Update medianomaly.py

medianomaly.py

@@ -61,34 +61,34 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
     
-def _split_generators(self, dl_manager):
-    config_name = self.config.name.lower()
-    if config_name not in _URLS:
-        raise NotImplementedError(f"{config_name} is not implemented in Medianomaly.")
-    archive_path = dl_manager.download_and_extract(_URLS[config_name])
-    print(archive_path)
-
-    if config_name == "rsna":
-        data_dir = os.path.join(archive_path, config_name)
-        with open(os.path.join(data_dir, "data.json"), "r") as f:
-            metadata = json.load(f)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
-                "samples": metadata["train"], "base_dir": data_dir, "config": config_name
-            }),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
-                "samples": metadata["test"], "base_dir": data_dir, "config": config_name
-            }),
-        ]
-    
+    def _split_generators(self, dl_manager):
+        config_name = self.config.name.lower()
+        if config_name not in _URLS:
+            raise NotImplementedError(f"{config_name} is not implemented in Medianomaly.")
+        archive_path = dl_manager.download_and_extract(_URLS[config_name])
+        print(archive_path)
     
-def _generate_examples(self, samples, base_dir, config):
-   if config == "rsna":
-        for label_str, items in samples.items():  # only "0" in train, "0"/"1" in test
-            label = int(label_str)
-            for idx, item in enumerate(items):
-                image_path = os.path.join(base_dir, item["image"])
-                yield idx, {
-                    "image": image_path,
-                    "label": label,
-                }
+        if config_name == "rsna":
+            data_dir = os.path.join(archive_path, config_name)
+            with open(os.path.join(data_dir, "data.json"), "r") as f:
+                metadata = json.load(f)
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                    "samples": metadata["train"], "base_dir": data_dir, "config": config_name
+                }),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
+                    "samples": metadata["test"], "base_dir": data_dir, "config": config_name
+                }),
+            ]
+        
+        
+    def _generate_examples(self, samples, base_dir, config):
+       if config == "rsna":
+            for label_str, items in samples.items():  # only "0" in train, "0"/"1" in test
+                label = int(label_str)
+                for idx, item in enumerate(items):
+                    image_path = os.path.join(base_dir, item["image"])
+                    yield idx, {
+                        "image": image_path,
+                        "label": label,
+                    }