Hey!
So, what kind of task do you want? You are absolutely correct as all the parameters have their requires_grad set to False.
If you only want to fine tune, you can just change the last layer according to number of output classes that you have.
model = models.resnet18(pretrained=True)
for param in model.parameters():
    param.requires_grad = False
num_filters = model.fc.in_features
model.fc = nn.Linear(num_filters, <no of output classes>)
model = model.to(device)

Rest of the pipeline remains the same.
However, if you want to actually turn on the training for particular layers, it can be a bit tricky
One way of actually keeping a track is you can print the different modules in the resnet model using the following code
for child in model_ft.children():
     print(child)

You will be able to analyze the various blocks.
One step further would be actually choosing the parameters and enabling the requires_grad for that particular layer.
for child in model_ft.children():
     for parameter in child.parameters():
        parameter.requires_grad = True

However you have to carefully make sure that you are enabling the correct parameter.