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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
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