Miscellaneous fixes (0.17 beta) (#21350)

* Fix genai callbacks in MQTT

* Cleanup cursor pointer for classification cards

* Cleanup

* Handle unknown SOCs for RKNN converter by only using known SOCs

* don't allow "none" as a classification class name

* change internal port user to admin and default unspecified username to viewer

* keep 5000 as anonymous user

* suppress tensorflow logging during classification training

* Always apply base log level suppressions for noisy third-party libraries even if no specific logConfig is provided

* remove decorator and specifically suppress TFLite delegate creation messages

---------

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

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

@@ -65,10 +65,15 @@ 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()
 

frigate/util/rknn_converter.py

@@ -8,6 +8,7 @@ import time
 from pathlib import Path
 from typing import Optional
 
+from frigate.const import SUPPORTED_RK_SOCS
 from frigate.util.file import FileLock
 
 logger = logging.getLogger(__name__)
@@ -68,9 +69,20 @@ def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
         True if the model is RKNN-compatible, False otherwise
     """
     soc = get_soc_type()
+
     if soc is None:
         return False
 
+    # Check if the SoC is actually a supported RK device
+    # This prevents false positives on non-RK devices (e.g., macOS Docker)
+    # where /proc/device-tree/compatible might exist but contain non-RK content
+    if soc not in SUPPORTED_RK_SOCS:
+        logger.debug(
+            f"SoC '{soc}' is not a supported RK device for RKNN conversion. "
+            f"Supported SoCs: {SUPPORTED_RK_SOCS}"
+        )
+        return False
+
     if not model_type:
         model_type = get_rknn_model_type(model_path)