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"""SMVB dataset""" |
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import sys |
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import io |
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import numpy as np |
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if sys.version_info < (3, 9): |
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from typing import Sequence, Generator, Tuple |
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else: |
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from collections.abc import Sequence, Generator |
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Tuple = tuple |
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from typing import Optional, IO |
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import datasets |
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import itertools |
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from huggingface_hub import HfFileSystem |
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_CITATION = """\ |
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@INPROCEEDINGS{karoly2024synthetic, |
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author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter}, |
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booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)}, |
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title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation}, |
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year={2024}, |
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volume={}, |
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number={}, |
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pages={}, |
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doi={}} |
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""" |
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_DESCRIPTION = """\ |
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Amultimodal video benchmark for evaluating models in multi-task learning scenarios. |
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""" |
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_HOMEPAGE = "https://huggingface.co/ABC-iRobotics/SMVB" |
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_LICENSE = "GNU General Public License v3.0" |
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_BASE_URL = "https://huggingface.co/" |
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_REPO = "datasets/ABC-iRobotics/SMVB" |
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_RESOURCE = "/resolve/main" |
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_VERSION = "1.0.0" |
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class SMVBDatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SMVB dataset.""" |
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def __init__(self, name: str, version: Optional[str] = None, **kwargs): |
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super(SMVBDatasetConfig, self).__init__(version=datasets.Version(version), name=name, **kwargs) |
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fs = HfFileSystem() |
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tarfiles = sorted(fs.glob(_REPO + "/**.tar.gz")) |
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self._data_urls = [p.replace(_REPO,_BASE_URL+_REPO+_RESOURCE) for p in tarfiles] |
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@property |
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def features(self): |
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return datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"mask": datasets.Image(), |
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"depth": datasets.Sequence(datasets.Value("float32")), |
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"flow": datasets.Sequence(datasets.Value("float32")), |
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"normal": datasets.Sequence(datasets.Value("float32")) |
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} |
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) |
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@property |
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def keys(self): |
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return ("image", "mask", "depth", "flow", "normal") |
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class SMVBDataset(datasets.GeneratorBasedBuilder): |
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"""SMVB dataset.""" |
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BUILDER_CONFIG_CLASS = SMVBDatasetConfig |
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BUILDER_CONFIGS = [ |
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SMVBDatasetConfig( |
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name = "all", |
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description = "Synthetic data with rich annotations", |
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version = _VERSION |
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), |
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] |
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DEFAULT_WRITER_BATCH_SIZE = 10 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=self.config.features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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version=self.config.version, |
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) |
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def _split_generators(self, dl_manager): |
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local_data_paths = dl_manager.download(self.config._data_urls) |
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local_data_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in local_data_paths]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data": local_data_gen |
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} |
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) |
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] |
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def _generate_examples( |
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self, |
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data: Generator[Tuple[str,IO], None, None] |
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): |
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file_infos = [] |
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keys = self.config.keys |
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for i, info in enumerate(data): |
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file_path, file_object = info |
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if i%len(keys) < 2: |
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file_infos.append((file_path, file_object.read())) |
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else: |
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file_infos.append((file_path, np.load(io.BytesIO(file_object.read())).flatten() if i%len(keys) == 3 else [0])) |
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if (i+1)%len(keys) == 0: |
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img_features_dict = {k:{'path':d[0],'bytes':d[1]} for k,d in zip(keys,file_infos) if k in ['image','mask']} |
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array_features_dict = {k:d[1] for k,d in zip(keys,file_infos) if not k in ['image','mask']} |
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data_dict = {**img_features_dict, **array_features_dict} |
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yield (i//len(keys))-1, data_dict |
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file_infos = [] |
