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Added the AI Anime Avatar Generator model with documentation, includi…
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Generative Models/AI Anime Avatar Generator/AnimeGANv2.py
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| from tools.ops import * | ||
| from tools.utils import * | ||
| from glob import glob | ||
| import time | ||
| import numpy as np | ||
| from net import generator | ||
| from net.discriminator import D_net | ||
| from tools.data_loader import ImageGenerator | ||
| from tools.vgg19 import Vgg19 | ||
|
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| class AnimeGANv2(object) : | ||
| def __init__(self, sess, args): | ||
| self.model_name = 'AnimeGANv2' | ||
| self.sess = sess | ||
| self.checkpoint_dir = args.checkpoint_dir | ||
| self.log_dir = args.log_dir | ||
| self.dataset_name = args.dataset | ||
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| self.epoch = args.epoch | ||
| self.init_epoch = args.init_epoch # args.epoch // 20 | ||
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| self.gan_type = args.gan_type | ||
| self.batch_size = args.batch_size | ||
| self.save_freq = args.save_freq | ||
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| self.init_lr = args.init_lr | ||
| self.d_lr = args.d_lr | ||
| self.g_lr = args.g_lr | ||
|
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| """ Weight """ | ||
| self.g_adv_weight = args.g_adv_weight | ||
| self.d_adv_weight = args.d_adv_weight | ||
| self.con_weight = args.con_weight | ||
| self.sty_weight = args.sty_weight | ||
| self.color_weight = args.color_weight | ||
| self.tv_weight = args.tv_weight | ||
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| self.training_rate = args.training_rate | ||
| self.ld = args.ld | ||
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| self.img_size = args.img_size | ||
| self.img_ch = args.img_ch | ||
|
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| """ Discriminator """ | ||
| self.n_dis = args.n_dis | ||
| self.ch = args.ch | ||
| self.sn = args.sn | ||
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| self.sample_dir = os.path.join(args.sample_dir, self.model_dir) | ||
| check_folder(self.sample_dir) | ||
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| self.real = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='real_A') | ||
| self.anime = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='anime_A') | ||
| self.anime_smooth = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='anime_smooth_A') | ||
| self.test_real = tf.placeholder(tf.float32, [1, None, None, self.img_ch], name='test_input') | ||
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| self.anime_gray = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch],name='anime_B') | ||
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| self.real_image_generator = ImageGenerator('./dataset/train_photo', self.img_size, self.batch_size) | ||
| self.anime_image_generator = ImageGenerator('./dataset/{}'.format(self.dataset_name + '/style'), self.img_size, self.batch_size) | ||
| self.anime_smooth_generator = ImageGenerator('./dataset/{}'.format(self.dataset_name + '/smooth'), self.img_size, self.batch_size) | ||
| self.dataset_num = max(self.real_image_generator.num_images, self.anime_image_generator.num_images) | ||
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| self.vgg = Vgg19() | ||
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| print() | ||
| print("##### Information #####") | ||
| print("# gan type : ", self.gan_type) | ||
| print("# dataset : ", self.dataset_name) | ||
| print("# max dataset number : ", self.dataset_num) | ||
| print("# batch_size : ", self.batch_size) | ||
| print("# epoch : ", self.epoch) | ||
| print("# init_epoch : ", self.init_epoch) | ||
| print("# training image size [H, W] : ", self.img_size) | ||
| print("# g_adv_weight,d_adv_weight,con_weight,sty_weight,color_weight,tv_weight : ", self.g_adv_weight,self.d_adv_weight,self.con_weight,self.sty_weight,self.color_weight,self.tv_weight) | ||
| print("# init_lr,g_lr,d_lr : ", self.init_lr,self.g_lr,self.d_lr) | ||
| print(f"# training_rate G -- D: {self.training_rate} : 1" ) | ||
| print() | ||
|
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||
| ################################################################################## | ||
