def apply_equalize3d(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Equalize a tensor volume or a batch of tensors volumes with given random parameters.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(*, C, D, H, W)`.
params (Dict[str, torch.Tensor]): shall be empty.
Returns:
torch.Tensor: Equalized input with shape :math:`(*, C, D, H, W)`.
"""
return equalize3d(input)
def apply_equalize3d(input: torch.Tensor,
params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Equalize a tensor volume or a batch of tensors volumes with given random parameters.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(D, H, W)`, :math:`(C, D, H, W)`,
:math:`(*, C, D, H, W)`.
params (Dict[str, torch.Tensor]): shall be empty.
Returns:
torch.Tensor: The equalized input. :math:`(D, H, W)`, :math:`(C, D, H, W)`, :math:`(*, C, D, H, W)`.
"""
input = _transform_input3d(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
return equalize3d(input)
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], transform: Optional[Tensor] = None
) -> Tensor:
return equalize3d(input)