add configs

Caltech-101.py

@@ -156,6 +156,22 @@ class Caltech101(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.0.0")
 
+    _BUILDER_CONFIG_WITH_BACKGROUND = datasets.BuilderConfig(
+        name="with_background_category",
+        version=VERSION,
+        description="Dataset containing only the 101 categories.",
+    )
+    _BUILDER_CONFIG_WITHOUT_BACKGROUND = datasets.BuilderConfig(
+        name="without_background_category",
+        version=VERSION,
+        description="Dataset containing the 101 categories and the additonnal background one.",
+    )
+
+    BUILDER_CONFIGS = [
+        _BUILDER_CONFIG_WITH_BACKGROUND,
+        _BUILDER_CONFIG_WITHOUT_BACKGROUND,
+    ]
+
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -188,6 +204,7 @@ class Caltech101(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepath": data_dir,
                     "split": "train",
+                    "config_name": self.config.name,
                 },
             ),
             datasets.SplitGenerator(
@@ -195,13 +212,14 @@ class Caltech101(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepath": data_dir,
                     "split": "test",
+                    "config_name": self.config.name,
                 },
             ),
         ]
 
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, filepath, split, config_name):
         # Same stratagy as the one proposed in TF datasets: 30 random examples from each class are added to the train
-        # split, and the remainder are added to the test split. 
+        # split, and the remainder are added to the test split.
         # Source: https://github.com/tensorflow/datasets/blob/1106d587f97c4fca68c5b593dc7dc48c790ffa8c/tensorflow_datasets/image_classification/caltech.py#L88-L140
 
         is_train_split = split == "train"
@@ -212,6 +230,7 @@ class Caltech101(datasets.GeneratorBasedBuilder):
         np.random.seed(1234)
 
         for class_dir in data_dir.iterdir():
+            # print(class_dir)
             fnames = [
                 image_path
                 for image_path in class_dir.iterdir()
@@ -231,6 +250,13 @@ class Caltech101(datasets.GeneratorBasedBuilder):
             test_fnames = set(fnames).difference(train_fnames)
             fnames_to_emit = train_fnames if is_train_split else test_fnames
 
+            if (
+                class_dir.name == "BACKGROUND_Google"
+                and config_name == self._BUILDER_CONFIG_WITHOUT_BACKGROUND.name
+            ):
+                print("skip BACKGROUND_Google")
+                continue
+
             for image_file in fnames_to_emit:
                 record = {
                     "image": str(image_file),