def build_model(self):
""" Graph Input """
# images
if self.custom_dataset :
Image_Data_Class = ImageData(self.img_size, self.c_dim)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
else :
self.inputs = tf.placeholder(tf.float32, [self.batch_size, self.img_size, self.img_size, self.c_dim], name='real_images')
# noises
self.z = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], name='z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else :
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate, beta1=self.beta1, beta2=self.beta2).minimize(self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.g_learning_rate, beta1=self.beta1, beta2=self.beta2).minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z, is_training=False, reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
def _input_fn(channel):
"""Returns a Dataset for reading from a SageMaker PipeMode channel."""
features = {
'data': tf.FixedLenFeature([], tf.string),
'labels': tf.FixedLenFeature([], tf.int64),
}
def parse(record):
parsed = tf.parse_single_example(record, features)
return ({
'data': tf.decode_raw(parsed['data'], tf.float64)
}, parsed['labels'])
ds = PipeModeDataset(channel)
if EPOCHS > 1:
ds = ds.repeat(EPOCHS)
ds = ds.prefetch(PREFETCH_SIZE)
ds = ds.apply(map_and_batch(parse, batch_size=BATCH_SIZE,
num_parallel_batches=NUM_PARALLEL_BATCHES))
return ds
def input_fn(params):
"""The actual input function."""
if use_tpu:
batch_size = params["batch_size"]
else:
batch_size = bsz
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
contrib_data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
def input_fn():
"""The actual input function."""
micro_batch_size = opts["micro_batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
"""
Not sure the repeat is in the right place.
Only for training.
"""
if is_training:
buffer_size = opts['buffer_size']
d = d.shuffle(buffer_size=buffer_size)
d = d.apply(
map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=micro_batch_size,
drop_remainder=True))
d = d.repeat()
return d
def _input_fn(channel):
"""Returns a Dataset for reading from a SageMaker PipeMode channel."""
features = {
"data": tf.FixedLenFeature([], tf.string),
"labels": tf.FixedLenFeature([], tf.int64),
}
def parse(record):
parsed = tf.parse_single_example(record, features)
return ({
"data": tf.decode_raw(parsed["data"], tf.float64)
}, parsed["labels"])
ds = PipeModeDataset(channel)
if EPOCHS > 1:
ds = ds.repeat(EPOCHS)
ds = ds.prefetch(PREFETCH_SIZE)
ds = ds.apply(
map_and_batch(parse,
batch_size=BATCH_SIZE,
num_parallel_batches=NUM_PARALLEL_BATCHES))
return ds
def _input_fn():
features = {
'data': tf.FixedLenFeature([], tf.string),
'labels': tf.FixedLenFeature([], tf.int64),
}
def parse(record):
parsed = tf.parse_single_example(record, features)
return ({
'data': tf.decode_raw(parsed['data'], tf.float64)
}, parsed['labels'])
ds = PipeModeDataset(config.channel, benchmark=True)
if config.epochs > 1:
ds = ds.repeat(config.epochs)
if config.prefetch_size > 0:
ds = ds.prefetch(config.prefetch_size)
ds = ds.apply(
map_and_batch(parse,
batch_size=config.batch_size,
num_parallel_batches=config.parallel_transform_calls))
return ds
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.Dataset.list_files(input_file, shuffle=False)
d = d.apply(
contrib_data.parallel_interleave(functools.partial(
tf.data.TFRecordDataset,
compression_type=FLAGS.compression_type),
cycle_length=32,
sloppy=is_training))
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
contrib_data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
def build_model(self):
if self.phase == 'train':
self.lr = tf.placeholder(tf.float32, name='learning_rate')
self.manual_d_loss = tf.placeholder(tf.float32,
name='manual_d_loss')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
# All optimized variables
# Discriminator
#self.D_optim = []
self.Discriminator_loss_total = []
#self.D_loss = []
# Generator
#self.real_A = []
#self.real_B = []
#self.fake_A = []
#self.fake_B = []
#self.G_optim = []
self.Generator_loss_total = []
#self.G_loss = []
# TODO(jhhuang): we should change how we load data here
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
# TODO(jhhuang): change GPU devices
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)
) #.apply(prefetch_to_device(gpu_device))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)
) #.apply(prefetch_to_device(gpu_device))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
# TODO(jhhuang): revisit the for-loop
reuse_vars = False
available_gpus = self.get_available_gpus()
num_gpus = len(available_gpus)
print(str(available_gpus))
for i in range(num_gpus):
print("Current GPU: " + str(available_gpus[i]))
with tf.device(
self.assign_to_device(available_gpus[i],
ps_device='/cpu:0')):
#gpu_device = '/gpu:0'
#trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device)).as_numpy_iterator()
#trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device)).as_numpy_iterator()
domain_A = trainA_iterator.get_next()
domain_B = trainB_iterator.get_next()
""" Define Generator, Discriminator """
x_ab, cam_ab = self.generate_a2b(
domain_A, reuse=reuse_vars) # real a
x_ba, cam_ba = self.generate_b2a(
domain_B, reuse=reuse_vars) # real b
x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b
x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a
x_aa, cam_aa = self.generate_b2a(domain_A,
reuse=True) # fake b
x_bb, cam_bb = self.generate_a2b(domain_B,
reuse=True) # fake a
real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(
domain_A, domain_B, reuse=reuse_vars)
fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(
x_ba, x_ab)
tf.print("real_A_logit: ", real_A_logit)
tf.print("real_A_cam_logit: ", real_A_cam_logit)
tf.print("real_B_logit: ", real_B_logit)
tf.print("real_B_cam_logit: ", real_B_cam_logit)
tf.print("fake_A_logit: ", fake_A_logit)
tf.print("fake_A_cam_logit: ", fake_A_cam_logit)
tf.print("fake_B_logit: ", fake_B_logit)
tf.print("fake_B_cam_logit: ", fake_B_cam_logit)
""" Define Loss """
if self.gan_type.__contains__(
'wgan') or self.gan_type == 'dragan':
GP_A, GP_CAM_A = self.gradient_panalty(
real=domain_A, fake=x_ba, scope="discriminator_A")
GP_B, GP_CAM_B = self.gradient_panalty(
real=domain_B, fake=x_ab, scope="discriminator_B")
else:
GP_A, GP_CAM_A = 0, 0
GP_B, GP_CAM_B = 0, 0
G_ad_loss_A = (
generator_loss(self.gan_type, fake_A_logit) +
generator_loss(self.gan_type, fake_A_cam_logit))
G_ad_loss_B = (
generator_loss(self.gan_type, fake_B_logit) +
generator_loss(self.gan_type, fake_B_cam_logit))
self.fool_discriminator_counter = tf.placeholder(
tf.int32, name="fool_discriminator_counter")
fool_dis = tf.cast(
(self.fool_discriminator_counter % 100 == 0), tf.bool)
D_ad_loss_A = tf.cond(
fool_dis, lambda: (discriminator_loss(
self.gan_type, fake_A_logit, real_A_logit
) + discriminator_loss(
self.gan_type, fake_A_cam_logit, real_A_cam_logit
) + GP_A + GP_CAM_A), lambda: (discriminator_loss(
self.gan_type, real_A_logit,
fake_A_logit) + discriminator_loss(
self.gan_type, real_A_cam_logit,
fake_A_cam_logit) + GP_A + GP_CAM_A))
D_ad_loss_B = tf.cond(
fool_dis, lambda: (discriminator_loss(
self.gan_type, fake_B_logit, real_B_logit
) + discriminator_loss(
self.gan_type, fake_B_cam_logit, real_B_cam_logit
) + GP_B + GP_CAM_B), lambda: (discriminator_loss(
self.gan_type, real_B_logit,
fake_B_logit) + discriminator_loss(
self.gan_type, real_B_cam_logit,
fake_B_cam_logit) + GP_B + GP_CAM_B))
#else :
# D_ad_loss_A = (discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) + discriminator_loss(self.gan_type, real_A_cam_logit, fake_A_cam_logit) + GP_A + GP_CAM_A)
# D_ad_loss_B = (discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) + discriminator_loss(self.gan_type, real_B_cam_logit, fake_B_cam_logit) + GP_B + GP_CAM_B)
#self.fool_discriminator_counter += 1
reconstruction_A = L1_loss(x_aba,
domain_A) # reconstruction
reconstruction_B = L1_loss(x_bab,
domain_B) # reconstruction
identity_A = L1_loss(x_aa, domain_A)
identity_B = L1_loss(x_bb, domain_B)
cam_A = cam_loss(source=cam_ba, non_source=cam_aa)
cam_B = cam_loss(source=cam_ab, non_source=cam_bb)
Generator_A_gan = self.adv_weight * G_ad_loss_A
Generator_A_cycle = self.cycle_weight * reconstruction_B
Generator_A_identity = self.identity_weight * identity_A
Generator_A_cam = self.cam_weight * cam_A
Generator_B_gan = self.adv_weight * G_ad_loss_B
Generator_B_cycle = self.cycle_weight * reconstruction_A
Generator_B_identity = self.identity_weight * identity_B
Generator_B_cam = self.cam_weight * cam_B
Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam
Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam
Discriminator_A_loss = self.adv_weight * D_ad_loss_A
Discriminator_B_loss = self.adv_weight * D_ad_loss_B
#self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss('generator')
self.Generator_loss_total.append(
Generator_A_loss + Generator_B_loss +
regularization_loss('generator'))
#self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss('discriminator') + self.manual_d_loss
self.Discriminator_loss_total.append(
Discriminator_A_loss + Discriminator_B_loss +
regularization_loss('discriminator') +
self.manual_d_loss)
reuse_vars = True
""" Result Image """
#self.fake_A = x_ba
#self.fake_A.append(x_ba)
#self.fake_B = x_ab
#self.fake_B.append(x_ab)
#self.real_A = self.domain_A
#self.real_A.append(self.domain_A)
#self.real_B = self.domain_B
#self.real_B.append(self.domain_B)
""" 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]
""" Compute Average Loss """
self.Generator_loss = tf.reduce_mean(self.Generator_loss_total)
self.Discriminator_loss = tf.reduce_mean(
self.Discriminator_loss_total)
print("G_vars : ")
for elemnt in G_vars:
print("element from G_vars : " + str(element))
self.G_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Generator_loss,
var_list=G_vars)
#self.G_optim.append(tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars))
self.D_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Discriminator_loss,
var_list=D_vars)
#self.D_optim.append(tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars))
"""" Summary """
self.all_G_loss = tf.summary.scalar("Generator_loss",
self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_A_gan = tf.summary.scalar("G_A_gan", Generator_A_gan)
self.G_A_cycle = tf.summary.scalar("G_A_cycle", Generator_A_cycle)
self.G_A_identity = tf.summary.scalar("G_A_identity",
Generator_A_identity)
self.G_A_cam = tf.summary.scalar("G_A_cam", Generator_A_cam)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.G_B_gan = tf.summary.scalar("G_B_gan", Generator_B_gan)
self.G_B_cycle = tf.summary.scalar("G_B_cycle", Generator_B_cycle)
self.G_B_identity = tf.summary.scalar("G_B_identity",
Generator_B_identity)
self.G_B_cam = tf.summary.scalar("G_B_cam", Generator_B_cam)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.rho_var = []
for var in tf.trainable_variables():
if 'rho' in var.name:
self.rho_var.append(tf.summary.histogram(var.name, var))
self.rho_var.append(
tf.summary.scalar(var.name + "_min",
tf.reduce_min(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_max",
tf.reduce_max(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_mean",
tf.reduce_mean(var)))
g_summary_list = [
self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity,
self.G_A_cam, self.G_B_loss, self.G_B_gan, self.G_B_cycle,
self.G_B_identity, self.G_B_cam, self.all_G_loss
]
g_summary_list.extend(self.rho_var)
d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]
self.G_loss = tf.summary.merge(g_summary_list)
#self.G_loss.append(tf.summary.merge(g_summary_list))
self.D_loss = tf.summary.merge(d_summary_list)
#self.D_loss.append(tf.summary.merge(d_summary_list))
else:
""" Test """
self.test_domain_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_A')
self.test_domain_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_B')
self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)
self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-s',
'--sample',
type=str,
default='',
help='Samples based on input song. Empty string means training.')
parser.add_argument('-c',
'--concat',
dest='concat',
action='store_true',
help='Enable concatenation.')
parser.add_argument('--no-concat',
dest='concat',
action='store_false',
help='Disable concatenation.')
parser.set_defaults(concat=False)
parser.add_argument('-r',
'--record',
dest='record',
action='store_true',
help='Enable recording.')
parser.add_argument('--no-record',
dest='record',
action='store_false',
help='Disable recording.')
parser.set_defaults(
record=False) # Warning: Windows kills python if enabled.
