ecoset.py

import csv
import json
import os
import os.path as op
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




# TODO: Add BibTeX citation
# 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}
}
"""

# TODO: Add description of the dataset here
# 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.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://www.kietzmannlab.org/ecoset/"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC BY NC SA 2.0"

# TODO: Add link to the official 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": "s3://codeocean-datasets/0ab003f4-ff2d-4de3-b4f8-b6e349c0e5e5/ecoset.zip",
}

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']

# 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):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        
        features=datasets.Features(
            {
                "image": datasets.Image(),
                #"label": datasets.ClassLabel(names=list(IMAGENET2012_CLASSES.values())),
                "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
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
            task_templates=[datasets.tasks.ImageClassification(image_column="image", label_column="label")],
        )
    

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        password = getpass("\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 abslist(path):
            return [op.join(path, p) for p in os.listdir(path)]
        
        def s3_zipfile_download(source_url, target_dir):
            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):
            # download
            urlinfo = urlparse(source_url, allow_fragments=False)
            if not op.exists(target_dir):
                os.makedirs(target_dir)
            zip_path = op.join(target_dir, "ecoset.zip")
            s3 = boto3.client(urlinfo.scheme, config=Config(signature_version=UNSIGNED))
            s3.download_file(urlinfo.netloc, urlinfo.path[1:], zip_path)

            # unzip
            # Expand-Archive -LiteralPath <PathToZipFile> -DestinationPath <PathToDestination>
            subprocess.call(["unzip", "-P", password.encode("ascii"), "-o", zip_path, "-d", target_dir], shell=False)

        if platform.system() in ("Linux", "Darwin"):
            print('Using "fast" Linux/Mac Download and Unzipping. This will take about 15h on a typical Computer.')
            archives = dl_manager.download_custom(_URLS["codeocean"], subprocess_download)
        else:
            print('Using slow Windows Download and Unzipping. This can take up to 70h on a typical Computer. Sorry.')
            archives = dl_manager.download_custom(_URLS["codeocean"], s3_zipfile_download)
            
        #archives = dl_manager.download(_URLS["codeocean"])
        print(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