def apply_adjust_brightness(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False) -> UnionType:
""" Wrapper for adjust_brightness for Torchvision-like param settings.
Args:
input (torch.Tensor): Image/Input to be adjusted in the shape of (*, N).
brightness_factor (Union[float, torch.Tensor]): Brightness adjust factor per element
in the batch. 0 gives a black image, 1 does not modify the input image and 2 gives a
white image, while any other number modify the brightness.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_brightness(
input, params['brightness_factor'].to(input.dtype) - 1)
if return_transform:
identity: torch.Tensor = torch.eye(3,
device=input.device,
dtype=input.dtype).repeat(
input.shape[0], 1, 1)
return transformed, identity
return transformed
def apply_adjust_brightness(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
""" Wrapper for adjust_brightness for Torchvision-like param settings.
Args:
input (torch.Tensor): Image/Input to be adjusted in the shape of (*, N).
params (Dict[str, torch.Tensor]):
- params['brightness_factor']: Brightness adjust factor per element
in the batch. 0 gives a black image, 1 does not modify the input image and 2 gives a
white image, while any other number modify the brightness.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_brightness(input, params['brightness_factor'].to(input.dtype))
return transformed