人脸老化预测（Python）

本次项目的文件

main.py主程序如下

1. 导入必要的库和模块：

   • 导入 Tensorflow 库以及自定义的 FaceAging 模块。
   • 导入操作系统库和参数解析库。

2. 定义 str2bool 函数：

   • 自定义函数用于将字符串转换为布尔值。

3. 创建命令行参数解析器：

   • 使用 argparse.ArgumentParser 创建解析器，设置命令行参数的相关信息，如是否训练、轮数、数据集名称等。

4. 主函数 main(_) 入口：

   • 打印设置的参数。
   • 配置 TensorFlow 会话，设置 GPU 使用等。

5. 在 with tf.Session(config=config) as session 中：

   • 创建 FaceAging 模型实例，传入会话、训练模式标志、保存路径和数据集名称。

6. 判断是否训练模式：

   • 如果是训练模式，根据参数决定是否使用预训练模型进行训练。
   • 如果不使用预训练模型，执行预训练步骤，并在预训练完成后开始正式训练。
   • 执行模型的训练方法，传入训练轮数等参数。

7. 如果不是训练模式：

   • 进入测试模式，执行模型的自定义测试方法，传入测试图像目录。

8. 在 __name__ == '__main__' 中执行程序：

   • 执行命令行参数解析和主函数。

import tensorflow as tffrom FaceAging import FaceAging  # 导入自定义的 FaceAging 模块from os import environimport argparse# 设置环境变量，控制 TensorFlow 输出日志等级environ['TF_CPP_MIN_LOG_LEVEL'] = '3'# 自定义一个函数用于将字符串转换为布尔值def str2bool(v):    if v.lower() in ('yes', 'true', 't', 'y', '1'):        return True    elif v.lower() in ('no', 'false', 'f', 'n', '0'):        return False    else:        raise argparse.ArgumentTypeError('Boolean value expected.')# 创建命令行参数解析器parser = argparse.ArgumentParser(description='CAAE')parser.add_argument('--is_train', type=str2bool, default=True, help='是否进行训练')parser.add_argument('--epoch', type=int, default=50, help='训练的轮数')parser.add_argument('--dataset', type=str, default='UTKFace', help='存储在./data目录中的训练数据集名称')parser.add_argument('--savedir', type=str, default='save', help='保存检查点、中间训练结果和摘要的目录')parser.add_argument('--testdir', type=str, default='None', help='测试图像所在的目录')parser.add_argument('--use_trained_model', type=str2bool, default=True, help='是否使用已有的模型进行训练')parser.add_argument('--use_init_model', type=str2bool, default=True, help='如果找不到已有模型，是否从初始模型开始训练')FLAGS = parser.parse_args()# 主函数入口def main(_):    # 打印设置参数    import pprint    pprint.pprint(FLAGS)    # 配置 TensorFlow 会话    config = tf.ConfigProto()    config.gpu_options.allow_growth = True    with tf.Session(config=config) as session:        # 创建 FaceAging 模型实例        model = FaceAging(            session,  # TensorFlow 会话            is_training=FLAGS.is_train,  # 是否为训练模式的标志            save_dir=FLAGS.savedir,  # 保存检查点、样本和摘要的路径            dataset_name=FLAGS.dataset  # 存储在 ./data 目录中的数据集名称        )        if FLAGS.is_train:            print ('\n\t训练模式')            if not FLAGS.use_trained_model:                print ('\n\t预训练网络')                model.train(                    num_epochs=10,  # 训练轮数                    use_trained_model=FLAGS.use_trained_model,                    use_init_model=FLAGS.use_init_model,                    weights=(0, 0, 0)                )                print ('\n\t预训练完成！训练将开始。')            model.train(                num_epochs=FLAGS.epoch,  # 训练轮数                use_trained_model=FLAGS.use_trained_model,                use_init_model=FLAGS.use_init_model            )        else:            print ('\n\t测试模式')            model.custom_test(                testing_samples_dir=FLAGS.testdir + '/*jpg'            )if __name__ == '__main__':    # 在主程序中执行命令行解析和执行主函数    tf.app.run()

 

FaceAging.py

主要流程

1. 导入必要的库和模块：

   • 导入所需的python库，如NumPy、TensorFlow等。
   • 导入自定义的操作（ops.py）。

2. 定义 FaceAging 类：

   • 在初始化方法中，设置了模型的各种参数，例如输入图像大小、网络层参数、训练参数等，并创建了 TensorFlow 图的输入节点。
   • 定义了图的结构，包括编码器、生成器、判别器等。
   • 定义了损失函数，包括生成器、判别器、总变差（TV）等。
   • 收集了需要用于TensorBoard可视化的摘要信息。

