Datasets:

SEACrowd
/

cub_bahasa

+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+import datasets
+import pandas as pd
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+_CITATION = """\
+@article{mahadi2023indonesian,
+    author    = {Made Raharja Surya Mahadi and Nugraha Priya Utama},
+    title     = {Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGAN},
+    journal   = {arXiv preprint arXiv:2303.14517},
+    year      = {2023},
+    url       = {https://arxiv.org/abs/2303.14517},
+}
+"""
+_DATASETNAME = "cub_bahasa"
+_DESCRIPTION = """\
+Semi-translated dataset of CUB-200-2011 into Indonesian. This dataset contains thousands
+of image-text annotation pairs of 200 subcategories belonging to birds. The natural
+language descriptions are collected through the Amazon Mechanical Turk (AMT) platform and
+are required at least 10 words, without any information on subcategories and actions.
+"""
+_LOCAL=False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_HOMEPAGE = "https://github.com/share424/Indonesian-Text-to-Image-synthesis-with-Sentence-BERT-and-FastGAN"
+_LICENSE = Licenses.UNKNOWN.value
+_URLS = {
+    "text": "https://raw.githubusercontent.com/share424/Indonesian-Text-to-Image-synthesis-with-Sentence-BERT-and-FastGAN/master/dataset/indo_cub_200_2011_captions.json",
+    "image": "https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz"
+}
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+class CubBahasaDataset(datasets.GeneratorBasedBuilder):
+    """CUB-200-2011 image-text dataset in Indonesian language for bird domain."""
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "imtext"
+    IMAGE_CLASS = {
+        1: '001.Black_footed_Albatross',
+        2: '002.Laysan_Albatross',
+        3: '003.Sooty_Albatross',
+        4: '004.Groove_billed_Ani',
+        5: '005.Crested_Auklet',
+        6: '006.Least_Auklet',
+        7: '007.Parakeet_Auklet',
+        8: '008.Rhinoceros_Auklet',
+        9: '009.Brewer_Blackbird',
+        10: '010.Red_winged_Blackbird',
+        11: '011.Rusty_Blackbird',
+        12: '012.Yellow_headed_Blackbird',
+        13: '013.Bobolink',
+        14: '014.Indigo_Bunting',
+        15: '015.Lazuli_Bunting',
+        16: '016.Painted_Bunting',
+        17: '017.Cardinal',
+        18: '018.Spotted_Catbird',
+        19: '019.Gray_Catbird',
+        20: '020.Yellow_breasted_Chat',
+        21: '021.Eastern_Towhee',
+        22: '022.Chuck_will_Widow',
+        23: '023.Brandt_Cormorant',
+        24: '024.Red_faced_Cormorant',
+        25: '025.Pelagic_Cormorant',
+        26: '026.Bronzed_Cowbird',
+        27: '027.Shiny_Cowbird',
+        28: '028.Brown_Creeper',
+        29: '029.American_Crow',
+        30: '030.Fish_Crow',
+        31: '031.Black_billed_Cuckoo',
+        32: '032.Mangrove_Cuckoo',
+        33: '033.Yellow_billed_Cuckoo',
+        34: '034.Gray_crowned_Rosy_Finch',
+        35: '035.Purple_Finch',
+        36: '036.Northern_Flicker',
+        37: '037.Acadian_Flycatcher',
+        38: '038.Great_Crested_Flycatcher',
+        39: '039.Least_Flycatcher',
+        40: '040.Olive_sided_Flycatcher',
+        41: '041.Scissor_tailed_Flycatcher',
+        42: '042.Vermilion_Flycatcher',
+        43: '043.Yellow_bellied_Flycatcher',
+        44: '044.Frigatebird',
+        45: '045.Northern_Fulmar',
+        46: '046.Gadwall',
+        47: '047.American_Goldfinch',
+        48: '048.European_Goldfinch',
+        49: '049.Boat_tailed_Grackle',
+        50: '050.Eared_Grebe',
+        51: '051.Horned_Grebe',
+        52: '052.Pied_billed_Grebe',
+        53: '053.Western_Grebe',
+        54: '054.Blue_Grosbeak',
+        55: '055.Evening_Grosbeak',
+        56: '056.Pine_Grosbeak',
+        57: '057.Rose_breasted_Grosbeak',
+        58: '058.Pigeon_Guillemot',
+        59: '059.California_Gull',
+        60: '060.Glaucous_winged_Gull',
+        61: '061.Heermann_Gull',
+        62: '062.Herring_Gull',
+        63: '063.Ivory_Gull',
+        64: '064.Ring_billed_Gull',
+        65: '065.Slaty_backed_Gull',
+        66: '066.Western_Gull',
+        67: '067.Anna_Hummingbird',
+        68: '068.Ruby_throated_Hummingbird',
+        69: '069.Rufous_Hummingbird',
+        70: '070.Green_Violetear',
+        71: '071.Long_tailed_Jaeger',
+        72: '072.Pomarine_Jaeger',
+        73: '073.Blue_Jay',
