latent_spec = [
(Uniform(62), False),
(Categorical(10), True),
(Uniform(1, fix_std=True), True),
(Uniform(1, fix_std=True), True),
]
model = RegularizedGAN(
output_dist=MeanBernoulli(dataset.image_dim),
latent_spec=latent_spec,
batch_size=batch_size,
image_shape=dataset.image_shape,
network_type="mnist",
)
algo = InfoGANTrainer(
model=model,
dataset=dataset,
batch_size=batch_size,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
max_epoch=max_epoch,
updates_per_epoch=updates_per_epoch,
info_reg_coeff=1.0,
generator_learning_rate=1e-3,
discriminator_learning_rate=2e-4,
)
algo.train()
def train(configPath):
with open(configPath, 'r') as f:
d = json.load(f)
train_dataset = d['train_dataset']
train_Folder = d['train_Folder']
val_dataset = d['val_dataset']
output_size = d['output_size']
categories = d['categories']
batch_size = d['batch_size']
if d.has_key('improvedGan'):
improvedGan = d['improvedGan']
else:
improvedGan = False
print("Using improvedGAN ", improvedGan)
if d.has_key('exp_name'):
exp_name = d['exp_name']
else:
filename = os.path.split(configPath)[-1]
assert (len(filename.split(".")) == 2)
exp_name = filename.split(".")[0]
arch = d['arch']
if d.has_key('semiSup'):
semiSup = d['semiSup']
else:
semiSup = False
if d.has_key('trainSplit'):
trainSplit = d['trainSplit']
else:
trainSplit = 0.7
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
root_log_dir = "logs/" + train_dataset
root_checkpoint_dir = "ckt/" + train_dataset
root_samples_dir = "samples/" + train_dataset
updates_per_epoch = 100
max_epoch = 50
if exp_name is None:
exp_name = "t-%s_v-%s_o-%d" % (train_dataset, val_dataset, output_size)
if not (categories is None):
exp_name = exp_name + "_c-%d" % (categories)
exp_name = exp_name + "_%s" % (timestamp)
print("Experiment Name: %s" % (exp_name))
log_dir = os.path.join(root_log_dir, exp_name)
checkpoint_dir = os.path.join(root_checkpoint_dir, exp_name)
samples_dir = os.path.join(root_samples_dir, exp_name)
mkdir_p(log_dir)
mkdir_p(checkpoint_dir)
mkdir_p(samples_dir)
output_dist = None
network_type = arch
if train_dataset == "mnist":
print("Creating train dataset ")
dataset = datasets.MnistDataset()
output_dist = MeanBernoulli(dataset.image_dim)
print("CREATED train dataset ")
network_type = 'mnist'
print("Creating VAL dataset ")
val_dataset = dataset
elif train_dataset == "dataFolder":
dataset = datasets.DataFolder(train_Folder,
batch_size,
out_size=output_size,
validation_proportion=(1 - trainSplit))
print("Folder datasets created ")
val_dataset = dataset
else:
dataset = datasets.Dataset(name=train_dataset,
batch_size=batch_size,
output_size=output_size)
val_dataset = datasets.Dataset(name=val_dataset,
batch_size=batch_size,
output_size=output_size)
latent_spec = [(Uniform(100), False)]
if categories is not None:
latent_spec.append((Categorical(categories), True))
is_reg = False
for x, y in latent_spec:
if y:
is_reg = True
model = RegularizedGAN(output_dist=output_dist,
latent_spec=latent_spec,
is_reg=is_reg,
batch_size=batch_size,
image_shape=dataset.image_shape,
network_type=network_type,
impr=improvedGan)
if (not improvedGan):
algo = InfoGANTrainer(model=model,
dataset=dataset,
val_dataset=val_dataset,
batch_size=batch_size,
isTrain=True,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
samples_dir=samples_dir,
max_epoch=max_epoch,
info_reg_coeff=1.0,
generator_learning_rate=2e-3,
discriminator_learning_rate=2e-3,
semiSup=semiSup)
else:
