def main():
tf.random.set_seed(222)
np.random.seed(222)
z_dim=100
epochs=3000000
batch_size=512
learning_rate=0.002
is_training=True
img_path=glob.glob(r'D:\BaiduNetdiskDownload\faces\faces\*.jpg')
dataset,img_shape,_=make_anime_dataset(img_path,batch_size)
print(dataset,img_shape)
sample=next(iter(dataset))
print(sample.shape)
dataset=dataset.repeat()
db_iter=iter(dataset)
generator=Genrator()
generator.build(input_shape=(None,z_dim))
dis=Discriminator()
dis.bulid(input_shape=(None,64,64,3))
g_optimaizer=tf.optimizers.Adam(learning_rate=learning_rate,beta_1=0.50)
d_optimaer=tf.optimizers.Adam(learning_rate=learning_rate,beta_2=0.5)
for epoch in range(epochs):
batch_z=tf.random.uniform([batch_size,z_dim],minval=-1,maxval=1)
batch_x=next(db_iter)
with tf.GradientTape() as tape:
d_loss=d_loss_fn(generator,dis,batch_z,batch_x,is_training)
grads=tape.gradient(d_loss,dis.trainable_varables)
d_optimaer.apply_gradients(zip(grads,dis.trainable_varables))
with tf.GradientTape() as tape:
def __init__(self,
batch_size=16,
D_lr=1e-3,
G_lr=1e-3,
r_dim=3072,
z_dim=100,
h_size=512,
use_gpu=False):
self.device = torch.device("cuda" if use_gpu else "cpu")
self.batch_size = batch_size
self.z_dim = z_dim
self.D = Discriminator(r_dim, h_size).to(device=self.device)
self.G = Generator(z_dim, r_dim, h_size).to(device=self.device)
self.criterion = nn.BCELoss(size_average=True)
self.D_optimizer = optim.Adam(self.D.parameters(), lr=D_lr)
self.G_optimizer = optim.Adam(self.G.parameters(), lr=G_lr)
root = '/home/lhq/PycharmProjects/gan.pytorch/datasets/data/test/'
text = '/home/lhq/PycharmProjects/gan.pytorch/datasets/data/labels.txt'
dataset = ListDatasets(root=root, fname_list=text)
self.dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
self.label_real = torch.ones(batch_size, 1, device=self.device)
self.label_fake = torch.zeros(batch_size, 1, device=self.device)
def main():
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
# hyper parameters
z_dim = 100
epochs = 3000000
batch_size = 512
learning_rate = 0.002
is_training = True
img_path = glob.glob(
r'C:\Users\Jackie Loong\Downloads\DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2-master\data\faces\*.jpg'
)
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
print(dataset, img_shape)
sample = next(iter(dataset))
print(sample.shape,
tf.reduce_max(sample).numpy(),
tf.reduce_min(sample).numpy())
dataset = dataset.repeat()
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
g_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epochs):
batch_z = tf.random.uniform([batch_size, z_dim], minval=-1., maxval=1.)
batch_x = next(db_iter)
# train D
with tf.GradientTape() as tape:
d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x,
is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd-loss:', float(d_loss), 'g-loss:', float(g_loss))
z = tf.random.uniform([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('images', 'gan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
def main():
tf.random.set_seed(233)
np.random.seed(233)
assert tf.__version__.startswith('2.')
# hyper parameters
z_dim = 100
epochs = 3000000
batch_size = 512
learning_rate = 0.0005
is_training = True
img_path = glob.glob(r'C:\Users\Jackie\Downloads\faces\*.jpg')
assert len(img_path) > 0
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
print(dataset, img_shape)
sample = next(iter(dataset))
print(sample.shape, tf.reduce_max(sample).numpy(),
tf.reduce_min(sample).numpy())
dataset = dataset.repeat()
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
z_sample = tf.random.normal([100, z_dim])
g_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epochs):
for _ in range(5):
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter)
# train D
with tf.GradientTape() as tape:
d_loss, gp = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
batch_z = tf.random.normal([batch_size, z_dim])
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd-loss:', float(d_loss), 'g-loss:', float(g_loss),
'gp:', float(gp))
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('images', 'wgan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
def train():
tf.random.set_seed(22)
np.random.seed(22)
data_iter = dataset.load_dataset()
# 利用数组形式实现多输入模型
generator = Generator()
generator.build(input_shape=[(None, z_dim), (None, 10)])
discriminator = Discriminator()
discriminator.build(input_shape=[(None, 28, 28, 1), (None, 10)])
g_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epochs):
for i in range(int(60000 / batch_size / epochs_d)):
batch_z = tf.random.uniform([batch_size, z_dim],
minval=0.,
maxval=1.)
batch_c = []
for k in range(batch_size):
batch_c.append(np.random.randint(0, 10))
batch_c = tf.one_hot(tf.convert_to_tensor(batch_c), 10)
# train D
for epoch_d in range(epochs_d):
batch_data = next(data_iter)
batch_x = batch_data[0]
batch_y = batch_data[1]
with tf.GradientTape() as tape:
d_loss = d_loss_fn(generator, discriminator, batch_z,
batch_c, batch_x, batch_y, is_training)
grads = tape.gradient(d_loss,
discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
# train G
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, batch_c,
is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(
zip(grads, generator.trainable_variables))
print('epoch : {epoch} d-loss : {d_loss} g-loss : {g_loss}'.format(
epoch=epoch, d_loss=d_loss, g_loss=g_loss))
z = tf.random.uniform([100, z_dim], minval=0., maxval=1.)
c = []
for i in range(10):
for j in range(10):
c.append(i)
c = tf.one_hot(tf.convert_to_tensor(c), 10)
fake_image = generator([z, c], training=False)
img_path = os.path.join('images', 'infogan-%d-final.png' % epoch)
saver.save_image(fake_image.numpy(), img_path, 10)
class Tree:
def __init__(self, location, forest, parent=None, generator=Generator(), name='unnamed'):
""" Constructor for a Tree. """
self.generator = generator
self.discriminator = Discriminator()
self.nn = load_model('new_nn.h5')
self.age = 1
self.location = location
self.parent = parent
self.forest = forest
self.neighbors = ([parent] if parent else [])
self.name = name
def __repr__(self):
""" Overwrite default str representation to output
T(<position in list of trees>) """
return 'T(' + str(self.forest.trees.index(self)) + ')'
def _newlocation(self):
""" Find a suitable location for a child. """
