remove decorator and specifically suppress TFLite delegate creation messages

frigate/data_processing/real_time/bird.py

@@ -13,7 +13,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.object import calculate_region
 
 from ..types import DataProcessorMetrics
@@ -80,8 +80,9 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         except Exception as e:
             logger.error(f"Failed to download {path}: {e}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
             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

@@ -21,7 +21,7 @@ from frigate.config.classification import (
     ObjectClassificationType,
 )
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.object import box_overlaps, calculate_region
@@ -72,7 +72,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.last_run = datetime.datetime.now().timestamp()
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
         try:
             from tflite_runtime.interpreter import Interpreter
@@ -89,6 +88,8 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
             self.interpreter = Interpreter(
                 model_path=model_path,
                 num_threads=2,
@@ -377,7 +378,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
@@ -389,6 +389,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
             self.interpreter = Interpreter(
                 model_path=model_path,
                 num_threads=2,

frigate/detectors/plugins/cpu_tfl.py

@@ -5,7 +5,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -28,8 +28,9 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, detector_config: CpuDetectorConfig):
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
             interpreter = Interpreter(
                 model_path=detector_config.model.path,
                 num_threads=detector_config.num_threads or 3,

frigate/embeddings/onnx/face_embedding.py

@@ -8,7 +8,7 @@ import numpy as np
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_runners import get_optimized_runner
 from frigate.embeddings.types import EnrichmentModelTypeEnum
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
@@ -57,12 +57,13 @@ class FaceNetEmbedding(BaseEmbedding):
             self._load_model_and_utils()
             logger.debug(f"models are already downloaded for {self.model_name}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def _load_model_and_utils(self):
         if self.runner is None:
             if self.downloader:
                 self.downloader.wait_for_download()
 
+            # Suppress TFLite delegate creation messages that bypass Python logging
+            with suppress_stderr_during("tflite_interpreter_init"):
                 self.runner = Interpreter(
                     model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
                     num_threads=2,

frigate/events/audio.py

@@ -34,7 +34,7 @@ from frigate.data_processing.real_time.audio_transcription import (
     AudioTranscriptionRealTimeProcessor,
 )
 from frigate.ffmpeg_presets import parse_preset_input
-from frigate.log import LogPipe, redirect_output_to_logger
+from frigate.log import LogPipe, suppress_stderr_during
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.util.process import FrigateProcess
@@ -367,16 +367,16 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, stop_event: threading.Event, num_threads=2):
         self.stop_event = stop_event
         self.num_threads = num_threads
         self.labels = load_labels("/audio-labelmap.txt", prefill=521)
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
             self.interpreter = Interpreter(
                 model_path="/cpu_audio_model.tflite",
                 num_threads=self.num_threads,
             )
-
             self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()