def get_split_info()

in data/dataset.py [0:0]

• 17 lines of code
• 7 McCabe index (conditional complexity)

    def get_split_info(self):
        data = torch.load(self.root + '/metadata_{}.t7'.format(self.split))
        train_data, val_data, test_data = [], [], []
        for instance in data:
            image, attr, obj, settype = instance['image'], instance[
                'attr'], instance['obj'], instance['set']

            if attr == 'NA' or (attr,
                                obj) not in self.pairs or settype == 'NA':
                # ignore instances with unlabeled attributes
                # ignore instances that are not in current split
                continue

            data_i = [image, attr, obj]
            if settype == 'train':
                train_data.append(data_i)
            elif settype == 'val':
                val_data.append(data_i)
            else:
                test_data.append(data_i)

        return train_data, val_data, test_data