fffa476e1e50ff0a59700a6fbfd3531d62fe692a,kornia/augmentation/augmentations.py,RandomGrayscale,forward,#RandomGrayscale#Any#Any#,234

Before Change

        if isinstance(input, tuple):

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

            return output, input[1]

        return F.apply_grayscale(input, params=params, return_transform=self.return_transform)

After Change

        if params is None:
            batch_size = self.infer_batch_size(input)
            params = RandomGrayscale.get_params(batch_size, self.p)
        return super().forward(input, params)

Instances