79fc09d66d3f4736b9fb4f5756a78725719f3808,train.py,,train,#Any#,27

Before Change

    optimizer = optim.Adam(model.parameters(), lr=opt.learning_rate)

    // Load the optimizer
    if infos.get("state_dict", None):
        optimizer.load_state_dict(infos["state_dict"])

    while True:
        if update_lr_flag:
                // Assign the learning rate
            if epoch > opt.learning_rate_decay_start and opt.learning_rate_decay_start >= 0:

After Change

                torch.save(model.state_dict(), checkpoint_path)
                print("model saved to {}".format(checkpoint_path))
                optimizer_path = os.path.join(opt.checkpoint_path, "optimizer.pth")
                torch.save(optimizer.state_dict(), optimizer_path)

                // Dump miscalleous informations
                infos["iter"] = iteration
                infos["epoch"] = epoch

Instances