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

Before Change

            if isinstance(input, tuple):
                batch_size = input[0].shape[0] if len(input[0].shape) == 4 else 1
            else:
                batch_size = input.shape[0] if len(input.shape) == 4 else 1
            params = RandomGrayscale.get_params(batch_size, self.p)

        if isinstance(input, tuple):

After Change

    def forward(self, input: UnionType, params: Optional[Dict[str, torch.Tensor]] = None) -> UnionType:  // type: ignore
        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