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cc3m_35l.py

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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 = "cc3m_35l"
+
+_DESCRIPTION = """\
+    CC3M-35L is created by translating Conceptual Captions 3M (Sharma et al., 2018),
+    originally in English, to the other 34 languages using Google's machine translation API.
+"""
+
+_HOMEPAGE = "https://google.github.io/crossmodal-3600/"
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+# the image URLs are contained in tsv file together with the original captions which can be downloaded locally using google account.
+# those tsv file originally can be found and downloaded from this page https://ai.google.com/research/ConceptualCaptions/download
+# there are no direct image folder ready, so it needs to be downloaded one by one
+# some warnings may occur when downloading due to reasons such as security certificate and others
+_URLS = {
+    "trans_train": "https://storage.googleapis.com/crossmodal-3600/cc3m_mt_train.jsonl.gz",
+    "trans_dev": "https://storage.googleapis.com/crossmodal-3600/cc3m_mt_dev.jsonl.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_LANGUAGES = ["fil", "ind", "tha", "vie"]
+
+_LOCAL = True
+
+
+class CC3M35L(datasets.GeneratorBasedBuilder):
+    """
+    CC3M-35L is created by translating Conceptual Captions 3M (Sharma et al., 2018),
+    originally in English, 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"cc3m_35l_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"cc3m_35l_{lang} source schema", schema="source", subset_id=f"cc3m_35l_{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 = "cc3m_35l_id_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 fill_img_path(self, df: pd.DataFrame, line: dict):
+        exceptions = []
+        selected_row = df.query('caption==@line["caption_tokenized"]')
+        # it may return several rows, skip of empty
+        if not selected_row.empty:
+            # for each row, download the image, use its path and put the translation
+            for idx, row in selected_row.iterrows():
+                row["trans_caption"] = line["translation_tokenized"]
+                row["backtrans_caption"] = line["backtranslation_tokenized"]
+                # if the image cannot be downloaded for some reason, skip it
+                # may cause difference in the total data each run
+                try:
+                    row["img_path"] = datasets.DownloadManager().download(row["img_url"])
+                except:
+                    exceptions.append(idx)
+
+        return selected_row, exceptions
+
+    def is_target(self, line: dict, trg_lang: str):
+        if line["trg_lang"] == trg_lang:
+            return line
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        dev_path = dl_manager.download_and_extract(_URLS["trans_dev"])
+        train_path = dl_manager.download_and_extract(_URLS["trans_train"])
+
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        else:
+            data_dir = self.config.data_dir
+
+        # read tsv from local train and validation files
+        gcc_val = os.path.join(data_dir, "Validation_GCC-1.1.0-Validation.tsv")
+        gcc_train = os.path.join(data_dir, "Train_GCC-training.tsv")
+
+        # make it into pandas dataframe
+        colnames = ["caption", "img_url"]
+        gcc_val_df = pd.read_csv(gcc_val, sep="\t", header=None, names=colnames)
+        gcc_train_df = pd.read_csv(gcc_train, sep="\t", header=None, names=colnames)
+
+        # add new column to keep the downloaded image path
+        gcc_val_df["img_path"] = None
+        gcc_train_df["img_path"] = None
+
+        # add new column to keep the translated caption
+        gcc_val_df["trans_caption"] = None
+        gcc_train_df["trans_caption"] = None
+
+        gcc_val_df["backtrans_caption"] = None
+        gcc_train_df["backtrans_caption"] = None
+
+        # match the original captions in the translated set to the dataframe caption
+        # download the images from the URL and use it as the filepath
+        train_exceptions = []
+        val_exceptions = []
+
+        current_lang = self.config.subset_id.split("_")[2]
+        val_caption_targets = []
+        train_caption_targets = []
+
+        # filter validation data
+        with jl.open(os.path.join(dev_path), mode="r") as j:
+            val_caption_targets = [line for line in j if line["trg_lang"] == current_lang]
+            
+            #for line in val_caption_targets[:100]: # this was for debugging only
+            for line in val_caption_targets:
+                res = self.fill_img_path(gcc_train_df, line)
+                val_exceptions.extend(res[1])
+                gcc_val_df.update(res[0])
+            
+        # clean the memory
+        val_caption_targets = []
+
+        # filter train data
+        with jl.open(os.path.join(train_path), mode="r") as j:
+            train_caption_targets = [line for line in j if line["trg_lang"] == current_lang]
+            
+                
+            #for line in train_caption_targets[:100]: # this was for debugging only
+            for line in train_caption_targets:
+                res = self.fill_img_path(gcc_val_df, line)
+                train_exceptions.extend(res[1])
+                gcc_train_df.update(res[0])
+
+        # clean the memory
+        train_caption_targets = []
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": gcc_train_df,
+                    "exceptions": train_exceptions,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": gcc_val_df,
+                    "exceptions": val_exceptions,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: dict, exceptions: list) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        for idx, row in filepath.iterrows():
+            if idx not in exceptions:
+                if self.config.schema == "source":
+                    yield idx, {
+                        "id": str(idx),
+                        "image_paths": row["img_path"],
+                        "src_lang": "en",
+                        "caption_tokenized": row["caption"],
+                        "trg_lang": self.config.subset_id.split("_")[2],
+                        "translation_tokenized": row["trans_caption"],
+                        "backtranslation_tokenized": row["backtrans_caption"],
+                    }
+
+                elif self.config.schema == "seacrowd_imtext":
+                    yield idx, {
+                        "id": str(idx),
+                        "image_paths": [row["img_path"]],
+                        "texts": row["trans_caption"],
+                        "metadata": {
+                            "context": None,
+                            "labels": None,
+                        },
+                    }
+
+                else:
+                    raise ValueError(f"Invalid config: {self.config.name}")