Face Aging Prediction (Python)

Documentation for this project

main.py main program is as follows

1. Import the necessary libraries and modules:

   • Import TensorFlow library and custom FaceAgingmodules.
   • Import the operating system library and the parameter parsing library.

2. Define str2boolthe function:

   • A custom function is used to convert a string to a boolean.

3. Create a command line argument parser:

   • Use to argparse.ArgumentParsercreate a parser, and set the relevant information of the command line parameters, such as whether to train, number of rounds, dataset name, etc.

4. Main function main(_)entry:

   • Print the set parameters.
   • Configure TensorFlow sessions, set GPU usage, etc.

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

   • Create FaceAginga model instance, passing in the session, training mode flag, save path and dataset name.

6. Determine whether to train mode:

   • If it is training mode, decide whether to use the pre-trained model for training according to the parameters.
   • If you do not use a pre-trained model, perform the pre-training step, and start the formal training after the pre-training is complete.
   • Execute the training method of the model, and pass in parameters such as the number of training rounds.

7. If not in training mode:

   • Enter the test mode, execute the custom test method of the model, and pass in the test image directory.

8. Execute the program in __name__ == '__main__':

   • Performs command line argument parsing and main function.

import tensorflow as tf
from FaceAging import FaceAging  # 导入自定义的 FaceAging 模块
from os import environ
import 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()

 

2.FaceAging.py

main process

1. Import the necessary libraries and modules:

   • Import required Python libraries such as NumPy, TensorFlow, etc.
   • Import custom ops (ops.py).

2. Define FaceAgingclass:

   • In the initialization method, various parameters of the model are set, such as input image size, network layer parameters, training parameters, etc., and the input nodes of the TensorFlow graph are created.
   • The structure of the graph is defined, including encoders, generators, discriminators, etc.
   • Loss functions are defined, including generators, discriminators, total variation (TV), etc.
   • The summary information needed for TensorBoard visualization is collected.

3. trainmethod:

   • A list of filenames to load the training dataset from.
   • The optimizer and loss function are defined, and then the model is trained.
   • In each epoch, randomly select a part of training image samples, calculate and update the parameters of the generator and discriminator, and output training progress and other information.
   • Save intermediate checkpoints of the model, generate sample images for visualization, and save the final model after training.

4. encodermethod:

   • The encoder structure is implemented to convert the input image into corresponding noise or features.

5. generatormethod:

   • The generator structure is realized, and noise features, age labels and gender labels are concatenated to generate face images of corresponding age groups.

6. discriminator_zand discriminator_imgmethod:

   • A discriminator structure is implemented to discriminate the input noise features or images.

7. save_checkpointand load_checkpointmethod:

   • Used to save and load model checkpoints during training.

8. sampleand testmethod:

   • Generate some sample images and save intermediate results during training as images.

9. custom_testmethod:

   • Run the model for custom testing, load the model and generate aging effects for specific faces.

from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
import numpy as np
from scipy.io import savemat
from 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 >_<!")
                # load init model
                if use_init_model:
                    if not os.path.exists('init_model/model-init.data-00000-of-00001'):
                        from init_model.zip_opt import join
                        try:
                            join('init_model/model_parts', 'init_model/model-init.data-00000-of-00001')
                        except:
                            raise Exception('Error joining files')
                    self.load_checkpoint(model_path='init_model')


        # epoch iteration
        num_batches = len(file_names) // self.size_batch
        for epoch in range(num_epochs):
            if enable_shuffle:
                np.random.shuffle(file_names)
            for ind_batch in range(num_batches):
                start_time = time.time()
                # read batch images and labels
                batch_files = file_names[ind_batch*self.size_batch:(ind_batch+1)*self.size_batch]
                batch = [load_image(
                    image_path=batch_file,
                    image_size=self.size_image,
                    image_value_range=self.image_value_range,
                    is_gray=(self.num_input_channels == 1),
                ) for batch_file in batch_files]
                if self.num_input_channels == 1:
                    batch_images = np.array(batch).astype(np.float32)[:, :, :, None]
                else:
                    batch_images = np.array(batch).astype(np.float32)
                batch_label_age = np.ones(
                    shape=(len(batch_files), self.num_categories),
                    dtype=np.float
                ) * self.image_value_range[0]
                batch_label_gender = np.ones(
                    shape=(len(batch_files), 2),
                    dtype=np.float
                ) * self.image_value_range[0]
                for i, label in enumerate(batch_files):
                    label = int(str(batch_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
                    batch_label_age[i, label] = self.image_value_range[-1]
                    gender = int(str(batch_files[i]).split('/')[-1].split('_')[1])
                    batch_label_gender[i, gender] = self.image_value_range[-1]

