parser.add_argument('--mode', choices=['AtoB', 'BtoA'], default='AtoB')
parser.add_argument('-b', '--batch', type=int, default=1)
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
BATCH = args.batch
if args.sample:
assert args.load
sample(args.data, args.load)
else:
logger.auto_set_dir()
data = QueueInput(get_data())
nr_tower = max(get_num_gpu(), 1)
if nr_tower == 1:
trainer = GANTrainer(data, Model())
else:
trainer = MultiGPUGANTrainer(nr_tower, data, Model())
trainer.train_with_defaults(
callbacks=[
PeriodicTrigger(ModelSaver(), every_k_epochs=3),
ScheduledHyperParamSetter('learning_rate', [(200, 1e-4)])
],
steps_per_epoch=data.size(),
max_epoch=300,
session_init=SaverRestore(args.load) if args.load else None)
initializer=1e-4,
trainable=False)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
return opt
if __name__ == '__main__':
args = DCGAN.get_args(default_batch=32, default_z_dim=64)
if args.sample:
DCGAN.sample(Model(), args.load, 'gen/conv4.3/output')
else:
logger.auto_set_dir()
input = QueueInput(DCGAN.get_data())
model = Model()
nr_tower = max(get_nr_gpu(), 1)
if nr_tower == 1:
trainer = GANTrainer(input, model)
else:
trainer = MultiGPUGANTrainer(nr_tower, input, model)
trainer.train_with_defaults(
callbacks=[
ModelSaver(),
StatMonitorParamSetter('learning_rate', 'measure',
lambda x: x * 0.5, 0, 10)
],
session_init=SaverRestore(args.load) if args.load else None,
steps_per_epoch=500,
max_epoch=400)
logger.auto_set_dir()
nr_tower = max(get_nr_gpu(), 1)
data = QueueInput(get_data(args.lmdb))
model = Model()
logger.info("run %i epochs", EPOCHS)
logger.info("use %i blocks", BLOCKS)
logger.info("use %i as batchsize", BATCH_SIZE)
if nr_tower == 1:
trainer = GANTrainer(data, model)
else:
trainer = MultiGPUGANTrainer(nr_tower, data, model)
callbacks = [
ModelSaver(),
MovingAverageSummary(),
ProgressBar(['d_loss', 'g_loss', 'alpha', 'loss-diff-g-d']),
MergeAllSummaries(),
RunUpdateOps()
]
trainer.train_with_defaults(
callbacks=callbacks,
session_init=SaverRestore(args.load, ignore=['global_step'])
if args.load else None,
steps_per_epoch=STEPS_PER_EPOCH,
max_epoch=EPOCHS,
)
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
return opt
if __name__ == '__main__':
args = DCGAN.get_args(default_batch=32, default_z_dim=64)
if args.sample:
DCGAN.sample(Model(), args.load, 'gen/conv4.3/output')
else:
logger.auto_set_dir()
input = QueueInput(DCGAN.get_data())
model = Model()
nr_tower = max(get_nr_gpu(), 1)
if nr_tower == 1:
trainer = GANTrainer(input, model)
else:
trainer = MultiGPUGANTrainer(nr_tower, input, model)
trainer.train_with_defaults(
callbacks=[
ModelSaver(),
StatMonitorParamSetter(
'learning_rate', 'measure', lambda x: x * 0.5, 0, 10)
],
session_init=SaverRestore(args.load) if args.load else None,
steps_per_epoch=500, max_epoch=400)
self.collect_variables()
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=1e-4,
trainable=False)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
return opt
if __name__ == '__main__':
args = DCGAN.get_args(default_batch=32, default_z_dim=64)
if args.sample:
DCGAN.sample(Model(), args.load, 'gen/conv4.3/output')
else:
logger.auto_set_dir()
input = QueueInput(DCGAN.get_data())
model = Model()
nr_tower = max(get_num_gpu(), 1)
trainer = GANTrainer(input, model, num_gpu=nr_tower)
trainer.train_with_defaults(callbacks=[
ModelSaver(),
StatMonitorParamSetter('learning_rate', 'losses/measure',
lambda x: x * 0.5, 0, 10)
],
session_init=SmartInit(args.load),
steps_per_epoch=500,
max_epoch=400)
