Update food101.py

food101.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2021 The TensorFlow Datasets Authors.
+# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,23 +12,27 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Dataset class for Food-101 dataset."""
+
+import json
+from pathlib import Path
+
 import datasets
 from datasets.tasks import ImageClassification
 
-import json
-import os
 
 _BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
 
+_HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/"
+
 _DESCRIPTION = (
     "This dataset consists of 101 food categories, with 101'000 images. For "
     "each class, 250 manually reviewed test images are provided as well as 750"
     " training images. On purpose, the training images were not cleaned, and "
     "thus still contain some amount of noise. This comes mostly in the form of"
     " intense colors and sometimes wrong labels. All images were rescaled to "
-    "have a maximum side length of 512 pixels.")
+    "have a maximum side length of 512 pixels."
+)
 
 _CITATION = """\
  @inproceedings{bossard14,
@@ -38,6 +42,7 @@ _CITATION = """\
   year = {2014}
 }
 """
+
 _NAMES = [
     "apple_pie",
     "baby_back_ribs",
@@ -142,56 +147,46 @@ _NAMES = [
     "waffles",
 ]
 
-class Food101(datasets.GeneratorBasedBuilder):
-  """Food-101 Images dataset."""
-
-  def _info(self):
-    """Define Dataset Info."""
 
-    features_dict = {
-        "image": datasets.Value("string"),
-        "label": datasets.features.ClassLabel(names=sorted(tuple(_NAMES))),
-    }
-
-    return datasets.DatasetInfo(
-        description=_DESCRIPTION,
-        features=datasets.Features(features_dict),
-        homepage="https://www.vision.ee.ethz.ch/datasets_extra/food-101/",
-        task_templates=[ImageClassification(image_file_path_column="image", label_column="label", labels=sorted(tuple(_NAMES)))],
-        citation=_CITATION,
-    )
-
-  def _split_generators(self, dl_manager):
-    """Define Splits."""
+class Food101(datasets.GeneratorBasedBuilder):
+    """Food-101 Images dataset."""
 
-    dl_path = dl_manager.download_and_extract(_BASE_URL)
-    meta_path = os.path.join(dl_path, "food-101", "meta")
-    image_dir_path = os.path.join(dl_path, "food-101", "images")
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            task_templates=[ImageClassification(image_file_path_column="image", label_column="label", labels=_NAMES)],
+            citation=_CITATION,
+        )
 
-    return [
-        datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "json_file_path": os.path.join(meta_path, "train.json"),
-                "image_dir_path": image_dir_path
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.VALIDATION,
-            gen_kwargs={
-                "json_file_path": os.path.join(meta_path, "test.json"),
-                "image_dir_path": image_dir_path
-            },
-        ),
-    ]
+    def _split_generators(self, dl_manager):
+        dl_path = Path(dl_manager.download_and_extract(_BASE_URL))
+        meta_path = dl_path / "food-101" / "meta"
+        image_dir_path = dl_path / "food-101" / "images"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"json_file_path": meta_path / "train.json", "image_dir_path": image_dir_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"json_file_path": meta_path / "test.json", "image_dir_path": image_dir_path},
+            ),
+        ]
 
-  def _generate_examples(self, json_file_path, image_dir_path):
-    # """Generate images and labels for splits."""
-    labels = self.info.features['label']
-    with open(json_file_path) as f:
-      data = json.loads(f.read())
-    for label, images in data.items():
-      for image_name in images:
-        image = os.path.join(image_dir_path, image_name + ".jpg")
-        features = {"image": image, "label": labels.encode_example(label)}
-        yield image_name, features
+    def _generate_examples(self, json_file_path, image_dir_path):
+        """Generate images and labels for splits."""
+        labels = self.info.features["label"]
+        data = json.loads(json_file_path.read_text())
+        for label, images in data.items():
+            for image_name in images:
+                image = image_dir_path / f"{image_name}.jpg"
+                features = {"image": str(image), "label": labels.encode_example(label)}
+                yield image_name, features