+        74: '074.Florida_Jay',
+        75: '075.Green_Jay',
+        76: '076.Dark_eyed_Junco',
+        77: '077.Tropical_Kingbird',
+        78: '078.Gray_Kingbird',
+        79: '079.Belted_Kingfisher',
+        80: '080.Green_Kingfisher',
+        81: '081.Pied_Kingfisher',
+        82: '082.Ringed_Kingfisher',
+        83: '083.White_breasted_Kingfisher',
+        84: '084.Red_legged_Kittiwake',
+        85: '085.Horned_Lark',
+        86: '086.Pacific_Loon',
+        87: '087.Mallard',
+        88: '088.Western_Meadowlark',
+        89: '089.Hooded_Merganser',
+        90: '090.Red_breasted_Merganser',
+        91: '091.Mockingbird',
+        92: '092.Nighthawk',
+        93: '093.Clark_Nutcracker',
+        94: '094.White_breasted_Nuthatch',
+        95: '095.Baltimore_Oriole',
+        96: '096.Hooded_Oriole',
+        97: '097.Orchard_Oriole',
+        98: '098.Scott_Oriole',
+        99: '099.Ovenbird',
+        100: '100.Brown_Pelican',
+        101: '101.White_Pelican',
+        102: '102.Western_Wood_Pewee',
+        103: '103.Sayornis',
+        104: '104.American_Pipit',
+        105: '105.Whip_poor_Will',
+        106: '106.Horned_Puffin',
+        107: '107.Common_Raven',
+        108: '108.White_necked_Raven',
+        109: '109.American_Redstart',
+        110: '110.Geococcyx',
+        111: '111.Loggerhead_Shrike',
+        112: '112.Great_Grey_Shrike',
+        113: '113.Baird_Sparrow',
+        114: '114.Black_throated_Sparrow',
+        115: '115.Brewer_Sparrow',
+        116: '116.Chipping_Sparrow',
+        117: '117.Clay_colored_Sparrow',
+        118: '118.House_Sparrow',
+        119: '119.Field_Sparrow',
+        120: '120.Fox_Sparrow',
+        121: '121.Grasshopper_Sparrow',
+        122: '122.Harris_Sparrow',
+        123: '123.Henslow_Sparrow',
+        124: '124.Le_Conte_Sparrow',
+        125: '125.Lincoln_Sparrow',
+        126: '126.Nelson_Sharp_tailed_Sparrow',
+        127: '127.Savannah_Sparrow',
+        128: '128.Seaside_Sparrow',
+        129: '129.Song_Sparrow',
+        130: '130.Tree_Sparrow',
+        131: '131.Vesper_Sparrow',
+        132: '132.White_crowned_Sparrow',
+        133: '133.White_throated_Sparrow',
+        134: '134.Cape_Glossy_Starling',
+        135: '135.Bank_Swallow',
+        136: '136.Barn_Swallow',
+        137: '137.Cliff_Swallow',
+        138: '138.Tree_Swallow',
+        139: '139.Scarlet_Tanager',
+        140: '140.Summer_Tanager',
+        141: '141.Artic_Tern',
+        142: '142.Black_Tern',
+        143: '143.Caspian_Tern',
+        144: '144.Common_Tern',
+        145: '145.Elegant_Tern',
+        146: '146.Forsters_Tern',
+        147: '147.Least_Tern',
+        148: '148.Green_tailed_Towhee',
+        149: '149.Brown_Thrasher',
+        150: '150.Sage_Thrasher',
+        151: '151.Black_capped_Vireo',
+        152: '152.Blue_headed_Vireo',
+        153: '153.Philadelphia_Vireo',
+        154: '154.Red_eyed_Vireo',
+        155: '155.Warbling_Vireo',
+        156: '156.White_eyed_Vireo',
+        157: '157.Yellow_throated_Vireo',
+        158: '158.Bay_breasted_Warbler',
+        159: '159.Black_and_white_Warbler',
+        160: '160.Black_throated_Blue_Warbler',
+        161: '161.Blue_winged_Warbler',
+        162: '162.Canada_Warbler',
+        163: '163.Cape_May_Warbler',
+        164: '164.Cerulean_Warbler',
+        165: '165.Chestnut_sided_Warbler',
+        166: '166.Golden_winged_Warbler',
+        167: '167.Hooded_Warbler',
+        168: '168.Kentucky_Warbler',
+        169: '169.Magnolia_Warbler',
+        170: '170.Mourning_Warbler',
+        171: '171.Myrtle_Warbler',
+        172: '172.Nashville_Warbler',
+        173: '173.Orange_crowned_Warbler',
+        174: '174.Palm_Warbler',
+        175: '175.Pine_Warbler',
+        176: '176.Prairie_Warbler',
+        177: '177.Prothonotary_Warbler',
+        178: '178.Swainson_Warbler',
+        179: '179.Tennessee_Warbler',
+        180: '180.Wilson_Warbler',
+        181: '181.Worm_eating_Warbler',
+        182: '182.Yellow_Warbler',
+        183: '183.Northern_Waterthrush',
+        184: '184.Louisiana_Waterthrush',
+        185: '185.Bohemian_Waxwing',
+        186: '186.Cedar_Waxwing',
+        187: '187.American_Three_toed_Woodpecker',
+        188: '188.Pileated_Woodpecker',
+        189: '189.Red_bellied_Woodpecker',
