def apply_adjust_hue(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False):
"""Wrapper for adjust_hue for Torchvision-like param settings.
Args:
input (torch.Tensor): Image/Tensor to be adjusted in the shape of (*, N).
hue_factor (float): How much to shift the hue channel. Should be in [-0.5, 0.5]. 0.5
and -0.5 give complete reversal of hue channel in HSV space in positive and negative
direction respectively. 0 means no shift. Therefore, both -PI and PI will give an
image with complementary colors while 0 gives the original image.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_hue(input,
params['hue_factor'].to(input.dtype) * 2 * pi)
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_hue(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Wrapper for adjust_hue for Torchvision-like param settings.
Args:
input (torch.Tensor): Image/Tensor to be adjusted in the shape of (*, N).
params (Dict[str, torch.Tensor]):
- params['hue_factor']: How much to shift the hue channel. Should be in [-0.5, 0.5].
0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative
direction respectively. 0 means no shift. Therefore, both -0.5 and 0.5 will give an
image with complementary colors while 0 gives the original image.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_hue(input, params['hue_factor'].to(input.dtype) * 2 * pi)
return transformed