args = parser.parse_args()
sampling = args.sample != ''
if not os.path.exists('Checkpoints_v1'):
os.makedirs('Checkpoints_v1')
if not os.path.exists('Logs_v1'):
os.makedirs('Logs_v1')
if not os.path.exists('Samples_v1'):
os.makedirs('Samples_v1')
if not os.path.exists('Timelines_v1'):
os.makedirs('Timelines_v1')
filename = 'Dataset/cond_dataset.tfrecord'
dataset = tf.data.TFRecordDataset(filename, num_parallel_reads=8)
def _parse(example_proto):
feature = {
'label': tf.FixedLenFeature([], tf.int64),
'data': tf.FixedLenFeature([], tf.string)
}
parsed = tf.parse_single_example(example_proto, feature)
data = tf.decode_raw(parsed['data'], tf.uint8)
label = tf.cast(parsed['label'], tf.uint8)
data = tf.py_func(func=np.unpackbits, inp=[data], Tout=tf.uint8)
label = tf.py_func(func=np.unpackbits, inp=[label], Tout=tf.uint8)
data = tf.cast(data, tf.float32)
label = tf.cast(label, tf.float32)
data = tf.reshape(data, [CHANNEL_NUM, CLASS_NUM, INPUT_LENGTH])
label.set_shape([8])
label = label[:CHANNEL_NUM]
label = tf.expand_dims(tf.expand_dims(label, axis=-1), axis=-1)
data = data * 2 - 1
return {'data': data, 'label': label}
dataset = dataset.apply(data.shuffle_and_repeat(buffer_size=16384))
dataset = dataset.apply(
data.map_and_batch(_parse,
batch_size=BATCH_SIZE,
num_parallel_batches=16,
drop_remainder=True))
dataset = dataset.prefetch(32)
dataset = dataset.apply(data.prefetch_to_device('/gpu:0'))
iterator = dataset.make_one_shot_iterator()
next_data = iterator.get_next()
real_input_3 = next_data['data']
label = next_data['label']
input_noise = tf.placeholder(dtype=tf.float32,
shape=[None, NOISE_LENGTH],
name='input_noise')
real_input_2 = tf.layers.average_pooling2d(inputs=real_input_3,
pool_size=2,
strides=2,
padding='same',
data_format='channels_first',
name='real_input_2')
real_input_2 -= tf.reduce_min(real_input_2)
real_input_2 /= (tf.reduce_max(real_input_2) + EPSILON)
real_input_2 = 2 * real_input_2 - 1
real_input_1 = tf.layers.average_pooling2d(inputs=real_input_2,
pool_size=2,
strides=2,
padding='same',
data_format='channels_first',
name='real_input_1')
real_input_1 -= tf.reduce_min(real_input_1)
real_input_1 /= (tf.reduce_max(real_input_1) + EPSILON)
real_input_1 = 2 * real_input_1 - 1
train = tf.placeholder(dtype=tf.bool, name='train')
# shape: [None, 6, 64, 256]
encode = encoder(inputs=real_input_3,
update_collection=SPECTRAL_UPDATE_OPS,
train=train)
# shape: [None, 64]
tf.summary.histogram('encode', encode)
print('Encoder set')
real_input_3_image = tf.expand_dims(real_input_3[:1],
axis=-1,
name='real_input_3_image')
# shape: [1, 6, 128, 512, 1]
real_input_2_image = tf.expand_dims(real_input_2[:1],
axis=-1,
name='real_input_2_image')
# shape: [1, 6, 64, 256, 1]
real_input_1_image = tf.expand_dims(real_input_1[:1],
axis=-1,
name='real_input_1_image')
# shape: [1, 6, 32, 128, 1]
for i in range(CHANNEL_NUM):
tf.summary.image('real_input_1_%d' % i, real_input_1_image[:, i])
tf.summary.image('real_input_2_%d' % i, real_input_2_image[:, i])
tf.summary.image('real_input_3_%d' % i, real_input_3_image[:, i])
shared_output = shared_gen(noise=input_noise,
label=label,
update_collection=SPECTRAL_UPDATE_OPS,
train=train)
# shape: [None, 64, 16, 64]
gen1 = generator1(inputs=shared_output,
label=label,
update_collection=SPECTRAL_UPDATE_OPS,
train=train)
output_gen1 = process(gen1, 1, train, SPECTRAL_UPDATE_OPS) * label
# shape: [None, 6, 32, 128]
gen2 = generator2(inputs=gen1,
label=label,
update_collection=SPECTRAL_UPDATE_OPS,
train=train)
output_gen2 = process(gen2, 2, train, SPECTRAL_UPDATE_OPS) * label
# shape: [None, 6, 64, 256]
gen3 = generator3(inputs=gen2,
label=label,
update_collection=SPECTRAL_UPDATE_OPS,
train=train)
output_gen3 = process(gen3, 3, train, SPECTRAL_UPDATE_OPS) * label
# shape: [None, 6, 128, 512]
print('Generators set')
dis1_real = discriminator1(inputs=real_input_1,
encode=encode,
update_collection=SPECTRAL_UPDATE_OPS)
dis1_gen = discriminator1(inputs=output_gen1,
encode=encode,
update_collection=NO_OPS)
dis2_real = discriminator2(inputs=real_input_2,
encode=encode,
update_collection=SPECTRAL_UPDATE_OPS)
dis2_gen = discriminator2(inputs=output_gen2,
encode=encode,
update_collection=NO_OPS)
dis3_real = discriminator3(inputs=real_input_3,
encode=encode,
update_collection=SPECTRAL_UPDATE_OPS)
dis3_gen = discriminator3(inputs=output_gen3,
encode=encode,
update_collection=NO_OPS)
print('Discriminators set')
loss_dis1 = tf.reduce_mean(dis1_gen - dis1_real) + lipschitz_penalty(
real=real_input_1,
gen=output_gen1,
encode=encode,
discriminator=discriminator1) + DRIFT * tf.reduce_mean(
tf.square(dis1_real))
loss_gen1 = -tf.reduce_mean(dis1_gen)
loss_dis2 = tf.reduce_mean(dis2_gen - dis2_real) + lipschitz_penalty(
real=real_input_2,
gen=output_gen2,
encode=encode,
discriminator=discriminator2) + DRIFT * tf.reduce_mean(
tf.square(dis2_real))
loss_gen2 = -tf.reduce_mean(dis2_gen)
loss_dis3 = tf.reduce_mean(dis3_gen - dis3_real) + lipschitz_penalty(
real=real_input_3,
gen=output_gen3,
encode=encode,
discriminator=discriminator3) + DRIFT * tf.reduce_mean(
tf.square(dis3_real))
loss_gen3 = -tf.reduce_mean(dis3_gen)
loss_gen = tf.add_n([loss_gen1, loss_gen2, loss_gen3]) / 3
tf.summary.scalar('loss_dis1', loss_dis1)
tf.summary.scalar('loss_gen1', loss_gen1)
tf.summary.scalar('dis1_real', tf.reduce_mean(dis1_real))
tf.summary.scalar('loss_dis2', loss_dis2)
tf.summary.scalar('loss_gen2', loss_gen2)
tf.summary.scalar('dis2_real', tf.reduce_mean(dis2_real))
tf.summary.scalar('loss_dis3', loss_dis3)
tf.summary.scalar('loss_gen3', loss_gen3)
tf.summary.scalar('dis3_real', tf.reduce_mean(dis3_real))
tf.summary.scalar('loss_gen', loss_gen)
print('Losses set')
gen_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Shared_generator')
#gen_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Encoder')
for i in range(CHANNEL_NUM):
gen_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Generator1_%d' % i)
gen_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Generator2_%d' % i)
gen_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Generator3_%d' % i)
dis1_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Discriminator1')
dis1_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Encoder')
dis2_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Discriminator2')
dis2_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Encoder')
dis3_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Discriminator3')
dis3_var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='Encoder')
gen_extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Shared_generator')
#gen_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Encoder')
for i in range(CHANNEL_NUM):
gen_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Generator1_%d' % i)
gen_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Generator2_%d' % i)
gen_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Generator3_%d' % i)
dis1_extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Discriminator1')
dis1_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Encoder')
dis2_extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Discriminator2')
dis2_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Encoder')
dis3_extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Discriminator3')
dis3_extra_update_ops += tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='Encoder')
spectral_norm_update_ops = tf.get_collection(SPECTRAL_UPDATE_OPS)
with tf.name_scope('optimizers'):
with tf.control_dependencies(dis1_extra_update_ops):
dis1_train = tf.train.AdamOptimizer(learning_rate=0.0004,
beta1=0.5,
beta2=0.9).minimize(
loss=loss_dis1,
var_list=dis1_var,
name='dis1_train')
print('dis1_train setup')
with tf.control_dependencies(dis2_extra_update_ops):
dis2_train = tf.train.AdamOptimizer(learning_rate=0.0004,
beta1=0.5,
beta2=0.9).minimize(
loss=loss_dis2,
var_list=dis2_var,
name='dis2_train')
print('dis2_train setup')
with tf.control_dependencies(dis3_extra_update_ops):
dis3_train = tf.train.AdamOptimizer(learning_rate=0.0004,
beta1=0.5,
beta2=0.9).minimize(
loss=loss_dis3,
var_list=dis3_var,
name='dis3_train')
print('dis3_train setup')
with tf.control_dependencies(gen_extra_update_ops):
gen_train = tf.train.AdamOptimizer(learning_rate=0.0001,
beta1=0.5,
beta2=0.9).minimize(
loss=loss_gen,
var_list=gen_var,
name='gen_train')
print('gen_train setup')
print('Optimizers set')
gpu_options = tf.GPUOptions(allow_growth=True, allocator_type='BFC')
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
merged = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
if tf.train.latest_checkpoint('Checkpoints_v1') is not None:
print('Restoring...')
saver.restore(sess, tf.train.latest_checkpoint('Checkpoints_v1'))
feed_dict = {input_noise: None, train: True}
print('preparing complete')
if sampling:
feed_dict = {input_noise: None, real_input_3: None, train: False}
path = args.sample
try:
feed_dict[real_input_3] = roll(path)[:BATCH_SIZE]
except:
print('Error while opening file.')
exit()
feed_dict[input_noise] = get_noise([BATCH_SIZE, NOISE_LENGTH])
samples = sess.run(output_gen3, feed_dict=feed_dict)
path = path.split('/')[-1]
if not os.path.exists('Samples_v1/sample_%s'):
os.mkdir('Samples_v1/sample_%s' % path)
np.save(file='Samples_v1/sample_%s' % path + '/%s' % path,
arr=samples)
unpack_sample(name='Samples_v1/sample_%s' % path +
'/%s.npy' % path,
concat=args.concat)
exit()
writer = tf.summary.FileWriter('Logs_v1', sess.graph)
run_options = tf.RunOptions(report_tensor_allocations_upon_oom=True)
epoch_num = 100001
for train_count in tqdm(range(epoch_num), smoothing=0.7):
for i in range(TRAIN_RATIO_DIS):
feed_dict[input_noise] = get_noise([BATCH_SIZE, NOISE_LENGTH])
feed_dict[train] = True
*_, loss_val_dis1, loss_val_dis2, loss_val_dis3 = sess.run(
[
dis1_train, dis2_train, dis3_train, loss_dis1,
loss_dis2, loss_dis3
],
feed_dict=feed_dict,
options=run_options)
for i in range(TRAIN_RATIO_GEN):
feed_dict[input_noise] = get_noise([BATCH_SIZE, NOISE_LENGTH])
feed_dict[train] = True
summary, _, loss_val_gen = sess.run(
[merged, gen_train, loss_gen],
feed_dict=feed_dict,
options=run_options)
sess.run(spectral_norm_update_ops)
writer.add_summary(summary, train_count)
tqdm.write('%06d' % train_count, end=' ')
tqdm.write('Discriminator1 loss : %.7f' % loss_val_dis1, end=' ')
tqdm.write('Discriminator2 loss : %.7f' % loss_val_dis2, end=' ')
tqdm.write('Discriminator3 loss : %.7f' % loss_val_dis3, end=' ')
tqdm.write('Generator loss : %.7f' % loss_val_gen)
if train_count % 1000 == 0:
feed_dict[input_noise] = get_noise([BATCH_SIZE, NOISE_LENGTH])
feed_dict[train] = False
samples = sess.run(output_gen3, feed_dict=feed_dict)
np.save(file='Samples_v1/song_%06d' % train_count, arr=samples)
unpack_sample('Samples_v1/song_%06d' % train_count)
save_path = saver.save(
sess, 'Checkpoints_v1/song_%06d' % train_count + '.ckpt')
tqdm.write('Model Saved: %s' % save_path)
if args.record:
trace_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE) # pylint: disable=E1101
run_metadata = tf.RunMetadata()
sess.run([dis1_train, dis2_train, dis3_train, gen_train],
feed_dict=feed_dict,
options=trace_options,
run_metadata=run_metadata)
writer.add_run_metadata(run_metadata,
'run_%d' % train_count)
tl = timeline.Timeline(run_metadata.step_stats) # pylint: disable=E1101
ctf = tl.generate_chrome_trace_format()
with open('Timelines_v1/timeline_%d.json' % train_count,
'w') as f:
f.write(ctf)
writer.close()
def build_model(self):
label_fix_onehot_list = []
""" Input Image"""
if self.dataset_name == 'celebA-HQ' or self.dataset_name == 'celebA':
img_class = ImageData_celebA(self.img_height, self.img_width,
self.img_ch, self.dataset_path,
self.label_list, self.augment_flag)
img_class.preprocess(self.phase)
else:
img_class = Image_data(self.img_height, self.img_width,
self.img_ch, self.dataset_path,
self.label_list, self.augment_flag)
img_class.preprocess()
label_fix_onehot_list = img_class.label_onehot_list
label_fix_onehot_list = tf.tile(
tf.expand_dims(label_fix_onehot_list, axis=1),
[1, self.batch_size, 1])
dataset_num = len(img_class.image)
print("Dataset number : ", dataset_num)
if self.phase == 'train':
self.lr = tf.placeholder(tf.float32, name='learning_rate')
if self.dataset_name == 'celebA-HQ' or self.dataset_name == 'celebA':
img_and_label = tf.data.Dataset.from_tensor_slices(
(img_class.image, img_class.label,
img_class.train_label_onehot_list))
else:
img_and_label = tf.data.Dataset.from_tensor_slices(
(img_class.image, img_class.label))
gpu_device = '/gpu:0'
img_and_label = img_and_label.apply(
shuffle_and_repeat(dataset_num)).apply(
map_and_batch(img_class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_label_iterator = img_and_label.make_one_shot_iterator()
if self.dataset_name == 'celebA-HQ' or self.dataset_name == 'celebA':
self.x_real, label_org, label_fix_onehot_list = img_and_label_iterator.get_next(
)
label_trg = tf.random_shuffle(
label_org) # Target domain labels
label_fix_onehot_list = tf.transpose(label_fix_onehot_list,
perm=[1, 0, 2])
else:
self.x_real, label_org = img_and_label_iterator.get_next()
label_trg = tf.random_shuffle(
label_org) # Target domain labels
""" Define Generator, Discriminator """
fake_style_code = tf.random_normal(
shape=[self.batch_size, self.style_dim])
x_fake = self.generator(self.x_real, label_trg,
fake_style_code) # real a
recon_style_code = tf.random_normal(
shape=[self.batch_size, self.style_dim])
x_recon = self.generator(x_fake,
label_org,
recon_style_code,
reuse=True) # real b
real_logit, real_cls, _ = self.discriminator(self.x_real)
fake_logit, fake_cls, fake_noise = self.discriminator(x_fake,
reuse=True)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_panalty(real=self.x_real, fake=x_fake)
else:
GP = 0
g_adv_loss = self.adv_weight * generator_loss(
self.gan_type, fake_logit)
g_cls_loss = self.cls_weight * classification_loss(logit=fake_cls,
label=label_trg)
g_rec_loss = self.rec_weight * L1_loss(self.x_real, x_recon)
g_noise_loss = self.noise_weight * L1_loss(fake_style_code,
fake_noise)
d_adv_loss = self.adv_weight * discriminator_loss(
self.gan_type, real_logit, fake_logit) + GP
d_cls_loss = self.cls_weight * classification_loss(logit=real_cls,
label=label_org)
d_noise_loss = self.noise_weight * L1_loss(fake_style_code,
fake_noise)
self.d_loss = d_adv_loss + d_cls_loss + d_noise_loss
self.g_loss = g_adv_loss + g_cls_loss + g_rec_loss + g_noise_loss
""" Result Image """
if self.dataset_name == 'celebA-HQ' or self.dataset_name == 'celebA':
self.x_fake_list = []
for _ in range(self.num_style):
random_style_code = tf.random_normal(
shape=[self.batch_size, self.style_dim])
self.x_fake_list.append(
tf.map_fn(lambda c: self.generator(
self.x_real, c, random_style_code, reuse=True),
label_fix_onehot_list,
dtype=tf.float32))
else:
self.x_fake_list = []
for _ in range(self.num_style):
random_style_code = tf.random_normal(
shape=[self.batch_size, self.style_dim])
self.x_fake_list.append(
tf.map_fn(lambda c: self.generator(
self.x_real, c, random_style_code, reuse=True),
label_fix_onehot_list,
dtype=tf.float32))
""" 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]
