# coding=utf-8 """Snacks Data Set""" import os import json import datasets from datasets.tasks import ImageClassification _CITATION = """ @misc{helff2023vlol, title={V-LoL: A Diagnostic Dataset for Visual Logical Learning}, author={Lukas Helff and Wolfgang Stammer and Hikaru Shindo and Devendra Singh Dhami and Kristian Kersting}, journal={Dataset available from https://sites.google.com/view/v-lol}, year={2023}, eprint={2306.07743}, archivePrefix={arXiv}, primaryClass={cs.AI} } """ _DESCRIPTION = "This is a diagnostic dataset for visual logical learning. " \ "It consists of 2D images of trains, where each train is either going eastbound or westbound. " \ "The trains are composed of multiple wagons, which are composed of multiple properties. " \ "The task is to predict the direction of the train. " \ "The dataset is designed to test the ability of machine learning models to learn logical rules from visual input." _HOMEPAGE = "https://huggingface.co/datasets/LukasHug/v-lol-trains/" _LICENSE = "cc-by-4.0" _IMAGES_URL = "https://huggingface.co/datasets/LukasHug/v-lol-trains/resolve/main/data" _DIR = _IMAGES_URL # _URL_DATA = { # "V-LoL-Trains-TheoryX": f"{_DIR}/V-LoL-Trains-TheoryX.zip", # "V-LoL-Trains-Numerical": f"{_DIR}/V-LoL-Trains-Numerical.zip", # "V-LoL-Trains-Complex": f"{_DIR}/V-LoL-Trains-Complex.zip", # "V-LoL-Blocks-TheoryX": f"{_DIR}/V-LoL-Blocks-TheoryX.zip", # "V-LoL-Blocks-Numerical": f"{_DIR}/V-LoL-Blocks-Numerical.zip", # "V-LoL-Blocks-Complex": f"{_DIR}/V-LoL-Blocks-Complex.zip", # "V-LoL-Trains-TheoryX-len7": f"{_DIR}/V-LoL-Trains-TheoryX-len7.zip", # "V-LoL-Trains-Numerical-len7": f"{_DIR}/V-LoL-Trains-Numerical-len7.zip", # "V-LoL-Trains-Complex-len7": f"{_DIR}/V-LoL-Trains-Complex-len7.zip", # "V-LoL-Random-Blocks-TheoryX": f"{_DIR}/V-LoL-Random-Blocks-TheoryX.zip", # "V-LoL-Random-Trains-TheoryX": f"{_DIR}/V-LoL-Random-Trains-TheoryX.zip", # } _URL_DATA = { "V-LoL-Trains-TheoryX": f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Trains-Numerical": f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Trains-Complex": f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Blocks-Numerical": f"{_DIR}/SimpleObjects_numerical_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Blocks-Complex": f"{_DIR}/SimpleObjects_complex_MichalskiTrains_base_scene_len_2-4.zip", "V-LoL-Trains-TheoryX-len7": {'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", 'test': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7-7.zip"}, "V-LoL-Trains-Numerical-len7": {'train': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_2-4.zip", 'test': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7-7.zip"}, "V-LoL-Trains-Complex-len7": {'train': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_2-4.zip", 'test': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7-7.zip"}, "V-LoL-Random-Blocks-TheoryX": {'train': f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip", 'test': f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_2-4.zip"}, "V-LoL-Random-Trains-TheoryX": {'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip", 'test': f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_2-4.zip"}, # "V-LoL-Trains-TheoryX-len7": f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7.zip", # "V-LoL-Trains-Numerical-len7": f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7.zip", # "V-LoL-Trains-Complex-len7": f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7.zip", # "V-LoL-Random-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_2-4.zip", # "V-LoL-Random-Trains-TheoryX": f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_2-4.zip", } _NAMES = ["westbound", "eastbound"] class VLoLConfig(datasets.BuilderConfig): """Builder Config for Food-101""" def __init__(self, data_url, **kwargs): """BuilderConfig for Food-101. Args: metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs **kwargs: keyword arguments forwarded to super. """ super(VLoLConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) if isinstance(data_url, dict): self.metadata_urls = data_url else: self.metadata_urls = {'train': data_url, 'test': None} class vloltrains(datasets.GeneratorBasedBuilder): '''v-lol-trains Data Set''' BUILDER_CONFIGS = [ VLoLConfig( name=name, description=name, data_url=data_url, ) for name, data_url in _URL_DATA.items() ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "image": datasets.Image(), "label": datasets.features.ClassLabel(names=_NAMES), } ), supervised_keys=("image", "label"), homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE, task_templates=ImageClassification(image_column="image", label_column="label"), ) def get_data(self, dl_manager, url): archive_path = os.path.join(dl_manager.download_and_extract(url), url.split('/')[-1].split('.')[0]) # print containg folders print(os.listdir(archive_path)) image_dir = os.path.join(archive_path, "images") metadata_pth = os.path.join(archive_path, "all_scenes", "all_scenes.json") images, y, trains, masks = [], [], [], [] # ds settings # load data with open(metadata_pth, 'r') as f: all_scenes = json.load(f) for scene in all_scenes['scenes']: images.append(scene['image_filename']) train = scene['train'] y.append(int(train.split(' ')[0] == 'east')) # depths.append(scene['depth_map_filename']) # if 'train' in scene: # # new json data format # train = scene['train'] # l = train.split(' ') # y = l[0] # y = int(l[0] == 'east') # train = MichalskiTrain.from_text(train, train_vis) # else: # # old json data format # train = scene['m_train'] # train = jsonpickle.decode(train) # # trains.append(train.replace('michalski_trains.m_train.', 'm_train.')) # # text = train.to_txt() # # t1 = MichalskiTrain.from_text(text, train_vis) # lab = int(train.get_label() == 'east') # y.append(lab) # trains.append(train) # masks.append(scene['car_masks']) return image_dir, y, images def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if self.config.metadata_urls['test'] is None: image_dir, y, images = self.get_data(dl_manager, self.config.metadata_urls['train']) image_dir_train, image_dir_test = image_dir, image_dir from sklearn.model_selection import train_test_split y_train, y_test, images_train, images_test = train_test_split(y, images, test_size=0.2, random_state=0) else: image_dir_train, y_train, images_train = self.get_data(dl_manager, self.config.metadata_urls['train']) image_dir_test, y_test, images_test = self.get_data(dl_manager, self.config.metadata_urls['test']) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"image_dir": image_dir_train, "labels": y_train, "images": images_train} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"image_dir": image_dir_test, "labels": y_test, "images": images_test} ), ] def _generate_examples(self, image_dir, labels, images): for i, (image, label) in enumerate(zip(images, labels)): yield i, {"image": os.path.join(image_dir, image), "label": label}