fffa476e1e50ff0a59700a6fbfd3531d62fe692a,kornia/augmentation/augmentations.py,ColorJitter,forward,#ColorJitter#Any#Any#,190

Before Change

                batch_size = input.shape[0] if len(input.shape) == 4 else 1
            params = ColorJitter.get_params(batch_size, self.brightness, self.contrast, self.saturation, self.hue)

        if isinstance(input, tuple):

            jittered: torch.Tensor = cast(
                torch.Tensor,
                F.apply_color_jitter(
                    input[0],
                    params=params,
                    return_transform=False))

            return jittered, input[1]

        return F.apply_color_jitter(input, params, self.return_transform)


class RandomGrayscale(AugmentationBase):

After Change

        if params is None:
            batch_size = self.infer_batch_size(input)
            params = ColorJitter.get_params(batch_size, self.brightness, self.contrast, self.saturation, self.hue)
        return super().forward(input, params)


class RandomGrayscale(AugmentationBase):
    rRandom Grayscale transformation according to a probability p value

Instances