Replace layer in pretrained Pytorch model

Question

I want to replace the linear layer of the 3D Resnet , which can be downloaded from the pytorch hub.

I can get the name of the linear layer by using the following code:

for name, layer in model.named_modules():
    if isinstance(layer, torch.nn.Linear):
        print(name, layer)

blocks.5.proj Linear(in_features=2048, out_features=400, bias=True)

I cannot simply use model.blocks.5.proj = nn.Linear(2048, 10) , because the .5. throws me a syntax error. Instead I tried to iterate over the modules and replace the linear layer:

for name, layer in model.named_modules():
    if isinstance(layer, torch.nn.Linear):
        model._modules[name] = torch.nn.Linear(2048, 10)

For some reason, this also doesn't work. Instead, it simply creates an additional linear layer with the same name:

blocks.5.proj Linear(in_features=2048, out_features=400, bias=True)

blocks.5.proj Linear(in_features=2048, out_features=10, bias=True)

Can someone help me out here?

Answer 1

The integer from the printed layer indicates that blocks is an nn.Sequential module. You can access a specific layer in the nn.Sequential module with regular array indexing.

Try something like:

blocks[5].proj = torch.nn.Linear(2048, 10)