mirror of
https://github.com/blakeblackshear/frigate.git
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Classification improvements (#19020)
* Move classification training to full process * Sort class images
This commit is contained in:
Nicolas Mowen
GitHub
parent
0f4cac736a
commit
2b4a773f9b
@ -20,106 +20,117 @@ LEARNING_RATE = 0.001
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logger = logging.getLogger(__name__)
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def __generate_representative_dataset_factory(dataset_dir: str):
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def generate_representative_dataset():
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image_paths = []
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for root, dirs, files in os.walk(dataset_dir):
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for file in files:
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if file.lower().endswith((".jpg", ".jpeg", ".png")):
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image_paths.append(os.path.join(root, file))
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class ClassificationTrainingProcess(FrigateProcess):
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def __init__(self, model_name: str) -> None:
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super().__init__(
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stop_event=None,
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name=f"model_training:{model_name}",
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)
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self.model_name = model_name
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for path in image_paths[:300]:
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img = cv2.imread(path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (224, 224))
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img_array = np.array(img, dtype=np.float32) / 255.0
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img_array = img_array[None, ...]
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yield [img_array]
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def run(self) -> None:
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self.pre_run_setup()
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self.__train_classification_model()
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return generate_representative_dataset
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def __generate_representative_dataset_factory(self, dataset_dir: str):
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def generate_representative_dataset():
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image_paths = []
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for root, dirs, files in os.walk(dataset_dir):
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for file in files:
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if file.lower().endswith((".jpg", ".jpeg", ".png")):
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image_paths.append(os.path.join(root, file))
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for path in image_paths[:300]:
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img = cv2.imread(path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (224, 224))
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img_array = np.array(img, dtype=np.float32) / 255.0
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img_array = img_array[None, ...]
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yield [img_array]
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@redirect_output_to_logger(logger, logging.DEBUG)
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def __train_classification_model(model_name: str) -> bool:
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"""Train a classification model."""
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return generate_representative_dataset
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# import in the function so that tensorflow is not initialized multiple times
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import tensorflow as tf
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from tensorflow.keras import layers, models, optimizers
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from tensorflow.keras.applications import MobileNetV2
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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@redirect_output_to_logger(logger, logging.DEBUG)
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def __train_classification_model(self) -> bool:
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"""Train a classification model."""
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logger.info(f"Kicking off classification training for {model_name}.")
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dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
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model_dir = os.path.join(MODEL_CACHE_DIR, model_name)
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num_classes = len(
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[
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d
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for d in os.listdir(dataset_dir)
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if os.path.isdir(os.path.join(dataset_dir, d))
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]
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)
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# import in the function so that tensorflow is not initialized multiple times
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import tensorflow as tf
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from tensorflow.keras import layers, models, optimizers
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from tensorflow.keras.applications import MobileNetV2
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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# Start with imagenet base model with 35% of channels in each layer
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base_model = MobileNetV2(
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input_shape=(224, 224, 3),
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include_top=False,
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weights="imagenet",
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alpha=0.35,
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)
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base_model.trainable = False # Freeze pre-trained layers
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logger.info(f"Kicking off classification training for {self.model_name}.")