num = randbelow(628) / 100
# A tree's roots are log10 of its age.
r = math.log10(self.age)
loc = (self.location[0] + round(math.cos(num) * r, 2), self.location[1] + round(math.sin(num) * r, 2))
for tree in self.forest.trees:
if (loc[0]-tree.location[0]) ** 2 + (loc[1]-tree.location[1]) ** 2 <= math.log10(tree.age) ** 2:
return None
return loc
def spawnChild(self):
""" Attempt to spawn a child. If successful, return it. If not, return None. """
for _ in range(10):
loc = self._newlocation()
if loc:
break
if not loc:
return None
r = math.log10(self.age)
child = Tree(location=loc, forest=self.forest, parent=self, name=self.forest.names.pop(0))
self.forest.connections[child] = [self]
self.forest.connections[self].append(child)
self.neighbors.append(child)
return child
def resetDiscriminator(self):
""" Reset this tree's discriminator. """
self.discriminator.reset()
def getnewneighbors(self):
""" Check for neighbors within radius of roots. If found, add all necessary
connections within both trees and within forest. """
for t in self.forest.trees:
if (t is not self.parent and t is not self and t not in self.neighbors and
((t.location[0] - self.location[0]) ** 2 + (t.location[1] - self.location[1]) ** 2) <= (1.4 * math.log10(self.age)) ** 2):
self.forest.connections[self].append(t)
self.forest.connections[t].append(self)
self.neighbors.append(t)
t.neighbors.append(self)
def main():
# 设计随机种子，方便复现
tf.random.set_seed(22)
np.random.seed(22)
# 设定相关参数
z_dim = 100
epochs = 3000000
batch_size = 512 # 根据自己的GPU能力设计
learning_rate = 0.002
is_training = True
# 加载数据（根据自己的路径更改），建立网络
img_path = glob.glob(
r'C:\Users\Jackie Loong\Downloads\DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2-master\data\faces\*.jpg'
)
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
# print(dataset, img_shape)
# sample = next(iter(dataset))
dataset = dataset.repeat()
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
# 建立优化器
g_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epochs):
# 随机取样出来的结果
batch_z = tf.random.uniform([batch_size, z_dim], minval=-1., maxval=1.)
batch_x = next(db_iter)
# 训练检测网络
with tf.GradientTape() as tape:
d_loss, gp = d_loss_fn(generator, discriminator, batch_z, batch_x,
is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
# 训练生成网络
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd-loss:', float(d_loss), 'g-loss:', float(g_loss),
'gp:', float(gp))
z = tf.random.uniform([100, z_dim])
fake_image = generator(z, training=False)
# 生成的图片保存，images文件夹下, 图片名为：wgan-epoch.png
img_path = os.path.join('images', 'wgan-%d.png' % epoch)
# 10*10, 彩色图片
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
def __init__(self, location, forest, parent=None, generator=Generator(), name='unnamed'):
""" Constructor for a Tree. """
self.generator = generator
self.discriminator = Discriminator()
self.nn = load_model('new_nn.h5')
self.age = 1
self.location = location
self.parent = parent
self.forest = forest
self.neighbors = ([parent] if parent else [])
self.name = name
def main():
''' This is a sample implementation of one generator training with three discriminators '''
# Setting up the gan network system
gan1 = GAN(generator=Generator(), discriminator=Discriminator(), nn=load_model("new_nn.h5"))
gan2 = GAN(generator=gan1.G, discriminator=Discriminator(), nn=load_model("new_nn.h5"))
gan3 = GAN(generator=gan1.G, discriminator=Discriminator(), nn=load_model("new_nn.h5"))
# Set the number of training iterations for the network
training_period = 5
# Train the system
for i in range(training_period):
gan1.train(testid=i)
gan2.train(testid=i)
gan3.train(testid=i)
def init_model(config, data_generator):
print('Initializing %s embedding model in %s mode...' % (config.model, config.mode))
npz = np.load(config.embedding_file) if config.load_embeddings else None
if config.model == 'transe':
em = EmbeddingModel.TransE(config, embeddings_dict=npz)
elif config.model == 'transd':
config.embedding_size = config.embedding_size / 2
em = EmbeddingModel.TransD(config, embeddings_dict=npz)
elif config.model == 'distmult':
em = EmbeddingModel.DistMult(config, embeddings_dict=npz)
else:
raise ValueError('Unrecognized model type: %s' % config.model)
if config.mode == 'disc':
model = Discriminator.BaseModel(config, em, data_generator)
elif config.mode == 'gen':
model = Generator.Generator(config, em, data_generator)
else:
raise ValueError('Unrecognized mode: %s' % config.mode)
if npz:
# noinspection PyUnresolvedReferences
npz.close()
model.build()
print('Built model.')
print('use semnet: %s' % model.use_semantic_network)
return model
def __init__(self, z_dim, h_dim, learning_rate, scale, generator_output_layer): self.z_dim = z_dim self.h_dim = h_dim self.g_net = Generator(z_dim, h_dim, generator_output_layer) self.d_net = Discriminator(h_dim) self.training = tf.placeholder(tf.bool, []) self.with_text = tf.placeholder(tf.float32, [None]) self.x = tf.placeholder(tf.float32, [None, 64, 64, 3]) self.x_w_ = tf.placeholder(tf.float32, [None, 64, 64, 3]) self.z = tf.placeholder(tf.float32, [None, self.z_dim]) # true h self.h = tf.placeholder(tf.float32, [None, h_dim]) # false h self.h_ = tf.placeholder(tf.float32, [None, h_dim]) # false image self.x_ = self.g_net(self.z, self.h, self.training) # true image, true h self.d = self.d_net(self.x, self.h, self.training, reuse=False) # fake image, true h self.d_ = self.d_net(self.x_, self.h, self.training) # wrong image, true h self.d_w_ = self.d_net(self.x_w_, self.h, self.training) # true image, false h self.d_h_ = self.d_net(self.x, self.h_, self.training) self.g_loss = - tf.reduce_mean(self.d_) #+ tf.reduce_mean(tf.square(self.x - self.x_)) self.d_loss = tf.reduce_mean(self.d) \ - ( 1 * tf.reduce_mean(self.d_) + 1 * tf.reduce_mean(self.d_h_) + 1 * tf.reduce_mean(self.d_w_)) / (1 + 1 + 1) # penalty distribution for "improved wgan" epsilon = tf.random_uniform([], 0.0, 1.0) x_hat = epsilon * self.x + (1 - epsilon) * self.x_ d_hat = self.d_net(x_hat, self.h, self.training) dx = tf.gradients(d_hat, x_hat)[0] dx_norm = tf.sqrt(tf.reduce_sum(tf.square(dx), axis=[1,2,3])) ddx = scale * tf.reduce_mean(tf.square(dx_norm - 1.0)) self.d_loss = -(self.d_loss - ddx) self.d_opt, self.g_opt = None, None with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): self.d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, beta2=0.9)\ .minimize(self.d_loss, var_list=self.d_net.vars) self.g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, beta2=0.9)\ .minimize(self.g_loss, var_list=self.g_net.vars)
def __init__(self, z_dim, h_dim, learning_rate, scale, generator_output_layer): self.z_dim = z_dim self.h_dim = h_dim self.g_net = Generator(z_dim, h_dim, generator_output_layer) self.d_net = Discriminator(h_dim) self.training = tf.placeholder(tf.bool, []) self.with_text = tf.placeholder(tf.float32, [None]) self.x = tf.placeholder(tf.float32, [None, 64, 64, 3]) self.x_w_ = tf.placeholder(tf.float32, [None, 64, 64, 3]) self.z = tf.placeholder(tf.float32, [None, self.z_dim]) # true h self.h = tf.placeholder(tf.float32, [None, h_dim]) # false h self.h_ = tf.placeholder(tf.float32, [None, h_dim]) # false image self.x_ = self.g_net(self.z, self.h, self.training) # true image, true h self.d = self.d_net(self.x, self.h, self.training, reuse=False) # fake image, true h self.d_ = self.d_net(self.x_, self.h, self.training) # wrong image, true h self.d_w_ = self.d_net(self.x_w_, self.h, self.training) # true image, false h self.d_h_ = self.d_net(self.x, self.h_, self.training) # self.g_loss = - tf.reduce_mean(self.d_) #+ tf.reduce_mean(tf.square(self.x - self.x_)) # self.d_loss = tf.reduce_mean(self.d) \ # - ( 1 * tf.reduce_mean(self.d_) + 1 * tf.reduce_mean(self.d_h_) + 1 * tf.reduce_mean(self.d_w_)) / (1 + 1 + 1) self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_, labels=tf.ones_like(self.d_))) self.d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d, labels=tf.ones_like(self.d))) \ + (tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_, labels=tf.zeros_like(self.d_))) + \ tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_w_, labels=tf.zeros_like(self.d_w_))) +\ tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_h_, labels=tf.zeros_like(self.d_h_))) ) / 3 self.d_opt, self.g_opt = None, None with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): self.d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, beta2=0.9)\ .minimize(self.d_loss, var_list=self.d_net.vars) self.g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, beta2=0.9)\ .minimize(self.g_loss, var_list=self.g_net.vars)
def build_model(self):
"""Create a generator and a discriminator."""
