Error loading saved Pytorch neural network

Hello. I am having trouble loading a saved Pytorch network and I have no idea why. I have done this several times before, and I have never had this error. This is the error I get:

RuntimeError: Error(s) in loading state_dict for NeuralNet:
	Unexpected key(s) in state_dict: "conv1.weight", "conv1.bias", "conv2.weight", "conv2.bias".

This is how I load my model:

model = NeuralNet()

This is my neural network structure:

    def __init__(self, **kwargs):
        super(NeuralNet, self).__init__(**kwargs)
        self.conv3 = nn.Conv2d(in_channels=24, out_channels=256, kernel_size=(2,2), stride=(2,2)) 
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(10,6), stride=(1,1))
        self.batch3 = nn.BatchNorm2d(512)
        self.batch4 = nn.BatchNorm2d(256)
        self.batch = nn.BatchNorm2d(24)


        self.deconv1 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=(10,6), stride=(1,1))
        self.deconv2 = nn.ConvTranspose2d(in_channels=256, out_channels=24, kernel_size=(2,2), stride=(2,2), padding=0)
        self.deconv3 = nn.ConvTranspose2d(in_channels=24, out_channels=3, kernel_size=(5,5), stride=(5,5), padding=0)

    def SqueezeExcite(self,channels,reduction,x):
        mid_cannels = channels // reduction

        self.pool = nn.AdaptiveAvgPool2d(output_size=1)
        self.conv1 = nn.Conv2d(in_channels=channels, out_channels=mid_cannels, kernel_size=(1,1), stride=(1,1), groups=1, bias=True)
        self.activ = nn.ReLU()
        self.conv2 = nn.Conv2d(in_channels=mid_cannels, out_channels=channels, kernel_size=(1,1), stride=(1,1), groups=1, bias=True)
        self.sigmoid = nn.Sigmoid()
        w = self.pool(x)
        w = self.conv1(w.float())
        w = self.activ(w.float())
        w = self.conv2(w.float())
        w = self.sigmoid(w.float())
        x = x * w.float()
        return x.float()
        encode1 = self.batch(torch.tanh(func.conv2d(input=x_in, weight=weights, stride=(5,5), padding=(0,0), groups=3)))

        encode2 = self.batch4(torch.tanh(self.conv3(encode1)))
        encode2 = self.SqueezeExcite(channels=256, reduction=16, x=encode2.float())
        encode3 = self.batch3(torch.tanh(self.conv4(encode2)))
        decode1 = torch.tanh(self.deconv1(encode3))
        decode5 = torch.tanh(self.deconv2(decode1))
        decode6 = func.relu(self.deconv3(decode5))
        decode6[:,0,:,:] = decode6[:,0,:,:] * x[:,0,:,:]
        decode6[:,1,:,:] = decode6[:,1,:,:] * x[:,0,:,:]
        decode6[:,2,:,:] = decode6[:,2,:,:] * x[:,0,:,:]
        return decode6

Usually I do not have problems, but recently I added this SqueezeAndExcitation block, and the error has occurred.

if im not mistaken, you try to load conv1 weight but in __init__ it got commented. also im not seeing conv2 either in __init__

Sorry, I don’t use that conv layer anymore. Just removed the comment to avoid confusion.

If that was the issue my training wouldn’t have run. It ran and I saved the trained model, I just cannot load it for some reason.

but the saved model you load had conv1 and conv2 weight, maybe you load weight before edit the model?

Yes, those are coming from the SqueezeExcite function.

maybe it cannot detect latter parameter assignment, since there is no conv1 and conv2 in the first place. can you check what model.parameters() or model.state_dict().keys() before load model?

That is true. It worked once I removed the function, and just assigned conv1 and conv2 without them being inside a function. Thanks!