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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 "
"source context $source_context to a target context without $source_context "
"Selected classes: $selected_classes"
)
_REPO = "https://huggingface.co/datasets/dgcnz/pcbm_survey/resolve/main"
class PCBMSurveyConfig(datasets.BuilderConfig):
"""Builder Config for PCBMSurvey"""
def __init__(
self,
metadata_path: str,
selected_classes: list[str],
spurious_class: str,
source_context: str,
**kwargs,
):
super(PCBMSurveyConfig, 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
class PCBMSurvey(datasets.GeneratorBasedBuilder):
"""PCBM Metashift Survey Images dataset"""
"""
task_1_bed_dog.json | airplane, bed, car, cow, keyboard | bed(dog)
task_2_keyboard_cat.json | beach, bus, airplane, keyboard, bird | keyboard(cat)
task_3_bed_cat.json | beach, car, airplane, bed, bird | bed(cat)
task_4_couch_cat.json | beach, motorcycle, airplane, couch, bird | couch(cat)
task_5_painting_lamp.json | bus, painting, cat, computer, snowboard | painting(lamp)
task_6_pillow_clock.json | bus, pillow, cat, computer, snowboard | pillow(clock)
task_7_television_fireplace.json | bus, television, cat, computer, snowboard | television(fireplace)
task_8_fork_tomato.json | car, fork, table, bed, computer | fork(tomato)
task_9_car_snow.json | dog, car, airplane, couch, bird | car(snow)
"""
BUILDER_CONFIGS = [
PCBMSurveyConfig(
name="task_1_bed_dog",
metadata_path="task_1_bed_dog.json",
selected_classes=["airplane", "bed", "car", "cow", "keyboard"],
spurious_class="bed",
source_context="dog",
),
PCBMSurveyConfig(
name="task_2_keyboard_cat",
metadata_path="task_2_keyboard_cat.json",
selected_classes=["beach", "bus", "airplane", "keyboard", "bird"],
spurious_class="keyboard",
source_context="cat",
),
PCBMSurveyConfig(
name="task_3_bed_cat",
metadata_path="task_3_bed_cat.json",
selected_classes=["beach", "car", "airplane", "bed", "bird"],
spurious_class="bed",
source_context="cat",
),
PCBMSurveyConfig(
name="task_4_couch_cat",
metadata_path="task_4_couch_cat.json",
selected_classes=["beach", "motorcycle", "airplane", "couch", "bird"],
spurious_class="couch",
source_context="cat",
),
PCBMSurveyConfig(
name="task_5_painting_lamp",
metadata_path="task_5_painting_lamp.json",
selected_classes=["bus", "painting", "cat", "computer", "snowboard"],
spurious_class="painting",
source_context="lamp",
),
PCBMSurveyConfig(
name="task_6_pillow_clock",
metadata_path="task_6_pillow_clock.json",
selected_classes=["bus", "pillow", "cat", "computer", "snowboard"],
spurious_class="pillow",
source_context="clock",
),
PCBMSurveyConfig(
name="task_7_television_fireplace",
metadata_path="task_7_television_fireplace.json",
selected_classes=["bus", "television", "cat", "computer", "snowboard"],
spurious_class="television",
source_context="fireplace",
),
PCBMSurveyConfig(
name="task_8_fork_tomato",
metadata_path="task_8_fork_tomato.json",
selected_classes=["car", "fork", "table", "bed", "computer"],
spurious_class="fork",
source_context="tomato",
),
PCBMSurveyConfig(
name="task_9_car_snow",
metadata_path="task_9_car_snow.json",
selected_classes=["dog", "car", "airplane", "couch", "bird"],
spurious_class="car",
source_context="snow",
),
]
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,
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}/scenarios/{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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