docker/main/Dockerfile

@@ -237,18 +237,8 @@ ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# TensorFlow C++ logging suppression (must be set before import)
+# TensorFlow error only
-# TF_CPP_MIN_LOG_LEVEL: 0=all, 1=INFO+, 2=WARNING+, 3=ERROR+ (we use 3 for errors only)
 ENV TF_CPP_MIN_LOG_LEVEL=3
-# Suppress verbose logging from TensorFlow C++ code
-ENV TF_CPP_MIN_VLOG_LEVEL=3
-# Disable oneDNN optimization messages ("optimized with oneDNN...")
-ENV TF_ENABLE_ONEDNN_OPTS=0
-# Suppress AutoGraph verbosity during conversion
-ENV AUTOGRAPH_VERBOSITY=0
-# Google Logging (GLOG) suppression for TensorFlow components
-ENV GLOG_minloglevel=3
-ENV GLOG_logtostderr=0
 
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.util.object import calculate_region
 
 from ..types import DataProcessorMetrics
@@ -80,14 +80,13 @@ 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
+        self.interpreter = Interpreter(
-        with suppress_stderr_during("tflite_interpreter_init"):
+            model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
-            self.interpreter = Interpreter(
+            num_threads=2,
-                model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
+        )
-                num_threads=2,
+        self.interpreter.allocate_tensors()
-            )
-            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
 

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.object import box_overlaps, calculate_region
@@ -72,6 +72,7 @@ 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
@@ -88,13 +89,11 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        # Suppress TFLite delegate creation messages that bypass Python logging
+        self.interpreter = Interpreter(
-        with suppress_stderr_during("tflite_interpreter_init"):
+            model_path=model_path,
-            self.interpreter = Interpreter(
+            num_threads=2,
-                model_path=model_path,
+        )
-                num_threads=2,
+        self.interpreter.allocate_tensors()
-            )
-            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)
@@ -378,6 +377,7 @@ 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")
@@ -389,13 +389,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        # Suppress TFLite delegate creation messages that bypass Python logging
+        self.interpreter = Interpreter(
-        with suppress_stderr_during("tflite_interpreter_init"):
+            model_path=model_path,
-            self.interpreter = Interpreter(
+            num_threads=2,
-                model_path=model_path,
+        )
-                num_threads=2,
+        self.interpreter.allocate_tensors()
-            )
-            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -28,13 +28,12 @@ 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
+        interpreter = Interpreter(
-        with suppress_stderr_during("tflite_interpreter_init"):
+            model_path=detector_config.model.path,
-            interpreter = Interpreter(
+            num_threads=detector_config.num_threads or 3,
-                model_path=detector_config.model.path,
+        )
-                num_threads=detector_config.num_threads or 3,
-            )
 
         tflite_init(self, interpreter)
 

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
@@ -57,18 +57,17 @@ 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
+            self.runner = Interpreter(
-            with suppress_stderr_during("tflite_interpreter_init"):
+                model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
-                self.runner = Interpreter(
+                num_threads=2,
-                    model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
+            )
-                    num_threads=2,
+            self.runner.allocate_tensors()
-                )
-                self.runner.allocate_tensors()
             self.tensor_input_details = self.runner.get_input_details()
             self.tensor_output_details = self.runner.get_output_details()
 

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, suppress_stderr_during
+from frigate.log import LogPipe, redirect_output_to_logger
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.util.process import FrigateProcess
@@ -367,17 +367,17 @@ 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
+        self.interpreter = Interpreter(
-        with suppress_stderr_during("tflite_interpreter_init"):
+            model_path="/cpu_audio_model.tflite",
-            self.interpreter = Interpreter(
+            num_threads=self.num_threads,
-                model_path="/cpu_audio_model.tflite",
+        )
-                num_threads=self.num_threads,
+
-            )
+        self.interpreter.allocate_tensors()
-            self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()

frigate/log.py

@@ -80,15 +80,10 @@ def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
     log_levels = {
         "absl": LogLevel.error,
         "httpx": LogLevel.error,
-        "h5py": LogLevel.error,
-        "keras": LogLevel.error,
         "matplotlib": LogLevel.error,
         "tensorflow": LogLevel.error,
-        "tensorflow.python": LogLevel.error,
         "werkzeug": LogLevel.error,
         "ws4py": LogLevel.error,
-        "PIL": LogLevel.warning,
-        "numba": LogLevel.warning,
         **log_levels,
     }
 
