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ultralytics/solutions/action_recognition.py

@@ -21,8 +21,6 @@ class ActionRecognition:
     def __init__(
         self,
         video_classifier_model="microsoft/xclip-base-patch32",
-        labels: Optional[List[str]] = None,
-        fp16: bool = False,
         crop_margin_percentage: int = 10,
         num_video_sequence_samples: int = 8,
         vid_stride: int = 2,
@@ -35,7 +33,6 @@ class ActionRecognition:
         Args:
             video_classifier_model (str): Name or path of the video classifier model. Defaults to "microsoft/xclip-base-patch32".
             labels (List[str], optional): List of labels for zero-shot classification. Defaults to predefined list.
-            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.
             vid_stride (int, optional): Number of frames to skip between detections. Defaults to 2.
@@ -47,22 +44,22 @@ class ActionRecognition:
             if labels is not None
             else ["walking", "running", "brushing teeth", "looking into phone", "weight lifting", "cooking", "sitting"]
         )
-        self.fp16 = fp16
+
         self.device = select_device(device)
+        self.fp16 = torch.cuda.is_available() and torch.cuda.is_bf16_supported() and 'cuda' in self.device
 
         # Check if environment supports imshow
         self.env_check = check_imshow(warn=True)
         self.window_name = "Ultralytics YOLOv8 Action Recognition"
 
         if video_classifier_model in TorchVisionVideoClassifier.available_model_names():
-            print("'fp16' is not supported for TorchVisionVideoClassifier. Setting fp16 to False.")
             print(
                 "'labels' is not used for TorchVisionVideoClassifier. Ignoring the provided labels and using Kinetics-400 labels."
             )
             self.video_classifier = TorchVisionVideoClassifier(video_classifier_model, device=self.device)
         else:
             self.video_classifier = HuggingFaceVideoClassifier(
-                self.labels, model_name=video_classifier_model, device=self.device, fp16=fp16
+                self.labels, model_name=video_classifier_model, device=self.device, fp16= self.fp16
             )
 
         self.track_history = defaultdict(list)