Hi,
I would like to implement a sharing weight ‘alexnet’ with two input batches of images, and the fully connected layers are concatenated together like this:
class ALEXNET_two_scale(nn.Module):
  def __init__(self, num_classes, 
               original_model = models.__dict__['alexnet'](pretrained=True)):
    super(ALEXNET_two_scale, self).__init__()
    
    self.features = original_model.features
    
    self.drop_out = nn.Dropout(p=0.75)
    
    self.fc6_scl1 = nn.Linear(256 * 6 * 6, 4096)
    self.fc6_scl2 = nn.Linear(256 * 6 * 6, 4096)
    
    self.relu = nn.ReLU(inplace=True)
    
    self.fc7_scl1 = nn.Linear(4096, 2048)
    self.fc7_scl2 = nn.Linear(4096, 2048)
    
    self.fc8 = nn.Linear(4096, num_classes)
    
    
  def forward(self, imgs_scl1, imgs_scl2):
    
    x_1 = self.features(imgs_scl1)
    x_2 = self.features(imgs_scl2)
    
    x_1 = self.drop_out(x_1)
    x_2 = self.drop_out(x_2)
    
    x_1 = self.fc6_scl1(x_1)
    x_2 = self.fc6_scl2(x_2)
    
    x_1 = self.relu(x_1)
    x_2 = self.relu(x_2)
    
    x_1 = self.drop_out(x_1)
    x_2 = self.drop_out(x_2)
    
    x_1 = self.fc7_scl1(x_1)
    x_2 = self.fc7_scl2(x_2)
    
    x = torch.cat([x_1, x_2], 1)
    
    y = self.fc8(x)
    
    return y

However, when I run it, I come across the following error:
/home/ga85nej/Documents/pytorch_WH_multiscale/pytorch_multiscale_VGG16 in <module>()
    396 
    397 if __name__ == '__main__':
--> 398   main()
    399 
    400 

/home/ga85nej/Documents/pytorch_WH_multiscale/pytorch_multiscale_VGG16 in main()
    369 
    370     # train for one epoch
--> 371     train_losses_per_epoch = train(train_img_lab_lists, model, train_transform, criterion, optimizer, epoch)
    372 
    373     training_metadata['epoch_{}_train_loss'.format(epoch)] = train_losses_per_epoch

/home/ga85nej/Documents/pytorch_WH_multiscale/pytorch_multiscale_VGG16 in train(train_img_lab_lists, model, train_transform, criterion, optimizer, epoch)
    143 
    144     # compute output
--> 145     output = model(batch_imgs_scale1_var, batch_imgs_scale2_var)
    146     loss = criterion(output, batch_labs_var)
    147 

/home/ga85nej/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    200 
    201     def __call__(self, *input, **kwargs):
--> 202         result = self.forward(*input, **kwargs)
    203         for hook in self._forward_hooks.values():
    204             hook_result = hook(self, input, result)

/home/ga85nej/Documents/pytorch_WH_multiscale/pytorch_multiscale_VGG16 in forward(self, imgs_scl1, imgs_scl2)
     70     x_2 = self.drop_out(x_2)
     71 
---> 72     x_1 = self.fc6_scl1(x_1)
     73     x_2 = self.fc6_scl2(x_2)
     74 

/home/ga85nej/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    200 
    201     def __call__(self, *input, **kwargs):
--> 202         result = self.forward(*input, **kwargs)
    203         for hook in self._forward_hooks.values():
    204             hook_result = hook(self, input, result)

/home/ga85nej/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input)
     52             return self._backend.Linear()(input, self.weight)
     53         else:
---> 54             return self._backend.Linear()(input, self.weight, self.bias)
     55 
     56     def __repr__(self):

/home/ga85nej/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/linear.py in forward(self, input, weight, bias)
      8         self.save_for_backward(input, weight, bias)
      9         output = input.new(input.size(0), weight.size(0))
---> 10         output.addmm_(0, 1, input, weight.t())
     11         if bias is not None:
     12             # cuBLAS doesn't support 0 strides in sger, so we can't use expand

RuntimeError: matrix and matrix expected at /data/users/soumith/miniconda2/conda-bld/pytorch-cuda80-0.1.10_1488758793045/work/torch/lib/THC/generic/THCTensorMathBlas.cu:235

Does anyone know about it?
Thank you very much.