mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
def forward_dummy(self, lq: torch.Tensor, *args, **kwargs):
        """Run test inference for restorer with backend wrapper.

        Args:
            lq (torch.Tensor): The input low-quality image of the model.

        Returns:
            list[np.ndarray] : High resolution image.
        """
        outputs = self.wrapper({'input': lq})
        outputs = self.wrapper.output_to_list(outputs)
        outputs = [out.detach().cpu().numpy() for out in outputs]
        return outputs
Run test inference for restorer with backend wrapper.

        Args:
            lq (torch.Tensor): The input low-quality image of the model.

        Returns:
            list[np.ndarray] : High resolution image.
        
Run test inference for restorer with backend wrapper.
Run test inference for restorer with backend wrapper .
def forward_dummy(self, lq: torch.Tensor, *args, **kwargs):
        outputs = self.wrapper({'input': lq})
        outputs = self.wrapper.output_to_list(outputs)
        outputs = [out.detach().cpu().numpy() for out in outputs]
        return outputs
def forward_dummy ( self , lq : torch . Tensor , * args , ** kwargs ) : outputs = self . wrapper ( { 'input' : lq } ) outputs = self . wrapper . output_to_list ( outputs ) outputs = [ out . detach ( ) . cpu ( ) . numpy ( ) for out in outputs ] return outputs
Run test inference for restorer with backend wrapper.
Run test inference for restorer with backend wrapper .
"""Run test inference for restorer with backend wrapper.

        Args:
            lq (torch.Tensor): The input low-quality image of the model.

        Returns:
            list[np.ndarray] : High resolution image.
        """
list[np.ndarray] : High resolution image.
list [ np . ndarray ] : High resolution image .
self
lq
torch.Tensor
The input low-quality image of the model.
The input low - quality image of the model .