import json import os from typing import Dict, List, Tuple # import csv import datasets import jsonlines as jl import pandas as pd from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @inproceedings{thapliyal-etal-2022-crossmodal, title = "Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset", author = "Thapliyal, Ashish V. and Pont Tuset, Jordi and Chen, Xi and Soricut, Radu", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.45", doi = "10.18653/v1/2022.emnlp-main.45", pages = "715--729", } """ _DATASETNAME = "coco_35l" _DESCRIPTION = """\ COCO-35L is a machine-generated image caption dataset, constructed by translating COCO Captions (Chen et al., 2015) to the other 34 languages using Google’s machine translation API. 152520 image ids are not found in the coco 2014 training caption. Validation set is ok Using COCO 2014 train and validation set. """ _HOMEPAGE = "https://google.github.io/crossmodal-3600/" _LICENSE = Licenses.CC_BY_4_0.value _URLS = { "coco2017_train_images": "http://images.cocodataset.org/zips/train2017.zip", "coco2014_train_images": "http://images.cocodataset.org/zips/train2014.zip", "coco2014_val_images": "http://images.cocodataset.org/zips/val2014.zip", "coco2014_train_val_annots": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip", "coco2017_train_val_annots": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip", "trans_train": "https://storage.googleapis.com/crossmodal-3600/coco_mt_train.jsonl.gz", "trans_dev": "https://storage.googleapis.com/crossmodal-3600/coco_mt_dev.jsonl.gz", } _SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" _LANGUAGES = {"fil": "fil", "ind": "id", "tha": "th", "vie": "vi"} _LOCAL = False class Coco35LDataset(datasets.GeneratorBasedBuilder): """ COCO-35L is a machine-generated image caption dataset, constructed by translating COCO Captions (Chen et al., 2015) to the other 34 languages using Google’s machine translation API. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME}_{lang} source schema", schema="source", subset_id=f"{_DATASETNAME}_{lang}", ) for lang in _LANGUAGES ] + [ SEACrowdConfig( name=f"{_DATASETNAME}_{lang}_seacrowd_imtext", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME}_{lang} SEACrowd schema", schema="seacrowd_imtext", subset_id=f"{_DATASETNAME}_{lang}", ) for lang in _LANGUAGES ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{sorted(_LANGUAGES)[0]}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "image_paths": datasets.Value("string"), "src_lang": datasets.Value("string"), "caption_tokenized": datasets.Value("string"), "trg_lang": datasets.Value("string"), "translation_tokenized": datasets.Value("string"), "backtranslation_tokenized": datasets.Value("string"), } ) elif self.config.schema == "seacrowd_imtext": features = schemas.image_text_features() return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" trans_train_path = dl_manager.download_and_extract(_URLS["trans_train"]) trans_val_path = dl_manager.download_and_extract(_URLS["trans_dev"]) coco2014_train_val_annots_path = dl_manager.download_and_extract(_URLS["coco2014_train_val_annots"]) coco2014_val_images_path = dl_manager.download_and_extract(_URLS["coco2014_val_images"]) coco2014_train_images_path = dl_manager.download_and_extract(_URLS["coco2014_train_images"]) trans_train_captions = {} trans_dev_captions = {} train_df = pd.DataFrame() val_df = pd.DataFrame() current_lang = _LANGUAGES[self.config.subset_id.split("_")[2]] # the COCO dataset structure has separated the captions and images information. The caption's "image_id" key will refer to the image's "id" key. # load the image informations from COCO 2014 dataset and put it into a dataframe with open(os.path.join(coco2014_train_val_annots_path, "annotations", "captions_val2014.json")) as json_captions: captions = json.load(json_captions) val_df = pd.DataFrame(captions["images"]) with open(os.path.join(coco2014_train_val_annots_path, "annotations", "captions_train2014.json")) as json_captions: captions = json.load(json_captions) train_df = pd.DataFrame(captions["images"]) # the translated caption has "image_id" which refers to the "image_id" in the COCO annotations. # however we can skip this and connect it to the images' "id" # the example of an "image_id" in the translated caption -> "123456_0" since an image can has many descriptions. # thus, the real image_id to map it into the COCO image dataset is the "123456" with jl.open(trans_train_path, mode="r") as j: total = 0 not_found = 0 missing_ids = [] for line in j: if line["trg_lang"] == current_lang: total += 1 trans_img_id = line["image_id"] coco2014_img_id = line["image_id"].split("_")[0] # unfortunately, not all image_id in the translated caption can be found in the original COCO 2014. # hence, we need to handle such errors try: filename = train_df.query(f"id=={int(coco2014_img_id)}")["file_name"].values[0] trans_train_captions[trans_img_id] = line trans_train_captions[trans_img_id]["filename"] = os.path.join(coco2014_train_images_path, "train2014", filename) except IndexError: missing_ids.append(trans_img_id) not_found += 1 pass # the validation set are strangely okay. with no missing image_id(s) with jl.open(trans_val_path, mode="r") as j: for line in j: if line["trg_lang"] == current_lang: trans_img_id = line["image_id"] trans_dev_captions[trans_img_id] = line coco2014_img_id = int(trans_img_id.split("_")[0]) filename = val_df.query(f"id=={coco2014_img_id}")["file_name"].values[0] trans_dev_captions[trans_img_id]["filename"] = os.path.join(coco2014_val_images_path, "val2014", filename) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": { "images": trans_train_captions, }, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": { "images": trans_dev_captions, }, "split": "dev", }, ), ] def _generate_examples(self, filepath: dict, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" counter = 0 for trans_img_id, data in filepath["images"].items(): if self.config.schema == "source": yield counter, { "id": trans_img_id + "_" + str(counter), "image_paths": data["filename"], "src_lang": data["src_lang"], "caption_tokenized": data["caption_tokenized"], "trg_lang": data["trg_lang"], "translation_tokenized": data["translation_tokenized"], "backtranslation_tokenized": data["backtranslation_tokenized"], } elif self.config.schema == "seacrowd_imtext": yield counter, { "id": trans_img_id + "_" + str(counter), "image_paths": [data["filename"]], "texts": data["translation_tokenized"], "metadata": { "context": None, "labels": None, }, } else: raise ValueError(f"Invalid config: {self.config.name}") counter += 1