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.trainer.monitors.put_image('testB-{}'.format(idx), vizB)
idx += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data', required=True,
help='the image directory. should contain trainA/trainB/testA/testB')
parser.add_argument('--load', help='load model')
args = parser.parse_args()
logger.auto_set_dir()
df = get_data(args.data)
df = PrintData(df)
data = QueueInput(df)
GANTrainer(data, Model()).train_with_defaults(
callbacks=[
ModelSaver(),
ScheduledHyperParamSetter(
'learning_rate',
[(100, 2e-4), (200, 0)], interp='linear'),
PeriodicTrigger(VisualizeTestSet(), every_k_epochs=3),
],
max_epoch=195,
steps_per_epoch=data.size(),
session_init=SaverRestore(args.load) if args.load else None
)
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true', help='run sampling')
parser.add_argument('--data', help='Image directory', required=True)
parser.add_argument('--mode', choices=['AtoB', 'BtoA'], default='AtoB')
parser.add_argument('-b', '--batch', type=int, default=1)
global args
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())
GANTrainer(data, Model()).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
)
input_names=['label', 'z'],
output_names=['gen/gen'])
ds = MapData(RandomZData((100, 100)),
lambda dp: [np.arange(100) % 10, dp[0]])
pred = SimpleDatasetPredictor(pred, ds)
for o in pred.get_result():
o = o[0] * 255.0
viz = stack_patches(o, nr_row=10, nr_col=10)
viz = cv2.resize(viz, (800, 800))
interactive_imshow(viz)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.sample:
sample(args.load)
else:
logger.auto_set_dir()
GANTrainer(QueueInput(get_data()), Model()).train_with_defaults(
callbacks=[ModelSaver()],
steps_per_epoch=500,
max_epoch=100,
session_init=SaverRestore(args.load) if args.load else None)
model=Model(),
input_names=['z'],
output_names=['gen/gen', 'z'])
pred = SimpleDatasetPredictor(pred, RandomZData((100, 100)))
for o in pred.get_result():
o, zs = o[0] + 1, o[1]
o = o * 128.0
o = o[:, :, :, ::-1]
viz = next(build_patch_list(o, nr_row=10, nr_col=10, viz=True))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true', help='run sampling')
parser.add_argument(
'--data', help='`image_align_celeba` directory of the celebA dataset')
args = parser.parse_args()
use_global_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.sample:
sample(args.load)
else:
assert args.data
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
GANTrainer(config).train()
opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
return opt
if __name__ == '__main__':
args = DCGAN.get_args()
if args.sample:
DCGAN.sample(Model(), args.load, 'gen/conv4.3/output')
else:
assert args.data
logger.auto_set_dir()
config = TrainConfig(
callbacks=[
ModelSaver(),
StatMonitorParamSetter('learning_rate', 'measure',
lambda x: x * 0.5, 0, 10)
],
steps_per_epoch=500,
max_epoch=400,
session_init=SaverRestore(args.load) if args.load else None,
)
input = QueueInput(DCGAN.get_data(args.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_config(config)
#assert np.all(
#dic['w_smile'] - dic['w_neutral'] \
#+ dic['m_neutral'] == dic['m_smile'])
#imgs = []
#for z in ['w_neutral', 'w_smile', 'm_neutral', 'm_smile']:
#z = dic[z]
#img = func([[z]])[0][0][:,:,::-1]
#img = (img + 1) * 128
#imgs.append(img)
#viz = next(build_patch_list(imgs, nr_row=1, nr_col=4, viz=True))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true', help='run sampling')
parser.add_argument(
'--data', help='`image_align_celeba` directory of the celebA dataset')
args = parser.parse_args()
use_global_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.sample:
sample(args.load)
else:
assert args.data
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
GANTrainer(config, g_vs_d=1).train()
output_names=['gen/gen'])
ds = MapData(RandomZData((100, 100)),
lambda dp: [np.arange(100) % 10, dp[0]])
pred = SimpleDatasetPredictor(pred, ds)
for o in pred.get_result():
o = o[0] * 255.0
viz = stack_patches(o, nr_row=10, nr_col=10)
viz = cv2.resize(viz, (800, 800))
interactive_imshow(viz)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.sample:
sample(args.load)
else:
logger.auto_set_dir()
GANTrainer(QueueInput(get_data()), Model()).train_with_defaults(
callbacks=[ModelSaver()],
steps_per_epoch=500,
max_epoch=100,
session_init=SmartInit(args.load),
)
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)
def get_data():
lmdb = "/graphics/projects/scratch/student_datasets/cgpraktikum17/vat/resized_8.lmdb"
ds = LMDBDataPoint(lmdb, shuffle=True)
ds = ImageDecode(ds, index=0)
ds = PrefetchDataZMQ(ds, 2)
ds = BatchData(ds, BATCH_SIZE)
return ds
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
args = parser.parse_args()
logger.auto_set_dir()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
GANTrainer(input=QueueInput(get_data()),
model=Model()).train_with_defaults(
callbacks=[ModelSaver()],
steps_per_epoch=1875, # <- 30.000 / BATCH_SIZE
max_epoch=20, # <- 600.000 / 30.000
session_init=SaverRestore(
args.load, 'loaded', ignore=['global_step'])
if args.load else None)
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)
parser.add_argument('--l2norm', type=float, default=0.00001)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
if __name__ == '__main__':
args = get_args()
assert args.data
opt.DATA_INFO = json.loads(str(np.load(args.data)['info']))
if args.sample > 0:
sample(args.sample, Model(), args.load, output_filename=args.output)
else:
logger.auto_set_dir(name=args.exp_name)
GANTrainer(
input=QueueInput(get_data(args.data)),
model=Model()).train_with_defaults(
callbacks=[
ModelSaver(),
],
steps_per_epoch=args.steps_per_epoch,
max_epoch=args.max_epoch,
session_init=SaverRestore(args.load) if args.load else None
)