self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim,
self.d_repeat_num)
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.print_network(self.G, 'G')
self.print_network(self.D, 'D')
self.G.to(self.device)
self.D.to(self.device)
"""Build the feature extractor"""
self.feature_model = f_model(model_path=DUMPED_MODEL,
freeze_param=True).cuda() #.cuda()
self.feature_model.eval()
def load_model(filename):
checkpoint = torch.load(filename, map_location=device)
global generator
global discriminator
generator = Generator(len(data['mapping']), data['max_length'],
latent_size)
generator.load_state_dict(checkpoint['generator_model_state_dict'])
generator.eval()
discriminator = Discriminator(len(data['mapping']))
discriminator.load_state_dict(checkpoint['discriminator_model_state_dict'])
discriminator.eval()
print('Loaded model')
parser = ArgumentParser()
parser.add_argument('--data', type=str)
parser.add_argument('--data-args', type=int, nargs='+')
parser.add_argument('--dbs', type=int, help='Discriminator Batch Size')
parser.add_argument('--dlr', type=float, help='Discriminator Learning Rate')
parser.add_argument('--glr', type=float, help='Generator Learning Rate')
parser.add_argument('--gbs', type=int, help='Generator Batch Size')
parser.add_argument('--gpu', type=int, help='GPU')
parser.add_argument('--nf', type=int, nargs='+', help='Number of Features')
parser.add_argument('--ni', type=int, help='Number of Iterations')
args = parser.parse_args()
device = th.device('cpu') if args.gpu < 0 else None # TODO
data_loader = getattr(data, args.data + 'Loader')(*args.data_args, device=device)
discriminator = Discriminator().to(device)
g_configs = [{'in_feats' : in_feats,
'out_feats' : out_feats,
'out_nodes' : out_nodes,
'aggregator' : 'mean'} for in_feats, out_feats, out_nodes in zip([data_loader.n_feats] + args.nf[:-1], args.nf[1:])]
generator = Generator().to(device)
d_optim = optim.Adam(discirminator.parameters(), args.dlr)
g_optim = optim.Adam(generator.parameters(), args.glr)
for i in range(args.ni):
for j in range(args.gbs):
generator()
x, adj = next(data_loader)
p, cost = discriminator(x, adj)
authentic =
def main():
tf.random.set_seed(3333)
np.random.seed(3333)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
z_dim = 100 # 隐藏向量z的长度
epochs = 3000000 # 训练步数
batch_size = 64
learning_rate = 0.0002
is_training = True
# 获取数据集路径
img_path = glob.glob(r'C:\Users\jay_n\.keras\datasets\faces\*.jpg') + \
glob.glob(r'C:\Users\jay_n\.keras\datasets\faces\*.png')
print('images num:', len(img_path))
# 构建数据集对象
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size, resize=64)
print(dataset, img_shape)
sample = next(iter(dataset)) # 采样
print(sample.shape, tf.reduce_max(sample).numpy(), tf.reduce_min(sample).numpy())
dataset = dataset.repeat(100)
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(4, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(4, 64, 64, 3))
# 分别为生成器和判别器创建优化器
g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
# generator.load_weights('generator.ckpt')
# discriminator.load_weights('discriminator.ckpt')
# print('Loaded ckpt!!')
d_losses, g_losses = [], []
for epoch in range(epochs):
# 1. 训练判别器
for _ in range(1):
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter) # 采样真实图片
# 判别器前向计算
with tf.GradientTape() as tape:
d_loss, _ = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
# 2. 训练生成器
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
# 生成器前向计算
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd-loss:', float(d_loss), 'g-loss:', float(g_loss))
# 可视化
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('gan_images', 'gan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
d_losses.append(float(d_loss))
g_losses.append(float(g_loss))
if epoch % 10000 == 1:
generator.save_weights('generator.ckpt')
discriminator.save_weights('discriminator.ckpt')
def train_style_transfer(args):
if not (args.train_data and args.valid_data):
print("must chose train_data and valid_data")
sys.exit()
# make dataset
trans = transforms.ToTensor()
train_dataset = FaceDataset(args.train_data, transform=trans)
label_dict = train_dataset.get_label_dict()
valid_dataset = FaceDataset(args.valid_data, transform=trans)
valid_dataset.give_label_dict(label_dict)
train_loader = data_utils.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
valid_loader = data_utils.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
train_size = len(train_dataset)
valid_size = len(valid_dataset)
loaders = {"train": train_loader, "valid": valid_loader}
dataset_sizes = {"train": train_size, "valid": valid_size}
if args.gpu:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# make network
if args.model_type == "VAE":
net = Autoencoder(train_dataset.label_num()).to(device)
optimizer = optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
best_model_wts = net.state_dict()
best_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
net.load_state_dict(torch.load(args.generator_model))
elif args.model_type == "VAEGAN":
generator = Autoencoder(train_dataset.label_num()).to(device)
discriminator = Discriminator().to(device)
classifier = Classifier(train_dataset.label_num()).to(device)
generator_optimizer = optim.Adam(generator.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
discriminator_optimizer = optim.Adam(discriminator.parameters(),
lr=args.lr * 0.1,
weight_decay=args.weight_decay)
best_generator_wts = generator.state_dict()
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = 1e10
best_discriminator_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
generator.load_state_dict(torch.load(args.generator_model))
if args.discriminator_model and os.path.exists(
args.discriminator_model):
discriminator.load_state_dict(torch.load(args.discriminator_model))
if args.classifier_model:
classifier.load_state_dict(torch.load(args.classifier_model))
# make loss function and optimizer
criterion = nn.BCELoss(reduction="sum")
classifier_criterion = nn.CrossEntropyLoss(reduction="sum")
# initialize loss
loss_history = {"train": [], "valid": []}
# start training
start_time = time.time()
for epoch in range(args.epochs):
print("epoch {}".format(epoch + 1))
for phase in ["train", "valid"]:
if phase == "train":
if args.model_type == "VAE":
net.train(True)
elif args.model_type == "VAEGAN":
generator.train(True)
discriminator.train(True)
else:
if args.model_type == "VAE":
net.train(False)
elif args.model_type == "VAEGAN":
generator.train(False)
