Update Carla-COCO-Object-Detection-Dataset.py

Carla-COCO-Object-Detection-Dataset.py

@@ -55,6 +55,8 @@ class CARLA_COCO(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
 
     def _info(self):
+        """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset"""
+
         features = datasets.Features(
             {
                 "image_id": datasets.Value("int64"),
@@ -81,16 +83,38 @@ class CARLA_COCO(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
+        """This method is tasked with downloading/extracting the data and defining the splits depending on the configuration"""
+
         downloaded_files = dl_manager.download_and_extract(_URLS)
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(downloaded_files["train"], "train.jsonl"),
+                    "split": "train"
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(downloaded_files["test"], "test.jsonl"),
+                    "split": "test"
+                }
+            ),
         ]
 
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath):
+        """
+        This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        """
+
         logger.info("generating examples from = %s", filepath)
 
         with open(filepath, encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                yield idx, json.loads(line)
+            for key, row in enumerate(f):
+                yield key, json.loads(row)