I have a question about the semi_supervised_evaluation.py script in FedSSL.

[ss3b3]

` # fine-tune model
if args.use_MLP:
logreg = MLP(num_features, n_classes, 4096)
logreg = logreg.to(device)
else:
logreg = nn.Sequential(nn.Linear(num_features, n_classes))
logreg = logreg.to(device)

# loss / optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=logreg.parameters(), lr=args.learning_rate)

# Train fine-tuned model
logreg.train()
resnet.train()
accs = []
for epoch in range(args.num_epochs):
    print("======epoch {}======".format(epoch))
    metrics = defaultdict(list)
    for step, (h, y) in enumerate(train_loader):
        h = h.to(device)
        y = y.to(device)

        outputs = logreg(resnet(h))

        loss = criterion(outputs, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # calculate accuracy and save metrics
        accuracy = (outputs.argmax(1) == y).sum().item() / y.size(0)
        metrics["Loss/train"].append(loss.item())
        metrics["Accuracy/train"].append(accuracy)

    print(f"Epoch [{epoch}/{args.num_epochs}]: " + "\t".join(
        [f"{k}: {np.array(v).mean()}" for k, v in metrics.items()]))

    if epoch % 1 == 0:
        acc = test_whole(resnet, logreg, device, test_loader, args.model_path)
        if epoch <= 100:
            accs.append(acc)
test_whole(resnet, logreg, device, test_loader, args.model_path)
print(args.model_path)
print(f"Best one for 100 epoch is {max(accs):.4f}")`

I have a question about the semi_supervised_evaluation.py script in FedSSL. During the finetuning process, the parameters of the Adam optimizer are set as logreg.parameters(). Does this mean that during finetuning, the parameters of the encoder won't be changed, and only the logreg model will be trained? Is this correct?