sample_info/modules/data_utils.py [387:413]:
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        if num_train_examples is not None:
            np.random.seed(args.get('seed', 42))
            if len(train_data) > num_train_examples:
                train_indices = np.random.choice(len(train_data), size=num_train_examples, replace=False)
                train_data = SubsetDataWrapper(train_data, include_indices=train_indices)

            if len(val_data) > num_train_examples:
                val_indices = np.random.choice(len(val_data), size=num_train_examples, replace=False)
                val_data = SubsetDataWrapper(val_data, include_indices=val_indices)

        # add label noise
        seed = args.get('seed', 42)
        error_prob = args.get('error_prob', 0.0)
        if error_prob > 0.0:
            train_data = UniformNoiseWrapper(train_data, error_prob=error_prob, num_classes=2, seed=seed)
            info = train_data.is_corrupted
            clean_validation = args.get('clean_validation', True)
            if not clean_validation:
                val_data = UniformNoiseWrapper(val_data, error_prob=error_prob, num_classes=2, seed=seed)

        if not build_loaders:
            return train_data, val_data, test_data, info

        train_loader, val_loader, test_loader = nnlib.nnlib.data_utils.base.get_loaders_from_datasets(
            train_data=train_data, val_data=val_data, test_data=test_data, **args)

        return train_loader, val_loader, test_loader, info
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sample_info/modules/data_utils.py [435:461]:
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        if num_train_examples is not None:
            np.random.seed(args.get('seed', 42))
            if len(train_data) > num_train_examples:
                train_indices = np.random.choice(len(train_data), size=num_train_examples, replace=False)
                train_data = SubsetDataWrapper(train_data, include_indices=train_indices)

            if len(val_data) > num_train_examples:
                val_indices = np.random.choice(len(val_data), size=num_train_examples, replace=False)
                val_data = SubsetDataWrapper(val_data, include_indices=val_indices)

        # add label noise
        seed = args.get('seed', 42)
        error_prob = args.get('error_prob', 0.0)
        if error_prob > 0.0:
            train_data = UniformNoiseWrapper(train_data, error_prob=error_prob, num_classes=2, seed=seed)
            info = train_data.is_corrupted
            clean_validation = args.get('clean_validation', True)
            if not clean_validation:
                val_data = UniformNoiseWrapper(val_data, error_prob=error_prob, num_classes=2, seed=seed)

        if not build_loaders:
            return train_data, val_data, test_data, info

        train_loader, val_loader, test_loader = nnlib.nnlib.data_utils.base.get_loaders_from_datasets(
            train_data=train_data, val_data=val_data, test_data=test_data, **args)

        return train_loader, val_loader, test_loader, info
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