                # prior distribution on the prior of z
                batch_z_prior = np.random.uniform(
                    self.image_value_range[0],
                    self.image_value_range[-1],
                    [self.size_batch, self.num_z_channels]
                ).astype(np.float32)

                # update
                _, _, _, EG_err, Ez_err, Dz_err, Dzp_err, Gi_err, DiG_err, Di_err, TV = self.session.run(
                    fetches = [
                        self.EG_optimizer,
                        self.D_z_optimizer,
                        self.D_img_optimizer,
                        self.EG_loss,
                        self.E_z_loss,
                        self.D_z_loss_z,
                        self.D_z_loss_prior,
                        self.G_img_loss,
                        self.D_img_loss_G,
                        self.D_img_loss_input,
                        self.tv_loss
                    ],
                    feed_dict={
                        self.input_image: batch_images,
                        self.age: batch_label_age,
                        self.gender: batch_label_gender,
                        self.z_prior: batch_z_prior
                    }
                )

                print("\nEpoch: [%3d/%3d] Batch: [%3d/%3d]\n\tEG_err=%.4f\tTV=%.4f" %
                    (epoch+1, num_epochs, ind_batch+1, num_batches, EG_err, TV))
                print("\tEz=%.4f\tDz=%.4f\tDzp=%.4f" % (Ez_err, Dz_err, Dzp_err))
                print("\tGi=%.4f\tDi=%.4f\tDiG=%.4f" % (Gi_err, Di_err, DiG_err))

                # estimate left run time
                elapse = time.time() - start_time
                time_left = ((num_epochs - epoch - 1) * num_batches + (num_batches - ind_batch - 1)) * elapse
                print("\tTime left: %02d:%02d:%02d" %
                      (int(time_left / 3600), int(time_left % 3600 / 60), time_left % 60))

                # add to summary
                summary = self.summary.eval(
                    feed_dict={
                        self.input_image: batch_images,
                        self.age: batch_label_age,
                        self.gender: batch_label_gender,
                        self.z_prior: batch_z_prior
                    }
                )
                self.writer.add_summary(summary, self.EG_global_step.eval())

            # save sample images for each epoch
            name = '{:02d}.png'.format(epoch+1)
            self.sample(sample_images, sample_label_age, sample_label_gender, name)
            self.test(sample_images, sample_label_gender, name)

            # save checkpoint for each 5 epoch
            if np.mod(epoch, 5) == 4:
                self.save_checkpoint()

        # save the trained model
        self.save_checkpoint()
        # close the summary writer
        self.writer.close()


    def encoder(self, image, reuse_variables=False):
        if reuse_variables:
            tf.get_variable_scope().reuse_variables()
        num_layers = int(np.log2(self.size_image)) - int(self.size_kernel / 2)
        current = image
        # conv layers with stride 2
        for i in range(num_layers):
            name = 'E_conv' + str(i)
            current = conv2d(
                    input_map=current,
                    num_output_channels=self.num_encoder_channels * (2 ** i),
                    size_kernel=self.size_kernel,
                    name=name
                )
            current = tf.nn.relu(current)

        # fully connection layer
        name = 'E_fc'
        current = fc(
            input_vector=tf.reshape(current, [self.size_batch, -1]),
            num_output_length=self.num_z_channels,
            name=name
        )

