feat: Added dataset builder

imagenette.py

@@ -0,0 +1,136 @@
+# 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.
+
+"""Imagenette dataset."""
+
+import os
+import json
+
+import datasets
+
+
+_HOMEPAGE = "https://github.com/fastai/imagenette"
+
+_LICENSE = "Apache License 2.0"
+
+_CITATION = """\
+@software{Howard_Imagenette_2019,
+    title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
+    author={Jeremy Howard},
+    year={2019},
+    month={March},
+    publisher = {GitHub},
+    url = {https://github.com/fastai/imagenette}
+}
+"""
+
+_DESCRIPTION = """\
+Imagenette is a subset of 10 easily classified classes from Imagenet
+(tench, English springer, cassette player, chain saw, church, French
+horn, garbage truck, gas pump, golf ball, parachute).
+"""
+
+_LABEL_MAP = [
+    'n01440764',
+    'n02102040',
+    'n02979186',
+    'n03000684',
+    'n03028079',
+    'n03394916',
+    'n03417042',
+    'n03425413',
+    'n03445777',
+    'n03888257',
+]
+
+
+
+class OpenFireConfig(datasets.BuilderConfig):
+    """BuilderConfig for OpenFire."""
+
+    def __init__(self, data_url, **kwargs):
+        """BuilderConfig for OpenFire.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(OpenFireConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+
+
+class OpenFire(datasets.GeneratorBasedBuilder):
+    """OpenFire dataset."""
+
+    BUILDER_CONFIGS = [
+        OpenFireConfig(
+            name="full_size",
+            description="All images are in their original size.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz",
+        ),
+        OpenFireConfig(
+            name="320px",
+            description="All images were resized on their shortest side to 320 pixels.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz",
+        ),
+        OpenFireConfig(
+            name="160px",
+            description="All images were resized on their shortest side to 160 pixels.",
+            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(
+                        names=[
+                            "tench",
+                            "English springer",
+                            "cassette player",
+                            "chain saw",
+                            "church",
+                            "French horn",
+                            "garbage truck",
+                            "gas pump",
+                            "golf ball",
+                            "parachute",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(self.config.data_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_folder": os.path.join(data_dir, "train"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "image_folder": os.path.join(data_dir, "val"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, image_folder, split):
+        idx = 0
+        for class_idx, class_folder in _LABEL_MAP:
+            for filepath in os.listdir(class_folder):
+                yield idx, {"image": os.path.join(image_folder, class_folder, filepath), "label": class_idx}
+                idx += 1