Finish dataset loading script

xd-violence.py

@@ -52,20 +52,42 @@ class XDViolence(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG_NAME = "video"
     BUILDER_CONFIG_CLASS = XDViolenceConfig
 
+    CODE2LABEL = {
+        "A": "Normal",
+        "B1": "Fighting",
+        "B2": "Shooting",
+        "B4": "Riot",
+        "B5": "Abuse",
+        "B6": "Car accident",
+        "G": "Explosion",
+    }
+
+    LABEL2IDX = {
+        "Normal": 0,
+        "Fighting": 1,
+        "Shooting": 2,
+        "Riot": 3,
+        "Abuse": 4,
+        "Car accident": 5,
+        "Explosion": 6,
+    }
+
     def _info(self):
         if self.config.name == "rgb":
             features = datasets.Features(
                 {
+                    "video_id": datasets.Value("string"),
                     "rgb_feats": datasets.Array3D(
                         shape=(None, 10, 2048),
                         dtype="float32",  # (num_frames, num_crops, feature_dim) use 10 crops by default as of now
                     ),
                     "binary_target": datasets.ClassLabel(
-                        names=["non-violence", "violence"]
+                        names=["Non-violence", "Violence"]
                     ),
                     "multilabel_targets": datasets.Sequence(
                         datasets.ClassLabel(
                             names=[
+                                "Normal",
                                 "Fighting",
                                 "Shooting",
                                 "Riot",
@@ -86,13 +108,15 @@ class XDViolence(datasets.GeneratorBasedBuilder):
         else:  # default = "video"
             features = datasets.Features(
                 {
+                    "video_id": datasets.Value("string"),
                     "video_path": datasets.Value("string"),
                     "binary_target": datasets.ClassLabel(
-                        names=["non-violence", "violence"]
+                        names=["Non-violence", "Violence"]
                     ),
                     "multilabel_targets": datasets.Sequence(
                         datasets.ClassLabel(
                             names=[
+                                "Normal",
                                 "Fighting",
                                 "Shooting",
                                 "Riot",
@@ -170,8 +194,8 @@ class XDViolence(datasets.GeneratorBasedBuilder):
 
             # Function to read annotations
             annotation_readers = {
-                "train": XDViolence._read_train_list,
-                "test": XDViolence._read_test_annotations,
+                "train": self._read_train_list,
+                "test": self._read_test_annotations,
             }
 
             return [
@@ -194,24 +218,34 @@ class XDViolence(datasets.GeneratorBasedBuilder):
             ]
 
     def _generate_examples(self, annotation_path, video_paths, annotation_reader):
-        # If test, read annotations from test_annotations.txt
-        # If train, read labels from train_list.txt
-
-        # for key, video_path in enumerate(zip(video_paths, )):
-        #     pass
-        print(annotation_reader)
-        ann_data = annotation_reader(self, annotation_path)
-        print(ann_data[:5])
-        for key, video_path, annotation in enumerate(zip(video_paths, ann_data)):
-            yield key, {"video_path": video_path}
-
-    def _read_train_list(cls, path):
+        ann_data = annotation_reader(annotation_path)
+
+        for key, (video_path, annotation) in enumerate(zip(video_paths, ann_data)):
+            video_id = annotation["video_id"]
+            frame_annotations = annotation.get("frame_annotations", [])
+
+            binary, multilabel = self.extract_labels(video_id)
+
+            yield key, {
+                "video_id": video_id,
+                "video_path": video_path,
+                "binary_target": binary,
+                "multilabel_targets": multilabel,
+                "frame_annotations": frame_annotations,
+            }
+
+    @staticmethod
+    def _read_train_list(path):
         """Reads the train_list.txt file and returns a list of video ids."""
-        return pd.read_csv(path, header=None, sep=" ", usecols=[0], names=["video_id"])[
-            "video_id"
-        ].to_list()
 
-    def _read_test_annotations(cls, path):
+        train_list = pd.read_csv(
+            path, header=None, sep=" ", usecols=[0], names=["video_id"]
+        )
+        train_list["video_id"] = train_list["video_id"].apply(lambda x: x.split("/")[1])
+        return train_list.to_dict("records")
+
+    @staticmethod
+    def _read_test_annotations(path):
         """Reads the test_annotations.txt file and returns a list of annotations."""
         url_components = urllib.parse.urlparse(path)
         is_url = url_components.scheme == "https" or url_components.scheme == "http"
@@ -250,3 +284,15 @@ class XDViolence(datasets.GeneratorBasedBuilder):
                     )
 
         return annotations
+
+    @classmethod
+    def extract_labels(cls, video_id):
+        """Extracts labels from the video id."""
+        codes = video_id.split("_")[-1].split("-")
+
+        binary = 1 if len(codes) > 1 else 0
+        multilabel = [
+            cls.LABEL2IDX[cls.CODE2LABEL[code]] for code in codes if code != "0"
+        ]
+
+        return binary, multilabel