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:

After Change

                if best_flag:
                    checkpoint_path = os.path.join(opt.checkpoint_path, "model-best.ckpt")
                    torch.save(model.state_dict(), checkpoint_path)
                    print("model saved to {}".format(checkpoint_path))
                    with open(os.path.join(opt.checkpoint_path, "infos_"+opt.id+"-best.pkl"), "wb") as f:
                        cPickle.dump(infos, f)

Instances