机器学习与深度学习算法-图片风格转化（含数据集）

from __future__ import division import os import time from glob import glob from collections import namedtuple from module import * from utils import * class cyclegan(object): def __init__(self, sess, args): self.sess = sess self.batch_size = args.batch_size self.image_size = args.fine_size self.input_c_dim = args.input_nc self.output_c_dim = args.output_nc self.L1_lambda = args.L1_lambda self.dataset_dir = args.dataset_dir self.discriminator = discriminator if args.use_resnet: self.generator = generator_resnet else: self.generator = generator_unet if args.use_lsgan: self.criterionGAN = mae_criterion else: self.criterionGAN = sce_criterion OPTIONS = namedtuple('OPTIONS', 'batch_size image_size \ gf_dim df_dim output_c_dim is_training') self.options = OPTIONS._make((args.batch_size, args.fine_size, args.ngf, args.ndf, args.output_nc, args.phase == 'train')) self._build_model() self.saver = tf.train.Saver() self.pool = ImagePool(args.max_size) def _build_model(self): self.real_data = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.input_c_dim + self.output_c_dim], name='real_A_and_B_images') self.real_A = self.real_data[:, :, :, :self.input_c_dim] self.real_B = self.real_data[:, :, :, self.input_c_dim:self.input_c_dim + self.output_c_dim] self.fake_B = self.generator(self.real_A, self.options, False, name="generatorA2B") self.fake_A_ = self.generator(self.fake_B, self.options, False, name="generatorB2A") self.fake_A = self.generator(self.real_B, self.options, True, name="generatorB2A") self.fake_B_ = self.generator(self.fake_A, self.options, True, name="generatorA2B") self.DB_fake = self.discriminator(self.fake_B, self.options, reuse=False, name="discriminatorB") self.DA_fake = self.discriminator(self.fake_A, self.options, reuse=False, name="discriminatorA") self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \ + self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \ + self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \ + self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \ + self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) self.g_loss = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \ + self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \ + self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \ + self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) self.fake_A_sample = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.input_c_dim], name='fake_A_sample') self.fake_B_sample = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.output_c_dim], name='fake_B_sample') self.DB_real = self.discriminator(self.real_B, self.options, reuse=True, name="discriminatorB") self.DA_real = self.discriminator(self.real_A, self.options, reuse=True, name="discriminatorA") self.DB_fake_sample = self.discriminator(self.fake_B_sample, self.options, reuse=True, name="discriminatorB") self.DA_fake_sample = self.discriminator(self.fake_A_sample, self.options, reuse=True, name="discriminatorA") self.db_loss_real = self.criterionGAN(self.DB_real, tf.ones_like(self.DB_real)) self.db_loss_fake = self.criterionGAN(self.DB_fake_sample, tf.zeros_like(self.DB_fake_sample)) self.db_loss = (self.db_loss_real + self.db_loss_fake) / 2 self.da_loss_real = self.criterionGAN(self.DA_real, tf.ones_like(self.DA_real)) self.da_loss_fake = self.criterionGAN(self.DA_fake_sample, tf.zeros_like(self.DA_fake_sample)) self.da_loss = (self.da_loss_real + self.da_loss_fake) / 2 self.d_loss = self.da_loss + self.db_loss self.g_loss_a2b_sum = tf.summary.scalar("g_loss_a2b", self.g_loss_a2b) self.g_loss_b2a_sum = tf.summary.scalar("g_loss_b2a", self.g_loss_b2a) self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss) self.g_sum = tf.summary.merge([self.g_loss_a2b_sum, self.g_loss_b2a_sum, self.g_loss_sum]) self.db_loss_sum = tf.summary.scalar("db_loss", self.db_loss) self.da_loss_sum = tf.summary.scalar("da_loss", self.da_loss) self.d_loss_sum = tf.summary.scalar("d_loss", self.d_loss) self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.db_loss_real) self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.db_loss_fake) self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.da_loss_real) self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.da_loss_fake) self.d_sum = tf.summary.merge( [self.da_loss_sum, self.da_loss_real_sum, self.da_loss_fake_sum, self.db_loss_sum, self.db_loss_real_sum, self.db_loss_fake_sum, self.d_loss_sum] ) self.test_A = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.input_c_dim], name='test_A') self.test_B = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.output_c_dim], name='test_B') self.testB = self.generator(self.test_A, self.options, True, name="generatorA2B") self.testA = self.generator(self.test_B, self.options, True, name="generatorB2A") t_vars = tf.trainable_variables() self.d_vars = [var for var in t_vars if 'discriminator' in var.name] self.g_vars = [var for var in t_vars if 'generator' in var.name] for var in t_vars: print(var.name) def train(self, args): """Train cyclegan""" self.lr = tf.placeholder(tf.float32, None, name='learning_rate') self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \ .minimize(self.d_loss, var_list=self.d_vars) self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \ .minimize(self.g_loss, var_list=self.g_vars) init_op = tf.global_variables_initializer() self.sess.run(init_op) self.writer = tf.summary.FileWriter("./logs/", self.sess.graph) counter = 1 start_time = time.time() if args.continue_train: if self.load(args.checkpoint_dir): print(" [*] Load SUCCESS") else: print(" [!] Load failed...") for epoch in range(args.epoch): dataA = glob('./{}/*.*'.format(self.dataset_dir + '/trainA')) dataB = glob('./{}/*.*'.format(self.dataset_dir + '/trainB')) np.random.shuffle(dataA) np.random.shuffle(dataB) batch_idxs = min(min(len(dataA), len(dataB)), args.train_size) // self.batch_size lr = args.lr if epoch < args.epoch_step else args.lr*(args.epoch-epoch)/(args.epoch-args.epoch_step) for idx in range(0, batch_idxs): batch_files = list(zip(dataA[idx * self.batch_size:(idx + 1) * self.batch_size], dataB[idx * self.batch_size:(idx + 1) * self.batch_size])) batch_ima