I am trying to use resnet18 and densenet121 as the pretrained models and have added 1 FC layer at the end of each network two change dimensions to 512 of both network and then have concatenated the outputs and passed to two final FC layers.
this can be seen here:
class classifier(nn.Module):
  def __init__(self,num_classes):
    super(classifier,self).__init__()

    self.resnet=models.resnet18(pretrained=True)

    self.rfc1=nn.Linear(512,512)
    
    self.densenet=models.densenet121(pretrained=True)
    self.dfc1=nn.Linear(1024,512)

    self.final_fc1=nn.Linear(1024,512)
    self.final_fc2=nn.Linear(512,num_classes)
    self.dropout=nn.Dropout(0.2)

  def forward(self,x):
    y=x.detach().clone()

    x=self.resnet.conv1(x)
    x=self.resnet.bn1(x)
    x=self.resnet.relu(x)
    x=self.resnet.maxpool(x)
    x=self.resnet.layer1(x)
    x=self.resnet.layer2(x)
    x=self.resnet.layer3(x)
    x=self.resnet.layer4(x)
    x=self.resnet.avgpool(x)
    x=x.view(x.size(0),-1)
    x=nn.functional.relu(self.rfc1(x))

    y=self.densenet.features(y)
    y=y.view(y.size(0),-1)
    y=nn.functional.relu(self.dfc1(y))

    x=torch.cat((x,y),0)
    x=nn.functional.relu(self.final_fc1(x))
    x=self.dropout(x)
    x=self.final_fc2(x)

    return x

Error:
size mismatch, m1: [1048576 x 1], m2: [1024 x 512] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:283

but as i could see the densenet outputs 1024 feature to the last layer
Questions:

Is the implementation correct ?
Can this implementation work as a Ensemble