        # output
        return tf.nn.tanh(current)

    def generator(self, z, y, gender, reuse_variables=False, enable_tile_label=True, tile_ratio=1.0):
        if reuse_variables:
            tf.get_variable_scope().reuse_variables()
        num_layers = int(np.log2(self.size_image)) - int(self.size_kernel / 2)
        if enable_tile_label:
            duplicate = int(self.num_z_channels * tile_ratio / self.num_categories)
        else:
            duplicate = 1
        z = concat_label(z, y, duplicate=duplicate)
        if enable_tile_label:
            duplicate = int(self.num_z_channels * tile_ratio / 2)
        else:
            duplicate = 1
        z = concat_label(z, gender, duplicate=duplicate)
        size_mini_map = int(self.size_image / 2 ** num_layers)
        # fc layer
        name = 'G_fc'
        current = fc(
            input_vector=z,
            num_output_length=self.num_gen_channels * size_mini_map * size_mini_map,
            name=name
        )
        # reshape to cube for deconv
        current = tf.reshape(current, [-1, size_mini_map, size_mini_map, self.num_gen_channels])
        current = tf.nn.relu(current)
        # deconv layers with stride 2
        for i in range(num_layers):
            name = 'G_deconv' + str(i)
            current = deconv2d(
                    input_map=current,
                    output_shape=[self.size_batch,
                                  size_mini_map * 2 ** (i + 1),
                                  size_mini_map * 2 ** (i + 1),
                                  int(self.num_gen_channels / 2 ** (i + 1))],
                    size_kernel=self.size_kernel,
                    name=name
                )
            current = tf.nn.relu(current)
        name = 'G_deconv' + str(i+1)
        current = deconv2d(
            input_map=current,
            output_shape=[self.size_batch,
                          self.size_image,
                          self.size_image,
                          int(self.num_gen_channels / 2 ** (i + 2))],
            size_kernel=self.size_kernel,
            stride=1,
            name=name
        )
        current = tf.nn.relu(current)
        name = 'G_deconv' + str(i + 2)
        current = deconv2d(
            input_map=current,
            output_shape=[self.size_batch,
                          self.size_image,
                          self.size_image,
                          self.num_input_channels],
            size_kernel=self.size_kernel,
            stride=1,
            name=name
        )

        # output
        return tf.nn.tanh(current)

    def discriminator_z(self, z, is_training=True, reuse_variables=False, num_hidden_layer_channels=(64, 32, 16), enable_bn=True):
        if reuse_variables:
            tf.get_variable_scope().reuse_variables()
        current = z
        # fully connection layer
        for i in range(len(num_hidden_layer_channels)):
            name = 'D_z_fc' + str(i)
            current = fc(
                    input_vector=current,
                    num_output_length=num_hidden_layer_channels[i],
                    name=name
                )
            if enable_bn:
                name = 'D_z_bn' + str(i)
                current = tf.contrib.layers.batch_norm(
                    current,
                    scale=False,
                    is_training=is_training,
                    scope=name,
                    reuse=reuse_variables
                )
            current = tf.nn.relu(current)
        # output layer
        name = 'D_z_fc' + str(i+1)
        current = fc(
            input_vector=current,
            num_output_length=1,
            name=name
        )
        return tf.nn.sigmoid(current), current

    def discriminator_img(self, image, y, gender, is_training=True, reuse_variables=False, num_hidden_layer_channels=(16, 32, 64, 128), enable_bn=True):
        if reuse_variables:
            tf.get_variable_scope().reuse_variables()
        num_layers = len(num_hidden_layer_channels)
        current = image
        # conv layers with stride 2
        for i in range(num_layers):
            name = 'D_img_conv' + str(i)
            current = conv2d(
                    input_map=current,
                    num_output_channels=num_hidden_layer_channels[i],
                    size_kernel=self.size_kernel,
                    name=name
                )
            if enable_bn:
                name = 'D_img_bn' + str(i)
                current = tf.contrib.layers.batch_norm(
                    current,
                    scale=False,
                    is_training=is_training,
                    scope=name,
                    reuse=reuse_variables
                )
            current = tf.nn.relu(current)
            if i == 0:
                current = concat_label(current, y)
                current = concat_label(current, gender, int(self.num_categories / 2))
        # fully connection layer
        name = 'D_img_fc1'
        current = fc(
            input_vector=tf.reshape(current, [self.size_batch, -1]),
            num_output_length=1024,
            name=name
        )
        current = lrelu(current)
        name = 'D_img_fc2'
        current = fc(
            input_vector=current,
            num_output_length=1,
            name=name
        )
        # output
        return tf.nn.sigmoid(current), current