discriminator.train(False)
# initialize running loss
generator_running_loss = 0.0
discriminator_running_loss = 0.0
for i, data in enumerate(loaders[phase]):
inputs, label = data
# wrap the in valiables
if phase == "train":
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(True)
else:
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(False)
# zero gradients
if args.model_type == "VAE":
optimizer.zero_grad()
mu, var, outputs = net(inputs, label)
loss = loss_func(inputs, outputs, mu, var)
if phase == "train":
loss.backward()
optimizer.step()
generator_running_loss += loss.item()
elif args.model_type == "VAEGAN":
real_label = Variable(
torch.ones((inputs.size()[0], 1), dtype=torch.float) -
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
fake_label = Variable(
torch.zeros((inputs.size()[0], 1), dtype=torch.float) +
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
discriminator_optimizer.zero_grad()
real_pred = discriminator(inputs)
real_loss = criterion(real_pred, real_label)
random_index = np.random.randint(0,
train_dataset.label_num(),
inputs.size()[0])
generate_label = Variable(
torch.zeros_like(label)).to(device)
for i, index in enumerate(random_index):
generate_label[i][index] = 1
mu, var, outputs = generator(inputs, label)
fake_pred = discriminator(outputs.detach())
fake_loss = criterion(fake_pred, fake_label)
discriminator_loss = real_loss + fake_loss
if phase == "train":
discriminator_loss.backward()
discriminator_optimizer.step()
generator_optimizer.zero_grad()
#class_loss = classifier_criterion(classifier(outputs), torch.max(label, 1)[1])
dis_loss = criterion(discriminator(outputs), real_label)
gen_loss = loss_func(inputs, outputs, mu, var)
generator_loss = dis_loss + gen_loss
if phase == "train":
generator_loss.backward()
generator_optimizer.step()
discriminator_running_loss += discriminator_loss.item()
generator_running_loss += generator_loss.item()
if args.model_type == "VAE":
epoch_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
loss_history[phase].append(epoch_loss)
print("{} loss {:.4f}".format(phase, epoch_loss))
if phase == "valid" and epoch_loss < best_loss:
best_model_wts = net.state_dict()
best_loss = epoch_loss
elif args.model_type == "VAEGAN":
epoch_generator_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
epoch_discriminator_loss = discriminator_running_loss / dataset_sizes[
phase] * args.batch_size
print("{} generator loss {:.4f}".format(
phase, epoch_generator_loss))
print("{} discriminator loss {:.4f}".format(
phase, epoch_discriminator_loss))
if phase == "valid" and epoch_generator_loss < best_generator_loss:
best_generator_wts = generator.state_dict()
best_generator_loss = epoch_generator_loss
if phase == "valid" and epoch_discriminator_loss < best_discriminator_loss:
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = epoch_discriminator_loss
elapsed_time = time.time() - start_time
print("training complete in {:.0f}s".format(elapsed_time))
if args.model_type == "VAE":
net.load_state_dict(best_model_wts)
return net, label_dict
elif args.model_type == "VAEGAN":
generator.load_state_dict(best_generator_wts)
discriminator.load_state_dict(best_discriminator_wts)
return (generator, discriminator), label_dict
# root_path = "C:/Users/Lucky/PycharmProjects/finalproject/"
# data_path = root_path + "face/"
# work_path = root_path + "workspace/"
# save_path = root_path + "workspace/new_picture/"
# os.makedirs(work_path, exist_ok=True)
# os.makedirs(save_path, exist_ok=True)
root_path = "/Users/yuming/OneDrive/sync/semester/ML/hw/project/dataset/"
data_path = root_path + "faces/"
save_path = root_path + "fake-faces/"
save_csv_loss_g = root_path + "csv/loss_g.csv"
save_csv_loss_d = root_path + "csv/loss_d.csv"
# Initialize generator and discriminator
generator = Generator(opt.latent_dim, img_shape)
discriminator = Discriminator(img_shape)
cuda_enabled = torch.cuda.is_available()
if cuda_enabled:
generator.cuda()
discriminator.cuda()
Tensor = torch.cuda.FloatTensor
else:
Tensor = torch.FloatTensor
# Tensor = torch.cuda.FloatTensor if cuda_enabled else torch.FloatTensor
image_raw_data = fetch_dataset(data_path)
data_loader = DataLoader(image_raw_data,
batch_size=opt.batch_size,
shuffle=True)
round(validation_split * len(dataset))
]
split = {
x: y
for x, y in zip(['train', 'val'],
data.random_split(dataset, lengths=split_len))
}
loader = {
x: data.DataLoader(split[x], batch_size, shuffle=True, num_workers=4)
for x in ['train', 'val']
}
# Create the GAN
netG = Generator(latent_size, n_features)
netD = Discriminator(n_features)
netG.to(device)
netD.to(device)
optimizerD = torch.optim.Adam(netD.parameters(), lr, betas)
optimizerG = torch.optim.Adam(netG.parameters(), lr, betas)
# Define penalties
class RoundNoGradient(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.round()
@staticmethod
def backward(ctx, g):
class GAN_Vanilla(object):
def __init__(self,
batch_size=16,
D_lr=1e-3,
G_lr=1e-3,
r_dim=3072,
z_dim=100,
h_size=512,
use_gpu=False):
self.device = torch.device("cuda" if use_gpu else "cpu")
self.batch_size = batch_size
self.z_dim = z_dim
self.D = Discriminator(r_dim, h_size).to(device=self.device)
self.G = Generator(z_dim, r_dim, h_size).to(device=self.device)
self.criterion = nn.BCELoss(size_average=True)
self.D_optimizer = optim.Adam(self.D.parameters(), lr=D_lr)
self.G_optimizer = optim.Adam(self.G.parameters(), lr=G_lr)
root = '/home/lhq/PycharmProjects/gan.pytorch/datasets/data/test/'
text = '/home/lhq/PycharmProjects/gan.pytorch/datasets/data/labels.txt'
dataset = ListDatasets(root=root, fname_list=text)
self.dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
self.label_real = torch.ones(batch_size, 1, device=self.device)
self.label_fake = torch.zeros(batch_size, 1, device=self.device)
def train(self, epoch, step_D, step_G, print_every):
for ep in range(epoch):
d_loss, g_loss = 0, 0
for i, data in enumerate(self.dataloader):
data = data.view(self.batch_size, -1)
if i % step_D == 0:
real_output = self.D(data)
d_loss_1 = self.criterion(real_output, self.label_real)
z = get_uniform_sampler(self.device)(self.batch_size,
self.z_dim)
fake_image = self.G(z).detach()
fake_output = self.D(fake_image)
d_loss_2 = self.criterion(fake_output, self.label_fake)
d_loss = d_loss_1 + d_loss_2
self.D_optimizer.zero_grad()
d_loss.backward()
self.D_optimizer.step()
if i % step_G == 0:
z = get_normal_sampler(0, 0.1,
self.device)(self.batch_size,
self.z_dim)
fake_image = self.G(z)
fake_output = self.D(fake_image)
