Miscellaneous Fixes (0.17 Beta) (#21320)

* Exclude D-FINE from using CUDA Graphs

* fix objects count in detail stream

* Add debugging for classification models

* validate idb stored stream name and reset if invalid

fixes https://github.com/blakeblackshear/frigate/discussions/21311

* ensure jina loading takes place in the main thread to prevent lazily importing tensorflow in another thread later

reverts atexit changes in https://github.com/blakeblackshear/frigate/pull/21301 and fixes https://github.com/blakeblackshear/frigate/discussions/21306

* revert old atexit change in bird too

* revert types

* ensure we bail in the live mode hook for empty camera groups

prevent infinite rendering on camera groups with no cameras

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

frigate/data_processing/real_time/bird.py

@@ -19,6 +19,11 @@ from frigate.util.object import calculate_region
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
 
+try:
+    from tflite_runtime.interpreter import Interpreter
+except ModuleNotFoundError:
+    from tensorflow.lite.python.interpreter import Interpreter
+
 logger = logging.getLogger(__name__)
 
 
@@ -30,7 +35,7 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics)
-        self.interpreter: Any | None = None
+        self.interpreter: Interpreter = None
         self.sub_label_publisher = sub_label_publisher
         self.tensor_input_details: dict[str, Any] = None
         self.tensor_output_details: dict[str, Any] = None
@@ -77,11 +82,6 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
 
     @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
-        try:
-            from tflite_runtime.interpreter import Interpreter
-        except ModuleNotFoundError:
-            from tensorflow.lite.python.interpreter import Interpreter
-
         self.interpreter = Interpreter(
             model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
             num_threads=2,

frigate/data_processing/real_time/custom_classification.py

@@ -29,6 +29,11 @@ from frigate.util.object import box_overlaps, calculate_region
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
 
+try:
+    from tflite_runtime.interpreter import Interpreter
+except ModuleNotFoundError:
+    from tensorflow.lite.python.interpreter import Interpreter
+
 logger = logging.getLogger(__name__)
 
 MAX_OBJECT_CLASSIFICATIONS = 16
@@ -47,7 +52,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.requestor = requestor
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Any | None = None
+        self.interpreter: Interpreter = None
         self.tensor_input_details: dict[str, Any] | None = None
         self.tensor_output_details: dict[str, Any] | None = None
         self.labelmap: dict[int, str] = {}
@@ -345,7 +350,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.model_config = model_config
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Any | None = None
+        self.interpreter: Interpreter = None
         self.sub_label_publisher = sub_label_publisher
         self.requestor = requestor
         self.tensor_input_details: dict[str, Any] | None = None
@@ -368,11 +373,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
     @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
-        try:
-            from tflite_runtime.interpreter import Interpreter
-        except ModuleNotFoundError:
-            from tensorflow.lite.python.interpreter import Interpreter
-
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
 

frigate/detectors/detection_runners.py

@@ -170,6 +170,7 @@ class CudaGraphRunner(BaseModelRunner):
 
         return model_type not in [
             ModelTypeEnum.yolonas.value,
+            ModelTypeEnum.dfine.value,
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v1.value,
             EnrichmentModelTypeEnum.jina_v2.value,

frigate/embeddings/maintainer.py

@@ -146,29 +146,6 @@ class EmbeddingMaintainer(threading.Thread):
         self.detected_license_plates: dict[str, dict[str, Any]] = {}
         self.genai_client = get_genai_client(config)
 
-        # Pre-import TensorFlow/tflite on main thread to avoid atexit registration issues
-        # when importing from worker threads later (e.g., during dynamic config updates)
-        if (
-            self.config.classification.bird.enabled
-            or len(self.config.classification.custom) > 0
-        ):
-            try:
-                from tflite_runtime.interpreter import Interpreter  # noqa: F401
-            except ModuleNotFoundError:
-                try:
-                    from tensorflow.lite.python.interpreter import (  # noqa: F401
-                        Interpreter,
-                    )
-
-                    logger.debug(
-                        "Pre-imported TensorFlow Interpreter on main thread for classification models"
-                    )
-                except Exception as e:
-                    logger.warning(
-                        f"Failed to pre-import TensorFlow Interpreter: {e}. "
-                        "Classification models may fail to load if added dynamically."
-                    )
-
         # model runners to share between realtime and post processors
         if self.config.lpr.enabled:
             lpr_model_runner = LicensePlateModelRunner(

frigate/embeddings/onnx/jina_v1_embedding.py

@@ -186,6 +186,9 @@ class JinaV1ImageEmbedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
+            # Avoid lazy loading in worker threads: block until downloads complete
+            # and load the model on the main thread during initialization.
+            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(

frigate/embeddings/onnx/jina_v2_embedding.py

@@ -65,6 +65,9 @@ class JinaV2Embedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
+            # Avoid lazy loading in worker threads: block until downloads complete
+            # and load the model on the main thread during initialization.
+            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(