ff68a9ef501e7286501dba1719024dfaaab4b473,eval_lm.py,,main,#Any#,18

Before Change

        args.max_target_positions = 1024

    use_cuda = torch.cuda.is_available() and not args.cpu
    dataset = data_loaders.load_dataset(args, [args.gen_subset], False)

    // Load ensemble
    print("| loading model(s) from {}".format(args.path))
    models, _ = utils.load_ensemble_for_inference(args.path.split(","), dataset.src_dict, dataset.dst_dict)

    print("| Dictionary: {} types".format(len(dataset.src_dict)))
    print("| {} {} {} examples".format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))

    // Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
    for model in models:
        model.make_generation_fast_()
        model.src_dict = dataset.src_dict
        model.dst_dict = dataset.dst_dict

    itr = dataset.eval_dataloader(
        args.gen_subset,

After Change

    // Load dataset splits
    task = tasks.setup_task(args)
    task.load_dataset(args.gen_subset)
    print("| {} {} {} examples".format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))

    // Load ensemble
    print("| loading model(s) from {}".format(args.path))

Instances