classification-models-tensorflow/train_alexnet.py

import os

import tensorflow as tf
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint, CSVLogger
from models.AlexNet import create_model
from tensorflow.keras.preprocessing import image_dataset_from_directory



def load_data(train_dir, val_dir, img_size=(224, 224), batch_size=32):
    # Define data augmentation for the training set
    # train_datagen = tf.keras.Sequential([
    #     tf.keras.layers.RandomFlip('horizontal'),
    #     tf.keras.layers.RandomRotation(0.2),
    #     tf.keras.layers.RandomZoom(0.2),
    #     tf.keras.layers.RandomContrast(0.2),
    # ])
    def augment(image):
        # Random horizontal flip
        image = tf.image.random_flip_left_right(image)
        # Random contrast adjustment
        image = tf.image.random_contrast(image, lower=0.8, upper=1.2)
        # Random brightness adjustment
        image = tf.image.random_brightness(image, max_delta=0.2)
        return image

    # Load training dataset
    train_dataset = image_dataset_from_directory(
        train_dir,
        image_size=img_size,  # Resize images to (224, 224)
        batch_size=batch_size,
        label_mode='categorical',  # Return integer labels
        shuffle=True
    )

    # Load validation dataset
    val_dataset = image_dataset_from_directory(
        val_dir,
        image_size=img_size,  # Resize images to (224, 224)
        batch_size=batch_size,
        label_mode='categorical',  # Return integer labels
        shuffle=False
    )

    # Normalize the datasets (rescale pixel values to [0, 1])
    train_dataset = train_dataset.map(
        lambda x, y: (augment(x) / 255.0, y),
        num_parallel_calls=tf.data.AUTOTUNE
    )

    val_dataset = val_dataset.map(
        lambda x, y: (x / 255.0, y),
        num_parallel_calls=tf.data.AUTOTUNE
    )

    # Prefetch to improve performance
    train_dataset = train_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
    val_dataset = val_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)

    return train_dataset, val_dataset


def train_model(args, train_data, val_data):
    # Create model
    model = create_model()

    # 调整学习率
    learning_rate = args.lr if args.lr else 1e-2

    # Select optimizer based on args.opt
    if args.opt == 'sgd':
        optimizer = SGD(learning_rate=learning_rate,
                                            momentum=args.momentum if args.momentum else 0.0)
    elif args.opt == 'adam':
        optimizer = Adam(learning_rate=learning_rate)
    else:
        optimizer = Adam(learning_rate=learning_rate)  # Default to Adam if unspecified

    # Compile model
    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

    # Check if a checkpoint exists and determine the initial_epoch
    latest_checkpoint = tf.train.latest_checkpoint(args.output_dir)
    if latest_checkpoint:
        initial_epoch = int(latest_checkpoint.split('_')[-1].split('.')[0])  # Get the last epoch from filename
        print(f"Resuming training from epoch {initial_epoch}")
    else:
        initial_epoch = 0

    # Define CSVLogger to log training history to a CSV file
    csv_logger = CSVLogger(os.path.join(args.output_dir, 'training_log.csv'), append=True)

    # Define ModelCheckpoint callback to save weights for each epoch
    checkpoint_callback = ModelCheckpoint(
        os.path.join(args.output_dir, 'alexnet_{epoch:03d}.h5'),  # Save weights as alexnet_{epoch}.h5
        save_weights_only=False,
        save_freq='epoch',  # Save after every epoch
        verbose=1
    )

    # Train the model
    history = model.fit(
        train_data,
        epochs=args.epochs,
        validation_data=val_data,
        initial_epoch=initial_epoch,
        callbacks=[csv_logger, checkpoint_callback],  # Add checkpoint callback
    )

    return history


def get_args_parser(add_help=True):
    import argparse

    parser = argparse.ArgumentParser(description="PyTorch Classification Training", add_help=add_help)

    parser.add_argument("--data-path", default="dataset/imagenette2-320", type=str, help="dataset path")
    parser.add_argument("--output-dir", default="checkpoints/alexnet", type=str, help="path to save outputs")

    parser.add_argument(
        "-b", "--batch-size", default=64, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
    )
    parser.add_argument("--epochs", default=90, type=int, metavar="N", help="number of total epochs to run")

    parser.add_argument("--opt", default="sgd", type=str, help="optimizer")
    parser.add_argument("--lr", default=0.1, type=float, help="initial learning rate")
    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")

    parser.add_argument(
        "--input-size", default=224, type=int, help="the random crop size used for training (default: 224)"
    )
    return parser

if __name__ == "__main__":
    args = get_args_parser().parse_args()

    # Set directories for your custom dataset
    train_dir = os.path.join(args.data_path, "train")
    val_dir = os.path.join(args.data_path, "val")

    # Set the directory where you want to save weights
    os.makedirs(args.output_dir, exist_ok=True)

    # Load data
    train_data, val_data = load_data(train_dir, val_dir, img_size=(args.input_size, args.input_size), batch_size=args.batch_size)

    # Start training
    train_model(args, train_data, val_data)