| # Generator | ||
| ################################################################################## | ||
|
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| def generator(self, x_init, reuse=False, scope="generator"): | ||
| with tf.variable_scope(scope, reuse=reuse): | ||
| G = generator.G_net(x_init) | ||
| return G.fake | ||
|
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||
| ################################################################################## | ||
| # Discriminator | ||
| ################################################################################## | ||
|
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| def discriminator(self, x_init, reuse=False, scope="discriminator"): | ||
| D = D_net(x_init, self.ch, self.n_dis, self.sn, reuse=reuse, scope=scope) | ||
| return D | ||
|
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||
| ################################################################################## | ||
| # Model | ||
| ################################################################################## | ||
| def gradient_panalty(self, real, fake, scope="discriminator"): | ||
| if self.gan_type.__contains__('dragan') : | ||
| eps = tf.random_uniform(shape=tf.shape(real), minval=0., maxval=1.) | ||
| _, x_var = tf.nn.moments(real, axes=[0, 1, 2, 3]) | ||
| x_std = tf.sqrt(x_var) # magnitude of noise decides the size of local region | ||
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| fake = real + 0.5 * x_std * eps | ||
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| alpha = tf.random_uniform(shape=[self.batch_size, 1, 1, 1], minval=0., maxval=1.) | ||
| interpolated = real + alpha * (fake - real) | ||
|
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| logit, _= self.discriminator(interpolated, reuse=True, scope=scope) | ||
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| grad = tf.gradients(logit, interpolated)[0] # gradient of D(interpolated) | ||
| grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm | ||
|
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| GP = 0 | ||
| # WGAN - LP | ||
| if self.gan_type.__contains__('lp'): | ||
| GP = self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.))) | ||
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| elif self.gan_type.__contains__('gp') or self.gan_type == 'dragan' : | ||
| GP = self.ld * tf.reduce_mean(tf.square(grad_norm - 1.)) | ||
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| return GP | ||
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| def build_model(self): | ||
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| """ Define Generator, Discriminator """ | ||
| self.generated = self.generator(self.real) | ||
| self.test_generated = self.generator(self.test_real, reuse=True) | ||
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| anime_logit = self.discriminator(self.anime) | ||
| anime_gray_logit = self.discriminator(self.anime_gray, reuse=True) | ||
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| generated_logit = self.discriminator(self.generated, reuse=True) | ||
| smooth_logit = self.discriminator(self.anime_smooth, reuse=True) | ||
|
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| """ Define Loss """ | ||
| if self.gan_type.__contains__('gp') or self.gan_type.__contains__('lp') or self.gan_type.__contains__('dragan') : | ||
| GP = self.gradient_panalty(real=self.anime, fake=self.generated) | ||
| else : | ||
| GP = 0.0 | ||
|
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| # init pharse | ||
| init_c_loss = con_loss(self.vgg, self.real, self.generated) | ||
| init_loss = self.con_weight * init_c_loss | ||
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| self.init_loss = init_loss | ||
|
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| # gan | ||
| c_loss, s_loss = con_sty_loss(self.vgg, self.real, self.anime_gray, self.generated) | ||
| tv_loss = self.tv_weight * total_variation_loss(self.generated) | ||
| t_loss = self.con_weight * c_loss + self.sty_weight * s_loss + color_loss(self.real,self.generated) * self.color_weight + tv_loss | ||
|
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| g_loss = self.g_adv_weight * generator_loss(self.gan_type, generated_logit) | ||
| d_loss = self.d_adv_weight * discriminator_loss(self.gan_type, anime_logit, anime_gray_logit, generated_logit, smooth_logit) + GP | ||