    def save_checkpoint(self):
        checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
        if not os.path.exists(checkpoint_dir):
            os.makedirs(checkpoint_dir)
        self.saver.save(
            sess=self.session,
            save_path=os.path.join(checkpoint_dir, 'model'),
            global_step=self.EG_global_step.eval()
        )

    def load_checkpoint(self, model_path=None):
        if model_path is None:
            print("\n\tLoading pre-trained model ...")
            checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
        else:
            print("\n\tLoading init model ...")
            checkpoint_dir = model_path
        checkpoints = tf.train.get_checkpoint_state(checkpoint_dir)
        if checkpoints and checkpoints.model_checkpoint_path:
            checkpoints_name = os.path.basename(checkpoints.model_checkpoint_path)
            try:
                self.saver.restore(self.session, os.path.join(checkpoint_dir, checkpoints_name))
                return True
            except:
                return False
        else:
            return False

    def sample(self, images, labels, gender, name):
        sample_dir = os.path.join(self.save_dir, 'samples')
        if not os.path.exists(sample_dir):
            os.makedirs(sample_dir)
        z, G = self.session.run(
            [self.z, self.G],
            feed_dict={
                self.input_image: images,
                self.age: labels,
                self.gender: gender
            }
        )
        size_frame = int(np.sqrt(self.size_batch))
        save_batch_images(
            batch_images=G,
            save_path=os.path.join(sample_dir, name),
            image_value_range=self.image_value_range,
            size_frame=[size_frame, size_frame]
        )

    def test(self, images, gender, name):
        test_dir = os.path.join(self.save_dir, 'test')
        if not os.path.exists(test_dir):
            os.makedirs(test_dir)
        images = images[:int(np.sqrt(self.size_batch)), :, :, :]
        gender = gender[:int(np.sqrt(self.size_batch)), :]
        size_sample = images.shape[0]
        labels = np.arange(size_sample)
        labels = np.repeat(labels, size_sample)
        query_labels = np.ones(
            shape=(size_sample ** 2, size_sample),
            dtype=np.float32
        ) * self.image_value_range[0]
        for i in range(query_labels.shape[0]):
            query_labels[i, labels[i]] = self.image_value_range[-1]
        query_images = np.tile(images, [self.num_categories, 1, 1, 1])
        query_gender = np.tile(gender, [self.num_categories, 1])
        z, G = self.session.run(
            [self.z, self.G],
            feed_dict={
                self.input_image: query_images,
                self.age: query_labels,
                self.gender: query_gender
            }
        )
        save_batch_images(
            batch_images=query_images,
            save_path=os.path.join(test_dir, 'input.png'),
            image_value_range=self.image_value_range,
            size_frame=[size_sample, size_sample]
        )
        save_batch_images(
            batch_images=G,
            save_path=os.path.join(test_dir, name),
            image_value_range=self.image_value_range,
            size_frame=[size_sample, size_sample]
        )

    def custom_test(self, testing_samples_dir):
        if not self.load_checkpoint():
            print("\tFAILED >_<!")
            exit(0)
        else:
            print("\tSUCCESS ^_^")

        num_samples = int(np.sqrt(self.size_batch))
        file_names = glob(testing_samples_dir)
        if len(file_names) < num_samples:
            print ('The number of testing images is must larger than %d' % num_samples)
            exit(0)
        sample_files = file_names[0:num_samples]
        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:
            images = np.array(sample).astype(np.float32)[:, :, :, None]
        else:
            images = np.array(sample).astype(np.float32)
        gender_male = np.ones(
            shape=(num_samples, 2),
            dtype=np.float32
        ) * self.image_value_range[0]
        gender_female = np.ones(
            shape=(num_samples, 2),
            dtype=np.float32
        ) * self.image_value_range[0]
        for i in range(gender_male.shape[0]):
            gender_male[i, 0] = self.image_value_range[-1]
            gender_female[i, 1] = self.image_value_range[-1]

        self.test(images, gender_male, 'test_as_male.png')
        self.test(images, gender_female, 'test_as_female.png')

        print ('\n\tDone! Results are saved as %s\n' % os.path.join(self.save_dir, 'test', 'test_as_xxx.png'))

 3.data, a total of 23708 photos 

 4. Those who are interested in the data set can follow

from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
import numpy as np
from scipy.io import savemat
from ops import *

#https://mbd.pub/o/bread/ZJ2UmJpp