define splits

ANAKIN.py

@@ -163,57 +163,62 @@ class Anakin(datasets.GeneratorBasedBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         metadata_dir = dl_manager.download_and_extract(_METADATA_URL)
+        folders = _FOLDERS[self.config.name]
 
         random.seed(47)
         root_url = "https://huggingface.co/datasets/AlexBlck/ANAKIN/resolve/main/"
         df = pd.read_csv(metadata_dir)
+        if "full" in folders:
+            df = df[df["full-available"] == True]
+        if "masks" in folders:
+            df = df[df["has-masks"] == True]
+
         ids = df["video-id"].to_list()
         random.shuffle(ids)
 
-        folders = _FOLDERS[self.config.name]
-        data_urls = [
-            {f"{folder}": root_url + f"{folder}/{idx}.mp4" for folder in folders}
-            for idx in ids
-        ]
-        data_dir = dl_manager.download(data_urls)
-        mask_dir = {
-            idx: dl_manager.iter_archive(
-                dl_manager.download(root_url + f"masks/{idx}.zip")
-            )
-            for idx in ids
-        }
+        train_end = int(len(df) * 0.7)
+        val_end = int(len(df) * 0.8)
+        data_dir = {}
+        mask_dir = {}
+
+        for split in [
+            datasets.Split.TRAIN,
+            datasets.Split.VALIDATION,
+            datasets.Split.TEST,
+        ]:
+            if split == datasets.Split.TRAIN:
+                split_ids = ids[:train_end]
+            elif split == datasets.Split.VALIDATION:
+                split_ids = ids[train_end:val_end]
+            else:
+                split_ids = ids[val_end:]
+            data_urls = [
+                {f"{folder}": root_url + f"{folder}/{idx}.mp4" for folder in folders}
+                for idx in split_ids
+            ]
+            data_dir[split] = dl_manager.download(data_urls)
+            mask_dir[split] = {
+                idx: dl_manager.iter_archive(
+                    dl_manager.download(root_url + f"masks/{idx}.zip")
+                )
+                for idx in split_ids
+            }
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
+                name=split,
                 gen_kwargs={
                     "files": data_dir,
                     "masks": mask_dir,
                     "df": df,
                     "return_time": "full" in folders,
                 },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "files": data_dir,
-                    "masks": mask_dir,
-                    "df": df,
-                    "return_time": "full" in folders,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "files": data_dir,
-                    "masks": mask_dir,
-                    "df": df,
-                    "return_time": "full" in folders,
-                },
-            ),
+            )
+            for split in [
+                datasets.Split.TRAIN,
+                datasets.Split.VALIDATION,
+                datasets.Split.TEST,
+            ]
         ]
 
     def _generate_examples(self, files, masks, df, return_time):