Add loading script

oe_dataset.py

@@ -0,0 +1,147 @@
+ # 
+ # 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"""
+
+from collections.abc import Sequence
+from typing import Optional, Generator, Tuple, IO
+
+import datasets
+
+
+# ---- 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/synthetic/photoreal/imgs"],
+            masks_urls = ["https://huggingface.co/datasets/ABC-iRobotics/oe_dataset/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"])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(train_imgs_paths),
+                    "masks": dl_manager.iter_archive(train_masks_paths),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(val_imgs_paths),
+                    "masks": dl_manager.iter_archive(val_masks_paths),
+                },
+            ),
+        ]
+
+    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()},
+            }