import sys import csv import json import os import os.path as op import base64 import zipfile from getpass import getpass from tqdm import tqdm import platform import subprocess from urllib.parse import urlparse import datasets from datasets.filesystems import S3FileSystem import boto3 from botocore import UNSIGNED from botocore.client import Config # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{mehrer2021ecologically, title={An ecologically motivated image dataset for deep learning yields better models of human vision}, author={Mehrer, Johannes and Spoerer, Courtney J and Jones, Emer C and Kriegeskorte, Nikolaus and Kietzmann, Tim C}, journal={Proceedings of the National Academy of Sciences}, volume={118}, number={8}, year={2021}, publisher={National Acad Sciences} } """ # You can copy an official description _DESCRIPTION = """\ Tired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images from 565 basic level categories, chosen to be both (i) frequent in linguistic usage, and (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’ is not). Here we collect resources associated with ecoset. This includes the dataset, trained deep neural network models, code to interact with them, and published papers using it. """ # official homepage for the dataset here _HOMEPAGE = "https://www.kietzmannlab.org/ecoset/" # licence for the dataset here _LICENSE = "CC BY NC SA 2.0" # dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { #"codeocean": "https://files.codeocean.com/datasets/verified/0ab003f4-ff2d-4de3-b4f8-b6e349c0e5e5/ecoset.zip?download", # codeocean cancels after 50GB "codeocean": "s3://codeocean-datasets/0ab003f4-ff2d-4de3-b4f8-b6e349c0e5e5/ecoset.zip", } # Define the labels available for ecoset labels = ['cymbals', 'bison', 'lemonade', 'crib', 'chestnut', 'mosquito', 'aloe', 'extinguisher', 'onion', 'starfish', 'basket', 'jar', 'snail', 'mushroom', 'coffin', 'joystick', 'raspberry', 'gearshift', 'tyrannosaurus', 'stadium', 'telescope', 'blueberry', 'hippo', 'cannabis', 'hairbrush', 'river', 'artichoke', 'wallet', 'city', 'bee', 'rifle', 'boar', 'bib', 'envelope', 'silverfish', 'shower', 'curtain', 'pinwheel', 'guillotine', 'snowplow', 'hut', 'jukebox', 'gecko', 'marshmallow', 'lobster', 'flashlight', 'breadfruit', 'cow', 'spoon', 'blender', 'croissant', 'greenhouse', 'church', 'antenna', 'monkey', 'zucchini', 'snake', 'manatee', 'child', 'table', 'winterberry', 'sloth', 'cannon', 'baguette', 'persimmon', 'candelabra', 'necklace', 'flag', 'geyser', 'thermos', 'tweezers', 'chandelier', 'kebab', 'mailbox', 'steamroller', 'crayon', 'lawnmower', 'pomegranate', 'fire', 'violin', 'matchstick', 'train', 'hamster', 'bobsleigh', 'boat', 'bullet', 'forklift', 'clock', 'saltshaker', 'anteater', 'crowbar', 'lightbulb', 'pier', 'muffin', 'paintbrush', 'crawfish', 'bench', 'nectarine', 'eyedropper', 'backpack', 'goat', 'hotplate', 'fishnet', 'robot', 'rice', 'shovel', 'candle', 'blimp', 'bridge', 'mountain', 'coleslaw', 'stagecoach', 'waterfall', 'ladle', 'radiator', 'drain', 'tray', 'house', 'key', 'skunk', 'lake', 'earpiece', 'gazebo', 'blackberry', 'groundhog', 'paperclip', 'cookie', 'milk', 'rug', 'thermostat', 'milkshake', 'scoreboard', 'bean', 'giraffe', 'antelope', 'newsstand', 'camcorder', 'sawmill', 'balloon', 'ladder', 'videotape', 'microphone', 'coin', 'hay', 'moth', 'octopus', 'honeycomb', 'wrench', 'cane', 'bobcat', 'banner', 'newspaper', 'reef', 'worm', 