self.g_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.g_loss,
var_list=G_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.d_loss,
var_list=D_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("g_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("d_loss", self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_cls_loss = tf.summary.scalar("g_cls_loss", g_cls_loss)
self.g_rec_loss = tf.summary.scalar("g_rec_loss", g_rec_loss)
self.g_noise_loss = tf.summary.scalar("g_noise_loss", g_noise_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
self.d_cls_loss = tf.summary.scalar("d_cls_loss", d_cls_loss)
self.d_noise_loss = tf.summary.scalar("d_noise_loss", d_noise_loss)
self.g_summary_loss = tf.summary.merge([
self.Generator_loss, self.g_adv_loss, self.g_cls_loss,
self.g_rec_loss, self.g_noise_loss
])
self.d_summary_loss = tf.summary.merge([
self.Discriminator_loss, self.d_adv_loss, self.d_cls_loss,
self.d_noise_loss
])
else:
""" Test """
if self.dataset_name == 'celebA-HQ' or self.dataset_name == 'celebA':
img_and_label = tf.data.Dataset.from_tensor_slices(
(img_class.test_image, img_class.test_label,
img_class.test_label_onehot_list))
dataset_num = len(img_class.test_image)
gpu_device = '/gpu:0'
img_and_label = img_and_label.apply(
shuffle_and_repeat(dataset_num)).apply(
map_and_batch(img_class.image_processing,
batch_size=self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_label_iterator = img_and_label.make_one_shot_iterator()
self.x_test, _, self.test_label_fix_onehot_list = img_and_label_iterator.get_next(
)
self.test_img_placeholder = tf.placeholder(
tf.float32,
[1, self.img_height, self.img_width, self.img_ch])
self.test_label_fix_placeholder = tf.placeholder(
tf.float32, [self.c_dim, 1, self.c_dim])
self.custom_image = tf.placeholder(
tf.float32,
[1, self.img_height, self.img_width, self.img_ch],
name='custom_image') # Custom Image
custom_label_fix_onehot_list = tf.transpose(
np.expand_dims(label2onehot(self.label_list), axis=0),
perm=[1, 0, 2]) # [c_dim, bs, c_dim]
""" Test Image """
test_random_style_code = tf.random_normal(
shape=[1, self.style_dim])
self.x_test_fake_list = tf.map_fn(
lambda c: self.generator(self.test_img_placeholder, c,
test_random_style_code),
self.test_label_fix_placeholder,
dtype=tf.float32)
self.custom_fake_image = tf.map_fn(lambda c: self.generator(
self.custom_image, c, test_random_style_code, reuse=True),
custom_label_fix_onehot_list,
dtype=tf.float32)
else:
self.custom_image = tf.placeholder(
tf.float32,
[1, self.img_height, self.img_width, self.img_ch],
name='custom_image') # Custom Image
custom_label_fix_onehot_list = tf.transpose(
np.expand_dims(label2onehot(self.label_list), axis=0),
perm=[1, 0, 2]) # [c_dim, bs, c_dim]
test_random_style_code = tf.random_normal(
shape=[1, self.style_dim])
self.custom_fake_image = tf.map_fn(
lambda c: self.generator(self.custom_image, c,
test_random_style_code),
custom_label_fix_onehot_list,
dtype=tf.float32)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.identity_A = trainA_iterator.get_next()
self.shape_A = trainA_iterator.get_next()
self.other_A = trainA_iterator.get_next()
self.shape_B = trainB_iterator.get_next()
self.test_identity_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_identity_A')
self.test_shape_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_shape_B')
""" Define Generator, Discriminator """
self.fake_same = self.generator(x_identity=self.identity_A,
x_shape=self.shape_A)
self.fake_diff = self.generator(x_identity=self.identity_A,
x_shape=self.shape_B,
reuse=True)
fake_diff_shape = self.generator(x_identity=self.shape_B,
x_shape=self.fake_diff,
reuse=True)
fake_diff_identity = self.generator(x_identity=self.fake_diff,
x_shape=self.shape_B,
reuse=True)
real_logit = self.discriminator(x_identity=self.identity_A,
x=self.other_A)
fake_logit = self.discriminator(x_identity=self.identity_A,
x=self.fake_diff,
reuse=True)
""" Define Loss """
g_identity_loss = self.adv_weight * generator_loss(
self.gan_type, fake_logit) * 64
g_shape_loss_same = self.L1_weight * L1_loss(self.fake_same,
self.shape_A)
g_shape_loss_diff_shape = self.L1_weight * L1_loss(
fake_diff_shape, self.shape_B) + self.L1_weight * L1_loss(
self.fake_diff, self.shape_B)
g_shape_loss_diff_identity = self.L1_weight * L1_loss(
fake_diff_identity, self.fake_diff)
self.Generator_loss = g_identity_loss + g_shape_loss_same + g_shape_loss_diff_shape + g_shape_loss_diff_identity
self.Discriminator_loss = self.adv_weight * discriminator_loss(
self.gan_type, real=real_logit, fake=fake_logit)
""" Result Image """
self.test_fake = self.generator(x_identity=self.test_identity_A,
x_shape=self.test_shape_B,
reuse=True)
""" 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]
self.G_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss,
var_list=D_vars)
"""" Summary """
self.G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_identity = tf.summary.scalar("G_identity", g_identity_loss)
self.G_shape_loss_same = tf.summary.scalar("G_shape_loss_same",
g_shape_loss_same)
self.G_shape_loss_diff_shape = tf.summary.scalar(
"G_shape_loss_diff_shape", g_shape_loss_diff_shape)
self.G_shape_loss_diff_identity = tf.summary.scalar(
"G_shape_loss_diff_identity", g_shape_loss_diff_identity)
self.G_loss_merge = tf.summary.merge([
self.G_loss, self.G_identity, self.G_shape_loss_same,
self.G_shape_loss_diff_shape, self.G_shape_loss_diff_identity
])
self.D_loss_merge = tf.summary.merge([self.D_loss])
def get_squad_dataset(opts, is_training):
seq_length = opts['seq_length']
name_to_features = {
"unique_ids": tf.FixedLenFeature([], tf.int64),
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
}
if is_training:
name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
micro_batch_size = opts['micro_batch_size']
tfrecord_dir = opts['tfrecord_dir']
opts['global_batch_size'] = opts['replicas'] * opts[
'micro_batch_size'] * opts['gradient_accumulation_count']
if opts["version_2_with_negative"]:
base_name_train = f"{opts['seq_length']}_{opts['doc_stride']}_{opts['max_query_length']}_SQuAD20"
base_name_eval = f"{opts['seq_length']}_{opts['doc_stride']}_{opts['max_query_length']}_{opts['global_batch_size']}_SQuAD20"
else:
base_name_train = f"{opts['seq_length']}_{opts['doc_stride']}_{opts['max_query_length']}_SQuAD11"
base_name_eval = f"{opts['seq_length']}_{opts['doc_stride']}_{opts['max_query_length']}_{opts['global_batch_size']}_SQuAD11"
if is_training:
filename = os.path.join(tfrecord_dir,
base_name_train + "_train.tfrecord")
input_file = opts['train_file']
else:
filename = os.path.join(tfrecord_dir,
base_name_eval + "_eval.tfrecord")
input_file = opts['predict_file']
if not os.path.exists(filename):
tf.logging.info(f'Preprocessing SQuAD input file: {filename}')
cache_path = os.path.dirname(filename)
if not os.path.exists(cache_path):
tf.logging.info(f'Creating SQuAD cache in {cache_path}')
os.makedirs(cache_path)
examples = read_squad_examples(input_file=input_file,
opts=opts,
is_training=is_training)
writer = FeatureWriter(filename=filename, is_training=is_training)
features = []
tokenizer = tokenization.FullTokenizer(
vocab_file=opts['vocab_file'], do_lower_case=opts['do_lower_case'])
def append_feature(feature):
features.append(feature)
writer.process_feature(feature)
if is_training:
# Don't need to pad for repeated dataset
padding_to = 1
else:
# Padding for pipeline depth
padding_to = opts['replicas'] * opts['micro_batch_size'] * opts[
'gradient_accumulation_count']
num_of_features = convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=opts['seq_length'],
doc_stride=opts['doc_stride'],
max_query_length=opts['max_query_length'],
is_training=is_training,
output_fn=append_feature,
padding_to=padding_to)
if is_training:
metadatafile = os.path.join(
tfrecord_dir, "train_" + base_name_train + ".metadata")
if not os.path.exists(metadatafile):
tf.logging.info(
f'Logging converted no. of SQuAD train features in {metadatafile}'
)
with open(metadatafile, 'w') as f:
f.write(str(num_of_features) + '\n')
else:
metadatafile = os.path.join(tfrecord_dir,
"eval_" + base_name_eval + ".metadata")
if not os.path.exists(metadatafile):
tf.logging.info(
f'Logging converted no. of SQuAD eval features in {metadatafile}'
)
with open(metadatafile, 'w') as f:
f.write(str(num_of_features) + '\n')
writer.close()
d = tf.data.TFRecordDataset(filename)
if is_training:
if opts['distributed_worker_count'] > 1:
d = d.shard(num_shards=opts['distributed_worker_count'],
index=opts['distributed_worker_index'])
d = d.shuffle(buffer_size=100000, reshuffle_each_iteration=False)
d = d.repeat()
if opts['generated_data']:
d = d.repeat()
d = d.apply(
map_and_batch(lambda record: _decode_record(record, name_to_features),
batch_size=micro_batch_size,
drop_remainder=True))
return d
def build_model(self, A):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_data_class = ImageData(load_size=self.img_size, channels=self.img_ch, data_path=self.dataset_path, selected_attrs=self.selected_attrs, augment_flag=self.augment_flag)
Image_data_class.preprocess()
train_dataset_num = len(Image_data_class.train_dataset)
test_dataset_num = len(Image_data_class.test_dataset)
train_dataset = tf.data.Dataset.from_tensor_slices((Image_data_class.train_dataset, Image_data_class.train_dataset_label, Image_data_class.train_dataset_fix_label))
test_dataset = tf.data.Dataset.from_tensor_slices((Image_data_class.test_dataset, Image_data_class.test_dataset_label, Image_data_class.test_dataset_fix_label))
gpu_device = '/gpu:0'
train_dataset = train_dataset.\
apply(shuffle_and_repeat(train_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
test_dataset = test_dataset.\
apply(shuffle_and_repeat(test_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
train_dataset_iterator = train_dataset.make_one_shot_iterator()
test_dataset_iterator = test_dataset.make_one_shot_iterator()
self.x_real, label_org, label_fix_list = train_dataset_iterator.get_next() # Input image / Original domain labels
label_trg = tf.random_shuffle(label_org) # Target domain labels
label_fix_list = tf.transpose(label_fix_list, perm=[1, 0, 2])
self.x_test, test_label_org, test_label_fix_list = test_dataset_iterator.get_next() # Input image / Original domain labels
test_label_fix_list = tf.transpose(test_label_fix_list, perm=[1, 0, 2])
self.custom_image = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='custom_image') # Custom Image
custom_label_fix_list = tf.transpose(create_labels(self.custom_label, self.selected_attrs), perm=[1, 0, 2])
""" Define Generator, Discriminator """
# binary label transformation
dist = tf_contrib.distributions.Categorical(probs=[0.25, 0.5, 0.25])
hat_c = tf.cast(dist.sample([self.batch_size, self.c_dim]) - 1, dtype ='float32')
x_fake, w_fake = self.generator(self.x_real, hat_c) # real a
x_recon, w_recon = self.generator(x_fake, -hat_c, reuse=True) # real b
real_logit, real_cls = self.discriminator(self.x_real)
fake_logit, fake_cls = self.discriminator(x_fake, reuse=True)
# warp cycle
A_fake = tf_contrib.image.dense_image_warp(A, w_fake)
A_cycle = tf_contrib.image.dense_image_warp(A_fake, w_recon)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :
GP = self.gradient_panalty(real=self.x_real, fake=x_fake)
else :
GP = 0
g_adv_loss = generator_loss(loss_func=self.gan_type, fake=fake_logit)
g_cls_loss = binary_label_loss(hat_c, fake_cls, self.c_dim)
warp_cycle_loss = tf.reduce_mean((A_cycle - A)** 2, axis=[1,2,3])
g_rec_loss = tf.reduce_mean(warp_cycle_loss)
smooth_loss = tf.reduce_mean(total_variation(w_fake))
d_adv_loss = discriminator_loss(loss_func=self.gan_type, real=real_logit, fake=fake_logit)
d_cls_loss = classification_loss(logit=real_cls, label=label_org)
self.d_loss = self.adv_weight * d_adv_loss + self.gp_weight * GP + self.cls_weight * d_cls_loss
self.g_loss = self.adv_weight * g_adv_loss + self.cls_weight * g_cls_loss + self.rec_weight * g_rec_loss + self.smooth_weight * smooth_loss
""" Result Image """
self.x_fake_list = tf.map_fn(lambda x : self.generator(self.x_real, x, reuse=True)[0], label_fix_list, dtype=tf.float32)
""" Test Image """
self.x_test_fake_list = tf.map_fn(lambda x : self.generator(self.x_test, x, reuse=True)[0], test_label_fix_list, dtype=tf.float32)
self.custom_fake_image = tf.map_fn(lambda x : self.generator(self.custom_image, x, reuse=True)[0], custom_label_fix_list, dtype=tf.float32)
""" 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]
self.g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.g_loss, var_list=G_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.d_loss, var_list=D_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("Generator_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("Discriminator_loss", self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_cls_loss = tf.summary.scalar("g_cls_loss", g_cls_loss)
self.g_rec_loss = tf.summary.scalar("g_rec_loss", g_rec_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
self.d_cls_loss = tf.summary.scalar("d_cls_loss", d_cls_loss)
self.g_summary_loss = tf.summary.merge([self.Generator_loss, self.g_adv_loss, self.g_cls_loss, self.g_rec_loss])
self.d_summary_loss = tf.summary.merge([self.Discriminator_loss, self.d_adv_loss, self.d_cls_loss])
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_data_class = ImageData(load_size=self.img_size,
channels=self.img_ch,
data_path=self.dataset_path,
dataset_name=self.dataset_name,
selected_attrs=self.selected_attrs,
augment_flag=self.augment_flag)
Image_data_class.preprocess()
train_dataset_num = len(Image_data_class.train_dataset)
test_dataset_num = len(Image_data_class.test_dataset)
train_dataset = tf.data.Dataset.from_tensor_slices(
(Image_data_class.train_dataset,
Image_data_class.train_dataset_label,
Image_data_class.train_dataset_fix_label))
test_dataset = tf.data.Dataset.from_tensor_slices(
(Image_data_class.test_dataset,
Image_data_class.test_dataset_label,
Image_data_class.test_dataset_fix_label))
gpu_device = '/gpu:0'
train_dataset = train_dataset.\
apply(shuffle_and_repeat(train_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
test_dataset = test_dataset.\
apply(shuffle_and_repeat(test_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
train_dataset_iterator = train_dataset.make_one_shot_iterator()
test_dataset_iterator = test_dataset.make_one_shot_iterator()
self.x_real, label_org, label_fix_list = train_dataset_iterator.get_next(
) # Input image / Original domain labels
label_trg = tf.random_shuffle(label_org) # Target domain labels
label_fix_list = tf.transpose(label_fix_list, perm=[1, 0, 2])
self.label_org = label_org
self.label_trg = label_trg
self.label_fix_list = label_fix_list
self.x_test, test_label_org, test_label_fix_list = test_dataset_iterator.get_next(
) # Input image / Original domain labels
test_label_fix_list = tf.transpose(test_label_fix_list, perm=[1, 0, 2])
self.custom_image = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='custom_image') # Custom Image
custom_label_fix_list = tf.transpose(create_labels(
self.custom_label, self.selected_attrs, self.dataset_name),
perm=[1, 0, 2])