g_loss = self.criterion(fake_output, self.label_real)
self.G_optimizer.zero_grad()
g_loss.backward()
self.G_optimizer.step()
print('{} epoch: D_loss: {}, G_loss: {}'.format(
ep, d_loss[0].item(), g_loss[0].item()))
if ep % print_every == 0:
print('{} epoch: D_loss: {}, G_loss: {}'.format(
ep, d_loss[0].item(), g_loss[0].item()))
def clip(a):
return 0 if a<0 else (255 if a>255 else a)
def array_to_img(im):
im = im*255
im = np.vectorize(clip)(im).astype(np.uint8)
im=im.transpose(1,2,0)
img=Image.fromarray(im)
return img
def save_img(img_array,save_path): #save from np.array (3,height,width)
img = array_to_img(img_array)
img.save(save_path)
Gen = Generator()
Dis = Discriminator()
gpu = -1
if gpu>=0:
xp = cuda.cupy
cuda.get_device(gpu).use()
Gen.to_gpu()
Dis.to_gpu()
else:
xp = np
optG = Gen.make_optimizer()
optD = Dis.make_optimizer()
optG.setup(Gen)
optD.setup(Dis)
style_size = 564
sample_batch = 4
z_size = 400
#########################################
def gen_rand_noise(batch_size, z_size, mean=0, std=0.001):
z_sample = np.random.normal(mean, std, size=[batch_size,
z_size]).astype(np.float32)
z = torch.from_numpy(z_sample)
return z
#########################################
G = Generator().cuda()
D = Discriminator().cuda()
Vgg = Vgg16().cuda()
bce = BCE_Loss()
mse = torch.nn.MSELoss()
optimizer_d = torch.optim.SGD(D.parameters(), lr=lr * 0.4)
optimizer_g = torch.optim.Adam(G.parameters(), lr=lr, betas=(beta1, 0.9))
#########################################
#########################################
for epoch in range(max_epoch):
for idx, (real_img, real_label, mask) in tqdm.tqdm(enumerate(trainloader)):
# trainD
make_trainable(D, True)
make_trainable(G, False)
D.zero_grad()
optimizer_d.zero_grad()
modules = list(
feature_extractor_network.children())[:-1] #Remove fully connected layer
modules.append(nn.Flatten())
feature_extractor_network = nn.Sequential(*modules)
feature_extractor_network = feature_extractor_network.to(device)
feature_extractor_network.load_state_dict(
torch.load(args.fe_model, map_location=device))
feature_extractor_network = feature_extractor_network.eval()
#Setup networks
generator_network = Generator(in_noise=args.noise_dem,
in_features=args.feature_dem,
ch=args.ch_multi,
norm_type=args.norm_type).to(device)
discriminator_network = Discriminator(channels=novel_images.shape[1],
in_features=args.feature_dem,
ch=args.ch_multi,
norm_type=args.norm_type).to(device)
#Setup optimizers
d_optimizer = optim.Adam(discriminator_network.parameters(),
lr=args.dlr,
betas=(0.0, 0.999))
g_optimizer = optim.Adam(generator_network.parameters(),
lr=args.glr,
betas=(0.0, 0.999))
#Create the save directory if it does note exist
if not os.path.isdir(args.save_dir + "/Models"):
os.makedirs(args.save_dir + "/Models")
if not os.path.isdir(args.save_dir + "/Samples"):
os.makedirs(args.save_dir + "/Samples")
m = 3
return np.random.normal(size=[Nbatch, m, dim]).astype(dtype=np.float32)
# ------- TRAIN MODELS --------
num_epochs = config_d['epochs']
print_every = config_d['print_every']
z_size = config_d['noise_dim']
n_critic = config_d['critic_iter']
lr = config_d['lr']
beta1 = config_d['beta1']
beta2 = config_d['beta2']
D = Discriminator()
G = Generator(z_size=z_size)
print(D)
print(G)
if cuda:
G.cuda()
D.cuda()
print('GPU available for training. Models moved to GPU')
else:
print('Training on CPU.')
d_optimizer = optim.Adam(D.parameters(), lr=lr, betas=[beta1, beta2])
g_optimizer = optim.Adam(G.parameters(), lr=lr, betas=[beta1, beta2])
losses_train = []
def main():
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
# hyper parameters
z_dim = 100
epochs = 3000000
batch_size = 128
learning_rate = 0.0002
is_training = True
# for validation purpose
assets_dir = './images'
if not os.path.isdir(assets_dir):
os.makedirs(assets_dir)
val_block_size = 10
val_size = val_block_size * val_block_size
# load mnist data
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255.
db = tf.data.Dataset.from_tensor_slices(x_train).shuffle(
batch_size * 4).batch(batch_size).repeat()
db_iter = iter(db)
inputs_shape = [-1, 28, 28, 1]
# create generator & discriminator
generator = Generator()
generator.build(input_shape=(batch_size, z_dim))
generator.summary()
discriminator = Discriminator()
discriminator.build(input_shape=(batch_size, 28, 28, 1))
discriminator.summary()
# prepare optimizer
d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
for epoch in range(epochs):
# no need labels
batch_x = next(db_iter)
# rescale images to -1 ~ 1
batch_x = tf.reshape(batch_x, shape=inputs_shape)
# -1 - 1
batch_x = batch_x * 2.0 - 1.0
# Sample random noise for G
batch_z = tf.random.uniform(shape=[batch_size, z_dim],
minval=-1.,
maxval=1.)
with tf.GradientTape() as tape:
d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x,
is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd loss:', float(d_loss), 'g loss:', float(g_loss))
# validation results at every epoch
val_z = np.random.uniform(-1, 1, size=(val_size, z_dim))
fake_image = generator(val_z, training=False)
image_fn = os.path.join('images',
'gan-val-{:03d}.png'.format(epoch + 1))
save_result(fake_image.numpy(),
val_block_size,
image_fn,
color_mode='L')
class Solver(object):
"""Solver for training and testing StarGAN."""
def __init__(self, data_loader, config):
"""Initialize configurations."""
# Data loader.
self.data_loader = data_loader
# Model configurations.
self.c_dim = config.c_dim
self.image_size = config.image_size
self.g_conv_dim = config.g_conv_dim
self.d_conv_dim = config.d_conv_dim
self.g_repeat_num = config.g_repeat_num
self.d_repeat_num = config.d_repeat_num
self.lambda_app = config.lambda_app
self.lambda_pose = config.lambda_pose
self.lambda_rec = config.lambda_rec
self.lambda_gp = config.lambda_gp
# Training configurations.
# self.dataset = config.dataset
self.batch_size = config.batch_size
self.num_iters = config.num_iters
self.num_iters_decay = config.num_iters_decay
self.g_lr = config.g_lr
self.d_lr = config.d_lr
self.n_critic = config.n_critic
self.beta1 = config.beta1
self.beta2 = config.beta2
self.resume_iters = config.resume_iters
# self.selected_attrs = config.selected_attrs
# loss functions
self.feature_loss = torch.nn.CosineEmbeddingLoss()
self.pose_loss = torch.nn.PairwiseDistance()
# Test configurations.
self.test_iters = config.test_iters
# Miscellaneous.
self.use_tensorboard = config.use_tensorboard
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# Directories.
