tensor) else actual
desired = desired.detach().cpu().numpy() if isinstance(
desired, torch.Tensor) else desired
np.testing.assert_allclose(actual, desired, *args, **kwargs)