""" Define Generator, Discriminator """
x_fake = self.generator(self.x_real, label_trg) # real a
x_recon = self.generator(x_fake, label_org, reuse=True) # real b
real_logit, real_cls = self.discriminator(self.x_real)
fake_logit, fake_cls = self.discriminator(x_fake, reuse=True)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_panalty(real=self.x_real, fake=x_fake)
else:
GP = 0
g_adv_loss = generator_loss(loss_func=self.gan_type, fake=fake_logit)
g_cls_loss = classification_loss(logit=fake_cls, label=label_trg)
g_rec_loss = L1_loss(self.x_real, x_recon)
d_adv_loss = discriminator_loss(
loss_func=self.gan_type, real=real_logit, fake=fake_logit) + GP
d_cls_loss = classification_loss(logit=real_cls, label=label_org)
self.d_loss = self.adv_weight * d_adv_loss + self.cls_weight * d_cls_loss
self.g_loss = self.adv_weight * g_adv_loss + self.cls_weight * g_cls_loss + self.rec_weight * g_rec_loss
""" Result Image """
self.x_fake_list = tf.map_fn(
lambda x: self.generator(self.x_real, x, reuse=True),
label_fix_list,
dtype=tf.float32)
""" Test Image """
self.x_test_fake_list = tf.map_fn(
lambda x: self.generator(self.x_test, x, reuse=True),
test_label_fix_list,
dtype=tf.float32)
self.custom_fake_image = tf.map_fn(
lambda x: self.generator(self.custom_image, x, reuse=True),
custom_label_fix_list,
dtype=tf.float32)
""" 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]
self.g_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.g_loss,
var_list=G_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.d_loss,
var_list=D_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("Generator_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("Discriminator_loss",
self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_cls_loss = tf.summary.scalar("g_cls_loss", g_cls_loss)
self.g_rec_loss = tf.summary.scalar("g_rec_loss", g_rec_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
self.d_cls_loss = tf.summary.scalar("d_cls_loss", d_cls_loss)
self.g_summary_loss = tf.summary.merge([
self.Generator_loss, self.g_adv_loss, self.g_cls_loss,
self.g_rec_loss
])
self.d_summary_loss = tf.summary.merge(
[self.Discriminator_loss, self.d_adv_loss, self.d_cls_loss])
def build_model(self):
if self.phase == 'train':
""" Input Image"""
img_data_class = Image_data(self.img_height, self.img_width,
self.img_ch, self.dataset_path,
self.augment_flag)
img_data_class.preprocess()
self.dataset_num = len(img_data_class.image_list)
img_and_class = tf.data.Dataset.from_tensor_slices(
(img_data_class.image_list, img_data_class.class_list))
gpu_device = '/gpu:0'
img_and_class = img_and_class.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing,
batch_size=self.batch_size * self.gpu_num,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_class_iterator = img_and_class.make_one_shot_iterator()
self.content_img, self.content_class = img_and_class_iterator.get_next(
)
self.style_img, self.style_class = img_and_class_iterator.get_next(
)
self.content_img = tf.split(self.content_img,
num_or_size_splits=self.gpu_num)
self.content_class = tf.split(self.content_class,
num_or_size_splits=self.gpu_num)
self.style_img = tf.split(self.style_img,
num_or_size_splits=self.gpu_num)
self.style_class = tf.split(self.style_class,
num_or_size_splits=self.gpu_num)
self.fake_img = []
d_adv_losses = []
g_adv_losses = []
g_recon_losses = []
g_feature_losses = []
for gpu_id in range(self.gpu_num):
with tf.device(
tf.DeviceSpec(device_type="GPU", device_index=gpu_id)):
with tf.variable_scope(tf.get_variable_scope(),
reuse=(gpu_id > 0)):
""" Define Generator, Discriminator """
content_code = self.content_encoder(
self.content_img[gpu_id])
style_class_code = self.class_encoder(
self.style_img[gpu_id])
content_class_code = self.class_encoder(
self.content_img[gpu_id])
fake_img = self.generator(content_code,
style_class_code)
recon_img = self.generator(content_code,
content_class_code)
real_logit, style_feature_map = self.discriminator(
self.style_img[gpu_id], self.style_class[gpu_id])
fake_logit, fake_feature_map = self.discriminator(
fake_img, self.style_class[gpu_id])
recon_logit, recon_feature_map = self.discriminator(
recon_img, self.content_class[gpu_id])
_, content_feature_map = self.discriminator(
self.content_img[gpu_id],
self.content_class[gpu_id])
""" Define Loss """
d_adv_loss = self.adv_weight * discriminator_loss(
self.gan_type, real_logit, fake_logit,
self.style_img[gpu_id])
g_adv_loss = 0.5 * self.adv_weight * (
generator_loss(self.gan_type, fake_logit) +
generator_loss(self.gan_type, recon_logit))
g_recon_loss = self.recon_weight * L1_loss(
self.content_img[gpu_id], recon_img)
content_feature_map = tf.reduce_mean(tf.reduce_mean(
content_feature_map, axis=2),
axis=1)
recon_feature_map = tf.reduce_mean(tf.reduce_mean(
recon_feature_map, axis=2),
axis=1)
fake_feature_map = tf.reduce_mean(tf.reduce_mean(
fake_feature_map, axis=2),
axis=1)
style_feature_map = tf.reduce_mean(tf.reduce_mean(
style_feature_map, axis=2),
axis=1)
g_feature_loss = self.feature_weight * (
L1_loss(recon_feature_map, content_feature_map) +
L1_loss(fake_feature_map, style_feature_map))
d_adv_losses.append(d_adv_loss)
g_adv_losses.append(g_adv_loss)
g_recon_losses.append(g_recon_loss)
g_feature_losses.append(g_feature_loss)
self.fake_img.append(fake_img)
self.g_loss = tf.reduce_mean(g_adv_losses) + \
tf.reduce_mean(g_recon_losses) + \
tf.reduce_mean(g_feature_losses) + regularization_loss('encoder') + regularization_loss('generator')
self.d_loss = tf.reduce_mean(d_adv_losses) + regularization_loss(
'discriminator')
""" Training """
t_vars = tf.trainable_variables()
G_vars = [
var for var in t_vars
if 'encoder' in var.name or 'generator' in var.name
]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
if self.gpu_num == 1:
prev_G_optim = tf.train.RMSPropOptimizer(
self.lr, decay=0.99,
epsilon=1e-8).minimize(self.g_loss, var_list=G_vars)
self.D_optim = tf.train.RMSPropOptimizer(
self.lr, decay=0.99,
epsilon=1e-8).minimize(self.d_loss, var_list=D_vars)
# Pytorch : decay=0.99, epsilon=1e-8
else:
prev_G_optim = tf.train.RMSPropOptimizer(
self.lr, decay=0.99,
epsilon=1e-8).minimize(self.g_loss,
var_list=G_vars,
colocate_gradients_with_ops=True)
self.D_optim = tf.train.RMSPropOptimizer(
self.lr, decay=0.99,
epsilon=1e-8).minimize(self.d_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
# Pytorch : decay=0.99, epsilon=1e-8
self.ema = tf.train.ExponentialMovingAverage(decay=self.ema_decay)
with tf.control_dependencies([prev_G_optim]):
self.G_optim = self.ema.apply(G_vars)
"""" Summary """
self.summary_g_loss = tf.summary.scalar("g_loss", self.g_loss)
self.summary_d_loss = tf.summary.scalar("d_loss", self.d_loss)
self.summary_g_adv_loss = tf.summary.scalar(
"g_adv_loss", tf.reduce_mean(g_adv_losses))
self.summary_g_recon_loss = tf.summary.scalar(
"g_recon_loss", tf.reduce_mean(g_recon_losses))
self.summary_g_feature_loss = tf.summary.scalar(
"g_feature_loss", tf.reduce_mean(g_feature_losses))
g_summary_list = [
self.summary_g_loss, self.summary_g_adv_loss,
self.summary_g_recon_loss, self.summary_g_feature_loss
]
d_summary_list = [self.summary_d_loss]
self.summary_merge_g_loss = tf.summary.merge(g_summary_list)
self.summary_merge_d_loss = tf.summary.merge(d_summary_list)
else:
""" Test """
self.ema = tf.train.ExponentialMovingAverage(decay=self.ema_decay)
self.test_content_img = tf.placeholder(
tf.float32, [1, self.img_height, self.img_width, self.img_ch])
self.test_class_img = tf.placeholder(
tf.float32,
[self.K, self.img_height, self.img_width, self.img_ch])
test_content_code = self.content_encoder(self.test_content_img)
test_style_class_code = tf.reduce_mean(self.class_encoder(
self.test_class_img),
axis=0,
keepdims=True)
self.test_fake_img = self.generator(test_content_code,
test_style_class_code)
def get_pretraining_dataset(opts, data_type, is_training=True, num_cpu_threads=4, use_static_mask=False):
if is_training:
input_file = opts['train_file']
else:
input_file = opts['test_file']
micro_batch_size = opts['micro_batch_size']
max_seq_length = opts['seq_length']
max_predictions_per_seq = opts['max_predictions_per_seq']
input_files = []
for input_pattern in input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
if use_static_mask:
# The masked tokens have been re-arranaged to always be at the first
# 'max_predictions_per_seq' positions.
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_position":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"mask_padding_index":
tf.FixedLenFeature([1], tf.int64),
"seq_padding_index":
tf.FixedLenFeature([1], tf.int64),
"masked_labels":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"next_sentence_labels":
tf.FixedLenFeature([1], tf.int64),
}
else:
# Default, the tokens have not been re-arranged.
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"next_sentence_labels":
tf.FixedLenFeature([1], tf.int64),
}
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
d = d.repeat()
# `cycle_length` is the number of parallel files that get read.
cycle_length = min(num_cpu_threads, len(input_files))
# `sloppy` mode means that the interleaving is not exact. This adds
# even more randomness to the training pipeline.
d = d.apply(parallel_interleave(tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length))
# `buffer_size` should be set big enough to keep data shuffle sufficiently.
if opts['distributed_worker_count'] > 1:
d = d.shard(num_shards=opts['distributed_worker_count'], index=opts['distributed_worker_index'])
d = d.shuffle(buffer_size=1000, seed=opts['seed'])
else:
d = d.shuffle(buffer_size=1000)
else:
d = tf.data.TFRecordDataset(input_files)
d = d.repeat()
d = d.apply(map_and_batch(
lambda record: _decode_record(record, name_to_features, data_type),
batch_size=micro_batch_size,
num_parallel_batches=num_cpu_threads,
drop_remainder=True))
return d
def build_model(self):
if self.custom_dataset:
Image_Data_Class = ImageData(self.img_size, self.c_dim)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
else:
self.inputs = tf.placeholder(
tf.float32,
[self.batch_size, self.img_size, self.img_size, self.c_dim],
name='real_images')
self.z = tf.placeholder(tf.float32,
[self.batch_size, 1, 1, self.z_dim],
name='z')
real_logits = self.discriminator(self.inputs)
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else:
GP = 0
self.d_loss = discriminator_loss(
self.gan_type, real=real_logits, fake=fake_logits) + GP
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.g_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.g_loss, var_list=g_vars)
self.fake_images = self.generator(self.z,
is_training=False,
reuse=True)
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
if not export_file:
fail_for_missing_file()
with open(export_file) as f:
export_json = load(f)
legend = export_json['legend']
tfrecord_paths = export_json['tfrecord_paths']
image_dim = 512
test_set_size = math.ceil(0.20 * len(tfrecord_paths))
training_dataset = (tf.data.TFRecordDataset(
tfrecord_paths).skip(test_set_size).map(_parse_tfrecord).apply(
shuffle_and_repeat(50)).apply(map_and_batch(_resize(image_dim),
8)))
test_dataset = (tf.data.TFRecordDataset(tfrecord_paths).take(
test_set_size).map(_parse_tfrecord).apply(
map_and_batch(_resize(image_dim), test_set_size)))
training_iterator = training_dataset.make_one_shot_iterator()
test_iterator = test_dataset.make_initializable_iterator()
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
handle, training_dataset.output_types, training_dataset.output_shapes)
images, labels = iterator.get_next()
number_of_classes = len(legend) + 1 # +1 to include background class
weight_decay = 0.0005
dropout_keep_prob = tf.placeholder(tf.float32, shape=[])
def build_model(self):
# some parameters
bs = self.batch_size
""" Graph Input """
# images
if self.custom_dataset:
Image_Data_Class = ImageData(self.output_height, self.c_dim)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=8,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
else:
self.inputs = tf.placeholder(tf.float32, [
self.batch_size, self.output_height, self.output_height,
self.c_dim
],
name='real_images')
# noises
self.z = tf.placeholder(tf.float32, [bs, self.z_dim], name='z')
""" Loss Function """
x_fake = self.generator(self.z, is_training=True, reuse=False)
x_real_encoder = self.encoder(self.inputs,
is_training=True,
reuse=False,
sn=True)
x_fake_encoder = self.encoder(x_fake,
is_training=True,
reuse=True,
sn=True)
x_real_fake = tf.subtract(x_real_encoder, x_fake_encoder)
x_fake_real = tf.subtract(x_fake_encoder, x_real_encoder)
x_real_fake_score = self.discriminator(x_real_fake,
reuse=False,
sn=True)
x_fake_real_score = self.discriminator(x_fake_real,
reuse=True,
sn=True)
# get loss for discriminator
self.d_loss = discriminator_loss(self.loss_type,
real=x_real_fake_score,
fake=x_fake_real_score)
# get loss for generator
self.g_loss = generator_loss(self.loss_type,
real=x_real_fake_score,
fake=x_fake_real_score)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [
var for var in t_vars
if 'discriminator' in var.name or 'encoder' in var.name
]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
with tf.control_dependencies(tf.get_collection(
tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.learning_rate, beta1=self.beta1) \
.minimize(self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.learning_rate*5, beta1=self.beta1) \
.minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z,
is_training=False,
reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='lr')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.domain_A = trainA_iterator.get_next()
self.domain_B = trainB_iterator.get_next()
""" Define Encoder, Generator, Discriminator """
random_z = tf.random_normal(shape=[self.batch_size, self.n_z],
mean=0.0,
stddev=1.0,
dtype=tf.float32)
# encode
content_a, attribute_a, mean_a, logvar_a = self.Encoder_A(
self.domain_A)
content_b, attribute_b, mean_b, logvar_b = self.Encoder_B(
self.domain_B)
# decode (fake, identity, random)
fake_a = self.Decoder_A(content_B=content_b, attribute_A=attribute_a)
fake_b = self.Decoder_B(content_A=content_a, attribute_B=attribute_b)
recon_a = self.Decoder_A(content_B=content_a,
attribute_A=attribute_a,
reuse=True)
recon_b = self.Decoder_B(content_A=content_b,
attribute_B=attribute_b,
reuse=True)
random_fake_a = self.Decoder_A(content_B=content_b,
attribute_A=random_z,
reuse=True)
random_fake_b = self.Decoder_B(content_A=content_a,
attribute_B=random_z,
reuse=True)
# encode & decode again for cycle-consistency
content_fake_a, attribute_fake_a, _, _ = self.Encoder_A(fake_a,
reuse=True)
content_fake_b, attribute_fake_b, _, _ = self.Encoder_B(fake_b,
reuse=True)
cycle_a = self.Decoder_A(content_B=content_fake_b,
attribute_A=attribute_fake_a,
reuse=True)
cycle_b = self.Decoder_B(content_A=content_fake_a,
attribute_B=attribute_fake_b,
reuse=True)
# for latent regression
_, attribute_fake_random_a, _, _ = self.Encoder_A(random_fake_a,
random_fake=True,
reuse=True)
_, attribute_fake_random_b, _, _ = self.Encoder_B(random_fake_b,
random_fake=True,
reuse=True)
# discriminate
real_A_logit, real_B_logit = self.discriminate_real(
