def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], transform: Optional[Tensor] = None
) -> Tensor:
transforms = [
lambda img: adjust_brightness(img, params["brightness_factor"] - 1),
lambda img: adjust_contrast(img, params["contrast_factor"]),
lambda img: adjust_saturation(img, params["saturation_factor"]),
lambda img: adjust_hue(img, params["hue_factor"] * 2 * pi),
]
jittered = input
for idx in params["order"].tolist():
t = transforms[idx]
jittered = t(jittered)
return jittered
def apply_adjust_saturation(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Apply saturation adjustment.
Wrapper for adjust_saturation for Torchvision-like param settings.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(*, C, H, W)`.
params (Dict[str, torch.Tensor]):
- params['saturation_factor']: How much to adjust the saturation. 0 will give a black
and white image, 1 will give the original image while 2 will enhance the saturation
by a factor of 2.
Returns:
torch.Tensor: Adjusted image with shape :math:`(B, C, H, W)`.
"""
transformed = adjust_saturation(input, params['saturation_factor'].to(input.dtype))
return transformed
def apply_adjust_saturation(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Wrapper for adjust_saturation for Torchvision-like param settings.
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['saturation_factor']: How much to adjust the saturation. 0 will give a black
and white image, 1 will give the original image while 2 will enhance the saturation
by a factor of 2.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_saturation(input, params['saturation_factor'].to(input.dtype))
return transformed
def color(x: Tensor, v: float) -> Tensor:
return E.adjust_saturation(x, v)