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dataset_dir = os.path.join(CLIPS_DIR, self.model_name, "dataset")
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model_dir = os.path.join(MODEL_CACHE_DIR, self.model_name)
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num_classes = len(
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[
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d
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for d in os.listdir(dataset_dir)
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if os.path.isdir(os.path.join(dataset_dir, d))
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]
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)
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model = models.Sequential(
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[
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base_model,
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layers.GlobalAveragePooling2D(),
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layers.Dense(128, activation="relu"),
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layers.Dropout(0.3),
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layers.Dense(num_classes, activation="softmax"),
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]
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)
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# Start with imagenet base model with 35% of channels in each layer
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base_model = MobileNetV2(
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input_shape=(224, 224, 3),
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include_top=False,
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weights="imagenet",
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alpha=0.35,
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)
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base_model.trainable = False # Freeze pre-trained layers
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model.compile(
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optimizer=optimizers.Adam(learning_rate=LEARNING_RATE),
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loss="categorical_crossentropy",
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metrics=["accuracy"],
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)
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model = models.Sequential(
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[
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base_model,
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layers.GlobalAveragePooling2D(),
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layers.Dense(128, activation="relu"),
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layers.Dropout(0.3),
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layers.Dense(num_classes, activation="softmax"),
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]
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)
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# create training set
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datagen = ImageDataGenerator(rescale=1.0 / 255, validation_split=0.2)
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train_gen = datagen.flow_from_directory(
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dataset_dir,
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target_size=(224, 224),
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batch_size=BATCH_SIZE,
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class_mode="categorical",
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subset="training",
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)
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model.compile(
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optimizer=optimizers.Adam(learning_rate=LEARNING_RATE),
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loss="categorical_crossentropy",
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metrics=["accuracy"],
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)
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# write labelmap
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class_indices = train_gen.class_indices
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index_to_class = {v: k for k, v in class_indices.items()}
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sorted_classes = [index_to_class[i] for i in range(len(index_to_class))]
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with open(os.path.join(model_dir, "labelmap.txt"), "w") as f:
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for class_name in sorted_classes:
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f.write(f"{class_name}\n")
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# create training set
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datagen = ImageDataGenerator(rescale=1.0 / 255, validation_split=0.2)
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train_gen = datagen.flow_from_directory(
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dataset_dir,
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target_size=(224, 224),
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batch_size=BATCH_SIZE,
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class_mode="categorical",
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subset="training",
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)
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# train the model
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model.fit(train_gen, epochs=EPOCHS, verbose=0)
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# write labelmap
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class_indices = train_gen.class_indices
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index_to_class = {v: k for k, v in class_indices.items()}
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sorted_classes = [index_to_class[i] for i in range(len(index_to_class))]
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with open(os.path.join(model_dir, "labelmap.txt"), "w") as f:
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for class_name in sorted_classes:
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f.write(f"{class_name}\n")
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# convert model to tflite
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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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converter.representative_dataset = __generate_representative_dataset_factory(
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dataset_dir
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)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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converter.inference_input_type = tf.uint8
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converter.inference_output_type = tf.uint8
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tflite_model = converter.convert()
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# train the model
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model.fit(train_gen, epochs=EPOCHS, verbose=0)
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# write model
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with open(os.path.join(model_dir, "model.tflite"), "wb") as f:
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f.write(tflite_model)
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# convert model to tflite
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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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converter.representative_dataset = (
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self.__generate_representative_dataset_factory(dataset_dir)
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)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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converter.inference_input_type = tf.uint8
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converter.inference_output_type = tf.uint8
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tflite_model = converter.convert()
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# write model
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with open(os.path.join(model_dir, "model.tflite"), "wb") as f:
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f.write(tflite_model)
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@staticmethod
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@ -138,12 +149,7 @@ def kickoff_model_training(
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# run training in sub process so that
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# tensorflow will free CPU / GPU memory
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# upon training completion
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training_process = FrigateProcess(
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None,
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target=__train_classification_model,
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name=f"model_training:{model_name}",
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args=(model_name,),
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)
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training_process = ClassificationTrainingProcess(model_name)
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training_process.start()
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training_process.join()
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@ -577,9 +577,14 @@ function DatasetGrid({
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}: DatasetGridProps) {
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const { t } = useTranslation(["views/classificationModel"]);
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const classData = useMemo(
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() => images.sort((a, b) => a.localeCompare(b)),
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[images],
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);
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return (
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<div className="flex flex-wrap gap-2 overflow-y-auto p-2">
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{images.map((image) => (
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{classData.map((image) => (
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<div
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className={cn(
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"flex w-60 cursor-pointer flex-col gap-2 rounded-lg bg-card outline outline-[3px]",
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