Problems initializing model in pytorch

Question

I can't initialize my model in pytorch and get:

TypeError                                 Traceback (most recent call last)
<ipython-input-82-9bfee30a439d> in <module>()
288 dataset = News_Dataset(true_path=args.true_news_file, 
                            fake_path=args.fake_news_file, 
289                         embeddings_path=args.embeddings_file)
 --> 290 classifier = News_classifier_resnet_based().cuda()
291 try:
292   classifier.load_state_dict(torch.load(args.model_state_file))

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in 
__call__(self, *input, **kwargs)
548             result = self._slow_forward(*input, **kwargs)
549         else:
--> 550             result = self.forward(*input, **kwargs)
551         for hook in self._forward_hooks.values():
552             hook_result = hook(self, input, result)

TypeError: forward() missing 1 required positional argument: 'input'

Here is my code:

class News_classifier_resnet_based(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.activation = torch.nn.ReLU6()
        self.sigmoid = torch.nn.Sigmoid()

        self.positional_encodings = PositionalEncoder()

        self.resnet = list(torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).children())

        self.to_appropriate_shape = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=77)

        self.conv1 = torch.nn.Conv2d(in_channels=1,out_channels=64,kernel_size=7,stride=2,padding=3)
        self.conv1.weight = torch.nn.Parameter(self.resnet[0].weight[:,0,:,:].data)
        self.center = torch.nn.Sequential(*self.resnet[1:-2])
        self.conv2 = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1)
        self.conv3 = torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=7)

        self.title_conv = torch.nn.Sequential(
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3,stride=3),
                    self.activation(),
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2),
                    self.activation(),
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2)
                )
       self.title_lin = torch.nn.Linear(25,1)

       self.year_lin = torch.nn.Linear(10,1)
       self.month_lin = torch.nn.Linear(12,1)
       self.day_lin = torch.nn.Linear(31,1)
       self.date_lin = torch.nn.Linear(3,1)

       self.final_lin = torch.nn.Linear(3,1)

   def forward(self,x_in):
       #input shape - (batch_size, 3+title_len+seq_len, embedding_dim)
       #output shape - (batch_size, 1)
       year = x_in[:,0,:10]
       month = x_in[:,1,:12]
       day = x_in[:,2,:31]

       title = x_in[:,3:3+args.title_len,:]
       text = x_in[:,3+args.title_len:,:]
       title = self.positional_encodings(title)
       text = self.positional_encodings(text)

       text = text.unsqueeze(1)
       text = self.activation(self.to_appropriate_shape(text))
       text = self.activation(self.conv1(text))
       text = self.activation(self.center(text))
       text = self.activation(self.conv2(text))
       text = self.activation(self.conv3(text))
       text = text.reshape(args.batch_size,-1)

       title = title.unsqueeze(1)
       title = self.activation(self.title_conv(title))
       title = title.reshape(args.batch_size,-1)
       title = self.activation(self.title_lin(title))

       year = self.activation(self.year_lin(year))
       month = self.activation(self.month_lin(month))
       day = self.activation(self.day_lin(day))
       date = torch.cat([year,month,day], dim=-1)
       date = self.activation(self.date_lin(date))

       final = torch.cat([date,title,text], dim=-1)
       final = self.sigmoid(self.final_lin(final))

       return final

classifier = News_classifier_resnet_based().cuda()

What should I do? I'm trying to classify texts using word embeddings but problem lies in last line. I am working in google colab. Also when I created some models in other code blocks, I've got no problems.

Could you show the model News_classifier_resnet_based please? — Madhoolika
– Madhoolika, Commented Jul 11, 2020 at 13:36
It might be a better choice to place the code here, else you might have to give access to your colab file to everyone. As of now it denies access. — Madhoolika
– Madhoolika, Commented Jul 11, 2020 at 13:51

Pedrolarben · Accepted Answer · 2020-07-11 15:47:22Z

2

The problem is in your init function. When you create title_conv, insted of passing the activation object previously created, your are calling the activation without arguments. You can fix it by changing that part of code with this:

self.title_conv = torch.nn.Sequential(
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3,stride=3),
                    self.activation, # Notice I have removed ()
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2),
                    self.activation, # Notice I have removed ()
                    torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2)
                )

edited Jul 11, 2020 at 15:47

answered Jul 11, 2020 at 14:28

Pedrolarben

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Comments

desertnaut · Accepted Answer · 2024-12-04 11:39:25Z

If the self.title_conv is changed in either of the below ways, the error doesn't appear.

self.title_conv = torch.nn.Sequential(
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3,stride=3),
                torch.nn.ReLU6(),
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2),
                torch.nn.ReLU6(),
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2)
            )

or as:

self.title_conv = torch.nn.Sequential(
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3,stride=3),
                self.activation,
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2),
                self.activation,
                torch.nn.Conv2d(in_channels=1,out_channels=1,kernel_size=2,stride=2)
            )

I am not sure of the reason. I think it could be because self.activation is a function and a call is made again by writing it as self.activation().

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