+        190: '190.Red_cockaded_Woodpecker',
+        191: '191.Red_headed_Woodpecker',
+        192: '192.Downy_Woodpecker',
+        193: '193.Bewick_Wren',
+        194: '194.Cactus_Wren',
+        195: '195.Carolina_Wren',
+        196: '196.House_Wren',
+        197: '197.Marsh_Wren',
+        198: '198.Rock_Wren',
+        199: '199.Winter_Wren',
+        200: '200.Common_Yellowthroat'
+    }
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "class_id": datasets.Value("int64"),
+                    "image_path": datasets.Value("string"),
+                    "class_name": datasets.Value("string"),
+                    "captions": [
+                        {
+                            "caption_eng": datasets.Value("string"),
+                            "caption_ind": datasets.Value("string"),
+                        }
+                    ]
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.image_text_features(label_names=list(self.IMAGE_CLASS.values()))
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # expect several minutes to download image data ~1.2GB
+        data_path = dl_manager.download_and_extract(_URLS)
+        # working with image dataset
+        image_meta = Path(data_path["image"]) / "CUB_200_2011" / "images.txt"
+        df_image = pd.read_csv(image_meta, sep=" ", names=["image_id", "image_path"])
+        df_image['image_path'] = df_image['image_path'].apply(lambda x: Path(image_meta.parent, 'images', x))
+        label_meta = Path(data_path["image"]) / "CUB_200_2011" / "image_class_labels.txt"
+        df_label = pd.read_csv(label_meta, sep=" ", names=["image_id", "class_id"])
+        # working with text dataset
+        text_path = Path(data_path["text"])
+        with open(text_path, "r") as f:
+            text_data = json.load(f)
+        df_text = pd.DataFrame([
+            {
+                'image_name': item['filename'],
+                'en_caption': caption['english'],
+                'id_caption': caption['indo']
+            } for item in text_data['dataset'] for caption in item['captions']
+        ])
+        grouped_text = df_text.groupby('image_name').agg(list).reset_index()
+        # working with split
+        split_dir = Path(data_path["image"]) / "CUB_200_2011" / "train_test_split.txt"
+        df_split = pd.read_csv(split_dir, sep=" ", names=["image_id", "is_train"])
+        # merge all data
+        df_image['image_name'] = df_image['image_path'].apply(lambda x: x.name)
+        df = pd.merge(df_image, grouped_text, on="image_name")
+        df.drop(columns=['image_name'], inplace=True)
+        df = pd.merge(df, df_label, on="image_id")
+        df = pd.merge(df, df_split, on="image_id")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data": df[df['is_train'] == 1],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data": df[df['is_train'] == 0],
+                    "split": "test",
+                },
+            ),
+        ]
+    def _generate_examples(self, data: pd.DataFrame, split: str) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            for key, row in data.iterrows():
+                example = {
+                    "image_id": row["image_id"],
+                    "class_id": row["class_id"],
+                    "image_path": row["image_path"],
+                    "class_name": self.IMAGE_CLASS[row["class_id"]],
+                    "captions": [
+                        {
+                            "caption_eng": row["en_caption"][i],
+                            "caption_ind": row["id_caption"][i],
+                        } for i in range(len(row["en_caption"]))
+                    ]
+                }
+                yield key, example
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            key = 0
+            for _, row in data.iterrows():
+                for i in range(len(row["id_caption"])):
+                    example = {
+                        "id": str(key),
+                        "image_paths": [row["image_path"]],
+                        "texts": row["id_caption"][i],
+                        "metadata": {
+                            "context": row["en_caption"][i],
+                            "labels": [self.IMAGE_CLASS[row["class_id"]]],
+                        }
+                    }
+                    yield key, example
+                    key += 1