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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pathfinder-X2 Segmentation Benchmark."""
import os
import pandas as pd
import datasets
_CITATION = """\
@article{suard2023pathfinder,
title={Pathfinder-X2: A Challenging Dataset for Evaluating Large Language Models on Long-Range Dependencies},
author={Suard, Tyler},
journal={},
year={2023}
}
"""
_DESCRIPTION = """\
The rapid progress of large language models has led to impressive results in a wide array of tasks. However, there remains a need for increasingly challenging datasets to evaluate these models' ability to handle long-range dependencies. In this paper, we present Pathfinder-X2, a novel dataset that builds upon the Pathfinder and Pathfinder-X datasets. Pathfinder-X2 comprises 512x512 pixel images, designed to test large language models' capacity to segment a specific white line dash "snake" with a circle at its tip among a collection of similar, distractor snakes. The increased image resolution and complexity of Pathfinder-X2 present a substantially more challenging task for large language models, contributing to the ongoing development and assessment of such models.
"""
_HOMEPAGE = "https://huggingface.co/datasets/Tylersuard/PathfinderX2/"
_LICENSE = "C.C. B.Y. 4.0"
_URLS = {
"instance_segmentation": {
"images": "https://pathfinder-x2.s3.us-west-1.amazonaws.com/Pathfinder-X2+images.zip",
"annotations": "https://pathfinder-x2.s3.us-west-1.amazonaws.com/Pathfinder-X2+masks.zip",
},
}
class PathfinderX2(datasets.GeneratorBasedBuilder):
"""Pathfinder X2 Segmentation Benchmark dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="instance_segmentation", version=VERSION, description="The instance segmentation variant."
),
]
DEFAULT_CONFIG_NAME = "instance_segmentation"
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
"annotation": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dirs = dl_manager.download_and_extract(urls) # change from download to download_and_extract
train_data = os.path.join(data_dirs["images"]), os.path.join(
data_dirs["annotations"]
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data": train_data,
"split": "training",
},
),
]
def _generate_examples(self, data, split):
if split == "training":
images_dir, annotations_dir = data
image_dict = {}
annotation_dict = {}
# loads the annotations for the split into RAM (less than 100 MB) to support streaming
for root, _, files in os.walk(annotations_dir):
for file_annot in files:
if file_annot.endswith(".png"):
image_id = os.path.splitext(file_annot)[0]
annotation_dict[image_id] = os.path.join(root, file_annot)
idx = 0
for root, _, files in os.walk(images_dir):
for file_img in files:
if file_img.endswith(".png"):
image_id = os.path.splitext(file_img)[0]
image_dict[image_id] = os.path.join(root, file_img)
path_img = image_dict[image_id]
path_annot = annotation_dict[image_id]
with open(path_img, "rb") as f_img:
bytes_img = f_img.read()
with open(path_annot, "rb") as f_annot:
bytes_annot = f_annot.read()
yield idx, {
"image": {"path": path_img, "bytes": bytes_img},
"annotation": {"path": path_annot, "bytes": bytes_annot},
}
idx += 1 |