| import os |
| from typing import Dict, List, Tuple |
|
|
| 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 = "xm3600" |
|
|
| _DESCRIPTION = """\ |
| Crossmodal-3600 dataset (XM3600 in short), a geographically-diverse set of 3600 images annotated with |
| human-generated reference captions in 36 languages. The images were selected from across the world, |
| covering regions where the languages are spoken, and annotated with captions that achieve consistency in |
| terms of style across all languages, while avoiding annotation artifacts due to direct translation. |
| The languages covered in the dataset include Filipino, Indonesian, Thai, and Vietnamnese |
| """ |
|
|
| _HOMEPAGE = "https://google.github.io/crossmodal-3600/" |
|
|
| _LICENSE = Licenses.CC_BY_4_0.value |
|
|
| _URLS = { |
| "captions": "https://google.github.io/crossmodal-3600/web-data/captions.zip", |
| "images": "https://open-images-dataset.s3.amazonaws.com/crossmodal-3600/images.tgz", |
| "image_attributions": "https://google.github.io/crossmodal-3600/web-data/image_attributions.csv", |
| } |
|
|
| _SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING] |
|
|
| _SOURCE_VERSION = "1.0.0" |
|
|
| _SEACROWD_VERSION = "2024.06.20" |
|
|
| _LANGUAGES = ["fil", "id", "th", "vi"] |
|
|
| _LOCAL = False |
|
|
|
|
| class XM3600Dataset(datasets.GeneratorBasedBuilder): |
| """ |
| Crossmodal-3600 dataset (XM3600 in short), a geographically-diverse set of 3600 images annotated with |
| human-generated reference captions in 36 languages. The images were selected from across the world, |
| covering regions where the languages are spoken, and annotated with captions that achieve consistency in |
| terms of style across all languages, while avoiding annotation artifacts due to direct translation. |
| The languages covered in the dataset include Filipino, Indonesian, Thai, and Vietnamnese |
| """ |
|
|
| 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"xm3600_{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"), |
| "texts": { |
| "caption": datasets.Value("string"), |
| "caption/tokenized": datasets.Value("string"), |
| "caption/tokenized/lowercase": 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.""" |
| captions_path = dl_manager.download_and_extract(_URLS["captions"]) |
| images_path = dl_manager.download_and_extract(_URLS["images"]) |
| attr_path = dl_manager.download(_URLS["image_attributions"]) |
|
|
| train_caps = {} |
| test_caps = {} |
| val_caps = {} |
|
|
| current_lang = self.config.subset_id.split("_")[1] |
|
|
| img_df = pd.read_csv(attr_path) |
|
|
| img_df_train = img_df.loc[img_df["Subset"] == "train"][["ImageID", "Subset"]] |
| img_df_test = img_df.loc[img_df["Subset"] == "test"][["ImageID", "Subset"]] |
| img_df_val = img_df.loc[img_df["Subset"] == "validation"][["ImageID", "Subset"]] |
|
|
| with jl.open(os.path.join(captions_path, "captions.jsonl"), mode="r") as jsonl_file: |
| for line in jsonl_file: |
| if line["image/key"] in img_df_train.ImageID.values: |
| train_caps[line["image/key"]] = line[current_lang] |
| elif line["image/key"] in img_df_test.ImageID.values: |
| test_caps[line["image/key"]] = line[current_lang] |
| elif line["image/key"] in img_df_val.ImageID.values: |
| val_caps[line["image/key"]] = line[current_lang] |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": {"img_ids": img_df_train.ImageID.values, "images": {img_id: os.path.join(images_path, img_id + ".jpg") for img_id in img_df_train.ImageID.values}, "captions": train_caps}, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": {"img_ids": img_df_test.ImageID.values, "images": {img_id: os.path.join(images_path, img_id + ".jpg") for img_id in img_df_test.ImageID.values}, "captions": test_caps}, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": {"img_ids": img_df_val.ImageID.values, "images": {img_id: os.path.join(images_path, img_id + ".jpg") for img_id in img_df_val.ImageID.values}, "captions": val_caps}, |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath: dict) -> Tuple[int, Dict]: |
| """Yields examples as (key, example) tuples.""" |
| counter = 0 |
| for img_id in filepath["img_ids"]: |
| cap = filepath["captions"][img_id] |
| for line in cap["caption"]: |
| cap_index = cap["caption"].index(line) |
| if self.config.schema == "source": |
| yield counter, { |
| "id": img_id + "_" + str(counter), |
| "image_paths": filepath["images"][img_id], |
| "texts": { |
| "caption": line, |
| "caption/tokenized": cap["caption/tokenized"][cap_index], |
| "caption/tokenized/lowercase": cap["caption/tokenized/lowercase"][cap_index], |
| }, |
| } |
|
|
| elif self.config.schema == "seacrowd_imtext": |
| yield counter, { |
| "id": img_id + "_" + str(counter), |
| "image_paths": [filepath["images"][img_id]], |
| "texts": line, |
| "metadata": { |
| "context": None, |
| "labels": None, |
| }, |
| } |
|
|
| else: |
| raise ValueError(f"Invalid config: {self.config.name}") |
|
|
| counter += 1 |
|
|