algo = ImprovedGAN(model=model,
dataset=dataset,
val_dataset=val_dataset,
batch_size=batch_size,
isTrain=True,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
samples_dir=samples_dir,
max_epoch=max_epoch,
info_reg_coeff=1.0,
generator_learning_rate=2e-3,
discriminator_learning_rate=2e-3,
semiSup=semiSup)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.03)
#config=tf.ConfigProto(gpu_options=gpu_options)
#device_name = "/gpu:0"
#with tf.device(device_name):
algo.init_opt()
with tf.Session() as sess:
algo.train(sess)
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
exp_name = "celebA_model_celebA_codes_color_img-align-celeba_10_%s" % timestamp
log_dir = os.path.join(root_log_dir, exp_name)
checkpoint_dir = os.path.join(root_checkpoint_dir, exp_name)
mkdir_p(log_dir)
mkdir_p(checkpoint_dir)
algo = InfoGANTrainer(
model=model,
dataset=dataset,
batch_size=batch_size,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
max_epoch=max_epoch,
updates_per_epoch=updates_per_epoch,
snapshot_interval=checkpoint_snapshot_interval,
info_reg_coeff=1.0,
generator_learning_rate=1e-3, # original paper's learning rate was 1e-3
discriminator_learning_rate=2e-4, # original paper's learning rate was 2e-4
)
# In[8]:
#algo.visualize_all_factors() # ... what does this do?
# In[9]:
output_dist=output_dist,
latent_spec=latent_spec,
is_reg=is_reg,
batch_size=batch_size,
image_shape=dataset.image_shape,
network_type=network_type,
)
algo = InfoGANTrainer(
model=model,
dataset=dataset,
val_dataset=val_dataset,
batch_size=batch_size,
isTrain=FLAGS.train,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
samples_dir=samples_dir,
max_epoch=max_epoch,
info_reg_coeff=1.0,
generator_learning_rate=2e-3,
discriminator_learning_rate=2e-3,
)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
algo.init_opt()
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if FLAGS.train:
algo.train(sess)
else:
restorer = tf.train.Saver()
#EVERYTHING SEEMS FINE. NETWORK ARCHITECTURE SHOULD
#BE CHANGED.
model = RegularizedGAN(
output_dist=MeanBernoulli(dataset.image_dim),
latent_spec=latent_spec,
batch_size=batch_size,
image_shape=dataset.image_shape,
network_type="heart",
)
#EVERYTHING SEEMS OKAY HERE
#JUST NEED TO MAKE SURE NEXT_BATCH
#IS READY TO GO
#LEARNING RATES MIGHT NEED TO CHANGE
algo = InfoGANTrainer(
model=model,
dataset=dataset,
batch_size=batch_size,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
max_epoch=max_epoch,
updates_per_epoch=updates_per_epoch,
info_reg_coeff=0.1, #1.0, RECAPUTULATE AZIMUTH FACE TEST
generator_learning_rate=5e-4, #1e-3,
discriminator_learning_rate=2e-4 #2e-4,
)
algo.train()
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
exp_name = "celebA_model_celebA_codes_color_img-align-celeba_10_%s" % timestamp
log_dir = os.path.join(root_log_dir, exp_name)
checkpoint_dir = os.path.join(root_checkpoint_dir, exp_name)
mkdir_p(log_dir)
mkdir_p(checkpoint_dir)
algo = InfoGANTrainer(
model=model,
dataset=dataset,
batch_size=batch_size,
exp_name=exp_name,
log_dir=log_dir,
checkpoint_dir=checkpoint_dir,
max_epoch=max_epoch,
updates_per_epoch=updates_per_epoch,
snapshot_interval=checkpoint_snapshot_interval,
info_reg_coeff=1.0,
generator_learning_rate=1e-3, # original paper's learning rate was 1e-3
discriminator_learning_rate=2e-4, # original paper's learning rate was 2e-4
)
# In[8]:
#algo.visualize_all_factors() # ... what does this do?
# In[9]:
sess = tf.Session()