update ckip_frame to vid_stride

docs/en/guides/action-recognition.md

@@ -71,7 +71,7 @@ cv2.destroyAllWindows()
 - `fp16`: Whether to use half-precision floating point. Defaults to `False`.
 - `crop_margin_percentage`: Percentage of margin to add around detected objects. Defaults to ``.
 - `num_video_sequence_samples`: Number of sequential video frames to use for action recognition. Defaults to `8`.
-- `skip_frame`: Number of frames to skip between detections. Defaults to `2`.
+- `vid_stride`: Number of frames to skip between detections. Defaults to `2`.
 - `video_cls_overlap_ratio`: Overlap ratio between video sequences. Defaults to `0.25`.
 - `device`: The device to run the model on. Defaults to `""`.
 

ultralytics/solutions/action_recognition.py

@@ -22,7 +22,7 @@ class ActionRecognition:
         fp16=False,
         crop_margin_percentage=10,
         num_video_sequence_samples=8,
-        skip_frame=2,
+        vid_stride=2,
         video_cls_overlap_ratio=0.25,
         device="",
     ):
@@ -35,7 +35,7 @@ class ActionRecognition:
             fp16 (bool, optional): Whether to use half-precision floating point. Defaults to False.
             crop_margin_percentage (int, optional): Percentage of margin to add around detected objects. Defaults to 10.
             num_video_sequence_samples (int, optional): Number of video frames to use for classification. Defaults to 8.
-            skip_frame (int, optional): Number of frames to skip between detections. Defaults to 2.
+            vid_stride (int, optional): Number of frames to skip between detections. Defaults to 2.
             video_cls_overlap_ratio (float, optional): Overlap ratio between video sequences. Defaults to 0.25.
             device (str or torch.device, optional): The device to run the model on. Defaults to "".
         """
@@ -69,7 +69,7 @@ class ActionRecognition:
         # Properties with default values
         self.crop_margin_percentage = crop_margin_percentage
         self.num_video_sequence_samples = num_video_sequence_samples
-        self.skip_frame = skip_frame
+        self.vid_stride = vid_stride
         self.video_cls_overlap_ratio = video_cls_overlap_ratio
 
     def process_tracks(self, tracks):
@@ -169,14 +169,14 @@ class ActionRecognition:
 
         self.process_tracks(tracks)
 
-        if self.frame_counter % self.skip_frame == 0:
+        if self.frame_counter % self.vid_stride == 0:
             crops_to_infer = []
             track_ids_to_infer = []
 
         for box, track_id in zip(self.boxes, self.track_ids):
             if (
                 len(self.track_history[track_id]) == self.num_video_sequence_samples
-                and self.frame_counter % self.skip_frame == 0
+                and self.frame_counter % self.vid_stride == 0
             ):
                 crops = self.video_classifier.preprocess_crops_for_video_cls(self.track_history[track_id])
                 crops_to_infer.append(crops)
@@ -185,7 +185,7 @@ class ActionRecognition:
         if crops_to_infer and (
             not pred_labels
             or self.frame_counter
-            % int(self.num_video_sequence_samples * self.skip_frame * (1 - self.video_cls_overlap_ratio))
+            % int(self.num_video_sequence_samples * self.vid_stride * (1 - self.video_cls_overlap_ratio))
             == 0
         ):
             crops_batch = torch.cat(crops_to_infer, dim=0)