def loss(self, pred: torch.Tensor, target: torch.Tensor,
             mask: torch.Tensor) -> torch.Tensor:
        """Generate loss.

        This method will expand mask to the size of the original image.

        Args:
            pred (torch.Tensor): The reconstructed image (B, C, H, W).
            target (torch.Tensor): The target image (B, C, H, W).
            mask (torch.Tensor): The mask of the target image.

        Returns:
            torch.Tensor: The reconstruction loss.
        """
        mask = mask.repeat_interleave(self.patch_size, 1).repeat_interleave(
            self.patch_size, 2).unsqueeze(1).contiguous()
        loss = self.loss_module(pred, target, mask)

        return loss
Generate loss.

        This method will expand mask to the size of the original image.

        Args:
            pred (torch.Tensor): The reconstructed image (B, C, H, W).
            target (torch.Tensor): The target image (B, C, H, W).
            mask (torch.Tensor): The mask of the target image.

        Returns:
            torch.Tensor: The reconstruction loss.
        
mmpretrain/models/heads/simmim_head.py