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| self.Generator_loss = t_loss + g_loss | ||
| self.Discriminator_loss = d_loss | ||
|
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| """ Training """ | ||
| t_vars = tf.trainable_variables() | ||
| G_vars = [var for var in t_vars if 'generator' in var.name] | ||
| D_vars = [var for var in t_vars if 'discriminator' in var.name] | ||
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| self.init_optim = tf.train.AdamOptimizer(self.init_lr, beta1=0.5, beta2=0.999).minimize(self.init_loss, var_list=G_vars) | ||
| self.G_optim = tf.train.AdamOptimizer(self.g_lr , beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars) | ||
| self.D_optim = tf.train.AdamOptimizer(self.d_lr , beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars) | ||
|
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| """" Summary """ | ||
| self.G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss) | ||
| self.D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss) | ||
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| self.G_gan = tf.summary.scalar("G_gan", g_loss) | ||
| self.G_vgg = tf.summary.scalar("G_vgg", t_loss) | ||
| self.G_init_loss = tf.summary.scalar("G_init", init_loss) | ||
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| self.V_loss_merge = tf.summary.merge([self.G_init_loss]) | ||
| self.G_loss_merge = tf.summary.merge([self.G_loss, self.G_gan, self.G_vgg, self.G_init_loss]) | ||
| self.D_loss_merge = tf.summary.merge([self.D_loss]) | ||
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| def train(self): | ||
| # initialize all variables | ||
| self.sess.run(tf.global_variables_initializer()) | ||
|
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| # saver to save model | ||
| self.saver = tf.train.Saver(max_to_keep=self.epoch) | ||
|
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| # summary writer | ||
| self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_dir, self.sess.graph) | ||
|
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| """ Input Image""" | ||
| real_img_op, anime_img_op, anime_smooth_op = self.real_image_generator.load_images(), self.anime_image_generator.load_images(), self.anime_smooth_generator.load_images() | ||
|
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| # restore check-point if it exits | ||
| could_load, checkpoint_counter = self.load(self.checkpoint_dir) | ||
| if could_load: | ||
| start_epoch = checkpoint_counter + 1 | ||
|
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| print(" [*] Load SUCCESS") | ||
| else: | ||
| start_epoch = 0 | ||
|
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| print(" [!] Load failed...") | ||
|
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| # loop for epoch | ||
| init_mean_loss = [] | ||
| mean_loss = [] | ||
| # training times , G : D = self.training_rate : 1 | ||
| j = self.training_rate | ||
| for epoch in range(start_epoch, self.epoch): | ||
| for idx in range(int(self.dataset_num / self.batch_size)): | ||
| anime, anime_smooth, real = self.sess.run([anime_img_op, anime_smooth_op, real_img_op]) | ||
| train_feed_dict = { | ||
| self.real:real[0], | ||
| self.anime:anime[0], | ||
| self.anime_gray:anime[1], | ||
| self.anime_smooth:anime_smooth[1] | ||
| } | ||
|
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| if epoch < self.init_epoch : | ||
| # Init G | ||
| start_time = time.time() | ||
|
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| real_images, generator_images, _, v_loss, summary_str = self.sess.run([self.real, self.generated, | ||
| self.init_optim, | ||
| self.init_loss, self.V_loss_merge], feed_dict = train_feed_dict) | ||
| self.writer.add_summary(summary_str, epoch) | ||
| init_mean_loss.append(v_loss) | ||
|
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| print("Epoch: %3d Step: %5d / %5d time: %f s init_v_loss: %.8f mean_v_loss: %.8f" % (epoch, idx,int(self.dataset_num / self.batch_size), time.time() - start_time, v_loss, np.mean(init_mean_loss))) | ||
| if (idx+1)%200 ==0: | ||
| init_mean_loss.clear() | ||
| else : | ||
| start_time = time.time() | ||
|
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| if j == self.training_rate: | ||
| # Update D | ||
| _, d_loss, summary_str = self.sess.run([self.D_optim, self.Discriminator_loss, self.D_loss_merge], | ||