self.domain_A, self.domain_B)
fake_A_logit, fake_B_logit = self.discriminate_fake(fake_a, fake_b)
random_fake_A_logit, random_fake_B_logit = self.discriminate_fake(
random_fake_a, random_fake_b)
content_A_logit, content_B_logit = self.discriminate_content(
content_a, content_b)
""" Define Loss """
g_adv_loss_a = generator_loss(self.gan_type,
fake_A_logit) + generator_loss(
self.gan_type, random_fake_A_logit)
g_adv_loss_b = generator_loss(self.gan_type,
fake_B_logit) + generator_loss(
self.gan_type, random_fake_B_logit)
g_con_loss_a = generator_loss(self.gan_type,
content_A_logit,
content=True)
g_con_loss_b = generator_loss(self.gan_type,
content_B_logit,
content=True)
g_cyc_loss_a = L1_loss(cycle_a, self.domain_A)
g_cyc_loss_b = L1_loss(cycle_b, self.domain_B)
g_rec_loss_a = L1_loss(recon_a, self.domain_A)
g_rec_loss_b = L1_loss(recon_b, self.domain_B)
g_latent_loss_a = L1_loss(attribute_fake_random_a, random_z)
g_latent_loss_b = L1_loss(attribute_fake_random_b, random_z)
if self.concat:
g_kl_loss_a = kl_loss(mean_a, logvar_a) + l2_regularize(content_a)
g_kl_loss_b = kl_loss(mean_b, logvar_b) + l2_regularize(content_b)
else:
g_kl_loss_a = l2_regularize(attribute_a) + l2_regularize(content_a)
g_kl_loss_b = l2_regularize(attribute_b) + l2_regularize(content_b)
d_adv_loss_a = discriminator_loss(self.gan_type, real_A_logit,
fake_A_logit)
d_adv_loss_b = discriminator_loss(self.gan_type, real_B_logit,
fake_B_logit)
d_con_loss = discriminator_loss(self.gan_type, content_A_logit,
content_B_logit)
Generator_A_domain_loss = self.domain_adv_w * g_adv_loss_a
Generator_A_content_loss = self.content_adv_w * g_con_loss_a
Generator_A_cycle_loss = self.cycle_w * g_cyc_loss_b
Generator_A_recon_loss = self.recon_w * g_rec_loss_a
Generator_A_latent_loss = self.latent_w * g_latent_loss_a
Generator_A_kl_loss = self.kl_w * g_kl_loss_a
Generator_A_loss = Generator_A_domain_loss + \
Generator_A_content_loss + \
Generator_A_cycle_loss + \
Generator_A_recon_loss + \
Generator_A_latent_loss + \
Generator_A_kl_loss
Generator_B_domain_loss = self.domain_adv_w * g_adv_loss_b
Generator_B_content_loss = self.content_adv_w * g_con_loss_b
Generator_B_cycle_loss = self.cycle_w * g_cyc_loss_a
Generator_B_recon_loss = self.recon_w * g_rec_loss_b
Generator_B_latent_loss = self.latent_w * g_latent_loss_b
Generator_B_kl_loss = self.kl_w * g_kl_loss_b
Generator_B_loss = Generator_B_domain_loss + \
Generator_B_content_loss + \
Generator_B_cycle_loss + \
Generator_B_recon_loss + \
Generator_B_latent_loss + \
Generator_B_kl_loss
Discriminator_A_loss = self.domain_adv_w * d_adv_loss_a
Discriminator_B_loss = self.domain_adv_w * d_adv_loss_b
Discriminator_content_loss = self.content_adv_w * d_con_loss
self.Generator_loss = Generator_A_loss + Generator_B_loss
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss
self.Discriminator_content_loss = Discriminator_content_loss
""" Training """
t_vars = tf.trainable_variables()
G_vars = [
var for var in t_vars
if 'endoer' in var.name or 'generator' in var.name
]
D_vars = [
var for var in t_vars
if 'discriminator' in var.name and 'content' not in var.name
]
D_content_vars = [
var for var in t_vars if 'content_discriminator' in var.name
]
grads, _ = tf.clip_by_global_norm(tf.gradients(
self.Discriminator_content_loss, D_content_vars),
clip_norm=5)
self.G_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss,
var_list=D_vars)
self.D_content_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5,
beta2=0.999).apply_gradients(zip(grads, D_content_vars))
"""" Summary """
self.lr_write = tf.summary.scalar("learning_rate", self.lr)
self.all_G_loss = tf.summary.scalar("Generator_loss",
self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_A_domain_loss = tf.summary.scalar("G_A_domain_loss",
Generator_A_domain_loss)
self.G_A_content_loss = tf.summary.scalar("G_A_content_loss",
Generator_A_content_loss)
self.G_A_cycle_loss = tf.summary.scalar("G_A_cycle_loss",
Generator_A_cycle_loss)
self.G_A_recon_loss = tf.summary.scalar("G_A_recon_loss",
Generator_A_recon_loss)
self.G_A_latent_loss = tf.summary.scalar("G_A_latent_loss",
Generator_A_latent_loss)
self.G_A_kl_loss = tf.summary.scalar("G_A_kl_loss",
Generator_A_kl_loss)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.G_B_domain_loss = tf.summary.scalar("G_B_domain_loss",
Generator_B_domain_loss)
self.G_B_content_loss = tf.summary.scalar("G_B_content_loss",
Generator_B_content_loss)
self.G_B_cycle_loss = tf.summary.scalar("G_B_cycle_loss",
Generator_B_cycle_loss)
self.G_B_recon_loss = tf.summary.scalar("G_B_recon_loss",
Generator_B_recon_loss)
self.G_B_latent_loss = tf.summary.scalar("G_B_latent_loss",
Generator_B_latent_loss)
self.G_B_kl_loss = tf.summary.scalar("G_B_kl_loss",
Generator_B_kl_loss)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.G_loss = tf.summary.merge([
self.G_A_loss, self.G_A_domain_loss, self.G_A_content_loss,
self.G_A_cycle_loss, self.G_A_recon_loss, self.G_A_latent_loss,
self.G_A_kl_loss, self.G_B_loss, self.G_B_domain_loss,
self.G_B_content_loss, self.G_B_cycle_loss, self.G_B_recon_loss,
self.G_B_latent_loss, self.G_B_kl_loss, self.all_G_loss
])
self.D_loss = tf.summary.merge(
[self.D_A_loss, self.D_B_loss, self.all_D_loss])
self.D_content_loss = tf.summary.scalar(
"Discriminator_content_loss", self.Discriminator_content_loss)
""" Image """
self.fake_A = fake_a
self.fake_B = fake_b
self.real_A = self.domain_A
self.real_B = self.domain_B
""" Test """
self.test_image = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_image')
self.test_random_z = tf.random_normal(shape=[1, self.n_z],
mean=0.0,
stddev=1.0,
dtype=tf.float32)
test_content_a, test_attribute_a, _, _ = self.Encoder_A(
self.test_image, is_training=False, reuse=True)
test_content_b, test_attribute_b, _, _ = self.Encoder_B(
self.test_image, is_training=False, reuse=True)
self.test_fake_A = self.Decoder_A(content_B=test_content_b,
attribute_A=self.test_random_z,
reuse=True)
self.test_fake_B = self.Decoder_B(content_A=test_content_a,
attribute_B=self.test_random_z,
reuse=True)
""" Guided Image Translation """
self.content_image = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='content_image')
self.attribute_image = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='guide_attribute_image')
guide_content_A, guide_attribute_A, _, _ = self.Encoder_A(
self.content_image, is_training=False, reuse=True)
guide_content_B, guide_attribute_B, _, _ = self.Encoder_B(
self.attribute_image, is_training=False, reuse=True)
self.guide_fake_A = self.Decoder_A(content_B=guide_content_B,
attribute_A=guide_attribute_A,
reuse=True)
self.guide_fake_B = self.Decoder_B(content_A=guide_content_A,
attribute_B=guide_attribute_B,
reuse=True)
def _initialize_tf_dataset(file_names,
augment,
over_sample,
shuffle,
target_size,
normalize,
nb_workers=8,
batch_size=64,
shuffle_buffer_size=3000,
input_type="img",
nr_epochs=1):
import tensorflow as tf
from tensorflow.contrib.data import map_and_batch
from tensorflow.contrib.data import shuffle_and_repeat
if not type(target_size) is list:
target_size = list(target_size)
with tf.name_scope('input_pipeline'):
dataset = tf.data.TFRecordDataset(file_names)
if shuffle:
dataset = dataset.apply(
shuffle_and_repeat(shuffle_buffer_size, nr_epochs))
def _decode_and_augment_image(example_proto):
keys_to_features = {
'label': tf.FixedLenFeature([], tf.int64),
'shape': tf.FixedLenFeature([], tf.string),
'image': tf.FixedLenFeature([], tf.string),
}
tfrecord_features = tf.parse_single_example(
example_proto, keys_to_features)
image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
shape = tf.decode_raw(tfrecord_features['shape'], tf.int64)
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
label = tfrecord_features['label']
if augment:
image = tf.image.random_flip_left_right(image)
image = tf.image.random_flip_up_down(image)
degrees = tf.random_uniform((), minval=-180, maxval=180)
image = tf.contrib.image.rotate(image, degrees)
width_shift = tf.random_uniform((), minval=0, maxval=0.05)
height_shift = tf.random_uniform((), minval=0, maxval=0.05)
horizontal_pad = tf.cast(tf.ceil(width_shift * target_size[0]),
tf.int32)
vertical_pad = tf.cast(tf.ceil(height_shift * target_size[1]),
tf.int32)
padding = tf.stack([
horizontal_pad, horizontal_pad, vertical_pad, vertical_pad,
tf.constant(0),
tf.constant(0)
])
padding = tf.reshape(padding, (3, 2))
image = tf.pad(image, padding)
image = tf.random_crop(image, target_size + [3])
zoom = tf.random_uniform((), minval=-0.1, maxval=0.1)
new_dim = tf.cast(tf.ceil((1 - zoom) * target_size[0]),
dtype=tf.int32)
image = tf.image.resize_image_with_crop_or_pad(
image, new_dim, new_dim)
image = tf.image.resize_images(
image, target_size, method=tf.image.ResizeMethod.BILINEAR)
if normalize:
std = tf.constant(np.array(
[70.53946096, 51.71475228, 43.03428563]),
dtype=tf.float32)
std = tf.expand_dims(tf.expand_dims(std, axis=0), axis=0)
mean = tf.constant(np.array(
[108.64628601, 75.86886597, 54.34005736]),
dtype=tf.float32)
mean = tf.expand_dims(tf.expand_dims(mean, axis=0), axis=0)
image = (tf.cast(image, dtype=tf.float32) - mean) / std
label = tf.reshape(label, [1])
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
return {'shape': shape, 'image': image}, label
dataset = dataset \
.apply(map_and_batch(_decode_and_augment_image, batch_size=batch_size, num_parallel_batches=nb_workers,
drop_remainder=True)) \
.prefetch(nb_workers)
# def _augment_images(example)
return dataset
def build_model(self):
""" Input Image"""
img_data_class = Image_data(self.img_height, self.img_width, self.img_ch, self.dataset_path, self.augment_flag)
train_captions, train_images, test_captions, test_images, idx_to_word, word_to_idx = img_data_class.preprocess()
"""
train_captions: (8855, 10, 18), test_captions: (2933, 10, 18)
train_images: (8855,), test_images: (2933,)
idx_to_word : 5450 5450
"""
if self.phase == 'train' :
self.lr = tf.placeholder(tf.float32, name='learning_rate')
self.dataset_num = len(train_images)
img_and_caption = tf.data.Dataset.from_tensor_slices((train_images, train_captions))
gpu_device = '/gpu:0'
img_and_caption = img_and_caption.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing, batch_size=self.batch_size, num_parallel_batches=16,
drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
img_and_caption_iterator = img_and_caption.make_one_shot_iterator()
real_img_256, caption = img_and_caption_iterator.get_next()
target_sentence_index = tf.random_uniform(shape=[], minval=0, maxval=10, dtype=tf.int32)
caption = tf.gather(caption, target_sentence_index, axis=1)
word_emb, sent_emb, mask = self.rnn_encoder(caption, n_words=len(idx_to_word),
embed_dim=self.embed_dim, drop_rate=0.5, n_hidden=128, n_layers=1,
bidirectional=True, rnn_type='lstm')
noise = tf.random_normal(shape=[self.batch_size, self.z_dim], mean=0.0, stddev=1.0)
fake_imgs, _, mu, logvar = self.generator(noise, sent_emb, word_emb, mask)
real_img_64, real_img_128 = resize(real_img_256, target_size=[64, 64]), resize(real_img_256, target_size=[128, 128])
fake_img_64, fake_img_128, fake_img_256 = fake_imgs[0], fake_imgs[1], fake_imgs[2]
uncond_real_logits, cond_real_logits = self.discriminator([real_img_64, real_img_128, real_img_256], sent_emb)
_, cond_wrong_logits = self.discriminator([real_img_64[:(self.batch_size - 1)], real_img_128[:(self.batch_size - 1)], real_img_256[:(self.batch_size - 1)]], sent_emb[1:self.batch_size])
uncond_fake_logits, cond_fake_logits = self.discriminator([fake_img_64, fake_img_128, fake_img_256], sent_emb)
self.g_adv_loss, self.d_adv_loss = 0, 0
for i in range(3):
self.g_adv_loss += self.adv_weight * (generator_loss(self.gan_type, uncond_fake_logits[i]) + generator_loss(self.gan_type, cond_fake_logits[i]))
uncond_real_loss, uncond_fake_loss = discriminator_loss(self.gan_type, uncond_real_logits[i], uncond_fake_logits[i])
cond_real_loss, cond_fake_loss = discriminator_loss(self.gan_type, cond_real_logits[i], cond_fake_logits[i])
_, cond_wrong_loss = discriminator_loss(self.gan_type, None, cond_wrong_logits[i])
self.d_adv_loss += self.adv_weight * (((uncond_real_loss + cond_real_loss) / 2) + (uncond_fake_loss + cond_fake_loss + cond_wrong_loss) / 3)
self.g_kl_loss = self.kl_weight * kl_loss(mu, logvar)
self.g_loss = self.g_adv_loss + self.g_kl_loss
self.d_loss = self.d_adv_loss
self.real_img = real_img_256
self.fake_img = fake_img_256
""" 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]
self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.g_loss, var_list=G_vars)
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.d_loss,var_list=D_vars)
"""" Summary """
self.summary_g_loss = tf.summary.scalar("g_loss", self.g_loss)
self.summary_d_loss = tf.summary.scalar("d_loss", self.d_loss)
self.summary_g_adv_loss = tf.summary.scalar("g_adv_loss", self.g_adv_loss)
self.summary_g_kl_loss = tf.summary.scalar("g_kl_loss", self.g_kl_loss)
self.summary_d_adv_loss = tf.summary.scalar("d_adv_loss", self.d_adv_loss)
g_summary_list = [self.summary_g_loss,
self.summary_g_adv_loss, self.summary_g_kl_loss]
d_summary_list = [self.summary_d_loss,
self.summary_d_adv_loss]
self.summary_merge_g_loss = tf.summary.merge(g_summary_list)
self.summary_merge_d_loss = tf.summary.merge(d_summary_list)
else :
""" Test """
self.dataset_num = len(test_captions)
gpu_device = '/gpu:0'
img_and_caption = tf.data.Dataset.from_tensor_slices((test_images, test_captions))
img_and_caption = img_and_caption.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing, batch_size=self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_caption_iterator = img_and_caption.make_one_shot_iterator()
real_img_256, caption = img_and_caption_iterator.get_next()
target_sentence_index = tf.random_uniform(shape=[], minval=0, maxval=10, dtype=tf.int32)
caption = tf.gather(caption, target_sentence_index, axis=1)
word_emb, sent_emb, mask = self.rnn_encoder(caption, n_words=len(idx_to_word),
embed_dim=self.embed_dim, drop_rate=0.5, n_hidden=128,
n_layers=1,
bidirectional=True, rnn_type='lstm',
is_training=False)
noise = tf.random_normal(shape=[self.batch_size, self.z_dim], mean=0.0, stddev=1.0)
fake_imgs, _, _, _ = self.generator(noise, sent_emb, word_emb, mask, is_training=False)
self.test_real_img = real_img_256
self.test_fake_img = fake_imgs[2]
def get_batch(datasets,
preprocess_name,
is_training,
batch_size,
num_gpu=1,
seed=None):
with tf.device('/cpu:0'):
num_class = datasets.num_class
file_name = datasets.source
feature = datasets.feature
decoder = datasets.decoder
name = datasets.description['name']
image_preprocessing_fn = get_preprocess_fn(preprocess_name)
dataset = tf.data.Dataset.from_tensor_slices(file_name)
if is_training:
# Shuffle the input files
dataset = dataset.shuffle(len(file_name),
seed=seed,
reshuffle_each_iteration=True)
'''
Convert to individual records.
cycle_length = 8 means 8 files will be read and deserialized in parallel.
This number is low enough to not cause too much contention on small systems
but high enough to provide the benefits of parallelization. You may want
to increase this number if you have a large number of CPU cores.
