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"""Imagenette dataset.""" |
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import os |
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import json |
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import datasets |
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_HOMEPAGE = "https://github.com/fastai/imagenette" |
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_LICENSE = "Apache License 2.0" |
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_CITATION = """\ |
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@software{Howard_Imagenette_2019, |
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title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet}, |
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author={Jeremy Howard}, |
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year={2019}, |
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month={March}, |
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publisher = {GitHub}, |
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url = {https://github.com/fastai/imagenette} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Imagenette is a subset of 10 easily classified classes from Imagenet |
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(tench, English springer, cassette player, chain saw, church, French |
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horn, garbage truck, gas pump, golf ball, parachute). |
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""" |
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_LABEL_MAP = [ |
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'n01440764', |
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'n02102040', |
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'n02979186', |
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'n03000684', |
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'n03028079', |
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'n03394916', |
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'n03417042', |
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'n03425413', |
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'n03445777', |
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'n03888257', |
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] |
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_REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata" |
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class OpenFireConfig(datasets.BuilderConfig): |
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"""BuilderConfig for OpenFire.""" |
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def __init__(self, data_url, metadata_urls, **kwargs): |
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"""BuilderConfig for OpenFire. |
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Args: |
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data_url: `string`, url to download the zip file from. |
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matadata_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(OpenFireConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.data_url = data_url |
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self.metadata_urls = metadata_urls |
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class OpenFire(datasets.GeneratorBasedBuilder): |
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"""OpenFire dataset.""" |
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BUILDER_CONFIGS = [ |
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OpenFireConfig( |
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name="full_size", |
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description="All images are in their original size.", |
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data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz", |
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metadata_urls={ |
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"train": f"{_REPO}/full_size/train.txt", |
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"validation": f"{_REPO}/imagenette2/val.txt", |
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}, |
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), |
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OpenFireConfig( |
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name="320px", |
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description="All images were resized on their shortest side to 320 pixels.", |
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data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz", |
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metadata_urls={ |
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"train": f"{_REPO}/imagenette2-320/train.txt", |
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"validation": f"{_REPO}/imagenette2-320/val.txt", |
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}, |
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), |
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OpenFireConfig( |
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name="160px", |
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description="All images were resized on their shortest side to 160 pixels.", |
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data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz", |
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metadata_urls={ |
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"train": f"{_REPO}/imagenette2-160/train.txt", |
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"validation": f"{_REPO}/imagenette2-160/val.txt", |
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}, |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION + self.config.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.ClassLabel( |
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names=[ |
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"tench", |
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"English springer", |
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"cassette player", |
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"chain saw", |
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"church", |
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"French horn", |
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"garbage truck", |
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"gas pump", |
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"golf ball", |
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"parachute", |
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] |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(self.config.data_url) |
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metadata_paths = dl_manager.download(self.config.metadata_urls) |
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archive_iter = dl_manager.iter_archive(archive_path) |
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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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"images": archive_iter, |
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"metadata_path": metadata_paths["train"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"images": archive_iter, |
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"metadata_path": metadata_paths["validation"], |
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}, |
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), |
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] |
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def _generate_examples(self, images, metadata_path): |
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with open(metadata_path, encoding="utf-8") as f: |
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files_to_keep = set(f.read().split("\n")) |
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idx = 0 |
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for file_path, file_obj in images: |
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if file_path in files_to_keep: |
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label = _LABEL_MAP.index(file_path.split("/")[-2]) |
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yield idx, { |
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"image": {"path": file_path, "bytes": file_obj.read()}, |
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"label": label, |
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} |
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idx += 1 |
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