[/content/mmediting/mmedit/models/restorers/basic_restorer.py](https://localhost:8080/#) in forward(self, lq, gt, test_mode, **kwargs)
     73 
     74         if test_mode:
---> 75             return self.forward_test(lq, gt, **kwargs)
     76 
     77         return self.forward_train(lq, gt)

[/content/mmediting/mmedit/models/restorers/stdf.py](https://localhost:8080/#) in forward_test(self, lq, gt, meta, save_image, save_path, iteration)
     59             dict: Output results.
     60         """
---> 61         output = self.generator(lq)
     62         if self.test_cfg is not None and self.test_cfg.get('metrics', None):
     63             assert gt is not None, (

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/content/mmediting/mmedit/models/backbones/sr_backbones/stdf.py](https://localhost:8080/#) in forward(self, x)
    223             Tensor: Out center frame with shape (n, c, h, w).
    224         """
--> 225         out = self.stdf(x)
    226         out = self.qenet(out)
    227 

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/content/mmediting/mmedit/models/backbones/sr_backbones/stdf.py](https://localhost:8080/#) in forward(self, x)
    100             up_conv = getattr(self, 'up_conv{}'.format(i))
    101             print(i, out.shape, out_lst[i].shape)
--> 102             out = up_conv(torch.cat([out, out_lst[i]], 1))
    103 
    104         # compute offset and mask

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 136 but got size 135 for tensor number 1 in the list.