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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 |