For ResNet model, you can use children attribute to access layers since ResNet model in pytorch consist of nn modules. (Tested on pytorch 0.4.1)
model = models.resnet152(pretrained=True)
newmodel = torch.nn.Sequential(*(list(model.children())[:-1]))
print(newmodel)
Update: Although there is not an universal answer for the question that can work on all pytorch models, it should work on all well structured ones. Existing layers you add to your model (such as torch.nn.Linear, torch.nn.Conv2d, torch.nn.BatchNorm2d…) all based on torch.nn.Module class. And if you implement a custom layer and add that to your network you should inherit it from pytorch’s torch.nn.Module class. As written in documentation, children attribute lets you access the modules of your class/model/network.
def children(self):
r"""Returns an iterator over immediate children modules.
Update: It is important to note that children() returns “immediate” modules, which means if last module of your network is a sequential, it will return whole sequential.