Add "id" field to the CIFAR datasets, for the user convenience.

This allows to individually identify the dataset examples examples.

This does not affect the sharding of the dataset, so the splits are still compatible with the previous version.

PiperOrigin-RevId: 302750645

Author: jpuigcerver

tensorflow_datasets/image/cifar.py

@@ -46,6 +46,11 @@ class Cifar10(tfds.core.GeneratorBasedBuilder):
   """CIFAR-10."""
 
   VERSION = tfds.core.Version("3.0.1")
+  SUPPORTED_VERSIONS = [
+      tfds.core.Version(
+          "3.0.2", experiments={tfds.core.Experiment.METADATA: True}
+      ),
+  ]
 
   def _info(self):
     return tfds.core.DatasetInfo(
@@ -54,6 +59,7 @@ def _info(self):
                      "images in 10 classes, with 6000 images per class. There "
                      "are 50000 training images and 10000 test images."),
         features=tfds.features.FeaturesDict({
+            "id": tfds.features.Text(),
             "image": tfds.features.Image(shape=_CIFAR_IMAGE_SHAPE),
             "label": tfds.features.ClassLabel(num_classes=10),
         }),
@@ -100,19 +106,26 @@ def gen_filenames(filenames):
     return [
         tfds.core.SplitGenerator(
             name=tfds.Split.TRAIN,
-            gen_kwargs={"filepaths": gen_filenames(cifar_info.train_files)}),
+            gen_kwargs={
+                "split_prefix": "train_",
+                "filepaths": gen_filenames(cifar_info.train_files)
+            }),
         tfds.core.SplitGenerator(
             name=tfds.Split.TEST,
-            gen_kwargs={"filepaths": gen_filenames(cifar_info.test_files)}),
+            gen_kwargs={
+                "split_prefix": "test_",
+                "filepaths": gen_filenames(cifar_info.test_files)
+            }),
     ]
 
-  def _generate_examples(self, filepaths):
+  def _generate_examples(self, split_prefix, filepaths):
     """Generate CIFAR examples as dicts.
 
     Shared across CIFAR-{10, 100}. Uses self._cifar_info as
     configuration.
 
     Args:
+      split_prefix (str): Prefix that identifies the split (e.g. "tr" or "te").
       filepaths (list[str]): The files to use to generate the data.
 
     Yields:
@@ -123,6 +136,10 @@ def _generate_examples(self, filepaths):
     for path in filepaths:
       for labels, np_image in _load_data(path, len(label_keys)):
         record = dict(zip(label_keys, labels))
+        # Note: "id" is only provided for the user convenience. To shuffle the
+        # dataset we use `index`, so that the sharding is compatible with
+        # earlier versions.
+        record["id"] = "{}{:05d}".format(split_prefix, index)
         record["image"] = np_image
         yield index, record
         index += 1
@@ -132,6 +149,11 @@ class Cifar100(Cifar10):
   """CIFAR-100 dataset."""
 
   VERSION = tfds.core.Version("3.0.1")
+  SUPPORTED_VERSIONS = [
+      tfds.core.Version(
+          "3.0.2", experiments={tfds.core.Experiment.METADATA: True}
+      ),
+  ]
 
   @property
   def _cifar_info(self):
@@ -156,6 +178,7 @@ def _info(self):
                      "(the class to which it belongs) and a \"coarse\" label "
                      "(the superclass to which it belongs)."),
         features=tfds.features.FeaturesDict({
+            "id": tfds.features.Text(),
             "image": tfds.features.Image(shape=_CIFAR_IMAGE_SHAPE),
             "label": tfds.features.ClassLabel(num_classes=100),
             "coarse_label": tfds.features.ClassLabel(num_classes=20),