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import datasets
import json
from string import Template
from pathlib import Path
_HOMEPAGE = ""
_CITATION = ""
_LICENSE = ""
_DESCRIPTION_TEMPLATE = Template(
"$num_classes-way image classification task "
"to test domain shift of class $spurious_class from "
"context $source_context to $target_context. "
"Selected classes: $selected_classes"
)
_REPO = "https://huggingface.co/datasets/dgcnz/pcbm-metashift/resolve/main"
_IMAGES_DIR = Path("data")
class PCBMMetashiftConfig(datasets.BuilderConfig):
"""Builder Config for Food-101"""
def __init__(
self,
metadata_path: str,
selected_classes: list[str],
spurious_class: str,
source_context: str,
target_context: str,
**kwargs,
):
"""BuilderConfig for Food-101.
Args:
data_url: `string`, url to download the zip file from.
metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
**kwargs: keyword arguments forwarded to super.
"""
super(PCBMMetashiftConfig, self).__init__(
version=datasets.Version("1.0.0"), **kwargs
)
self.metadata_path = metadata_path
self.selected_classes = selected_classes
self.spurious_class = spurious_class
self.source_context = source_context
self.target_context = target_context
class PCBMMetashift(datasets.GeneratorBasedBuilder):
"""Food-101 Images dataset"""
BUILDER_CONFIGS = [
PCBMMetashiftConfig(
name="task_abcck_bed_cat_dog",
description="Task 1: bed(cat) -> bed(dog)",
metadata_path="configs/task_abcck_bed_cat_dog.json",
selected_classes=["airplane", "bed", "car", "cow", "keyboard"],
spurious_class="bed",
source_context="cat",
target_context="dog",
),
PCBMMetashiftConfig(
name="task_abcck_bed_dog_cat",
description="Task 1: bed(dog) -> bed(cat)",
metadata_path="configs/task_abcck_bed_dog_cat.json",
selected_classes=["airplane", "bed", "car", "cow", "keyboard"],
spurious_class="bed",
source_context="dog",
target_context="cat",
),
PCBMMetashiftConfig(
name="task_abcck_car_cat_dog",
description="Task 1: car(cat) -> car(dog)",
metadata_path="configs/task_abcck_car_cat_dog.json",
selected_classes=["airplane", "bed", "car", "cow", "keyboard"],
spurious_class="car",
source_context="cat",
target_context="dog",
),
PCBMMetashiftConfig(
name="task_abcck_car_dog_cat",
description="Task 1: car(dog) -> car(cat)",
metadata_path="configs/task_abcck_car_dog_cat.json",
selected_classes=["airplane", "bed", "car", "cow", "keyboard"],
spurious_class="car",
source_context="dog",
target_context="cat",
),
PCBMMetashiftConfig(
name="task_bcmst_table_books_cat",
description="Task 2: table(books) -> table(cat)",
metadata_path="configs/task_bcmst_table_books_cat.json",
selected_classes=["beach", "computer", "motorcycle", "stove", "table"],
spurious_class="table",
source_context="books",
target_context="cat",
),
PCBMMetashiftConfig(
name="task_bcmst_table_books_dog",
description="Task 2: table(books) -> table(dog)",
metadata_path="configs/task_bcmst_table_books_dog.json",
selected_classes=["beach", "computer", "motorcycle", "stove", "table"],
spurious_class="table",
source_context="books",
target_context="dog",
),
PCBMMetashiftConfig(
name="task_bcmst_table_cat_dog",
description="Task 2: table(cat) -> table(dog)",
metadata_path="configs/task_bcmst_table_cat_dog.json",
selected_classes=["beach", "computer", "motorcycle", "stove", "table"],
spurious_class="table",
source_context="cat",
target_context="dog",
),
PCBMMetashiftConfig(
name="task_bcmst_table_dog_cat",
description="Task 2: table(dog) -> table(cat)",
metadata_path="configs/task_bcmst_table_dog_cat.json",
selected_classes=["beach", "computer", "motorcycle", "stove", "table"],
spurious_class="table",
source_context="dog",
target_context="cat",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION_TEMPLATE.substitute(
num_classes=len(self.config.selected_classes),
spurious_class=self.config.spurious_class,
source_context=self.config.source_context,
target_context=self.config.target_context,
selected_classes=", ".join(self.config.selected_classes),
),
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=self.config.selected_classes),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
task_templates=[
datasets.ImageClassification(image_column="image", label_column="label")
],
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(f"{_REPO}/data/images.tar.gz")
metadata_path = dl_manager.download(f"{_REPO}/{self.config.metadata_path}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": dl_manager.iter_archive(archive_path),
"metadata_path": metadata_path,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": dl_manager.iter_archive(archive_path),
"metadata_path": metadata_path,
"split": "test",
},
),
]
def _generate_examples(self, images, metadata_path: str, split: str):
"""Generate images and labels for splits."""
with open(metadata_path, encoding="utf-8") as f:
metadata = json.load(f)
split_data = metadata["data_splits"][split]
ids_to_keep = set()
for _, ids in split_data.items():
ids_to_keep.update([Path(id).stem for id in ids])
files = dict()
for file_path, file_obj in images:
image_id = Path(file_path).stem
if image_id in ids_to_keep:
files[image_id] = (file_obj.read(), file_path)
for cls, ids in split_data.items():
for image_id in ids:
image_id = Path(image_id).stem
file_obj, file_path = files[image_id]
yield f"{cls}_{image_id}", {
"image": {"path": file_path, "bytes": file_obj},
"label": cls,
}
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