@@ -323,31 +318,3 @@ def suppress_os_output(func: Callable) -> Callable:
         return result
 
     return wrapper
-
-
-@contextmanager
-def suppress_stderr_during(operation_name: str) -> Generator[None, None, None]:
-    """
-    Context manager to suppress stderr output during a specific operation.
-
-    Useful for silencing LLVM debug output, CUDA messages, and other native
-    library logging that cannot be controlled via Python logging or environment
-    variables. Completely redirects file descriptor 2 (stderr) to /dev/null.
-
-    Usage:
-        with suppress_stderr_during("model_conversion"):
-            converter = tf.lite.TFLiteConverter.from_keras_model(model)
-            tflite_model = converter.convert()
-
-    Args:
-        operation_name: Name of the operation for debugging purposes
-    """
-    original_stderr_fd = os.dup(2)
-    devnull = os.open(os.devnull, os.O_WRONLY)
-    try:
-        os.dup2(devnull, 2)
-        yield
-    finally:
-        os.dup2(original_stderr_fd, 2)
-        os.close(devnull)
-        os.close(original_stderr_fd)

frigate/util/classification.py

@@ -19,7 +19,7 @@ from frigate.const import (
     PROCESS_PRIORITY_LOW,
     UPDATE_MODEL_STATE,
 )
-from frigate.log import redirect_output_to_logger, suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
@@ -250,20 +250,15 @@ class ClassificationTrainingProcess(FrigateProcess):
             logger.debug(f"Converting {self.model_name} to TFLite...")
 
             # convert model to tflite
-            # Suppress stderr during conversion to avoid LLVM debug output
+            converter = tf.lite.TFLiteConverter.from_keras_model(model)
-            # (fully_quantize, inference_type, MLIR optimization messages, etc)
+            converter.optimizations = [tf.lite.Optimize.DEFAULT]
-            with suppress_stderr_during("tflite_conversion"):
+            converter.representative_dataset = (
-                converter = tf.lite.TFLiteConverter.from_keras_model(model)
+                self.__generate_representative_dataset_factory(dataset_dir)
-                converter.optimizations = [tf.lite.Optimize.DEFAULT]
+            )
-                converter.representative_dataset = (
+            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-                    self.__generate_representative_dataset_factory(dataset_dir)
+            converter.inference_input_type = tf.uint8
-                )
+            converter.inference_output_type = tf.uint8
-                converter.target_spec.supported_ops = [
+            tflite_model = converter.convert()
-                    tf.lite.OpsSet.TFLITE_BUILTINS_INT8
-                ]
-                converter.inference_input_type = tf.uint8
-                converter.inference_output_type = tf.uint8
-                tflite_model = converter.convert()
 
             # write model
             model_path = os.path.join(model_dir, "model.tflite")

frigate/util/process.py

@@ -65,15 +65,10 @@ class FrigateProcess(BaseProcess):
         logging.basicConfig(handlers=[], force=True)
         logging.getLogger().addHandler(QueueHandler(self.__log_queue))
 
-        # Always apply base log level suppressions for noisy third-party libraries
-        # even if no specific logConfig is provided
         if logConfig:
             frigate.log.apply_log_levels(
                 logConfig.default.value.upper(), logConfig.logs
             )
-        else:
-            # Apply default INFO level with standard library suppressions
-            frigate.log.apply_log_levels("INFO", {})
 
         self._setup_memray()