turn_into_zip (#1)

- :sparkles: Added zip file support for faster download and errorless csv reading (0674e0e203226d313757af0ddadc011f324fbe76)

TID2008.py

@@ -21,6 +21,7 @@ _HOMEPAGE = "https://www.ponomarenko.info/tid2008.htm"
 
 # _LICENSE = ""
 
+
 class TID2008(datasets.GeneratorBasedBuilder):
     """TID2008 Image Quality Dataset"""
 
@@ -32,7 +33,7 @@ class TID2008(datasets.GeneratorBasedBuilder):
             {
                 "reference": datasets.Image(),
                 "distorted": datasets.Image(),
-                "mos": datasets.Value("float")
+                "mos": datasets.Value("float"),
             }
         )
         return datasets.DatasetInfo(
@@ -45,32 +46,41 @@ class TID2008(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_path = dl_manager.download("image_pairs_mos.csv")
-        data = pd.read_csv(data_path, index_col=0)
-        
-        reference_paths = data["Reference"].apply(lambda x: os.path.join("reference_images", x)).to_list()
-        distorted_paths = data["Distorted"].apply(lambda x: os.path.join("distorted_images", x)).to_list()
-
-        reference_paths = dl_manager.download(reference_paths)
-        distorted_paths = dl_manager.download(distorted_paths)
+        data_path = dl_manager.download("data.zip")
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "reference": reference_paths,
-                    "distorted": distorted_paths,
-                    "mos": data["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)):
-            yield key, {
-                "reference": ref,
-                "distorted": dist,
-                "mos": m,
-            }
+    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,
+                {
+                    "reference": ref,
+                    "distorted": dist,
+                    "mos": m,
+                },
+            )
+

data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c048c69418cb0146fe8363f637a35e16623ca6ce25a8b6bfcdd9fb47e85ecaf6
+size 704640392