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Move classification training to full process

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This commit is contained in:
Nicolas Mowen 2025-06-28 13:31:45 -06:00
parent 0f4cac736a
commit d49a7425c8
1 changed files with 98 additions and 92 deletions
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36
frigate/util/classification.py
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@ -20,7 +20,19 @@ LEARNING_RATE = 0.001
logger = logging.getLogger(__name__)
def __generate_representative_dataset_factory(dataset_dir: str):
class ClassificationTrainingProcess(FrigateProcess):
def __init__(self, model_name: str) -> None:
super().__init__(
stop_event=None,
name=f"model_training:{model_name}",
)
self.model_name = model_name
def run(self) -> None:
self.pre_run_setup()
self.__train_classification_model()
def __generate_representative_dataset_factory(self, dataset_dir: str):
def generate_representative_dataset():
image_paths = []
for root, dirs, files in os.walk(dataset_dir):
@ -38,9 +50,8 @@ def __generate_representative_dataset_factory(dataset_dir: str):
return generate_representative_dataset
@redirect_output_to_logger(logger, logging.DEBUG)
def __train_classification_model(model_name: str) -> bool:
@redirect_output_to_logger(logger, logging.DEBUG)
def __train_classification_model(self) -> bool:
"""Train a classification model."""
# import in the function so that tensorflow is not initialized multiple times
@ -49,9 +60,9 @@ def __train_classification_model(model_name: str) -> bool:
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
logger.info(f"Kicking off classification training for {model_name}.")
dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
model_dir = os.path.join(MODEL_CACHE_DIR, model_name)
logger.info(f"Kicking off classification training for {self.model_name}.")
dataset_dir = os.path.join(CLIPS_DIR, self.model_name, "dataset")
model_dir = os.path.join(MODEL_CACHE_DIR, self.model_name)
num_classes = len(
[
d
@ -109,8 +120,8 @@ def __train_classification_model(model_name: str) -> bool:
# convert model to tflite
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = __generate_representative_dataset_factory(
dataset_dir
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
@ -138,12 +149,7 @@ def kickoff_model_training(
# run training in sub process so that
# tensorflow will free CPU / GPU memory
# upon training completion
training_process = FrigateProcess(
None,
target=__train_classification_model,
name=f"model_training:{model_name}",
args=(model_name,),
)
training_process = ClassificationTrainingProcess(model_name)
training_process.start()
training_process.join()