'''
cycle_length = min(10, len(file_name))
dataset = dataset.apply(
data.parallel_interleave(tf.data.TFRecordDataset,
cycle_length=cycle_length))
# We prefetch a batch at a time, This can help smooth out the time taken to
# load input files as we go through shuffling and processing.
dataset = dataset.prefetch(buffer_size=batch_size)
if is_training:
dataset = dataset.apply(
data.shuffle_and_repeat(buffer_size=10000, seed=seed))
else:
dataset = dataset.repeat()
def map_func(record):
parsed = tf.parse_single_example(record, feature)
image = decoder(parsed['image/encoded'])
# Perform additional preprocessing on the parsed data.
image = image_preprocessing_fn(image,
datasets,
is_training=is_training)
label = parsed['image/class/label']
label = tf.one_hot(label, num_class)
return image, label
'''
Parse the raw records into images and labels. Testing has shown that setting
num_parallel_batches > 1 produces no improvement in throughput, since
batch_size is almost always much greater than the number of CPU cores.
'''
dataset = dataset.apply(
data.map_and_batch(map_func=map_func,
batch_size=batch_size,
num_parallel_batches=1))
'''
Operations between the final prefetch and the get_next call to the iterator
will happen synchronously during run time. We prefetch here again to
background all of the above processing work and keep it out of the
critical training path.
'''
dataset = dataset.prefetch(buffer_size=32)
iterator = dataset.make_one_shot_iterator()
return iterator
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=3)
dataset = tf.data.TFRecordDataset('Dataset/dataset_1.tfrecord')
def _parse(example_proto):
feature = {'roll' : tf.FixedLenFeature([], tf.string)}
parsed = tf.parse_single_example(example_proto, feature)
data = tf.decode_raw(parsed['roll'], tf.uint8)
data = tf.py_func(func=np.unpackbits, inp=[data], Tout=tf.uint8)
data = tf.cast(data, tf.float32)
data = tf.reshape(data, [CLASS_NUM, 600])
data = data * 2 - 1
return data
dataset = dataset.apply(data.shuffle_and_repeat(buffer_size=60000, count=3))
dataset = dataset.apply(data.map_and_batch(_parse, batch_size=batch_size, num_parallel_batches=2))
iterator = dataset.prefetch(batch_size).make_one_shot_iterator()
real_input_next_element = iterator.get_next()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if os.path.exists(model_path):
load_path = saver.restore(sess, model_path)
print("Model restored from file: %s" % load_path)
for i in range(total_batch):
tfdata = sess.run(real_input_next_element)
reshape_tfdata = tfdata.reshape([-1, CLASS_NUM, 600]) #
cuted_tfdata = reshape_tfdata[:, :, :INPUT_LENGTH]
reshape_cuted_tfdata = cuted_tfdata.reshape([-1, CLASS_NUM*INPUT_LENGTH])
def build_model(self):
""" Graph """
if self.phase == 'train':
self.d_loss_per_res = {}
self.g_loss_per_res = {}
self.generator_optim = {}
self.discriminator_optim = {}
self.alpha_summary_per_res = {}
self.d_summary_per_res = {}
self.g_summary_per_res = {}
self.train_fake_images = {}
for res in self.resolutions[self.resolutions.index(self.start_res
):]:
g_loss_per_gpu = []
d_loss_per_gpu = []
train_fake_images_per_gpu = []
batch_size = self.batch_sizes.get(res, self.batch_size_base)
global_step = tf.get_variable(
'global_step_{}'.format(res),
shape=[],
dtype=tf.float32,
initializer=tf.initializers.zeros(),
trainable=False,
aggregation=tf.VariableAggregation.ONLY_FIRST_TOWER)
alpha_const, zero_constant = get_alpha_const(
self.iteration // 2, batch_size * self.gpu_num,
global_step)
# smooth transition variable
do_train_trans = self.train_with_trans[res]
alpha = tf.get_variable(
'alpha_{}'.format(res),
shape=[],
dtype=tf.float32,
initializer=tf.initializers.ones()
if do_train_trans else tf.initializers.zeros(),
trainable=False,
aggregation=tf.VariableAggregation.ONLY_FIRST_TOWER)
if do_train_trans:
alpha_assign_op = tf.assign(alpha, alpha_const)
else:
alpha_assign_op = tf.assign(alpha, zero_constant)
with tf.control_dependencies([alpha_assign_op]):
for gpu_id in range(self.gpu_num):
with tf.device(
tf.DeviceSpec(device_type="GPU",
device_index=gpu_id)):
with tf.variable_scope(tf.get_variable_scope(),
reuse=(gpu_id > 0)):
# images
gpu_device = '/gpu:{}'.format(gpu_id)
image_class = ImageData(res)
inputs = tf.data.Dataset.from_tensor_slices(
self.dataset)
inputs = inputs. \
apply(shuffle_and_repeat(self.dataset_num)). \
apply(map_and_batch(image_class.image_processing, batch_size, num_parallel_batches=16, drop_remainder=True)). \
apply(prefetch_to_device(gpu_device, None))
# When using dataset.prefetch, use buffer_size=None to let it detect optimal buffer size
inputs_iterator = inputs.make_one_shot_iterator(
)
real_img = inputs_iterator.get_next()
z = tf.random_normal(
shape=[batch_size, self.z_dim])
fake_img = self.generator(z, alpha, res)
real_img = smooth_crossfade(real_img, alpha)
real_logit = self.discriminator(
real_img, alpha, res)
fake_logit = self.discriminator(
fake_img, alpha, res)
# compute loss
d_loss, g_loss = compute_loss(
real_img, real_logit, fake_logit)
d_loss_per_gpu.append(d_loss)
g_loss_per_gpu.append(g_loss)
train_fake_images_per_gpu.append(fake_img)
print("Create graph for {} resolution".format(res))
# prepare appropriate training vars
d_vars, g_vars = filter_trainable_variables(res)
d_loss = tf.reduce_mean(d_loss_per_gpu)
g_loss = tf.reduce_mean(g_loss_per_gpu)
d_lr = self.d_learning_rates.get(res, self.learning_rate_base)
g_lr = self.g_learning_rates.get(res, self.learning_rate_base)
if self.gpu_num == 1:
colocate_grad = False
else:
colocate_grad = True
d_optim = tf.train.AdamOptimizer(
d_lr, beta1=0.0, beta2=0.99, epsilon=1e-8).minimize(
d_loss,
var_list=d_vars,
colocate_gradients_with_ops=colocate_grad)
g_optim = tf.train.AdamOptimizer(
g_lr, beta1=0.0, beta2=0.99, epsilon=1e-8).minimize(
g_loss,
var_list=g_vars,
global_step=global_step,
colocate_gradients_with_ops=colocate_grad)
self.discriminator_optim[res] = d_optim
self.generator_optim[res] = g_optim
self.d_loss_per_res[res] = d_loss
self.g_loss_per_res[res] = g_loss
self.train_fake_images[res] = tf.concat(
train_fake_images_per_gpu, axis=0)
""" Summary """
self.alpha_summary_per_res[res] = tf.summary.scalar(
"alpha_{}".format(res), alpha)
self.d_summary_per_res[res] = tf.summary.scalar(
"d_loss_{}".format(res), self.d_loss_per_res[res])
self.g_summary_per_res[res] = tf.summary.scalar(
"g_loss_{}".format(res), self.g_loss_per_res[res])
else:
"""" Testing """
test_z = tf.random_normal(shape=[self.batch_size, self.z_dim])
alpha = tf.constant(0.0, dtype=tf.float32, shape=[])
self.fake_images = self.generator(test_z,
alpha=alpha,
target_img_size=self.img_size,
is_training=False)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
img_class = Image_data(self.img_height, self.img_width, self.img_ch,
self.segmap_ch, self.dataset_path,
self.augment_flag)
img_class.preprocess()
self.dataset_num = len(img_class.image)
self.test_dataset_num = len(img_class.segmap_test)
img_and_segmap = tf.data.Dataset.from_tensor_slices(
(img_class.image, img_class.segmap))
segmap_test = tf.data.Dataset.from_tensor_slices(img_class.segmap_test)
gpu_device = '/gpu:0'
img_and_segmap = img_and_segmap.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
segmap_test = segmap_test.apply(shuffle_and_repeat(
self.dataset_num)).apply(
map_and_batch(img_class.test_image_processing,
batch_size=self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
img_and_segmap_iterator = img_and_segmap.make_one_shot_iterator()
segmap_test_iterator = segmap_test.make_one_shot_iterator()
self.real_x, self.real_x_segmap, self.real_x_segmap_onehot = img_and_segmap_iterator.get_next(
)
self.real_x_segmap_test, self.real_x_segmap_test_onehot = segmap_test_iterator.get_next(
)
""" Define Generator, Discriminator """
fake_x, x_mean, x_var = self.image_translate(
segmap_img=self.real_x_segmap_onehot, x_img=self.real_x)
real_logit, fake_logit = self.image_discriminate(
segmap_img=self.real_x_segmap_onehot,
real_img=self.real_x,
fake_img=fake_x)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.real_x,
segmap=self.real_x_segmap_onehot,
fake=fake_x)
else:
GP = 0
""" Define Loss """
g_adv_loss = self.adv_weight * generator_loss(self.gan_type,
fake_logit)
g_kl_loss = self.kl_weight * kl_loss(x_mean, x_var)
g_vgg_loss = self.vgg_weight * VGGLoss()(self.real_x, fake_x)
g_feature_loss = self.feature_weight * feature_loss(
real_logit, fake_logit)
g_reg_loss = regularization_loss('generator') + regularization_loss(
'encoder')
d_adv_loss = self.adv_weight * (
discriminator_loss(self.gan_type, real_logit, fake_logit) + GP)
d_reg_loss = regularization_loss('discriminator')
self.g_loss = g_adv_loss + g_kl_loss + g_vgg_loss + g_feature_loss + g_reg_loss
self.d_loss = d_adv_loss + d_reg_loss
""" Result Image """
self.fake_x = fake_x
self.random_fake_x, _, _ = self.image_translate(
segmap_img=self.real_x_segmap_onehot,
random_style=True,
reuse=True)
""" Test """
self.test_segmap_image = tf.placeholder(tf.float32, [
1, self.img_height, self.img_width,
len(img_class.color_value_dict)
])
self.random_test_fake_x, _, _ = self.image_translate(
segmap_img=self.test_segmap_image, random_style=True, reuse=True)
self.test_guide_image = tf.placeholder(
tf.float32, [1, self.img_height, self.img_width, self.img_ch])
self.guide_test_fake_x, _, _ = self.image_translate(
segmap_img=self.test_segmap_image,
x_img=self.test_guide_image,
reuse=True)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [
var for var in t_vars
if 'encoder' in var.name or 'generator' in var.name
]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
if self.TTUR:
beta1 = 0.0
beta2 = 0.9
g_lr = self.lr / 2
d_lr = self.lr * 2
else:
beta1 = self.beta1
beta2 = self.beta2
g_lr = self.lr
d_lr = self.lr
self.G_optim = tf.train.AdamOptimizer(
g_lr, beta1=beta1, beta2=beta2).minimize(self.g_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(
d_lr, beta1=beta1, beta2=beta2).minimize(self.d_loss,
var_list=D_vars)
"""" Summary """
self.summary_g_loss = tf.summary.scalar("g_loss", self.g_loss)
self.summary_d_loss = tf.summary.scalar("d_loss", self.d_loss)
self.summary_g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.summary_g_kl_loss = tf.summary.scalar("g_kl_loss", g_kl_loss)
self.summary_g_vgg_loss = tf.summary.scalar("g_vgg_loss", g_vgg_loss)
self.summary_g_feature_loss = tf.summary.scalar(
"g_feature_loss", g_feature_loss)
g_summary_list = [
self.summary_g_loss, self.summary_g_adv_loss,
self.summary_g_kl_loss, self.summary_g_vgg_loss,
self.summary_g_feature_loss
]
d_summary_list = [self.summary_d_loss]
self.G_loss = tf.summary.merge(g_summary_list)
self.D_loss = tf.summary.merge(d_summary_list)
def build_model(self):
if self.phase == 'train':
#初始化步长
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
#将图片地址切片
#print('ok trainA:',trainA)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(100)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
trainB = trainB.apply(shuffle_and_repeat(100)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
#打乱原图片排列
#map_and_batch 1：将tensor的嵌套结构映射到另一个tensor嵌套结构的函数
# 2：要在此数据集合并的单个batch中的连续元素数（一个batch 32 个元素，即输出是32维的元素）
# 3：要并行创建的batch数。一方面，较高的值可以帮助减轻落后者的影响。另一方面，如果CPU空闲，较高的值可能会增加竞争。
# 4：表示是否应丢弃最后一个batch，以防其大小小于所需值
#返回 32*64*64*3 的随机增强的数组
#应用gpu加速
#print('ok trainA:',trainA.shape)
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.domain_A = trainA_iterator.get_next()
self.domain_B = trainB_iterator.get_next()
""" Define Generator, Discriminator """
x_ab, cam_ab = self.generate_a2b(self.domain_A) # real a
#self.domain_A 是 卡通图片
#x_ab是由self.domain_A经过下采样 再 上采样得到的图片
x_ba, cam_ba = self.generate_b2a(self.domain_B) # real b
#generate_a2b和generate_b2a是两套不同的参数
x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b
x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a
#固定参数不变，再将generate_a2b生成的图片，用generate_b2a生成一遍
#generate_b2a 尝试 将真人图生成卡通图
#generate_a2b 尝试 将卡通图生成真人图
#可以看做将generate_a2b与generate_b2a作为逆变换
x_aa, cam_aa = self.generate_b2a(self.domain_A,
reuse=True) # fake b
x_bb, cam_bb = self.generate_a2b(self.domain_B,
reuse=True) # fake a
#固定参数不变
#***将卡通图生成卡通图
#确保在generate_b2a和generate_a2b过程中，颜色区域是不变的
real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(
self.domain_A, self.domain_B)
#鉴别 真卡通图 与 真真人图
fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(
x_ba, x_ab)
#鉴别 假卡通图 与 假真人图
#输入的是生成器生成的图片
#输出的是图片经过卷积的32*8*8*1的张量和池化结果的连接(32, 2)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP_A, GP_CAM_A = self.gradient_panalty(real=self.domain_A,
fake=x_ba,
scope="discriminator_A")
GP_B, GP_CAM_B = self.gradient_panalty(real=self.domain_B,
fake=x_ab,
scope="discriminator_B")
else:
GP_A, GP_CAM_A = 0, 0
GP_B, GP_CAM_B = 0, 0
#接下来是对于假图片判别器discriminate_fake 真图片判别器discriminate_real的损失计算
#对判别器和生成器的损失计算
'''
一、对抗损失（T）
用的最小二乘损失
'''
#fake_A_logit[0],fake_A_logit[1]与1的平方差的和+fake_A_cam_logit[0],fake_A_cam_logit[1]与1的平方差的和
#从生成器的角度看，真图片会被discriminate鉴别器判为0，生成的图片经过判别器（1-fake_*），也要尽量被判别为0
G_ad_loss_A = (generator_loss(self.gan_type, fake_A_logit) +
generator_loss(self.gan_type, fake_A_cam_logit))
#生成假卡通图的损失
G_ad_loss_B = (generator_loss(self.gan_type, fake_B_logit) +
generator_loss(self.gan_type, fake_B_cam_logit))
#生成假真人图的损失
#生成器的损失
D_ad_loss_A = (
discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) +
discriminator_loss(self.gan_type, real_A_cam_logit,
fake_A_cam_logit) + GP_A + GP_CAM_A)
#对卡通图的鉴别损失
#discriminator_loss力求将真图片判断为1，假图片判断为0
D_ad_loss_B = (
discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) +
discriminator_loss(self.gan_type, real_B_cam_logit,
fake_B_cam_logit) + GP_B + GP_CAM_B)
#鉴别器的损失
'''
二、Cycle 损失(T)
the image should be successfully translated back to the original domain
'''
reconstruction_A = L1_loss(x_aba, self.domain_A) # reconstruction
#由卡通图生成真人图再生成卡通图的损失
reconstruction_B = L1_loss(x_bab, self.domain_B) # reconstruction
#由真人图生成卡通图再生成真人图的损失
'''
三、Identity 损失
确保在A B相互变化时，身份信息不丢失
'''
identity_A = L1_loss(x_aa, self.domain_A)
identity_B = L1_loss(x_bb, self.domain_B)
#将卡通图生成卡通图的损失
'''
四、CAM 损失
利用辅助分类器，使得G和D知道在哪里进行强化变换
在A变B时，热力图应该有明显显示
在A变A时，热力图应该没有显示
'''
cam_A = cam_loss(source=cam_ba, non_source=cam_aa)
#cam_ba是从真人图到卡通图的全连接（两次，用不同方法池化）
#cam_aa是从卡通图到卡通图的全连接
cam_B = cam_loss(source=cam_ab, non_source=cam_bb)
#开始时的比重是如何决定的？？？
#
Generator_A_gan = self.adv_weight * G_ad_loss_A
#1
#网络由真人图生成卡通图的损失*相对的比重
Generator_A_cycle = self.cycle_weight * reconstruction_B
#10
#self.generate_a2b(self.generate_b2a(self.domain_B), reuse=True)
#由真人图生成卡通图再生成真人图的损失*相对的比重
Generator_A_identity = self.identity_weight * identity_A