'cucumber', 'beach', 'couch', 'streetlamp', 'rhino', 'ceiling', 'cupcake', 'hourglass', 'caterpillar', 'tamale', 'asparagus', 'flower', 'frog', 'dog', 'knife', 'lamp', 'walnut', 'grape', 'scone', 'peanut', 'ferret', 'kettle', 'elephant', 'oscilloscope', 'weasel', 'guava', 'gramophone', 'stove', 'bamboo', 'chicken', 'guacamole', 'toolbox', 'tractor', 'tiger', 'butterfly', 'coffeepot', 'bus', 'meteorite', 'fish', 'graveyard', 'blowtorch', 'grapefruit', 'cat', 'jellyfish', 'carousel', 'wheat', 'tadpole', 'kazoo', 'raccoon', 'typewriter', 'scissors', 'pothole', 'earring', 'drawers', 'cup', 'warthog', 'wall', 'lighthouse', 'burrito', 'cassette', 'nacho', 'sink', 'seashell', 'bed', 'noodles', 'woman', 'rabbit', 'fence', 'pistachio', 'pencil', 'hotdog', 'ball', 'ship', 'strawberry', 'pan', 'custard', 'dolphin', 'tent', 'bun', 'tortilla', 'tumbleweed', 'playground', 'scallion', 'anchor', 'hare', 'waterspout', 'dough', 'burner', 'kale', 'razor', 'chocolate', 'doughnut', 'squeegee', 'bandage', 'beaver', 'refrigerator', 'cork', 'anvil', 'microchip', 'banana', 'thumbtack', 'chair', 'sharpener', 'bird', 'castle', 'wand', 'doormat', 'celery', 'steak', 'ant', 'apple', 'cave', 'scaffolding', 'bell', 'towel', 'mantis', 'thimble', 'bowl', 'chess', 'pickle', 'lollypop', 'leek', 'barrel', 'dollhouse', 'tapioca', 'spareribs', 'fig', 'apricot', 'strongbox', 'brownie', 'beaker', 'manhole', 'piano', 'whale', 'hammer', 'dishrag', 'pecan', 'highlighter', 'pretzel', 'earwig', 'cogwheel', 'trashcan', 'syringe', 'turnip', 'pear', 'lettuce', 'hedgehog', 'guardrail', 'bubble', 'pineapple', 'burlap', 'moon', 'spider', 'fern', 'binoculars', 'gravel', 'plum', 'scorpion', 'cube', 'squirrel', 'book', 'crouton', 'bag', 'lantern', 'parsley', 'jaguar', 'thyme', 'oyster', 'kumquat', 'chinchilla', 'cherry', 'umbrella', 'bicycle', 'eggbeater', 'pig', 'kitchen', 'fondue', 'treadmill', 'casket', 'papaya', 'beetle', 'shredder', 'grasshopper', 'anthill', 'chili', 'bottle', 'calculator', 'gondola', 'pizza', 'compass', 'mop', 'hamburger', 'chipmunk', 'bagel', 'outhouse', 'pliers', 'wolf', 'matchbook', 'corn', 'salamander', 'lasagna', 'stethoscope', 'eggroll', 'avocado', 'eggplant', 'mouse', 'walrus', 'sprinkler', 'glass', 'cauldron', 'parsnip', 'canoe', 'pancake', 'koala', 'deer', 'chalk', 'urinal', 'toilet', 'cabbage', 'platypus', 'lizard', 'leopard', 'cake', 'hammock', 'defibrillator', 'sundial', 'beet', 'popcorn', 'spinach', 'cauliflower', 'canyon', 'spacecraft', 'teapot', 'tunnel', 'porcupine', 'jail', 'spearmint', 'dustpan', 'calipers', 'toast', 'drum', 'phone', 'wire', 'alligator', 'vase', 'motorcycle', 'toothpick', 'coconut', 'lion', 'turtle', 'cheetah', 'bugle', 'casino', 'fountain', 'pie', 'bread', 'meatball', 'windmill', 'gun', 'projector', 'chameleon', 'tomato', 'nutmeg', 'plate', 'bulldozer', 'camel', 'sphinx', 'mall', 'hanger', 'ukulele', 'wheelbarrow', 'ring', 'dildo', 'loudspeaker', 'odometer', 'ruler', 'mousetrap', 'breadbox', 'parachute', 'bolt', 'bracelet', 'library', 'otter', 'airplane', 'pea', 'tongs', 'cactus', 'knot', 'shrimp', 'computer', 'sheep', 'television', 'melon', 'kangaroo', 'helicopter', 'birdcage', 'pumpkin', 'dishwasher', 'crocodile', 'stairs', 'garlic', 'barnacle', 'crate', 'lime', 'axe', 'hairpin', 'egg', 'emerald', 'candy', 'stegosaurus', 'broom', 'mistletoe', 'submarine', 'fireworks', 'peach', 'ape', 'chalkboard', 'bumblebee', 'potato', 'battery', 'guitar', 'opossum', 