self.log_dir = config.log_dir
self.sample_dir = config.sample_dir
self.model_save_dir = config.model_save_dir
self.result_dir = config.result_dir
# Step size.
self.log_step = config.log_step
self.sample_step = config.sample_step
self.model_save_step = config.model_save_step
self.lr_update_step = config.lr_update_step
# Build the model and tensorboard.
self.build_model()
if self.use_tensorboard:
self.build_tensorboard()
def build_model(self):
"""Create a generator and a discriminator."""
self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim,
self.d_repeat_num)
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.print_network(self.G, 'G')
self.print_network(self.D, 'D')
self.G.to(self.device)
self.D.to(self.device)
"""Build the feature extractor"""
self.feature_model = f_model(model_path=DUMPED_MODEL,
freeze_param=True).cuda() #.cuda()
self.feature_model.eval()
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("The number of parameters: {}".format(num_params))
def restore_model(self, resume_iters):
"""Restore the trained generator and discriminator."""
print(
'Loading the trained models from step {}...'.format(resume_iters))
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(resume_iters))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(resume_iters))
self.G.load_state_dict(
torch.load(G_path, map_location=lambda storage, loc: storage))
self.D.load_state_dict(
torch.load(D_path, map_location=lambda storage, loc: storage))
def build_tensorboard(self):
"""Build a tensorboard logger."""
from logger import Logger
self.logger = Logger(self.log_dir)
def update_lr(self, g_lr, d_lr):
"""Decay learning rates of the generator and discriminator."""
for param_group in self.g_optimizer.param_groups:
param_group['lr'] = g_lr
for param_group in self.d_optimizer.param_groups:
param_group['lr'] = d_lr
def reset_grad(self):
"""Reset the gradient buffers."""
self.g_optimizer.zero_grad()
self.d_optimizer.zero_grad()
def normalize(self, x):
pass
def denorm(self, x):
"""Convert the range from [-1, 1] to [0, 1]."""
out = (x + 1) / 2
return out.clamp_(0, 1)
def gradient_penalty(self, y, x):
"""Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
weight = torch.ones(y.size()).to(self.device)
dydx = torch.autograd.grad(outputs=y,
inputs=x,
grad_outputs=weight,
retain_graph=True,
create_graph=True,
only_inputs=True)[0]
dydx = dydx.view(dydx.size(0), -1)
dydx_l2norm = torch.sqrt(torch.sum(dydx**2, dim=1))
return torch.mean((dydx_l2norm - 1)**2)
pass
def feat_extract(self, resized_data):
# input: N * 3 * 224 * 224
resized_data = resized_data.to(self.device) #.cuda()
# output: N * num_classes, N * inter_dim, N * C' * 7 * 7
out, inter_out, x = self.feature_model(resized_data)
# return batch feature vector
return out.clamp_(0, 1)
def pose_extract(self, batch_img, bbx=None):
# input [N, 224, 224, 3] in RGB
# output [N, 2, 18]
# permute = [2, 1, 0]
# # crop_batch = np.zeros(shape=())
# for i in range(batch_img.shape[0]):
# img = crop(batch_img[i], bbx[i])
# # RGB TO BGR
# img = img[:, permute, : , :]
# crop_batch[i, :, :] = img
# pose_vectors = get_batch_body_vector(crop_batch.numpy())
batch_img = np.transpose(batch_img, (0, 2, 3, 1))
# print(batch_img.shape)
pose_vectors = get_batch_body_vector(batch_img)
pose_vectors = torch.from_numpy(pose_vectors)
return pose_vectors
def appreance_cos_similarity(self, feat_fake, feat_real):
label = torch.ones(feat_real.shape[0]).cuda()
cos_simi = self.feature_loss(feat_real, feat_fake, label)
return cos_simi
def compute_pose_loss(self, pose_fake, pose_real, mask):
mask = mask.view(-1, 1, 18).double()
pose_fake = torch.mul(pose_fake, mask)
pose_real = torch.mul(pose_real.double(), mask)
distance = self.pose_loss(pose_real, pose_fake)
return distance.float().sum()
def train(self):
"""Train StarGAN within a single dataset."""
# Fetch fixed inputs for debugging.
data_iter = iter(self.data_loader)
a_fixed, b_fixed, bbox_fixed, b_fixed_pose_feat, mask_fixed = next(
data_iter)
a_fixed = a_fixed.to(self.device)
b_fixed = b_fixed.to(self.device)
bbox_fixed = bbox_fixed.to(self.device)
# c_fixed_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
# Start training from scratch or resume training.
start_iters = 0
if self.resume_iters:
start_iters = self.resume_iters
self.restore_model(self.resume_iters)
# Start training.
print('Start training...')
start_time = time.time()
for step in range(start_iters, self.num_iters):
# =================================================================================== #
# 1. Preprocess input data #
# =================================================================================== #
# Fetch real images and labels.
try:
a_real, b_real, bbox, b_pose_feat, mask = next(data_iter)
except:
data_iter = iter(self.data_loader)
a_real, b_real, bbox, b_pose_feat, mask = next(data_iter)
a_real = a_real.to(self.device) # Input images.
b_real = b_real.to(self.device)
bbox = bbox.to(self.device)
b_pose_feat = b_pose_feat.to(self.device)
mask = mask.to(self.device)
# extract appearance feature
a_app_feat = self.feat_extract(a_real)
a_app_feat = a_app_feat.to(self.device)
# # extract pose feature
# b_pose_feat = self.pose_extract(b_real)
# =================================================================================== #
# 2. Train the discriminator #
# =================================================================================== #
# Compute loss with real images.
out_src = self.D(b_real)
d_loss_real = -torch.mean(out_src)
# d_loss_cls = self.classification_loss(out_cls, label_org, self.dataset)
# Compute loss with fake images.
# con_feat = torch.cat([a_app_feat, bbox/416.0], dim=1)
con_feat = a_app_feat
x_fake = self.G(b_real, con_feat)
out_src = self.D(x_fake.detach())
d_loss_fake = torch.mean(out_src)
# fake_app_feat = self.feat_extract(x_fake)
# fake_pose_feat = self.pose_extract(x_fake, bbox)
# d_loss_app = self.appreance_cos_similarity(fake_app_feat, a_app_feat)
# d_loss_pose = - self.pose_loss(fake_pose_feat, b_pose_feat)
# Compute loss for gradient penalty.
alpha = torch.rand(b_real.size(0), 1, 1, 1).to(self.device)
x_hat = (alpha * b_real.data +
(1 - alpha) * x_fake.data).requires_grad_(True)
out_src = self.D(x_hat)
d_loss_gp = self.gradient_penalty(out_src, x_hat)
