classification-models-tensorflow/train_vgg16.py

import os

import tensorflow as tf
from keras.callbacks import ModelCheckpoint, CSVLogger
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16


def load_data(train_dir, val_dir, img_size=(224, 224), batch_size=32):
    # 使用 ImageDataGenerator 加载图像并进行预处理
    train_datagen = ImageDataGenerator(
        rescale=1.0 / 255.0,  # 归一化
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest'
    )

    val_datagen = ImageDataGenerator(rescale=1.0 / 255.0)

    train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=img_size,  # VGG16 输入大小
        batch_size=batch_size,
        class_mode='categorical'  # 多分类任务
    )

    val_generator = val_datagen.flow_from_directory(
        val_dir,
        target_size=img_size,
        batch_size=batch_size,
        class_mode='categorical'
    )

    return train_generator, val_generator


def train_model(args, train_generator, val_generator):
    # Create model
    model = VGG16(weights=None, include_top=True, input_shape=(224, 224, 3), classes=10)

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

    # 编译模型
    model.compile(optimizer=tf.keras.optimizers.Adam(),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    # 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, 'vgg16_{epoch:03d}.h5'),  # Save weights as vgg16_{epoch}.h5
        save_weights_only=False,
        save_freq='epoch',  # Save after every epoch
        verbose=1
    )

    # 训练模型
    history = model.fit(
        train_generator,
        steps_per_epoch=train_generator.samples // train_generator.batch_size,
        epochs=args.epochs,
        validation_data=val_generator,
        validation_steps=val_generator.samples // val_generator.batch_size,
        callbacks=[csv_logger, 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("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
    parser.add_argument(
        "-b", "--batch-size", default=2, 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("--lr-scheduler", default="steplr", type=str, help="the lr scheduler (default: steplr)")
    parser.add_argument("--lr-warmup-epochs", default=0, type=int, help="the number of epochs to warmup (default: 0)")
    parser.add_argument(
        "--lr-warmup-method", default="constant", type=str, help="the warmup method (default: constant)"
    )
    parser.add_argument("--lr-warmup-decay", default=0.01, type=float, help="the decay for lr")
    parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
    parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
    parser.add_argument("--lr-min", default=0.0, type=float, help="minimum lr of lr schedule (default: 0.0)")
    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")

    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_generator, val_generator = 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_generator, val_generator)