Hi;
I would like to use fine-tune resnet 18 on another dataset. I would like to do a study to see the performance of the network based on freezing the different layers of the network.
As of now to make make all the layers learnable I do the following
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_featuresmodel_ft.fc = nn.Linear(num_ftrs, 2)
To make all layers learnable
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
To make only classification layer learnable
for param in model_ft.parameters():
param.requires_grad = False
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
optimizer_ft = optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9)
To make the block and the classification layer learnable:
lt=8
cntr=0
for child in model_ft.children():
cntr+=1
if cntr < lt:
    # print child
    for param in child.parameters():
        param.requires_grad = False

num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,2)
optimizer_ft = optim.SGD(filter(lambda p: p.requires_grad, model_ft.parameters()), lr=0.001, momentum=0.9)
Now my query is within a block there are few convolution layer, how do I access them and set them to freeze.