mirror of
https://github.com/blakeblackshear/frigate.git
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1c75ff59f1
* Setup basic training structure * Build out route * Handle model configs * Add image fetch APIs * Implement model training screen with dataset selection * Implement viewing of training images * Adjust directories * Implement viewing of images * Add support for deleting images * Implement full deletion * Implement classification model training * Improve naming * More renaming * Improve layout * Reduce logging * Cleanup
115 lines
3.5 KiB
Python
115 lines
3.5 KiB
Python
"""Util for classification models."""
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import logging
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import os
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import cv2
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import numpy as np
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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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from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
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BATCH_SIZE = 16
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EPOCHS = 50
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LEARNING_RATE = 0.001
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@staticmethod
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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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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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return generate_representative_dataset
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@staticmethod
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def train_classification_model(model_name: str) -> bool:
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"""Train a classification model."""
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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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tf.get_logger().setLevel(logging.ERROR)
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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 = 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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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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# 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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# 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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# train the model
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model.fit(train_gen, epochs=EPOCHS, verbose=0)
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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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# 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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