# Backward and optimize.
# d_loss = d_loss_real + d_loss_fake + self.lambda_app * d_loss_cls + self.lambda_gp * d_loss_gp
# d_loss = d_loss_fake + d_loss_real + self.lambda_app * d_loss_app + self.lambda_pose * d_loss_pose
# d_loss = d_loss_fake + d_loss_real + self.lambda_gp * d_loss_gp
d_loss = d_loss_fake + d_loss_real + self.lambda_gp * d_loss_gp
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
# Logging.
loss = {}
loss['D/loss_real'] = d_loss_real.item()
loss['D/loss_fake'] = d_loss_fake.item()
# loss['D/loss_app'] = d_loss_app.item()
# loss['D/loss_pose'] = d_loss_pose.item()
loss['D/loss_gp'] = d_loss_gp.item()
# =================================================================================== #
# 3. Train the generator #
# =================================================================================== #
if (step + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(b_real, con_feat)
# print(x_fake[0,:,200:205,200:205])
out_src = self.D(x_fake)
g_loss_fake = -torch.mean(out_src)
crop_batch = torch.zeros((x_fake.shape[0], 3, 224, 224))
b = bbox.detach().cpu().numpy().astype(int)
for i in range(x_fake.shape[0]):
# img = crop(x_fake[i], bbox[i])
x1, x2, y1, y2 = b[i, 0], b[i, 0] + b[i, 2], b[
i, 1], b[i, 1] + b[i, 3]
x1 = min(max(x1, 0), 416)
x2 = min(max(x2, 0), 416)
y1 = min(max(y1, 0), 416)
y2 = min(max(y2, 0), 416)
img = x_fake[i, :, x1:x2, y1:y2].cpu().data.numpy()
img = img.transpose((1, 2, 0))
resized_img = np.zeros(shape=(224, 224, 3))
resized_img = cv2.resize(img, (224, 224),
interpolation=cv2.INTER_AREA)
crop_batch[i, :, :, :] = torch.from_numpy(
resized_img.transpose((2, 0, 1)))
fake_app_feat = self.feat_extract(crop_batch)
fake_pose_feat = self.pose_extract(crop_batch.numpy())
# #**** debug ****#
# fake_images = (x_fake.cpu().data).numpy()
# permute = [2, 1, 0]
# fake_images = fake_images[:, permute, :, :].transpose((0,2,3,1))
# resized_data = np.zeros(shape=(fake_images.shape[0], 224, 224, 3))
# for j in range(fake_images.shape[0]):
# resized_data[j,:,:,:] = cv2.resize(fake_images[j,:,:,:], (224, 224), interpolation = cv2.INTER_AREA)
# resized_data = np.transpose(resized_data, (0, 3, 1, 2))
# resized_tensor = torch.from_numpy(resized_data)
# resized_tensor = resized_tensor.to(self.device, dtype=torch.float)
# fake_app_feat = self.feat_extract(resized_tensor)
# fake_pose_feat = self.pose_extract(resized_data, bbox)
fake_app_feat = fake_app_feat.to(self.device)
fake_pose_feat = fake_pose_feat.to(self.device)
#**** debug ****#
# g_loss_cls = self.classification_loss(out_cls, label_trg, self.dataset)
g_loss_app = -self.appreance_cos_similarity(
fake_app_feat, a_app_feat) # -similarity
# print(fake_pose_feat.size(), b_pose_feat.size(), mask.size())
g_loss_pose = self.compute_pose_loss(fake_pose_feat,
b_pose_feat,
mask) # joints distance
# Backward and optimize.
# g_loss = g_loss_fake + self.lambda_rec * g_loss_rec + self.lambda_app * g_loss_cls
# g_loss = g_loss_fake + self.lambda_app * g_loss_app + self.lambda_pose * g_loss_pose
g_loss = g_loss_fake + self.lambda_app * g_loss_app + self.lambda_pose * g_loss_pose
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss['G/loss_fake'] = g_loss_fake.item()
# loss['G/loss_rec'] = g_loss_rec.item()
loss['G/loss_app'] = g_loss_app.item() * self.lambda_app
loss['G/loss_pose'] = g_loss_pose.item() * self.lambda_pose
# =================================================================================== #
# 4. Miscellaneous #
# =================================================================================== #
# Print out training information.
if (step + 1) % self.log_step == 0:
et = time.time() - start_time
et = str(datetime.timedelta(seconds=et))[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, step + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
if self.use_tensorboard:
for tag, value in loss.items():
self.logger.scalar_summary(tag, value, i + 1)
# Translate fixed images for debugging.
if (step + 1) % self.sample_step == 0:
# if (step + 1) % 1 == 0:
with torch.no_grad():
# a fix: [N, 3, 224, 224]
# a_real, b_real, bbox, b_pose_feat, mask
a_resized = torch.zeros(size=(a_real.shape[0], 3, 416,
416))
b_drawed = torch.zeros(size=(a_real.shape[0], 3, 416, 416))
for i in range(a_real.shape[0]):
img = a_real[i].cpu().data.numpy()
img = img.transpose((1, 2, 0))
resized_img = np.zeros(shape=(416, 416, 3))
resized_img = cv2.resize(img, (416, 416),
interpolation=cv2.INTER_AREA)
a_resized[i, :, :, :] = torch.from_numpy(
resized_img.transpose((2, 0, 1)))
trans1 = transforms.ToPILImage()
trans2 = transforms.ToTensor()
b_img = trans1(b_real[i].cpu())
draw = ImageDraw.Draw(b_img)
b = bbox[i].cpu().data.numpy().astype(int)
x, y, w, h = b
x2, y2 = x + w, y + h
draw.rectangle([x, y, x2, y2],
outline="green",
width=20)
b_drawed[i, :, :, :] = trans2(b_img)
b_drawed = b_drawed.to(self.device)
a_resized = a_resized.to(self.device)
picture_list = [a_resized, b_drawed]
a_visual_feat = self.feat_extract(a_real)
# a feature: [N, 20]; bbox: [N,4]
# con_visual_feat = torch.cat([a_visual_feat, bbox/416.0], dim=1) # [N, 24]
con_visual_feat = a_visual_feat
# print(b_real, con_visual_feat)
x_fake = self.G(b_real,
con_visual_feat) # [N, 3, 416, 416]
# print(a_fixed.size(), b_fixed.size(), x_fake.size())
picture_list.append(x_fake)
picture_concat = torch.cat(picture_list, dim=0)
# print(picture_concat.size())
sample_path = os.path.join(
self.sample_dir, '{}-images.jpg'.format(step + 1))
save_image(self.denorm(picture_concat.data.cpu()),
sample_path,
nrow=4,
padding=0)
print('Saved real and fake images into {}...'.format(
sample_path))
# Save model checkpoints.
if (step + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(step + 1))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(step + 1))
torch.save(self.G.state_dict(), G_path)
torch.save(self.D.state_dict(), D_path)
print('Saved model checkpoints into {}...'.format(
self.model_save_dir))
# Decay learning rates.
if (step + 1) % self.lr_update_step == 0 and (step + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= (self.g_lr / float(self.num_iters_decay))
d_lr -= (self.d_lr / float(self.num_iters_decay))
self.update_lr(g_lr, d_lr)
print('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(
g_lr, d_lr))
def test(self):
"""Translate images using StarGAN trained on a single dataset."""
