def apply_posterize(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Posterize an image.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(*, C, H, W)`.
params (Dict[str, torch.Tensor]):
- params['bits_factor']: uint8 bits number ranged from 0 to 8.
Returns:
torch.Tensor: Adjusted image with shape :math:`(B, C, H, W)`.
"""
bits = params['bits_factor']
return posterize(input, bits)
def apply_posterize(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Posterize an image.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
params (Dict[str, torch.Tensor]):
- params['bits_factor']: uint8 bits number ranged from 0 to 8.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
bits = params['bits_factor']
return posterize(input, bits)
def posterize(x: Tensor, v: float) -> Tensor:
v = int(v)
return E.posterize(x, v)
def apply_transform(self,
input: Tensor,
params: Dict[str, Tensor],
transform: Optional[Tensor] = None) -> Tensor:
return posterize(input, params["bits_factor"].to(input.device))