#10
#由卡通图生成卡通图的损失*相对的比重
Generator_A_cam = self.cam_weight * cam_A
#1000
#从真人图到卡通图的全连接*相对的比重
Generator_B_gan = self.adv_weight * G_ad_loss_B
Generator_B_cycle = self.cycle_weight * reconstruction_A
Generator_B_identity = self.identity_weight * identity_B
Generator_B_cam = self.cam_weight * cam_B
print('ok 5')
Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam
#所有生成卡通图的损失
Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam
Discriminator_A_loss = self.adv_weight * D_ad_loss_A
#对生成的卡通图 和 真的卡通图的鉴别损失+生成的卡通图的全连接 和 真的卡通图的全连接的鉴别损失*权重
Discriminator_B_loss = self.adv_weight * D_ad_loss_B
self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss(
'generator')
#生成器的总损失
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss(
'discriminator')
#鉴别器的总损失
print('55')
""" Result Image """
#生成的假图片（用于储存）
self.fake_A = x_ba
self.fake_B = x_ab
#输入的真图片
self.real_A = self.domain_A
self.real_B = self.domain_B
self.imgba = imgba
self.imgab = imgab
""" 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]
self.G_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Discriminator_loss,
var_list=D_vars)
#var_list：在优化时每次要迭代更新的参数集合
"""" Summary """
# self.all_G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
# self.all_D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
# self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
# self.G_A_gan = tf.summary.scalar("G_A_gan", Generator_A_gan)
# self.G_A_cycle = tf.summary.scalar("G_A_cycle", Generator_A_cycle)
# self.G_A_identity = tf.summary.scalar("G_A_identity", Generator_A_identity)
# self.G_A_cam = tf.summary.scalar("G_A_cam", Generator_A_cam)
# self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
# self.G_B_gan = tf.summary.scalar("G_B_gan", Generator_B_gan)
# self.G_B_cycle = tf.summary.scalar("G_B_cycle", Generator_B_cycle)
# self.G_B_identity = tf.summary.scalar("G_B_identity", Generator_B_identity)
# self.G_B_cam = tf.summary.scalar("G_B_cam", Generator_B_cam)
# self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
# self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
'''
画图
'''
# self.rho_var = []
# for var in tf.trainable_variables():
# if 'rho' in var.name:
# self.rho_var.append(tf.summary.histogram(var.name, var))
# self.rho_var.append(tf.summary.scalar(var.name + "_min", tf.reduce_min(var)))
# self.rho_var.append(tf.summary.scalar(var.name + "_max", tf.reduce_max(var)))
# self.rho_var.append(tf.summary.scalar(var.name + "_mean", tf.reduce_mean(var)))
# print('ok 7')
# g_summary_list = [self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity, self.G_A_cam,
# self.G_B_loss, self.G_B_gan, self.G_B_cycle, self.G_B_identity, self.G_B_cam,
# self.all_G_loss]
# g_summary_list.extend(self.rho_var)
# d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]
# self.G_loss = tf.summary.merge(g_summary_list)
# self.D_loss = tf.summary.merge(d_summary_list)
else:
""" Test """
self.test_domain_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_A')
self.test_domain_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_B')
self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)
self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)
def build_model(self):
if self.phase == 'train':
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.domain_A = trainA_iterator.get_next()
self.domain_B = trainB_iterator.get_next()
""" Define Generator, Discriminator """
x_ab, cam_ab = self.generate_a2b(self.domain_A) # real a
x_ba, cam_ba = self.generate_b2a(self.domain_B) # real b
x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b
x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a
x_aa, cam_aa = self.generate_b2a(self.domain_A,
reuse=True) # fake b
x_bb, cam_bb = self.generate_a2b(self.domain_B,
reuse=True) # fake a
real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(
self.domain_A, self.domain_B)
fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(
x_ba, x_ab)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP_A, GP_CAM_A = self.gradient_panalty(real=self.domain_A,
fake=x_ba,
scope="discriminator_A")
GP_B, GP_CAM_B = self.gradient_panalty(real=self.domain_B,
fake=x_ab,
scope="discriminator_B")
else:
GP_A, GP_CAM_A = 0, 0
GP_B, GP_CAM_B = 0, 0
G_ad_loss_A = (generator_loss(self.gan_type, fake_A_logit) +
generator_loss(self.gan_type, fake_A_cam_logit))
G_ad_loss_B = (generator_loss(self.gan_type, fake_B_logit) +
generator_loss(self.gan_type, fake_B_cam_logit))
D_ad_loss_A = (
discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) +
discriminator_loss(self.gan_type, real_A_cam_logit,
fake_A_cam_logit) + GP_A + GP_CAM_A)
D_ad_loss_B = (
discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) +
discriminator_loss(self.gan_type, real_B_cam_logit,
fake_B_cam_logit) + GP_B + GP_CAM_B)
reconstruction_A = L1_loss(x_aba, self.domain_A) # reconstruction
reconstruction_B = L1_loss(x_bab, self.domain_B) # reconstruction
identity_A = L1_loss(x_aa, self.domain_A)
identity_B = L1_loss(x_bb, self.domain_B)
cam_A = cam_loss(source=cam_ba, non_source=cam_aa)
cam_B = cam_loss(source=cam_ab, non_source=cam_bb)
Generator_A_gan = self.adv_weight * G_ad_loss_A
Generator_A_cycle = self.cycle_weight * reconstruction_B
Generator_A_identity = self.identity_weight * identity_A
Generator_A_cam = self.cam_weight * cam_A
Generator_B_gan = self.adv_weight * G_ad_loss_B
Generator_B_cycle = self.cycle_weight * reconstruction_A
Generator_B_identity = self.identity_weight * identity_B
Generator_B_cam = self.cam_weight * cam_B
Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam
Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam
Discriminator_A_loss = self.adv_weight * D_ad_loss_A
Discriminator_B_loss = self.adv_weight * D_ad_loss_B
self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss(
'generator')
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss(
'discriminator')
""" Result Image """
self.fake_A = x_ba
self.fake_B = x_ab
self.real_A = self.domain_A
self.real_B = self.domain_B
""" 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]
self.G_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Discriminator_loss,
var_list=D_vars)
"""" Summary """
self.all_G_loss = tf.summary.scalar("Generator_loss",
self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_A_gan = tf.summary.scalar("G_A_gan", Generator_A_gan)
self.G_A_cycle = tf.summary.scalar("G_A_cycle", Generator_A_cycle)
self.G_A_identity = tf.summary.scalar("G_A_identity",
Generator_A_identity)
self.G_A_cam = tf.summary.scalar("G_A_cam", Generator_A_cam)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.G_B_gan = tf.summary.scalar("G_B_gan", Generator_B_gan)
self.G_B_cycle = tf.summary.scalar("G_B_cycle", Generator_B_cycle)
self.G_B_identity = tf.summary.scalar("G_B_identity",
Generator_B_identity)
self.G_B_cam = tf.summary.scalar("G_B_cam", Generator_B_cam)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.rho_var = []
for var in tf.trainable_variables():
if 'rho' in var.name:
self.rho_var.append(tf.summary.histogram(var.name, var))
self.rho_var.append(
tf.summary.scalar(var.name + "_min",
tf.reduce_min(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_max",
tf.reduce_max(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_mean",
tf.reduce_mean(var)))
g_summary_list = [
self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity,
self.G_A_cam, self.G_B_loss, self.G_B_gan, self.G_B_cycle,
self.G_B_identity, self.G_B_cam, self.all_G_loss
]
g_summary_list.extend(self.rho_var)
d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]
self.G_loss = tf.summary.merge(g_summary_list)
self.D_loss = tf.summary.merge(d_summary_list)
else:
""" Test """
self.test_domain_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_A')
self.test_domain_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_B')
self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)
self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)
def _initialize_tf_dataset(file_names,
augment,
over_sample,
shuffle,
target_size,
normalize,
nb_workers=8,
batch_size=64,
shuffle_buffer_size=3000,
input_type="img",
nr_epochs=1):
import tensorflow as tf
from tensorflow.contrib.data import map_and_batch
from tensorflow.contrib.data import shuffle_and_repeat
if not type(target_size) is list:
target_size = list(target_size)
with tf.name_scope('input_pipeline'):
dataset = tf.data.TFRecordDataset(file_names)
if shuffle:
dataset = dataset.apply(
shuffle_and_repeat(shuffle_buffer_size, nr_epochs))
def _decode_and_augment_image(example_proto):
keys_to_features = {
'label': tf.FixedLenFeature([], tf.int64),
'shape': tf.FixedLenFeature([], tf.string),
'image': tf.FixedLenFeature([], tf.string),
}
tfrecord_features = tf.parse_single_example(example_proto,
keys_to_features)
image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
shape = tf.decode_raw(tfrecord_features['shape'], tf.int64)
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
label = tfrecord_features['label']
if augment:
image = tf.image.random_flip_left_right(image)
image = tf.image.random_flip_up_down(image)
degrees = tf.random_uniform((), minval=-180, maxval=180)
image = tf.contrib.image.rotate(image, degrees)
width_shift = tf.random_uniform((), minval=0, maxval=0.05)
height_shift = tf.random_uniform((), minval=0, maxval=0.05)
horizontal_pad = tf.cast(
tf.ceil(width_shift * target_size[0]), tf.int32)
vertical_pad = tf.cast(tf.ceil(height_shift * target_size[1]), tf.int32)
padding = tf.stack([
horizontal_pad, horizontal_pad, vertical_pad, vertical_pad,
tf.constant(0),
tf.constant(0)
])
padding = tf.reshape(padding, (3, 2))
image = tf.pad(image, padding)
image = tf.random_crop(image, target_size + [3])
zoom = tf.random_uniform((), minval=-0.1, maxval=0.1)
new_dim = tf.cast(tf.ceil((1 - zoom) * target_size[0]), dtype=tf.int32)
image = tf.image.resize_image_with_crop_or_pad(image, new_dim, new_dim)
image = tf.image.resize_images(
image, target_size, method=tf.image.ResizeMethod.BILINEAR)
if normalize:
std = tf.constant(
np.array([70.53946096, 51.71475228, 43.03428563]), dtype=tf.float32)
std = tf.expand_dims(tf.expand_dims(std, axis=0), axis=0)
mean = tf.constant(
np.array([108.64628601, 75.86886597, 54.34005736]),
dtype=tf.float32)
mean = tf.expand_dims(tf.expand_dims(mean, axis=0), axis=0)
image = (tf.cast(image, dtype=tf.float32) - mean) / std
label = tf.reshape(label, [1])
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
return {'shape': shape, 'image': image}, label
dataset = dataset \
.apply(map_and_batch(_decode_and_augment_image, batch_size=batch_size, num_parallel_batches=nb_workers,
drop_remainder=True)) \
.prefetch(nb_workers)
# def _augment_images(example)
return dataset
def build_model(self):
self.ema = tf.train.ExponentialMovingAverage(decay=self.ema_decay)
if self.phase == 'train' :
""" Input Image"""
img_class = Image_data(self.img_height, self.img_width, self.img_ch, self.dataset_path, self.label_list,
self.augment_flag)
img_class.preprocess()
dataset_num = len(img_class.image)
print("Dataset number : ", dataset_num)
self.lr = tf.placeholder(tf.float32, name='learning_rate')
self.ds_weight_placeholder = tf.placeholder(tf.float32, name='ds_weight')
img_and_label = tf.data.Dataset.from_tensor_slices((img_class.image, img_class.label))
gpu_device = '/gpu:0'
img_and_label = img_and_label.apply(shuffle_and_repeat(dataset_num)).apply(
map_and_batch(img_class.image_processing, self.batch_size * self.gpu_num, num_parallel_batches=16,
drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
img_and_label_iterator = img_and_label.make_one_shot_iterator()
self.x_real, label_org = img_and_label_iterator.get_next() # [bs, 256, 256, 3], [bs, 1]
# label_trg = tf.random_shuffle(label_org) # Target domain labels
label_trg = tf.random_uniform(shape=tf.shape(label_org), minval=0, maxval=self.c_dim, dtype=tf.int32) # Target domain labels
""" split """
x_real_gpu_split = tf.split(self.x_real, num_or_size_splits=self.gpu_num, axis=0)
label_org_gpu_split = tf.split(label_org, num_or_size_splits=self.gpu_num, axis=0)
label_trg_gpu_split = tf.split(label_trg, num_or_size_splits=self.gpu_num, axis=0)
g_adv_loss_per_gpu = []
g_sty_recon_loss_per_gpu = []
g_sty_diverse_loss_per_gpu = []
g_cyc_loss_per_gpu = []
g_loss_per_gpu = []
d_adv_loss_per_gpu = []
d_loss_per_gpu = []
for gpu_id in range(self.gpu_num):
with tf.device(tf.DeviceSpec(device_type="GPU", device_index=gpu_id)):
with tf.variable_scope(tf.get_variable_scope(), reuse=(gpu_id > 0)):
x_real_split = tf.split(x_real_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
label_org_split = tf.split(label_org_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
label_trg_split = tf.split(label_trg_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
g_adv_loss = None
g_sty_recon_loss = None
g_sty_diverse_loss = None
g_cyc_loss = None
d_adv_loss = None
d_simple_gp = None
d_gp = None
for each_bs in range(self.batch_size) :
""" Define Generator, Discriminator """
x_real_each = x_real_split[each_bs] # [1, 256, 256, 3]
label_org_each = tf.squeeze(label_org_split[each_bs], axis=[0, 1]) # [1, 1] -> []
label_trg_each = tf.squeeze(label_trg_split[each_bs], axis=[0, 1])
random_style_code = tf.random_normal(shape=[1, self.style_dim])
random_style_code_1 = tf.random_normal(shape=[1, self.style_dim])
random_style_code_2 = tf.random_normal(shape=[1, self.style_dim])
random_style = tf.gather(self.mapping_network(random_style_code), label_trg_each)
random_style_1 = tf.gather(self.mapping_network(random_style_code_1), label_trg_each)
random_style_2 = tf.gather(self.mapping_network(random_style_code_2), label_trg_each)
x_fake = self.generator(x_real_each, random_style) # for adversarial objective
x_fake_1 = self.generator(x_real_each, random_style_1) # for style diversification
x_fake_2 = self.generator(x_real_each, random_style_2) # for style diversification
x_real_each_style = tf.gather(self.style_encoder(x_real_each), label_org_each) # for cycle consistency
x_fake_style = tf.gather(self.style_encoder(x_fake), label_trg_each) # for style reconstruction
x_cycle = self.generator(x_fake, x_real_each_style) # for cycle consistency
real_logit = tf.gather(self.discriminator(x_real_each), label_org_each)
fake_logit = tf.gather(self.discriminator(x_fake), label_trg_each)
""" Define loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_panalty(real=x_real_each, fake=x_fake, real_label=label_org_each)
else:
GP = tf.constant([0], tf.float32)