# Load the trained generator.
self.restore_model(self.test_iters)
# Set data loader.
data_loader = self.data_loader
with torch.no_grad():
for i, (a_real, b_real) in enumerate(data_loader):
# Prepare input images and target domain labels.
a_real = a_real.to(self.device)
b_real = b_real.to(self.device)
# Translate images.
a_fake_list = [a_real, b_real]
a_fixed_feat = self.feat_extract(a_real)
a_fake_list.append(self.G(b_real, a_fixed_feat))
# Save the translated images.
x_concat = torch.cat(a_fake_list, dim=3)
result_path = os.path.join(self.result_dir,
'{}-images.jpg'.format(i + 1))
save_image(self.denorm(x_concat.data.cpu()),
result_path,
nrow=1,
padding=0)
print('Saved real and fake images into {}...'.format(
result_path))
def main():
parser = argparse.ArgumentParser(description='DCGAN for mnist')
parser.add_argument('--batchsize',
'-b',
type=int,
default=1,
help='# of each mini-batch size')
parser.add_argument('--epoch',
'-e',
type=int,
default=500,
help='# of epoch')
parser.add_argument(
'--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (if you want to use gpu, set positive value)')
parser.add_argument('--dataset',
'-d',
type=str,
default='',
help='path of training dataset path.')
parser.add_argument('--out_dir',
'-o',
type=str,
default='result',
help='path of output the result.')
parser.add_argument('--n_hidden',
'-n',
type=int,
default=100,
help='# of hidden unit(z)')
args = parser.parse_args()
logger = set_logger()
logger.debug('=' * 10)
logger.debug('GPU: {}'.format(args.gpu))
logger.debug('#batchsize: {}'.format(args.batchsize))
logger.debug('#epoch: {}'.format(args.epoch))
logger.debug('n_hidden: {}'.format(args.n_hidden))
logger.debug('dataset: {}'.format(args.dataset))
logger.debug('out_dir: {}'.format(args.out_dir))
logger.debug('=' * 10)
print()
logger.debug('setup models')
# Setup networks
generator = Generator(z_dim=args.n_hidden)
discriminator = Discriminator(z_dim=args.n_hidden)
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
generator.to_gpu()
discriminator.to_gpu()
# Setup optimizers
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001),
'hook_dec')
return optimizer
opt_generator = make_optimizer(generator)
opt_discriminator = make_optimizer(discriminator)
if args.dataset == '':
train, _ = chainer.datasets.get_mnist(withlabel=False,
ndim=3,
scale=255.)
else:
pass
# Setup an iterator
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
# Setup an Updater
updater = GANUpdater(models=(generator, discriminator),
iterator=train_iter,
optimizer={
'gen': opt_generator,
'dis': opt_discriminator
},
device=args.gpu)
# Setup a trainer
trainer = training.Trainer(updater, (args.epoch, 'epoch'),
out=args.out_dir)
snapshot_interval = (1000, 'iteration')
display_interval = (100, 'iteration')
trainer.extend(
extensions.snapshot(filename='snapshot_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
generator, 'generator_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
discriminator, 'discriminator_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.LogReport(trigger=snapshot_interval))
trainer.extend(extensions.PrintReport([
'epoch',
'iteration',
'gen/loss',
'dis/loss',
]),
trigger=display_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
# Start train
logger.debug('Training Start.')
print()
print()
trainer.run()
# 1. what happens if you use larger networks?
# 2. better normalization with Batch Normalization algorithm
# 3. different learning rates (is there a better one?)
# 4. change architecture to a CNN
# Hyperparameters etc.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
lr = 3e-4
z_dim = 64 # 128, 256
image_dim = 28 * 28 * 1 # 784
batch_size = 32
num_epochs = 200
disc_model_file = 'parameters/gan_mnist/discriminator.pth'
gen_model_file = 'parameters/gan_mnist/generator.pth'
disc = Discriminator(image_dim).to(device)
gen = Generator(z_dim, image_dim).to(device)
load_model(disc, disc_model_file, device)
load_model(gen, gen_model_file, device)
fixed_noise = torch.randn((batch_size, z_dim)).to(device)
transforms = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5), (0.5))])
dataset = datasets.MNIST(root='../datasets/',
transform=transforms,
download=True)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
opt_disc = optim.Adam(disc.parameters(), lr=lr)
opt_gen = optim.Adam(gen.parameters(), lr=lr)
criterion = nn.BCELoss()
def main():
tf.random.set_seed(233)
np.random.seed(233)
z_dim = 100
epochs = 3000000
batch_size = 512
learning_rate = 2e-4
# ratios = D steps:G steps
ratios = 2
img_path = glob.glob(os.path.join('faces', '*.jpg'))
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
print(dataset, img_shape)
sample = next(iter(dataset))
print(sample.shape,
tf.reduce_max(sample).numpy(),
tf.reduce_min(sample).numpy())
dataset = dataset.repeat()
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
# generator.load_weights(os.path.join('checkpoints', 'generator-5000'))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
# discriminator.load_weights(os.path.join('checkpoints', 'discriminator-5000'))
g_optimizer = tf.optimizers.Adam(learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate, beta_1=0.5)
# a fixed noise for sampling
z_sample = tf.random.normal([100, z_dim])
g_loss_meter = keras.metrics.Mean()
d_loss_meter = keras.metrics.Mean()
gp_meter = keras.metrics.Mean()
for epoch in range(epochs):
# train D
for step in range(ratios):
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter)
with tf.GradientTape() as tape:
d_loss, gp = d_loss_fn(generator, discriminator, batch_z,
batch_x)
d_loss_meter.update_state(d_loss)
gp_meter.update_state(gp)
gradients = tape.gradient(d_loss,
discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(gradients, discriminator.trainable_variables))
# train G
batch_z = tf.random.normal([batch_size, z_dim])
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z)
g_loss_meter.update_state(g_loss)
gradients = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(
zip(gradients, generator.trainable_variables))
if epoch % 100 == 0:
fake_image = generator(z_sample, training=False)
print(epoch, 'd-loss:',
d_loss_meter.result().numpy(), 'g-loss',
g_loss_meter.result().numpy(), 'gp',
gp_meter.result().numpy())
d_loss_meter.reset_states()
g_loss_meter.reset_states()
gp_meter.reset_states()
# save generated image samples
img_path = os.path.join('images_wgan_gp', 'wgan_gp-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
if epoch + 1 % 2000 == 0:
generator.save_weights(
os.path.join('checkpoints_gp', 'generator-%d' % epoch))
discriminator.save_weights(
os.path.join('checkpoints_gp', 'discriminator-%d' % epoch))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batchSize, sampler=sampler,
shuffle=True, num_workers=int(args.workers))
device = torch.device("cuda" if args.cuda else "cpu")
nz = args.nz
ngf = args.ngf
ndf = args.ndf
G = Generator().to(device)
G.apply(weights_init)
if args.G != '':
G.load_state_dict(clean_state_dict(torch.load(args.G)))
D = Discriminator().to(device)
D.apply(weights_init)
if args.D != '':
D.load_state_dict(clean_state_dict(torch.load(args.D)))
if torch.cuda.device_count() > 1 and args.cuda:
print("Let's use {} GPUs".format(torch.cuda.device_count()))
G = nn.DataParallel(G)
D = nn.DataParallel(D)
criterion = nn.BCELoss()
# fixed noise & label
fixed_noise = torch.randn(args.batchSize, nz, 1, 1, device=device)
real_label = 1
fake_label = 0