'volcano', 'llama', 'ashtray', 'sieve', 'coliseum', 'cinnamon', 'moose', 'tree', 'donkey', 'wasp', 'corkscrew', 'gargoyle', 'taco', 'macadamia', 'camera', 'mandolin', 'kite', 'cranberry', 'thermometer', 'tofu', 'closet', 'hovercraft', 'escalator', 'horseshoe', 'wristwatch', 'lemon', 'sushi', 'rat', 'rainbow', 'pillow', 'radish', 'granola', 'okra', 'pastry', 'mango', 'dragonfly', 'flashbulb', 'chalice', 'acorn', 'birdhouse', 'gooseberry', 'locker', 'padlock', 'missile', 'clarinet', 'panda', 'iceberg', 'road', 'flea', 'hazelnut', 'cockroach', 'needle', 'omelet', 'desert', 'condom', 'graffiti', 'iguana', 'bucket', 'photocopier', 'blanket', 'microscope', 'horse', 'nest', 'screwdriver', 'toaster', 'car', 'doll', 'salsa', 'man', 'zebra', 'stapler', 'grate', 'truck', 'bear', 'carrot', 'auditorium', 'cashew', 'shield', 'crown', 'altar', 'pudding', 'cheese', 'rhubarb', 'broccoli', 'tower', 'cumin', 'elevator', 'wheelchair', 'flyswatter'] # handle password entry _PWD_MSG = "\nIn order to use ecoset, please read the README and License agreement found under:\nhttps://codeocean.com/capsule/9570390\nand enter the mentioned password.\n\nPlease Enter Password:\n" def check_pass(pw): if base64.b64encode(pw.encode("ascii")) != (b"ZWNvc2V0X21zamtr"): raise AttributeError("Wrong password! Please try again.") else: print("Password correct.\n") # Name of the dataset usually match the script name with CamelCase instead of snake_case class Ecoset(datasets.GeneratorBasedBuilder): """Ecoset is a large clean and ecologically valid image dataset.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="Full", version=VERSION, description="We could do different splits of the dataset here. But we don't"), ] DEFAULT_CONFIG_NAME = "Full" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # define dataset features features=datasets.Features( { "image": datasets.Image(), "label": datasets.ClassLabel(names=labels), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, # creating a template task_templates=[datasets.tasks.ImageClassification(image_column="image", label_column="label")], ) def _split_generators(self, dl_manager): # Ask password of user. This could be also handled through dataset config def abslist(path): """Helper function to give abspaths of os.listdir""" return [op.join(path, p) for p in os.listdir(path)] def subprocess_call_print(command_list): """Execute a subprocess while printing the command line output""" p = subprocess.Popen(command_list, stdout=subprocess.PIPE) while True: line = p.stdout.readline() if not line: break print(line.strip()) sys.stdout.flush() def s3_zipfile_download(source_url, target_dir): """Extremely slow download""" # ask password password = getpass(_PWD_MSG) check_pass(password) # download and unzip print('Using slow Python-based download and unzipping. This can take up to 70h on a typical computer. Sorry.') s3 = S3FileSystem(anon=True, use_ssl=False, default_block_size=int(15 * 2**20)) with s3.open(source_url, "rb") as raw_filw: with ZipFile(raw_filw, compression=zipfile.ZIP_DEFLATED, allowZip64=True) as zip_file: member_list = zip_file.namelist() for member in tqdm(member_list, total=len(member_list), desc="Extracting ecoset to disc"): zip_file.extract(member, target_dir, pwd=password.encode("ascii")) def subprocess_download(source_url, target_dir): """Moderately slow download""" # ask password password = getpass(_PWD_MSG) check_pass(password) # download print('Using