Datasets:
File size: 2,743 Bytes
06c63b4 dea2ab5 06c63b4 2f474c6 06c63b4 dea2ab5 5c084cc 06c63b4 dea2ab5 06c63b4 dea2ab5 06c63b4 dea2ab5 06c63b4 dea2ab5 06c63b4 6f870be dea2ab5 06c63b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import csv
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """"""
_DESCRIPTION = """Flickr30k filtered and translated to Persian made by Sajjad Ayoubi https://www.kaggle.com/datasets/sajjadayobi360/flickrfa"""
_DOWNLOAD_URLS = {
"train": "https://huggingface.co./datasets/hezarai/flickr30k-fa/resolve/main/annotations_train.csv",
"test": "https://huggingface.co./datasets/hezarai/flickr30k-fa/resolve/main/annotations_test.csv",
"data": "https://huggingface.co./datasets/hezarai/flickr30k-fa/resolve/main/images.zip",
}
ZIP_IMAGES_DIR = "images"
class Flickr30kFaConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(Flickr30kFaConfig, self).__init__(**kwargs)
class Flickr30kFa(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
Flickr30kFaConfig(
name="Persian flickr30k",
version=datasets.Version("1.0.0"),
description=_DESCRIPTION,
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image_path": datasets.Value("string"),
"label": datasets.Value("string"),
}
),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""
Return SplitGenerators.
"""
train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
archive_path = dl_manager.download(_DOWNLOAD_URLS["data"])
images_dir = dl_manager.extract(archive_path) if not dl_manager.is_streaming else ""
images_dir = os.path.join(images_dir, ZIP_IMAGES_DIR)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"annotations_file": train_path, "images_dir": images_dir}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"annotations_file": test_path, "images_dir": images_dir}
),
]
def _generate_examples(self, annotations_file, images_dir):
logger.info("⏳ Generating examples from = %s", annotations_file)
with open(annotations_file, encoding="utf-8") as csv_file:
csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
# Skip header
next(csv_reader, None)
for id_, row in enumerate(csv_reader):
filename, label = row
image_path = os.path.join(images_dir, filename)
yield id_, {"image_path": image_path, "label": label}
|