How to freeze the pytorch network parameters from training?

The basic idea is that all models have a function model.children() which returns it’s layers. Within each layer, there are parameters (or weights), which can be obtained using .param() on any children (i.e. layer). Now, every parameter has an attribute called requires_grad which is by default True. True means it will be backpropagrated and hence to freeze a layer you need to set requires_grad to False for all parameters of a layer. This can be done like this -

model_ft = models.resnet50(pretrained=True)
ct = 0
for child in model_ft.children():
    ct += 1
    if ct < 7:
        for param in child.parameters():
            param.requires_grad = False

This freezes layers 1-6 in the total 10 layers of Resnet50. Hope this helps!

p/s: https://discuss.pytorch.org/t/how-the-pytorch-freeze-network-in-some-layers-only-the-rest-of-the-training/7088/2