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"""Snacks Data Set""" |
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import os |
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import json |
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import datasets |
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from datasets.tasks import ImageClassification |
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_CITATION = """ |
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@misc{helff2023vlol, |
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title={V-LoL: A Diagnostic Dataset for Visual Logical Learning}, |
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author={Lukas Helff and Wolfgang Stammer and Hikaru Shindo and Devendra Singh Dhami and Kristian Kersting}, |
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journal={Dataset available from https://sites.google.com/view/v-lol}, |
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year={2023}, |
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eprint={2306.07743}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI} |
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} |
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""" |
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_DESCRIPTION = "This is a diagnostic dataset for visual logical learning. " \ |
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"It consists of 2D images of trains, where each train is either going eastbound or westbound. " \ |
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"The trains are composed of multiple wagons, which are composed of multiple properties. " \ |
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"The task is to predict the direction of the train. " \ |
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"The dataset is designed to test the ability of machine learning models to learn logical rules from visual input." |
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_HOMEPAGE = "https://huggingface.co/datasets/LukasHug/v-lol-trains/" |
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_LICENSE = "cc-by-4.0" |
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_IMAGES_URL = "https://huggingface.co/datasets/LukasHug/v-lol-trains/resolve/main/data" |
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_DIR = _IMAGES_URL |
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_URL_DATA = { |
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"V-LoL-Trains-TheoryX": f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Trains-Numerical": f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Trains-Complex": f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Blocks-Numerical": f"{_DIR}/SimpleObjects_numerical_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Blocks-Complex": f"{_DIR}/SimpleObjects_complex_MichalskiTrains_base_scene_len_2-4.zip", |
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"V-LoL-Trains-TheoryX-len7": |
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{'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", |
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'test': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7-7.zip"}, |
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"V-LoL-Trains-Numerical-len7": |
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{'train': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_2-4.zip", |
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'test': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7-7.zip"}, |
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"V-LoL-Trains-Complex-len7": |
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{'train': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_2-4.zip", |
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'test': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7-7.zip"}, |
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"V-LoL-Random-Blocks-TheoryX": |
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{'train': f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip", |
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'test': f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_2-4.zip"}, |
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"V-LoL-Random-Trains-TheoryX": |
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{'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", |
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'test': f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_2-4.zip"}, |
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} |
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_NAMES = ["westbound", "eastbound"] |
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class VLoLConfig(datasets.BuilderConfig): |
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"""Builder Config for Food-101""" |
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def __init__(self, data_url, **kwargs): |
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"""BuilderConfig for Food-101. |
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Args: |
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metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(VLoLConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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if isinstance(data_url, dict): |
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self.metadata_urls = data_url |
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else: |
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self.metadata_urls = {'train': data_url, 'test': None} |
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class vloltrains(datasets.GeneratorBasedBuilder): |
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'''v-lol-trains Data Set''' |
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BUILDER_CONFIGS = [ |
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VLoLConfig( |
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name=name, |
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description=name, |
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data_url=data_url, |
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) for name, data_url in _URL_DATA.items() |
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] |
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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=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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task_templates=ImageClassification(image_column="image", label_column="label"), |
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) |
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def get_data(self, dl_manager, url): |
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archive_path = os.path.join(dl_manager.download_and_extract(url), url.split('/')[-1].split('.')[0]) |
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print(os.listdir(archive_path)) |
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image_dir = os.path.join(archive_path, "images") |
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metadata_pth = os.path.join(archive_path, "all_scenes", "all_scenes.json") |
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images, y, trains, masks = [], [], [], [] |
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with open(metadata_pth, 'r') as f: |
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all_scenes = json.load(f) |
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for scene in all_scenes['scenes']: |
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images.append(scene['image_filename']) |
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train = scene['train'] |
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y.append(int(train.split(' ')[0] == 'east')) |
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return image_dir, y, images |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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if self.config.metadata_urls['test'] is None: |
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image_dir, y, images = self.get_data(dl_manager, self.config.metadata_urls['train']) |
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image_dir_train, image_dir_test = image_dir, image_dir |
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from sklearn.model_selection import train_test_split |
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y_train, y_test, images_train, images_test = train_test_split(y, images, test_size=0.2, random_state=0) |
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else: |
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image_dir_train, y_train, images_train = self.get_data(dl_manager, self.config.metadata_urls['train']) |
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image_dir_test, y_test, images_test = self.get_data(dl_manager, self.config.metadata_urls['test']) |
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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={"image_dir": image_dir_train, "labels": y_train, "images": images_train} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"image_dir": image_dir_test, "labels": y_test, "images": images_test} |
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), |
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] |
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def _generate_examples(self, image_dir, labels, images): |
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for i, (image, label) in enumerate(zip(images, labels)): |
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yield i, {"image": os.path.join(image_dir, image), "label": label} |
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