def load_dataset()

in src/fairseq/fairseq/tasks/sentence_ranking.py [0:0]

• 79 lines of code
• 11 McCabe index (conditional complexity)

    def load_dataset(self, split, combine=False, **kwargs):
        """Load a given dataset split (e.g., train, valid, test)."""

        def get_path(type, split):
            return os.path.join(self.args.data, type, split)

        def make_dataset(type, dictionary):
            split_path = get_path(type, split)

            dataset = data_utils.load_indexed_dataset(
                split_path,
                self.source_dictionary,
                self.args.dataset_impl,
                combine=combine,
            )
            return dataset

        input0 = make_dataset('input0', self.source_dictionary)
        input_options = [
            make_dataset(
                'input{idx}'.format(idx=idx + 1),
                self.source_dictionary
            )
            for idx in range(self.args.num_classes)
        ]

        if self.args.separator_token is not None:
            input0 = PrependTokenDataset(input0, self.args.separator_token)

        src_tokens = []
        for input_option in input_options:
            if self.args.init_token is not None:
                input_option = PrependTokenDataset(input_option, self.args.init_token)
            if self.args.max_option_length is not None:
                input_option = TruncateDataset(input_option, self.args.max_option_length)
            src_token = ConcatSentencesDataset(input_option, input0)
            src_token = maybe_shorten_dataset(
                src_token,
                split,
                self.args.shorten_data_split_whitelist,
                self.args.shorten_method,
                self.args.max_positions,
                self.args.seed,
            )
            src_tokens.append(src_token)

        with data_utils.numpy_seed(self.args.seed):
            shuffle = np.random.permutation(len(src_tokens[0]))

        dataset = {
            'id': IdDataset(),
            'nsentences': NumSamplesDataset(),
            'ntokens': NumelDataset(src_tokens[0], reduce=True),
        }

        for src_token_idx in range(len(src_tokens)):
            dataset.update(
                {
                    'net_input{idx}'.format(idx=src_token_idx+1): {
                        'src_tokens': RightPadDataset(
                            src_tokens[src_token_idx],
                            pad_idx=self.source_dictionary.pad(),
                        ),
                        'src_lengths': NumelDataset(src_tokens[src_token_idx], reduce=False),
                    }
                }
            )

        label_path = '{}.label'.format(get_path('label', split))
        if os.path.exists(label_path):
            with open(label_path) as h:
                dataset.update(
                    target=RawLabelDataset([
                        int(x.strip()) for x in h.readlines()
                    ])
                )

        nested_dataset = NestedDictionaryDataset(
            dataset,
            sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])],
        )

        if self.args.no_shuffle:
            dataset = nested_dataset
        else:
            dataset = SortDataset(
                nested_dataset,
                # shuffle
                sort_order=[shuffle],
            )

        logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset)))

        self.datasets[split] = dataset
        return self.datasets[split]