native OS unzipping. This will take about 15h on a typical Linux/Mac and 8h on a typical Windows Computer.') urlinfo = urlparse(source_url, allow_fragments=False) # create destination path if not existing if not op.exists(target_dir): os.makedirs(target_dir) # download zip file if not existing zip_path = op.join(target_dir, "ecoset.zip") if not op.exists(zip_path): s3 = boto3.client(urlinfo.scheme, config=Config(signature_version=UNSIGNED)) object_size = s3.head_object(Bucket=urlinfo.netloc, Key=urlinfo.path[1:])["ContentLength"] with tqdm(total=object_size, unit="MB", unit_scale=True, desc="Downloading ecoset") as pbar: s3.download_file(Bucket=urlinfo.netloc, Key=urlinfo.path[1:], Filename=zip_path, Callback=lambda bytes_transferred: pbar.update(bytes_transferred)) # unzip using platform-based subprocess if platform.system() in ("Linux", "Darwin"): #subprocess.call(["unzip", "-n", "-P", password.encode("ascii"), "-o", zip_path, "-d", target_dir], shell=False) subprocess_call_print(["unzip", "-n", "-P", password.encode("ascii"), "-o", zip_path, "-d", target_dir]) else: #subprocess.call(["tar.exe", "-xf", zip_path, "-C", target_dir, "--passphrase", password], shell=False) subprocess_call_print(["tar.exe", "-xf", zip_path, "-C", target_dir, "--passphrase", password]) def subprocess_download2(source_url, target_dir): """Moderately slow download""" # ask password password = getpass(_PWD_MSG) check_pass(password) # download print('Using native OS unzipping. This will take about 15h on a typical Linux/Mac and 8h on a typical Windows Computer.') urlinfo = urlparse(source_url, allow_fragments=False) # create destination path if not existing if not op.exists(target_dir): os.makedirs(target_dir) # download zip file if not existing zip_path = op.join(target_dir, "ecoset.zip") if not op.exists(zip_path): s3 = boto3.client(urlinfo.scheme, config=Config(signature_version=UNSIGNED)) s3.download_file(urlinfo.netloc, urlinfo.path[1:], zip_path) # unzip using platform-based subprocess if platform.system() in ("Linux", "Darwin"): subprocess.call(["unzip", "-n", "-P", password.encode("ascii"), "-o", zip_path, "-d", target_dir], shell=False) else: subprocess.call(["tar.exe", "-xf", zip_path, "-C", target_dir, "--passphrase", password], shell=False) # download and unzip using subprocess. S3 download was discontinued due to being extremely slow archives = dl_manager.download_custom(_URLS["codeocean"], subprocess_download) print("Ecoset files are stored under: \n", archives) # create a dict containing all files split_dict = {split:[] for split in ("train", "val", "test")} for split in split_dict.keys(): fnames = abslist(op.join(archives, split)) for f in fnames: split_dict[split].extend(abslist(f)) # return data splits return [datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "archives": split_dict["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "archives": split_dict["val"], "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "archives": split_dict["test"], "split": "test", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, archives, split): """Yields examples.""" idx = 0 for archive in archives: if any(archive.endswith(i) for i in (".JPEG", ".JPG", ".jpeg", ".jpg")): # extract file, label, etc file = open(archive, 'rb') synset_id, label = archive.split("/")[-2].split("_") ex = {"image": {"path": archive, "bytes": file.read()}, "label": label} yield idx, ex idx += 1