I’m currently trying to use a pretrained DenseNet in my model. I’m following this tutorial: https://pytorch.org/hub/pytorch_vision_densenet/, and it works well, with an input of [1,3,244,244], it returns a [1,1000] tensor, exactly as expected.
However, currently I’m using this code to load a pretrained Densenet into my model, and use it as a “feature extraction” model. This is the code in the init function
base_model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet121', pretrained=True)

self.base_model = nn.Sequential(*list(base_model.children())[:-1])

And it is being used like this in the forward function
x = self.base_model(x)


This however, taking the same input, returns a tensor of the size: ([1, 1024, 7, 7]). I can not figure out what is not working, I think it is due to the fact that DenseNet connects all the layers together, but I do not know how to get it to work in the same method. Any tips in how to use pretrained DenseNet in my own model?