PyTorch CNN Different Input Size

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Hello Guys I have a question about different Input Sizes.

My training set and validation dataset have an input Size of 256 and for my prediction (with an unseen Test Dataset) I have an input size of 496.

class Net(nn.Module):
    def __init__(self, shape):
        super(Net,self).__init__()
        self.conv1 = nn.Conv1d(shape,1,1)
        self.batch1 = nn.BatchNorm1d(1)
        self.avgpl1 = nn.AvgPool1d(1, stride=1)
        self.fc1 = nn.Linear(1,3)
    
    #forward method 
    def forward(self,x):
        x = self.conv1(x)
        x = self.batch1(x)
        x = F.relu(x)
        x = self.avgpl1(x)
        x = torch.flatten(x,1)
        x = F.log_softmax(self.fc1(x))
        return x

I saved the model and wanna use it also for my prediction.

Error Message is:

Input In [244], in predict_data(prediction_data, model_path, data_config, context)
     25 new_model = Net(shape_preprocessed_data)
     26 # load the previously saved state_dict
---> 27 new_model.load_state_dict(torch.load("NetModel.pth"))
     29 # check if predictions of models are equal
     30 
     31 # generate random input of size (N,C,H,W)
     32 
     33 # switch to eval mode for both models
     34 model = model.eval()

    RuntimeError: Error(s) in loading state_dict for Net:
    size mismatch for conv1.weight: copying a param with shape 
    torch.Size([1, 256, 1]) from checkpoint, the shape in current model is torch.Size([1, 494, 1]).

How can I solve this?
torch conv-neural-network pytorch python
3 Answers

It seems that the saved model was initialized with shape, the number of input channels equal to 256, while the model you are trying to load the weight onto new_model was initialized with 494.

But this value refers to the feature size, not the sequence length. I believe you might have mixed up the two things. The feature size should remain constant. But in your case it's hard to say what you are trying to do since you are not providing information about the kind of dataset used.

user_63313690

You could reshape/downsample the input as the first step of the forward pass in your model. This can be done using the torch.nn.functional.interpolate function.

For example:

class Net(nn.Module):
def __init__(self, shape):
    super(Net,self).__init__()
    self.input_shape = shape
    self.conv1 = nn.Conv1d(shape,1,1)
    self.batch1 = nn.BatchNorm1d(1)
    self.avgpl1 = nn.AvgPool1d(1, stride=1)
    self.fc1 = nn.Linear(1,3)

#forward method 
def forward(self,x):
    x = torch.nn.functional.interpolate(x, size=self.input_shape)
    x = self.conv1(x)
    x = self.batch1(x)
    x = F.relu(x)
    x = self.avgpl1(x)
    x = torch.flatten(x,1)
    x = F.log_softmax(self.fc1(x))
    return x

Your test images would then be downsampled to size 256 in order to be compatible with the model.

user_192909260

Try using nn.AdaptiveAvgPool1d(output_size) instead of nn.AvgPool1d, and mention the desired output size. Refer to this for detailed explanation of how Adaptive average pooling works in Pytorch.

user_67929080
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