Pytorch TypeError: forward() takes 2 positional arguments but 4 were given

Pytorch TypeError: forward() takes 2 positional arguments but 4 were given

from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class Graphconvlayer(nn.Module):
  def __init__(self,adj,input_feature_neurons,output_neurons):
    super(Graphconvlayer, self).__init__()
    self.adj=adj
    self.input_feature_neurons=input_feature_neurons
    self.output_neurons=output_neurons
    self.weights=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons,output_neurons)))
    self.bias=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons)))
  
  def forward(self,inputfeaturedata):
    output1= torch.mm(self.adj,inputfeaturedata)
    print(output1.shape)
    print(self.weights.shape)
    print(self.bias.shape)
    output2= torch.matmul(output1,self.weights.t())+ self.bias
    return output2 

class GCN(nn.Module):
   def __init__(self,lr,dropoutvalue,adjmatrix,inputneurons,hidden,outputneurons):
     super(GCN, self).__init__()
     self.lr=lr
     self.dropoutvalue=dropoutvalue
     self.adjmatrix=adjmatrix
     self.inputneurons=inputneurons
     self.hidden=hidden
     self.outputneurons=outputneurons
     self.gcn1 = Graphconvlayer(adjmatrix,inputneurons,hidden)
     self.gcn2 = Graphconvlayer(adjmatrix,hidden,outputneurons)
  
   def forward(self,x,adj):
     x= F.relu(self.gcn1(adj,x,64))
     x= F.dropout(x,self.dropoutvalue)
     x= self.gcn2(adj,x,7)
     return F.log_softmax(x,dim=1)

a=GCN(lr=0.001,dropoutvalue=0.5,adjmatrix=adj,inputneurons=features.shape[1],hidden=64,outputneurons=7)
a.forward(adj,features)

TypeError                                 Traceback (most recent call last)
<ipython-input-85-7d1a2a73ecad> in <module>()
     37 
     38 a=GCN(lr=0.001,dropoutvalue=0.5,adjmatrix=adj,inputneurons=features.shape[1],hidden=64,outputneurons=7)
---> 39 a.forward(adj,features)

1 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self.forward(*input, **kwargs)
    888         for hook in itertools.chain(
--> 889                 _global_forward_hooks.values(),
    890                 self._forward_hooks.values()):
    891             hook_result = hook(self, input, result)

TypeError: forward() takes 2 positional arguments but 4 were given
print(a)
>>>
GCN(
  (gcn1): Graphconvlayer()
  (gcn2): Graphconvlayer()
)

这是一个图神经网络。我想要得到的是前向层的输出。我不确定为什么会出现上述错误以及我应该更改哪些代码才能正常工作。 谁能指导我解决这个问题?

此外,如果我将 class graphconvlayer 传递给 class GCN,我现在是否必须分别将它的每个参数也传递给 class GCN 的对象 ä?

你的GCN由两个Graphconvlayer组成。
正如您发布的代码中所定义的,Graphconvlayerforward 方法只需要 一个 输入参数:inputfeaturedata。但是,当 GCN 调用 self.gcn1self.gcn2(在其 forward 方法中)时,它会传递 3 个参数:self.gcn1(adj,x,64)self.gcn2(adj,x,7).
因此,self.gcn1self.gcn2 接收的不是单个输入参数,而是 3——这就是您遇到的错误。