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#
# This file is part of the oe_dataset distribution (https://huggingface.co/datasets/ABC-iRobotics/oe_dataset).
# Copyright (c) 2023 ABC-iRobotics.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
"""OE dataset"""
import sys
if sys.version_info < (3, 9):
from typing import Sequence, Generator, Tuple
else:
from collections.abc import Sequence, Generator
Tuple = tuple
from typing import Optional, IO
import datasets
import itertools
# ---- Constants ----
_CITATION = """\
@ARTICLE{10145828,
author={Károly, Artúr István and Tirczka, Sebestyén and Gao, Huijun and Rudas, Imre J. and Galambos, Péter},
journal={IEEE Transactions on Cybernetics},
title={Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data},
year={2023},
volume={},
number={},
pages={1-14},
doi={10.1109/TCYB.2023.3276485}}
"""
_DESCRIPTION = """\
An instance segmentation dataset for robotic manipulation in a tabletop environment.
The dataset incorporates real and synthetic images for testing sim-to-real model transfer after fine-tuning.
"""
_HOMEPAGE = "https://huggingface.co/ABC-iRobotics/oe_dataset"
_LICENSE = "GNU General Public License v3.0"
_LATEST_VERSIONS = {
"all": "1.0.0",
"real": "1.0.0",
"synthetic": "1.0.0",
"photoreal": "1.0.0",
"random": "1.0.0",
}
# ---- OE dataset Configs ----
class OEDatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for OE dataset."""
def __init__(self, name: str, imgs_urls: Sequence[str], masks_urls: Sequence[str], version: Optional[str] = None, **kwargs):
_version = _LATEST_VERSIONS[name] if version is None else version
_name = f"{name}_v{_version}"
super(OEDatasetConfig, self).__init__(version=datasets.Version(_version), name=_name, **kwargs)
self._imgs_urls = {"train": [url + "/train.tar.gz" for url in imgs_urls], "val": [url + "/val.tar.gz" for url in imgs_urls]}
self._masks_urls = {"train": [url + "/train.tar.gz" for url in masks_urls], "val": [url + "/val.tar.gz" for url in masks_urls]}
@property
def features(self):
return datasets.Features(
{
"image": datasets.Image(),
"mask": datasets.Image(),
}
)
@property
def supervised_keys(self):
return ("image", "mask")
# ---- OE dataset Loader ----
class OEDataset(datasets.GeneratorBasedBuilder):
"""OE dataset."""
BUILDER_CONFIG_CLASS = OEDatasetConfig
BUILDER_CONFIGS = [
OEDatasetConfig(
name = "photoreal",
description = "Photorealistic synthetic images",
imgs_urls = ["https://huggingface.co/datasets/ABC-iRobotics/oe_dataset/resolve/main/synthetic/photoreal/imgs"],
masks_urls = ["https://huggingface.co/datasets/ABC-iRobotics/oe_dataset/resolve/main/synthetic/photoreal/masks"]
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=self.config.features,
supervised_keys=self.config.supervised_keys,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=self.config.version,
)
def _split_generators(self, dl_manager):
train_imgs_paths = dl_manager.download(self.config._imgs_urls["train"])
val_imgs_paths = dl_manager.download(self.config._imgs_urls["val"])
train_masks_paths = dl_manager.download(self.config._masks_urls["train"])
val_masks_paths = dl_manager.download(self.config._masks_urls["val"])
train_imgs_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in train_imgs_paths])
val_imgs_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in val_imgs_paths])
train_masks_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in train_masks_paths])
val_masks_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in val_masks_paths])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": train_imgs_gen,
"masks": train_masks_gen,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"images": val_imgs_gen,
"masks": val_masks_gen,
},
),
]
def _generate_examples(
self,
images: Generator[Tuple[str,IO], None, None],
masks: Generator[Tuple[str,IO], None, None],
):
for i, (img_info, mask_info) in enumerate(zip(images, masks)):
img_file_path, img_file_obj = img_info
mask_file_path, mask_file_obj = mask_info
yield i, {
"image": {"path": img_file_path, "bytes": img_file_obj.read()},
"mask": {"path": mask_file_path, "bytes": mask_file_obj.read()},
} |