Upload coco_35l.py with huggingface_hub

coco_35l.py

@@ -0,0 +1,230 @@
+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