| feed_dict=train_feed_dict) | ||
| self.writer.add_summary(summary_str, epoch) | ||
|
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| # Update G | ||
| real_images, generator_images, _, g_loss, summary_str = self.sess.run([self.real, self.generated,self.G_optim, | ||
| self.Generator_loss, self.G_loss_merge], feed_dict = train_feed_dict) | ||
| self.writer.add_summary(summary_str, epoch) | ||
|
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| mean_loss.append([d_loss, g_loss]) | ||
| if j == self.training_rate: | ||
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| print( | ||
| "Epoch: %3d Step: %5d / %5d time: %f s d_loss: %.8f, g_loss: %.8f -- mean_d_loss: %.8f, mean_g_loss: %.8f" % ( | ||
| epoch, idx, int(self.dataset_num / self.batch_size), time.time() - start_time, d_loss, g_loss, np.mean(mean_loss, axis=0)[0], | ||
| np.mean(mean_loss, axis=0)[1])) | ||
| else: | ||
| print( | ||
| "Epoch: %3d Step: %5d / %5d time: %f s , g_loss: %.8f -- mean_g_loss: %.8f" % ( | ||
| epoch, idx, int(self.dataset_num / self.batch_size), time.time() - start_time, g_loss, np.mean(mean_loss, axis=0)[1])) | ||
|
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| if (idx + 1) % 200 == 0: | ||
| mean_loss.clear() | ||
|
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| j = j - 1 | ||
| if j < 1: | ||
| j = self.training_rate | ||
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| if (epoch + 1) >= self.init_epoch and np.mod(epoch + 1, self.save_freq) == 0: | ||
| self.save(self.checkpoint_dir, epoch) | ||
|
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| if epoch >= self.init_epoch -1: | ||
| """ Result Image """ | ||
| val_files = glob('./dataset/{}/*.*'.format('val')) | ||
| save_path = './{}/{:03d}/'.format(self.sample_dir, epoch) | ||
| check_folder(save_path) | ||
| for i, sample_file in enumerate(val_files): | ||
| print('val: '+ str(i) + sample_file) | ||
| sample_image = np.asarray(load_test_data(sample_file, self.img_size)) | ||
| test_real,test_generated = self.sess.run([self.test_real,self.test_generated],feed_dict = {self.test_real:sample_image} ) | ||
| save_images(test_real, save_path+'{:03d}_a.jpg'.format(i), None) | ||
| save_images(test_generated, save_path+'{:03d}_b.jpg'.format(i), None) | ||
|
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| @property | ||
| def model_dir(self): | ||
| return "{}_{}_{}_{}_{}_{}_{}_{}_{}".format(self.model_name, self.dataset_name, | ||
| self.gan_type, | ||
| int(self.g_adv_weight), int(self.d_adv_weight), | ||
| int(self.con_weight), int(self.sty_weight), | ||
| int(self.color_weight), int(self.tv_weight)) | ||
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| def save(self, checkpoint_dir, step): | ||
| checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir) | ||
| if not os.path.exists(checkpoint_dir): | ||
| os.makedirs(checkpoint_dir) | ||
| self.saver.save(self.sess, os.path.join(checkpoint_dir, self.model_name + '.model'), global_step=step) | ||
|
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| def load(self, checkpoint_dir): | ||
| print(" [*] Reading checkpoints...") | ||
| checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir) | ||
|
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| ckpt = tf.train.get_checkpoint_state(checkpoint_dir) # checkpoint file information | ||
|
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| if ckpt and ckpt.model_checkpoint_path: | ||
| ckpt_name = os.path.basename(ckpt.model_checkpoint_path) # first line | ||
| self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name)) | ||
| counter = int(ckpt_name.split('-')[-1]) | ||
| print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name))) | ||
| return True, counter | ||
| else: | ||
| print(" [*] Failed to find a checkpoint") | ||
| return False, 0 |
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| MIT License | ||
|
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| Copyright (c) 2024 RAMESWAR BISOYI | ||
|
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| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
|
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| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
|
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| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. |
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