feat: Added builder script

imagewoof.py

@@ -0,0 +1,161 @@
+# Copyright (C) 2022, François-Guillaume Fernandez.
+
+# This program is licensed under the Apache License 2.0.
+# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
+
+"""Imagewoof dataset."""
+
+import os
+import json
+
+import datasets
+
+
+_HOMEPAGE = "https://github.com/fastai/imagenette#imagewoof"
+
+_LICENSE = "Apache License 2.0"
+
+_CITATION = """\
+@software{Howard_Imagewoof_2019,
+    title={Imagewoof: a subset of 10 classes from Imagenet that aren't so easy to classify},
+    author={Jeremy Howard},
+    year={2019},
+    month={March},
+    publisher = {GitHub},
+    url = {https://github.com/fastai/imagenette#imagewoof}
+}
+"""
+
+_DESCRIPTION = """\
+Imagewoof is a subset of 10 classes from Imagenet that aren't so 
+easy to classify, since they're all dog breeds. The breeds are: 
+Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, 
+English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, 
+Old English sheepdog.
+"""
+
+_LABEL_MAP = [
+    'n02086240',
+    'n02087394',
+    'n02088364',
+    'n02089973',
+    'n02093754',
+    'n02096294',
+    'n02099601',
+    'n02105641',
+    'n02111889',
+    'n02115641',
+]
+
+_REPO = "https://huggingface.co/datasets/frgfm/imagewoof/resolve/main/metadata"
+
+
+class ImagewoofConfig(datasets.BuilderConfig):
+    """BuilderConfig for OpenFire."""
+
+    def __init__(self, data_url, metadata_urls, **kwargs):
+        """BuilderConfig for OpenFire.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ImagewoofConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+        self.metadata_urls = metadata_urls
+
+
+class Imagewoof(datasets.GeneratorBasedBuilder):
+    """Imagewoof dataset."""
+
+    BUILDER_CONFIGS = [
+        ImagewoofConfig(
+            name="full_size",
+            description="All images are in their original size.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2.tgz",
+            metadata_urls={
+                "train": f"{_REPO}/imagewoof2/train.txt",
+                "validation": f"{_REPO}/imagewoof2/val.txt",
+            },
+        ),
+        ImagewoofConfig(
+            name="320px",
+            description="All images were resized on their shortest side to 320 pixels.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2-320.tgz",
+            metadata_urls={
+                "train": f"{_REPO}/imagewoof2-320/train.txt",
+                "validation": f"{_REPO}/imagewoof2-320/val.txt",
+            },
+        ),
+        ImagewoofConfig(
+            name="160px",
+            description="All images were resized on their shortest side to 160 pixels.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2-160.tgz",
+            metadata_urls={
+                "train": f"{_REPO}/imagewoof2-160/train.txt",
+                "validation": f"{_REPO}/imagewoof2-160/val.txt",
+            },
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(
+                        names=[
+                            "Australian terrier",
+                            "Border terrier",
+                            "Samoyed",
+                            "Beagle",
+                            "Shih-Tzu",
+                            "English foxhound",
+                            "Rhodesian ridgeback",
+                            "Dingo",
+                            "Golden retriever",
+                            "Old English sheepdog",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(self.config.data_url)
+        metadata_paths = dl_manager.download(self.config.metadata_urls)
+        archive_iter = dl_manager.iter_archive(archive_path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": archive_iter,
+                    "metadata_path": metadata_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": archive_iter,
+                    "metadata_path": metadata_paths["validation"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        with open(metadata_path, encoding="utf-8") as f:
+            files_to_keep = set(f.read().split("\n"))
+        idx = 0
+        for file_path, file_obj in images:
+            if file_path in files_to_keep:
+                label = _LABEL_MAP.index(file_path.split("/")[-2])
+                yield idx, {
+                    "image": {"path": file_path, "bytes": file_obj.read()},
+                    "label": label,
+                }
+                idx += 1