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import csv
import datasets
from datasets import DatasetDict
LABELS = {"aerial", "interior", "exterior", "upshot", "skyline", "night"}
_DATA_URL = {
"train": [f"data/train_images.tar.gz" for i in range(5)],
"validation": ["data/validation_images.tar.gz"],
"test": ["data/test_images.tar.gz"],
}
class Sample(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
def _info(self):
return datasets.DatasetInfo(
description="A sample dataset to illustrate how to use HF APIs",
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=list(LABELS)),
}
),
homepage="github.com/SOM-Enterprise/hf-dataset-sample-representation",
citation="None",
task_templates=[
datasets.ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager):
archives = dl_manager.download(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
"split": "train",
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"archives": [dl_manager.iter_archive(archive) for archive in archives["validation"]],
"split": "validation",
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"archives": [dl_manager.iter_archive(archive) for archive in archives["test"]],
"split": "test",
}
)
]
def _generate_examples(self, archives, split):
labels_dict = {}
with open('metadata.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
labels_dict[row['id']] = set(row['label'].split('|'))
idx = 0
for archive in archives:
for path, file in archive:
if path.endswith(".jpeg"):
if split != "test":
labels = labels_dict.get(path.split('/')[-1])
label = labels if labels else ['']
else:
label = -1
ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
yield idx, ex
idx += 1 |