if each_bs == 0 :
g_adv_loss = self.adv_weight * generator_loss(self.gan_type, fake_logit)
g_sty_recon_loss = self.sty_weight * L1_loss(random_style, x_fake_style)
g_sty_diverse_loss = self.ds_weight_placeholder * L1_loss(x_fake_1, x_fake_2)
g_cyc_loss = self.cyc_weight * L1_loss(x_real_each, x_cycle)
d_adv_loss = self.adv_weight * discriminator_loss(self.gan_type, real_logit, fake_logit)
d_simple_gp = self.adv_weight * simple_gp(real_logit, fake_logit, x_real_each, x_fake, r1_gamma=self.r1_weight, r2_gamma=0.0)
d_gp = self.adv_weight * GP
else :
g_adv_loss = tf.concat([g_adv_loss, self.adv_weight * generator_loss(self.gan_type, fake_logit)], axis=0)
g_sty_recon_loss = tf.concat([g_sty_recon_loss, self.sty_weight * L1_loss(random_style, x_fake_style)], axis=0)
g_sty_diverse_loss = tf.concat([g_sty_diverse_loss, self.ds_weight_placeholder * L1_loss(x_fake_1, x_fake_2)], axis=0)
g_cyc_loss = tf.concat([g_cyc_loss, self.cyc_weight * L1_loss(x_real_each, x_cycle)], axis=0)
d_adv_loss = tf.concat([d_adv_loss, self.adv_weight * discriminator_loss(self.gan_type, real_logit, fake_logit)], axis=0)
d_simple_gp = tf.concat([d_simple_gp, self.adv_weight * simple_gp(real_logit, fake_logit, x_real_each, x_fake, r1_gamma=self.r1_weight, r2_gamma=0.0)], axis=0)
d_gp = tf.concat([d_gp, self.adv_weight * GP], axis=0)
g_adv_loss = tf.reduce_mean(g_adv_loss)
g_sty_recon_loss = tf.reduce_mean(g_sty_recon_loss)
g_sty_diverse_loss = tf.reduce_mean(g_sty_diverse_loss)
g_cyc_loss = tf.reduce_mean(g_cyc_loss)
d_adv_loss = tf.reduce_mean(d_adv_loss)
d_simple_gp = tf.reduce_mean(tf.reduce_sum(d_simple_gp, axis=[1, 2, 3]))
d_gp = tf.reduce_mean(d_gp)
g_loss = g_adv_loss + g_sty_recon_loss - g_sty_diverse_loss + g_cyc_loss
d_loss = d_adv_loss + d_simple_gp + d_gp
g_adv_loss_per_gpu.append(g_adv_loss)
g_sty_recon_loss_per_gpu.append(g_sty_recon_loss)
g_sty_diverse_loss_per_gpu.append(g_sty_diverse_loss)
g_cyc_loss_per_gpu.append(g_cyc_loss)
d_adv_loss_per_gpu.append(d_adv_loss)
g_loss_per_gpu.append(g_loss)
d_loss_per_gpu.append(d_loss)
g_adv_loss = tf.reduce_mean(g_adv_loss_per_gpu)
g_sty_recon_loss = tf.reduce_mean(g_sty_recon_loss_per_gpu)
g_sty_diverse_loss = tf.reduce_mean(g_sty_diverse_loss_per_gpu)
g_cyc_loss = tf.reduce_mean(g_cyc_loss_per_gpu)
self.g_loss = tf.reduce_mean(g_loss_per_gpu)
d_adv_loss = tf.reduce_mean(d_adv_loss_per_gpu)
self.d_loss = tf.reduce_mean(d_loss_per_gpu)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
E_vars = [var for var in t_vars if 'encoder' in var.name]
F_vars = [var for var in t_vars if 'mapping' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
if self.gpu_num == 1 :
prev_g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=G_vars)
prev_e_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=E_vars)
prev_f_optimizer = tf.train.AdamOptimizer(self.lr * 0.01, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=F_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.d_loss, var_list=D_vars)
else :
prev_g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=G_vars,
colocate_gradients_with_ops=True)
prev_e_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss,
var_list=E_vars,
colocate_gradients_with_ops=True)
prev_f_optimizer = tf.train.AdamOptimizer(self.lr * 0.01, beta1=0, beta2=0.99).minimize(self.g_loss,
var_list=F_vars,
colocate_gradients_with_ops=True)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.d_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
with tf.control_dependencies([prev_g_optimizer, prev_e_optimizer, prev_f_optimizer]):
self.g_optimizer = self.ema.apply(G_vars)
self.e_optimizer = self.ema.apply(E_vars)
self.f_optimizer = self.ema.apply(F_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("g_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("d_loss", self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_sty_recon_loss = tf.summary.scalar("g_sty_recon_loss", g_sty_recon_loss)
self.g_sty_diverse_loss = tf.summary.scalar("g_sty_diverse_loss", g_sty_diverse_loss)
self.g_cyc_loss = tf.summary.scalar("g_cyc_loss", g_cyc_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
g_summary_list = [self.Generator_loss, self.g_adv_loss, self.g_sty_recon_loss, self.g_sty_diverse_loss, self.g_cyc_loss]
d_summary_list = [self.Discriminator_loss, self.d_adv_loss]
self.g_summary_loss = tf.summary.merge(g_summary_list)
self.d_summary_loss = tf.summary.merge(d_summary_list)
""" Result Image """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.x_fake_list = []
first_x_real = tf.expand_dims(self.x_real[0], axis=0)
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
for _ in range(self.num_style):
random_style_code = tf.truncated_normal(shape=[1, self.style_dim])
self.x_fake_list.append(tf.map_fn(
lambda c: return_g_images(self.generator,
first_x_real,
tf.gather(self.mapping_network(random_style_code), c)),
label_fix_list, dtype=tf.float32))
elif self.phase == 'refer_test':
""" Test """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.custom_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='custom_image')
self.refer_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='refer_image')
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
self.refer_fake_image = tf.map_fn(
lambda c : return_g_images(self.generator,
self.custom_image,
tf.gather(self.style_encoder(self.refer_image), c)),
label_fix_list, dtype=tf.float32)
else :
""" Test """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.custom_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='custom_image')
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
random_style_code = tf.truncated_normal(shape=[1, self.style_dim])
self.custom_fake_image = tf.map_fn(
lambda c : return_g_images(self.generator,
self.custom_image,
tf.gather(self.mapping_network(random_style_code), c)),
label_fix_list, dtype=tf.float32)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch, self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
trainB_smooth = tf.data.Dataset.from_tensor_slices(self.trainB_smooth_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, self.batch_size))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, self.batch_size))
trainB_smooth = trainB_smooth.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, self.batch_size))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
trainB_smooth_iterator = trainB_smooth.make_one_shot_iterator()
self.real_A = trainA_iterator.get_next()
self.real_B = trainB_iterator.get_next()
self.real_B_smooth = trainB_smooth_iterator.get_next()
self.test_real_A = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='test_real_A')
""" Define Generator, Discriminator """
self.fake_B = self.generator(self.real_A)
real_B_logit = self.discriminator(self.real_B)
fake_B_logit = self.discriminator(self.fake_B, reuse=True)
real_B_smooth_logit = self.discriminator(self.real_B_smooth, reuse=True)
""" 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.real_B, fake=self.fake_B) + self.gradient_panalty(self.real_B, fake=self.real_B_smooth)
else :
GP = 0.0
v_loss = self.vgg_weight * vgg_loss(self.real_A, self.fake_B)
g_loss = self.adv_weight * generator_loss(self.gan_type, fake_B_logit)
d_loss = self.adv_weight * discriminator_loss(self.gan_type, real_B_logit, fake_B_logit, real_B_smooth_logit) + GP
self.Vgg_loss = v_loss
self.Generator_loss = g_loss + v_loss
self.Discriminator_loss = d_loss
""" Result Image """
self.test_fake_B = self.generator(self.test_real_A, reuse=True)
""" 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]
self.init_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Vgg_loss, var_list=G_vars)
self.G_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)
"""" Summary """
self.G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
self.G_gan = tf.summary.scalar("G_gan", g_loss)
self.G_vgg = tf.summary.scalar("G_vgg", v_loss)
self.V_loss_merge = tf.summary.merge([self.G_vgg])
self.G_loss_merge = tf.summary.merge([self.G_loss, self.G_gan, self.G_vgg])
self.D_loss_merge = tf.summary.merge([self.D_loss])
def build_model(self):
""" Graph Input """
# images
if self.custom_dataset:
Image_Data_Class = ImageData(self.img_size, self.c_dim)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
else:
self.inputs = tf.placeholder(
tf.float32,
[self.batch_size, self.img_size, self.img_size, self.c_dim],
name='real_images')
# noises
self.z = tf.placeholder(tf.float32,
[self.batch_size, 1, 1, self.z_dim],
name='z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('gp') or self.gan_type.__contains__(
'lp') or self.gan_type.__contains__('dragan'):
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else:
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(
self.Ra, self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.Ra,
self.gan_type,
real=real_logits,
fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.g_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z,
is_training=False,
reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
def build_model(self):
if self.phase == 'train':
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
img_data_class = Image_data(self.img_height, self.img_width,
self.img_ch, self.dataset_path,
self.augment_flag)
img_data_class.preprocess()
self.dataset_num = len(img_data_class.image_list)
img_and_embedding = tf.data.Dataset.from_tensor_slices(
(img_data_class.image_list, img_data_class.embedding))
gpu_device = '/gpu:0'
img_and_embedding = img_and_embedding.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing,
batch_size=self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_embedding_iterator = img_and_embedding.make_one_shot_iterator(
)
self.real_img_256, self.embedding = img_and_embedding_iterator.get_next(
)
sentence_index = tf.random.uniform(shape=[],
minval=0,
maxval=10,
dtype=tf.int32)
self.embedding = tf.gather(self.embedding,
indices=sentence_index,
axis=1) #[bs, 1024]
noise = tf.random_normal(shape=[self.batch_size, self.z_dim])
self.fake_img_64, mu_64, logvar_64 = self.generator_1(
self.embedding, noise)
self.fake_img_256, mu_256, logvar_256 = self.generator_2(
self.fake_img_64, self.embedding)
self.real_img_64 = tf.image.resize_bilinear(self.real_img_256,
size=[64, 64])
self.real_img = [self.real_img_64, self.real_img_256]
self.fake_img = [self.fake_img_64, self.fake_img_256]
real_logit_64 = self.discriminator_1(self.real_img_64, mu_64)
fake_logit_64 = self.discriminator_1(self.fake_img_64, mu_64)
real_logit_256 = self.discriminator_2(self.real_img_256, mu_256)
fake_logit_256 = self.discriminator_2(self.fake_img_256, mu_256)
g_adv_loss_64 = generator_loss(self.gan_type,
fake_logit_64) * self.adv_weight
g_kl_loss_64 = kl_loss(mu_64, logvar_64) * self.kl_weight
d_adv_loss_64 = discriminator_loss(self.gan_type, real_logit_64,
fake_logit_64) * self.adv_weight
g_loss_64 = g_adv_loss_64 + g_kl_loss_64
d_loss_64 = d_adv_loss_64
g_adv_loss_256 = generator_loss(self.gan_type,
fake_logit_256) * self.adv_weight
g_kl_loss_256 = kl_loss(mu_256, logvar_256) * self.kl_weight
d_adv_loss_256 = discriminator_loss(
self.gan_type, real_logit_256,
fake_logit_256) * self.adv_weight
g_loss_256 = g_adv_loss_256 + g_kl_loss_256
d_loss_256 = d_adv_loss_256
self.g_loss = [g_loss_64, g_loss_256]
self.d_loss = [d_loss_64, d_loss_256]
""" Training """
t_vars = tf.trainable_variables()
G1_vars = [var for var in t_vars if 'generator_1' in var.name]
G2_vars = [var for var in t_vars if 'generator_2' in var.name]
D1_vars = [var for var in t_vars if 'discriminator_1' in var.name]
D2_vars = [var for var in t_vars if 'discriminator_2' in var.name]
g1_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(g_loss_64,
var_list=G1_vars)
g2_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(g_loss_256,
var_list=G2_vars)
d1_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(d_loss_64,
var_list=D1_vars)
d2_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(d_loss_256,
var_list=D2_vars)
self.g_optim = [g1_optim, g2_optim]
self.d_optim = [d1_optim, d2_optim]
"""" Summary """
self.summary_g_loss_64 = tf.summary.scalar("g_loss_64", g_loss_64)
self.summary_g_loss_256 = tf.summary.scalar(
"g_loss_256", g_loss_256)
self.summary_d_loss_64 = tf.summary.scalar("d_loss_64", d_loss_64)
self.summary_d_loss_256 = tf.summary.scalar(
"d_loss_256", d_loss_256)
self.summary_g_adv_loss_64 = tf.summary.scalar(
"g_adv_loss_64", g_adv_loss_64)
self.summary_g_adv_loss_256 = tf.summary.scalar(
"g_adv_loss_256", g_adv_loss_256)
self.summary_g_kl_loss_64 = tf.summary.scalar(
"g_kl_loss_64", g_kl_loss_64)
self.summary_g_kl_loss_256 = tf.summary.scalar(
"g_kl_loss_256", g_kl_loss_256)
self.summary_d_adv_loss_64 = tf.summary.scalar(
"d_adv_loss_64", d_adv_loss_64)
self.summary_d_adv_loss_256 = tf.summary.scalar(
"d_adv_loss_256", d_adv_loss_256)
g_summary_list = [
self.summary_g_loss_64, self.summary_g_loss_256,
self.summary_g_adv_loss_64, self.summary_g_adv_loss_256,
self.summary_g_kl_loss_64, self.summary_g_kl_loss_256
]
d_summary_list = [
self.summary_d_loss_64, self.summary_d_loss_256,
self.summary_d_adv_loss_64, self.summary_d_adv_loss_256
]
self.summary_merge_g_loss = tf.summary.merge(g_summary_list)
self.summary_merge_d_loss = tf.summary.merge(d_summary_list)
else:
""" Test """
""" Input Image"""
img_data_class = Image_data(self.img_height,
self.img_width,
self.img_ch,
self.dataset_path,
augment_flag=False)
img_data_class.preprocess()
self.dataset_num = len(img_data_class.image_list)
img_and_embedding = tf.data.Dataset.from_tensor_slices(
(img_data_class.image_list, img_data_class.embedding))
gpu_device = '/gpu:0'
img_and_embedding = img_and_embedding.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing,
batch_size=32,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_embedding_iterator = img_and_embedding.make_one_shot_iterator(
)
self.real_img_256, self.embedding = img_and_embedding_iterator.get_next(
)
sentence_index = tf.random.uniform(shape=[],
minval=0,
maxval=10,
dtype=tf.int32)
self.embedding = tf.gather(self.embedding,
indices=sentence_index,
axis=1) # [bs, 1024]
noise = tf.random_normal(shape=[self.batch_size, self.z_dim])
self.fake_img_64, mu_64, logvar_64 = self.generator_1(
self.embedding, noise, is_training=False)
self.fake_img_256, mu_256, logvar_256 = self.generator_2(
self.fake_img_64, self.embedding, is_training=False)
self.test_fake_img = self.fake_img_256
self.test_real_img = self.real_img_256
self.test_fake_img_64 = self.fake_img_64
