114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
from torch.distributions import Normal
|
|
|
|
import time
|
|
import os
|
|
|
|
from argparse import Namespace
|
|
from argparse import ArgumentParser
|
|
from distutils.util import strtobool
|
|
|
|
from networks.auto_encoder import AutoEncoder, AutoEncoder_LO
|
|
from networks.variational_auto_encoder import VariationalAE, VAE_LO
|
|
from networks.adverserial_auto_encoder import AdversarialAE_LO, AdversarialAE
|
|
from networks.seperating_adversarial_auto_encoder import SeperatingAAE, SeparatingAAE_LO
|
|
from networks.modules import LightningModule
|
|
|
|
from pytorch_lightning import Trainer
|
|
from test_tube import Experiment
|
|
|
|
|
|
args = ArgumentParser()
|
|
args.add_argument('--step', default=5)
|
|
args.add_argument('--features', default=6)
|
|
args.add_argument('--size', default=9)
|
|
args.add_argument('--latent_dim', default=2)
|
|
args.add_argument('--model', default='AE_Model')
|
|
args.add_argument('--refresh', type=strtobool, default=False)
|
|
|
|
|
|
class AE_Model(AutoEncoder_LO, LightningModule):
|
|
|
|
def __init__(self, parameters):
|
|
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
|
self.size = parameters.size
|
|
self.latent_dim = parameters.latent_dim
|
|
self.features = parameters.features
|
|
self.step = parameters.step
|
|
super(AE_Model, self).__init__()
|
|
self.network = AutoEncoder(self.latent_dim, self.features)
|
|
|
|
|
|
class VAE_Model(VAE_LO, LightningModule):
|
|
|
|
def __init__(self, parameters):
|
|
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
|
self.size = parameters.size
|
|
self.latent_dim = parameters.latent_dim
|
|
self.features = parameters.features
|
|
self.step = parameters.step
|
|
super(VAE_Model, self).__init__()
|
|
self.network = VariationalAE(self.latent_dim, self.features)
|
|
|
|
|
|
class AAE_Model(AdversarialAE_LO, LightningModule):
|
|
|
|
def __init__(self, parameters: Namespace):
|
|
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
|
self.size = parameters.size
|
|
self.latent_dim = parameters.latent_dim
|
|
self.features = parameters.features
|
|
self.step = parameters.step
|
|
super(AAE_Model, self).__init__()
|
|
self.normal = Normal(0, 1)
|
|
self.network = AdversarialAE(self.latent_dim, self.features)
|
|
pass
|
|
|
|
|
|
class SAAE_Model(SeparatingAAE_LO, LightningModule):
|
|
|
|
def __init__(self, parameters: Namespace):
|
|
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
|
self.size = parameters.size
|
|
self.latent_dim = parameters.latent_dim
|
|
self.features = parameters.features
|
|
self.step = parameters.step
|
|
super(SAAE_Model, self).__init__()
|
|
self.normal = Normal(0, 1)
|
|
self.network = SeperatingAAE(self.latent_dim, self.features)
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
arguments = args.parse_args()
|
|
|
|
model = globals()[arguments.model](arguments)
|
|
|
|
# PyTorch summarywriter with a few bells and whistles
|
|
outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
|
|
os.makedirs(outpath, exist_ok=True)
|
|
exp = Experiment(save_dir=outpath)
|
|
exp.tag(tag_dict=arguments.__dict__)
|
|
|
|
from pytorch_lightning.callbacks import ModelCheckpoint
|
|
|
|
checkpoint_callback = ModelCheckpoint(
|
|
filepath=os.path.join(outpath, 'weights.ckpt'),
|
|
save_best_only=False,
|
|
verbose=True,
|
|
period=4
|
|
)
|
|
|
|
trainer = Trainer(experiment=exp,
|
|
max_nb_epochs=250,
|
|
gpus=[0],
|
|
add_log_row_interval=1000,
|
|
# checkpoint_callback=checkpoint_callback
|
|
)
|
|
|
|
trainer.fit(model)
|
|
trainer.save_checkpoint(os.path.join(outpath, 'weights.ckpt'))
|
|
|
|
# view tensorflow logs
|
|
print(f'View tensorboard logs by running\ntensorboard --logdir {outpath}')
|
|
print('and going to http://localhost:6006 on your browser')
|