rollback

TID2013.py

@@ -59,34 +59,34 @@ class TID2013(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         data_path = dl_manager.download("data.zip")
-        data_path = dl_manager.download_and_extract(data_path)
-        df = pd.read_csv(os.path.join(data_path, "image_pairs_mos.csv"), index_col=0)
-        reference_paths = (
-            df["Reference"]
-            .apply(lambda x: os.path.join(data_path, "reference_images", x))
-            .to_list()
-        )
-        distorted_paths = (
-            df["Distorted"]
-            .apply(lambda x: os.path.join(data_path, "distorted_images", x))
-            .to_list()
-        )
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "reference": reference_paths,
-                    "distorted": distorted_paths,
-                    "mos": df["MOS"],
+                    "data": dl_manager.download_and_extract(data_path),
                     "split": "train",
                 },
             )
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, reference, distorted, mos, split):
-        for key, (ref, dist, m) in enumerate(zip(reference, distorted, mos)):
+    def _generate_examples(self, data, split):
+        df = pd.read_csv(os.path.join(data, "image_pairs_mos.csv"), index_col=0)
+        reference_paths = (
+            df["Reference"]
+            .apply(lambda x: os.path.join(data, "reference_images", x))
+            .to_list()
+        )
+        distorted_paths = (
+            df["Distorted"]
+            .apply(lambda x: os.path.join(data, "distorted_images", x))
+            .to_list()
+        )
+
+        for key, (ref, dist, m) in enumerate(
+            zip(reference_paths, distorted_paths, df["MOS"])
+        ):
             yield (
                 key,
                 {