def apply_adjust_contrast(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False):
"""Wrapper for adjust_contrast for Torchvision-like param settings.
Args:
input (torch.Tensor): Image to be adjusted in the shape of (*, N).
params['contrast_factor'] (Union[float, torch.Tensor]):
Contrast adjust factor per element in the batch.
0 generates a compleatly black image, 1 does not modify
the input image while any other non-negative number modify the
brightness by this factor.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_contrast(input,
params['contrast_factor'].to(input.dtype))
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_contrast(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Wrapper for adjust_contrast for Torchvision-like param settings.
Args:
input (torch.Tensor): Image to be adjusted in the shape of (*, N).
params (Dict[str, torch.Tensor]):
- params['contrast_factor']: Contrast adjust factor per element in the batch.
0 generates a compleatly black image, 1 does not modify the input image while any other
non-negative number modify the brightness by this factor.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_contrast(input, params['contrast_factor'].to(input.dtype))
return transformed