3. train 方法：

   • 从文件中加载训练数据集的文件名列表。
   • 定义了优化器和损失函数，然后进行模型的训练。
   • 在每个epoch中，随机选择一部分训练图像样本，计算并更新生成器和判别器的参数，输出训练进度等信息。
   • 保存模型的中间检查点，生成样本图像用于可视化，训练结束后保存最终模型。

4. encoder 方法：

   • 实现了编码器结构，将输入图像转化为对应的噪声或特征。

5. generator 方法：

   • 实现了生成器结构，将噪声特征、年龄标签和性别标签拼接，生成相应年龄段的人脸图像。

6. discriminator_z 和 discriminator_img 方法：

   • 实现了判别器结构，对输入的噪声特征或图像进行判别。

7. save_checkpoint 和 load_checkpoint 方法：

   • 用于保存和加载训练过程中的模型检查点。

8. sample 和 test 方法：

   • 生成一些样本图像以及将训练过程中的中间结果保存为图片。

9. custom_test 方法：

   • 运行模型进行自定义测试，加载模型并生成特定人脸的年龄化效果。

from __future__ import divisionimport osimport timefrom glob import globimport tensorflow as tfimport numpy as npfrom scipy.io import savematfrom ops import *class FaceAging(object):    def __init__(self,                 session,  # TensorFlow session                 size_image=128,  # size the input images                 size_kernel=5,  # size of the kernels in convolution and deconvolution                 size_batch=100,  # mini-batch size for training and testing, must be square of an integer                 num_input_channels=3,  # number of channels of input images                 num_encoder_channels=64,  # number of channels of the first conv layer of encoder                 num_z_channels=50,  # number of channels of the layer z (noise or code)                 num_categories=10,  # number of categories (age segments) in the training dataset                 num_gen_channels=1024,  # number of channels of the first deconv layer of generator                 enable_tile_label=True,  # enable to tile the label                 tile_ratio=1.0,  # ratio of the length between tiled label and z                 is_training=True,  # flag for training or testing mode                 save_dir='./save',  # path to save checkpoints, samples, and summary                 dataset_name='UTKFace'  # name of the dataset in the folder ./data                 ):        self.session = session        self.image_value_range = (-1, 1)        self.size_image = size_image        self.size_kernel = size_kernel        self.size_batch = size_batch        self.num_input_channels = num_input_channels        self.num_encoder_channels = num_encoder_channels        self.num_z_channels = num_z_channels        self.num_categories = num_categories        self.num_gen_channels = num_gen_channels        self.enable_tile_label = enable_tile_label        self.tile_ratio = tile_ratio        self.is_training = is_training        self.save_dir = save_dir        self.dataset_name = dataset_name        # ************************************* input to graph ********************************************************        self.input_image = tf.placeholder(            tf.float32,            [self.size_batch, self.size_image, self.size_image, self.num_input_channels],            name='input_images'        )        self.age = tf.placeholder(            tf.float32,            [self.size_batch, self.num_categories],            name='age_labels'        )        self.gender = tf.placeholder(            tf.float32,            [self.size_batch, 2],            name='gender_labels'        )        self.z_prior = tf.placeholder(            tf.float32,            [self.size_batch, self.num_z_channels],            name='z_prior'        )        # ************************************* build the graph *******************************************************        print ('\n\tBuilding graph ...')        # encoder: input image --> z        self.z = self.encoder(            image=self.input_image        )        # generator: z + label --> generated image        self.G = self.generator(            z=self.z,            y=self.age,            gender=self.gender,            enable_tile_label=self.enable_tile_label,            tile_ratio=self.tile_ratio        )        # discriminator on z        self.D_z, self.D_z_logits = self.discriminator_z(            z=self.z,            is_training=self.is_training        )        # discriminator on G        self.D_G, self.D_G_logits = self.discriminator_img(            image=self.G,            y=self.age,            gender=self.gender,            is_training=self.is_training        )        # discriminator on z_prior        self.D_z_prior, self.D_z_prior_logits = self.discriminator_z(            z=self.z_prior,            is_training=self.is_training,            reuse_variables=True        )        # discriminator on input image        self.D_input, self.D_input_logits = self.discriminator_img(            image=self.input_image,            y=self.age,            gender=self.gender,            is_training=self.is_training,            reuse_variables=True        )        # ************************************* loss functions *******************************************************        # loss function of encoder + generator        #self.EG_loss = tf.nn.l2_loss(self.input_image - self.G) / self.size_batch  # L2 loss        self.EG_loss = tf.reduce_mean(tf.abs(self.input_image - self.G))  # L1 loss        # loss function of discriminator on z        self.D_z_loss_prior = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_prior_logits, labels=tf.ones_like(self.D_z_prior_logits))        )        self.D_z_loss_z = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_logits, labels=tf.zeros_like(self.D_z_logits))        )        self.E_z_loss = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_logits, labels=tf.ones_like(self.D_z_logits))        )        # loss function of discriminator on image        self.D_img_loss_input = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_input_logits, labels=tf.ones_like(self.D_input_logits))        )        self.D_img_loss_G = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_G_logits, labels=tf.zeros_like(self.D_G_logits))        )        self.G_img_loss = tf.reduce_mean(            tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_G_logits, labels=tf.ones_like(self.D_G_logits))        )        # total variation to smooth the generated image        tv_y_size = self.size_image        tv_x_size = self.size_image        self.tv_loss = (            (tf.nn.l2_loss(self.G[:, 1:, :, :] - self.G[:, :self.size_image - 1, :, :]) / tv_y_size) +            (tf.nn.l2_loss(self.G[:, :, 1:, :] - self.G[:, :, :self.size_image - 1, :]) / tv_x_size)) / self.size_batch        # *********************************** trainable variables ****************************************************        trainable_variables = tf.trainable_variables()        # variables of encoder        self.E_variables = [var for var in trainable_variables if 'E_' in var.name]        # variables of generator        self.G_variables = [var for var in trainable_variables if 'G_' in var.name]        # variables of discriminator on z        self.D_z_variables = [var for var in trainable_variables if 'D_z_' in var.name]        # variables of discriminator on image        self.D_img_variables = [var for var in trainable_variables if 'D_img_' in var.name]        # ************************************* collect the summary ***************************************        self.z_summary = tf.summary.histogram('z', self.z)        self.z_prior_summary = tf.summary.histogram('z_prior', self.z_prior)        self.EG_loss_summary = tf.summary.Scalar('EG_loss', self.EG_loss)        self.D_z_loss_z_summary = tf.summary.scalar('D_z_loss_z', self.D_z_loss_z)        self.D_z_loss_prior_summary = tf.summary.scalar('D_z_loss_prior', self.D_z_loss_prior)        self.E_z_loss_summary = tf.summary.scalar('E_z_loss', self.E_z_loss)        self.D_z_logits_summary = tf.summary.histogram('D_z_logits', self.D_z_logits)        self.D_z_prior_logits_summary = tf.summary.histogram('D_z_prior_logits', self.D_z_prior_logits)        self.D_img_loss_input_summary = tf.summary.scalar('D_img_loss_input', self.D_img_loss_input)        self.D_img_loss_G_summary = tf.summary.scalar('D_img_loss_G', self.D_img_loss_G)        self.G_img_loss_summary = tf.summary.scalar('G_img_loss', self.G_img_loss)        self.D_G_logits_summary = tf.summary.histogram('D_G_logits', self.D_G_logits)        self.D_input_logits_summary = tf.summary.histogram('D_input_logits', self.D_input_logits)        # for saving the graph and variables        self.saver = tf.train.Saver(max_to_keep=2)    def train(self,              num_epochs=200,  # number of epochs              learning_rate=0.0002,  # learning rate of optimizer              beta1=0.5,  # parameter for Adam optimizer              decay_rate=1.0,  # learning rate decay (0, 1], 1 means no decay              enable_shuffle=True,  # enable shuffle of the dataset              use_trained_model=True,  # use the saved checkpoint to initialize the network              use_init_model=True,  # use the init model to initialize the network              weigts=(0.0001, 0, 0)  # the weights of adversarial loss and TV loss              ):        # *************************** load file names of images ******************************************************        file_names = glob(os.path.join('./data', self.dataset_name, '*.jpg'))        size_data = len(file_names)        np.random.seed(seed=2017)        if enable_shuffle:            np.random.shuffle(file_names)        # *********************************** optimizer **************************************************************        # over all, there are three loss functions, weights may differ from the paper because of different datasets        self.loss_EG = self.EG_loss + weigts[0] * self.G_img_loss + weigts[1] * self.E_z_loss + weigts[2] * self.tv_loss # slightly increase the params        self.loss_Dz = self.D_z_loss_prior + self.D_z_loss_z        self.loss_Di = self.D_img_loss_input + self.D_img_loss_G        # set learning rate decay        self.EG_global_step = tf.Variable(0, trainable=False, name='global_step')        EG_learning_rate = tf.train.exponential_decay(            learning_rate=learning_rate,            global_step=self.EG_global_step,            decay_steps=size_data / self.size_batch * 2,            decay_rate=decay_rate,            staircase=True        )        # optimizer for encoder + generator        with tf.variable_scope('opt', reuse=tf.AUTO_REUSE):            self.EG_optimizer = tf.train.AdamOptimizer(                learning_rate=EG_learning_rate,                beta1=beta1            ).minimize(                loss=self.loss_EG,                global_step=self.EG_global_step,                var_list=self.E_variables + self.G_variables            )            # optimizer for discriminator on z            self.D_z_optimizer = tf.train.AdamOptimizer(                learning_rate=EG_learning_rate,                beta1=beta1            ).minimize(                loss=self.loss_Dz,                var_list=self.D_z_variables            )            # optimizer for discriminator on image            self.D_img_optimizer = tf.train.AdamOptimizer(                learning_rate=EG_learning_rate,                beta1=beta1            ).minimize(                loss=self.loss_Di,                var_list=self.D_img_variables            )        # *********************************** tensorboard *************************************************************        # for visualization (TensorBoard): $ tensorboard --logdir path/to/log-directory        self.EG_learning_rate_summary = tf.summary.scalar('EG_learning_rate', EG_learning_rate)        self.summary = tf.summary.merge([            self.z_summary, self.z_prior_summary,            self.D_z_loss_z_summary, self.D_z_loss_prior_summary,            self.D_z_logits_summary, self.D_z_prior_logits_summary,            self.EG_loss_summary, self.E_z_loss_summary,            self.D_img_loss_input_summary, self.D_img_loss_G_summary,            self.G_img_loss_summary, self.EG_learning_rate_summary,            self.D_G_logits_summary, self.D_input_logits_summary        ])        self.writer = tf.summary.FileWriter(os.path.join(self.save_dir, 'summary'), self.session.graph)        # ************* get some random samples as testing data to visualize the learning process *********************        sample_files = file_names[0:self.size_batch]        file_names[0:self.size_batch] = []        sample = [load_image(            image_path=sample_file,            image_size=self.size_image,            image_value_range=self.image_value_range,            is_gray=(self.num_input_channels == 1),        ) for sample_file in sample_files]        if self.num_input_channels == 1:            sample_images = np.array(sample).astype(np.float32)[:, :, :, None]        else:            sample_images = np.array(sample).astype(np.float32)        sample_label_age = np.ones(            shape=(len(sample_files), self.num_cateGories),            dtype=np.float32        ) * self.image_value_range[0]        sample_label_gender = np.ones(            shape=(len(sample_files), 2),            dtype=np.float32        ) * self.image_value_range[0]        for i, label in enumerate(sample_files):            label = int(str(sample_files[i]).split('/')[-1].split('_')[0])            if 0 <= label <= 5:                label = 0            elif 6 <= label <= 10:                label = 1            elif 11 <= label <= 15:                label = 2            elif 16 <= label <= 20:                label = 3            elif 21 <= label <= 30:                label = 4            elif 31 <= label <= 40:                label = 5            elif 41 <= label <= 50:                label = 6            elif 51 <= label <= 60:                label = 7            elif 61 <= label <= 70:                label = 8            else:                label = 9            sample_label_age[i, label] = self.image_value_range[-1]            gender = int(str(sample_files[i]).split('/')[-1].split('_')[1])            sample_label_gender[i, gender] = self.image_value_range[-1]        # ******************************************* training *******************************************************        # initialize the graph        tf.global_variables_initializer().run()        # load check point        if use_trained_model:            if self.load_checkpoint():                print("\tSUCCESS ^_^")            else:                print("\tFAILED >__

from __future__ import divisionimport osimport timefrom glob import globimport tensorflow as tfimport numpy as npfrom scipy.io import savematfrom ops import *#https://mbd.pub/o/bread/ZJ2UmJpp