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uit_viic.py

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+# coding=utf-8
+import json
+import os.path
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_DATASETNAME = "uit_viic"
+_CITATION = """\
+@InProceedings{10.1007/978-3-030-63007-2_57,
+author="Lam, Quan Hoang
+and Le, Quang Duy
+and Nguyen, Van Kiet
+and Nguyen, Ngan Luu-Thuy",
+editor="Nguyen, Ngoc Thanh
+and Hoang, Bao Hung
+and Huynh, Cong Phap
+and Hwang, Dosam
+and Trawi{\'{n}}ski, Bogdan
+and Vossen, Gottfried",
+title="UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning",
+booktitle="Computational Collective Intelligence",
+year="2020",
+publisher="Springer International Publishing",
+address="Cham",
+pages="730--742",
+abstract="Image Captioning (IC), the task of automatic generation of image captions, has attracted
+attentions from researchers in many fields of computer science, being computer vision, natural language
+processing and machine learning in recent years. This paper contributes to research on Image Captioning
+task in terms of extending dataset to a different language - Vietnamese. So far, there has been no existed
+Image Captioning dataset for Vietnamese language, so this is the foremost fundamental step for developing
+Vietnamese Image Captioning. In this scope, we first built a dataset which contains manually written
+captions for images from Microsoft COCO dataset relating to sports played with balls, we called this dataset
+UIT-ViIC (University Of Information Technology - Vietnamese Image Captions). UIT-ViIC consists of 19,250
+Vietnamese captions for 3,850 images. Following that, we evaluated our dataset on deep neural network models
+and did comparisons with English dataset and two Vietnamese datasets built by different methods. UIT-ViIC
+is published on our lab website (https://sites.google.com/uit.edu.vn/uit-nlp/) for research purposes.",
+isbn="978-3-030-63007-2"
+}
+"""
+
+_DESCRIPTION = """
+UIT-ViIC contains manually written captions for images from Microsoft COCO dataset relating to sports
+played with ball. UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images. For each image,
+UIT-ViIC provides five Vietnamese captions annotated by five annotators.
+"""
+
+_HOMEPAGE = "https://drive.google.com/file/d/1YexKrE6o0UiJhFWpE8M5LKoe6-k3AiM4"
+_PAPER_URL = "https://arxiv.org/abs/2002.00175"
+_LICENSE = Licenses.UNKNOWN.value
+_HF_URL = ""
+_LANGUAGES = ["vi"]
+_LOCAL = False
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_URLS = "https://drive.google.com/uc?export=download&id=1YexKrE6o0UiJhFWpE8M5LKoe6-k3AiM4"
+_Split_Path = {
+    "train": "UIT-ViIC/uitviic_captions_train2017.json",
+    "validation": "UIT-ViIC/uitviic_captions_val2017.json",
+    "test": "UIT-ViIC/uitviic_captions_test2017.json",
+}
+
+
+class UITViICDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="source"),
+        SEACrowdConfig(name=f"{_DATASETNAME}_seacrowd_imtext", version=datasets.Version(_SEACROWD_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="seacrowd_imtext"),
+    ]
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "license": datasets.Value("int32"),
+                    "file_name": datasets.Value("string"),
+                    "coco_url": datasets.Value("string"),
+                    "flickr_url": datasets.Value("string"),
+                    "height": datasets.Value("int32"),
+                    "width": datasets.Value("int32"),
+                    "date_captured": datasets.Value("string"),
+                    "image_id": datasets.Value("int32"),
+                    "caption": datasets.Value("string"),
+                    "cap_id": datasets.Value("int32"),
+                }
+            )
+        elif self.config.schema == "seacrowd_imtext":
+            features = schemas.image_text_features()
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            license=_LICENSE,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        file_paths = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["train"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["validation"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["test"])},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            json_dict = json.load(f)
+            images = {itm["id"]: itm for itm in json_dict["images"]}
+            captns = json_dict["annotations"]
+
+            for idx, capt in enumerate(captns):
+                image_id = capt["image_id"]
+                if self.config.schema == "source":
+                    yield idx, {
+                        "license": images[image_id]["license"],
+                        "file_name": images[image_id]["file_name"],
+                        "coco_url": images[image_id]["coco_url"],
+                        "flickr_url": images[image_id]["flickr_url"],
+                        "height": images[image_id]["height"],
+                        "width": images[image_id]["width"],
+                        "date_captured": images[image_id]["date_captured"],
+                        "image_id": capt["image_id"],
+                        "caption": capt["caption"],
+                        "cap_id": capt["id"],
+                    }
+                elif self.config.schema == "seacrowd_imtext":
+                    yield idx, {
+                        "id": capt["id"],
+                        "image_paths": [images[image_id]["coco_url"], images[image_id]["flickr_url"]],
+                        "texts": capt["caption"],
+                        "metadata": {
+                            "context": "",
+                            "labels": ["Yes"],
+                        },
+                    }