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Miscellaneous fixes (0.17 beta) (#21350)
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Browse Source 
* 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>
This commit is contained in:
Nicolas Mowen
2025-12-18 15:12:10 -07:00
committed by GitHub
co-authored by Josh Hawkins
parent 6a0e31dcf9
commit e636449d56
20 changed files with 156 additions and 67 deletions
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frigate/embeddings/onnx/face_embedding.py
+8 -7
View File
@@ -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
@@ -57,17 +57,18 @@ 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()
self.runner = Interpreter(
model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
num_threads=2,
)
self.runner.allocate_tensors()
# 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,
)
self.runner.allocate_tensors()
self.tensor_input_details = self.runner.get_input_details()
self.tensor_output_details = self.runner.get_output_details()