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_contrast(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor: """Apply contrast adjustment. Wrapper for adjust_contrast 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['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 with shape :math:`(B, C, H, W)`. """ transformed = adjust_contrast(input, params['contrast_factor'].to(input.dtype)) 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): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W). 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
def contrast(x: Tensor, v: float) -> Tensor: return E.adjust_contrast(x, v)