ea65d92a41ba1e8171cd0814dc9c50373f8e2c4f,eval_ch.py,,main,#,197

Before Change

        log_packages=args.log_packages,
        log_pip_packages=args.log_pip_packages)

    ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
    ds_metainfo.update(args=args)
    assert (ds_metainfo.ml_type != "imgseg") or (args.batch_size == 1)
    assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune

    global_config.train = False
    use_gpus = prepare_ch_context(args.num_gpus)

    net = prepare_model(
        model_name=args.model,
        use_pretrained=args.use_pretrained,
        pretrained_model_file_path=args.resume.strip(),
        use_gpus=use_gpus,
        net_extra_kwargs=ds_metainfo.net_extra_kwargs,
        num_classes=args.num_classes,
        in_channels=args.in_channels)
    assert (hasattr(net, "classes"))
    assert (hasattr(net, "in_size"))

    if args.data_subset == "val":
        get_test_data_source_class = get_val_data_source
        test_metric = get_composite_metric(
            metric_names=ds_metainfo.val_metric_names,
            metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
    else:
        get_test_data_source_class = get_test_data_source
        test_metric = get_composite_metric(
            metric_names=ds_metainfo.test_metric_names,
            metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
    test_data = get_test_data_source_class(
        ds_metainfo=ds_metainfo,
        batch_size=args.batch_size,
        num_workers=args.num_workers)

After Change

    if args.all:
        args.use_pretrained = True
        for model_name, model_metainfo in (_model_sha1.items() if version_info[0] >= 3 else _model_sha1.iteritems()):
            error, checksum, repo_release_tag = model_metainfo
            args.model = model_name
            logging.info("==============")
            logging.info("Checking model: {}".format(model_name))
            acc_value = test_model(args=args)
            if acc_value is not None:
                exp_value = int(error) * 1e-4
                if abs(acc_value - exp_value) > 2e-4:
                    logging.info("----> Wrong value detected (expected value: {})!".format(exp_value))
    else:
        test_model(args=args)

Instances