def run(args, kwargs):
args.model_signature = str(datetime.datetime.now())[0:19]
model_name = args.dataset_name + '_' + args.model_name + '_' + args.prior + '(K_' + str(args.number_components) + ')' + '_wu(' + str(args.warmup) + ')' + '_z1_' + str(args.z1_size) + '_z2_' + str(args.z2_size)
# DIRECTORY FOR SAVING
snapshots_path = 'snapshots/'
dir = snapshots_path + args.model_signature + '_' + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
# LOAD DATA=========================================================================================================
print('load data')
# loading data
train_loader, val_loader, test_loader, args = load_dataset(args, **kwargs)
# CREATE MODEL======================================================================================================
print('create model')
# importing model
if args.model_name == 'vae':
from models.VAE import VAE
elif args.model_name == 'hvae_2level':
from models.HVAE_2level import VAE
elif args.model_name == 'convhvae_2level':
from models.convHVAE_2level import VAE
elif args.model_name == 'pixelhvae_2level':
from models.PixelHVAE_2level import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(args)
if args.cuda:
model.cuda()
optimizer = AdamNormGrad(model.parameters(), lr=args.lr)
# optimizer = optim.Adam(model.parameters(), lr=args.lr)
# ======================================================================================================================
print(args)
with open('vae_experiment_log.txt', 'a') as f:
print(args, file=f)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(args, train_loader, val_loader, test_loader, model, optimizer, dir, model_name = args.model_name)
# ======================================================================================================================
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
with open('vae_experiment_log.txt', 'a') as f:
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n', file=f)
def run(args, kwargs):
args.model_signature = str(datetime.datetime.now())[0:19]
model_name = args.dataset_name + '_' + args.model_name + '_' + args.prior + '(K_' + str(args.number_components) + ')' + '_wu(' + str(args.warmup) + ')' + '_z1_' + str(args.z1_size) + '_z2_' + str(args.z2_size)
# DIRECTORY FOR SAVING
snapshots_path = 'snapshots/'
dir = snapshots_path + args.model_signature + '_' + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
# LOAD DATA=========================================================================================================
print('load data')
# loading data
train_loader, val_loader, test_loader, args = load_dataset(args, **kwargs)
# CREATE MODEL======================================================================================================
print('create model')
# importing model
if args.model_name == 'vae':
from models.VAE import VAE
elif args.model_name == 'hvae_2level':
from models.HVAE_2level import VAE
elif args.model_name == 'convhvae_2level':
from models.convHVAE_2level import VAE
elif args.model_name == 'pixelhvae_2level':
from models.PixelHVAE_2level import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(args)
if args.cuda:
model.cuda()
optimizer = AdamNormGrad(model.parameters(), lr=args.lr)
# ======================================================================================================================
print(args)
with open('vae_experiment_log.txt', 'a') as f:
print(args, file=f)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(args, train_loader, val_loader, test_loader, model, optimizer, dir, model_name = args.model_name)
# ======================================================================================================================
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
with open('vae_experiment_log.txt', 'a') as f:
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n', file=f)
def run(train_loader, val_loader, test_loader, model_name, warmup, z1_size,
z2_size, input_size, dataset_name, lr):
if torch.cuda.is_available():
cuda = True
torch.cuda.manual_seed(1)
# DIRECTORY FOR SAVING
snapshots_path = 'snapshots/'
dir = snapshots_path + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
print('create model')
# importing model
if model_name == 'vae':
from models.generative.autoencoders.VAE import VAE
elif model_name == 'vae_HF':
from models.generative.autoencoders.VAE_HF import VAE
elif model_name == 'vae_ccLinIAF':
from models.generative.autoencoders.VAE_ccLinIAF import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(input_size, z1_size, number_of_flows=3)
if cuda:
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=lr)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(train_loader,
val_loader,
test_loader,
model,
optimizer,
dir,
model_name=model_name)
# ======================================================================================================================
print(
'-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-'
)
with open('vae_experiment_log.txt', 'a') as f:
print(
'-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n',
file=f)
def run(args, kwargs):
args.model_signature = str(datetime.datetime.now())[0:19]
model_name = args.dataset_name + '_' + args.model_name + '_' + args.prior + '(M_' + str(
args.M) + ')' + '(F_' + str(args.F) + ')' + '_wu(' + str(
args.warmup) + ')' + '_z1_' + str(args.z1_size) + '_hidden_' + str(
args.number_hidden) + '_ksi_' + str(args.ksi)
if args.FI is True:
model_name += '_FI'
else:
args.F = 0
if args.MI is True:
model_name += '_MI'
else:
args.M = 0
# DIRECTORY FOR SAVING
#snapshots_path = 'snapshots/'
snapshots_path = args.snapshot_dir
if not os.path.exists(snapshots_path):
os.makedirs(snapshots_path)
dir = snapshots_path + args.model_signature + '_' + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
# LOAD DATA=========================================================================================================
print('load data')
# loading data
train_loader, val_loader, test_loader, args = load_dataset(args, **kwargs)
# CREATE MODEL======================================================================================================
print('create model')
if args.dataset_name == "celeba":
args.model_name = "celebavae"
# importing model
if args.model_name == 'vae':
from models.VAE import VAE
elif args.model_name == 'hvae_2level':
from models.HVAE_2level import VAE
elif args.model_name == 'convhvae_2level':
from models.convHVAE_2level import VAE
elif args.model_name == 'convvae':
from models.convVAE import VAE
elif args.model_name == 'pixelhvae_2level':
from models.PixelHVAE_2level import VAE
elif args.model_name == 'pixelvae':
from models.pixelVAE import VAE
elif args.model_name == 'iaf_vae':
from models.VAE_ccLinIAF import VAE
elif args.model_name == 'rev_vae':
from models.REV_VAE import VAE
elif args.model_name == 'rev_pixelvae':
from models.REV_pixelVAE import VAE
elif args.model_name == 'celebavae':
from models.CelebaVAE import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(args)
if args.cuda:
model.cuda()
optimizer = AdamNormGrad(model.parameters(), lr=args.lr)
# ======================================================================================================================
print(args)
with open(dir + 'vae_experiment_log.txt', 'a') as f:
print(args, file=f)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(args,
train_loader,
val_loader,
test_loader,
model,
optimizer,
dir,
model_name=args.model_name)
# ======================================================================================================================
print(
'-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-'
)
with open(dir + '/vae_experiment_log.txt', 'a') as f:
print(
'-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n',
file=f)
def run():
model_name = args.model_name
if model_name == 'vae_HF':
args.number_combination = 0
elif model_name == 'vae_ccLinIAF':
args.number_of_flows = 1
if args.model_name == 'vae_HF':
model_name = model_name + '(T_' + str(args.number_of_flows) + ')'
elif args.model_name == 'vae_ccLinIAF':
model_name = model_name + '(K_' + str(args.number_combination) + ')'
model_name = model_name + '_wu(' + str(args.warmup) + ')' + '_z1_' + str(args.z1_size)
if args.z2_size > 0:
model_name = model_name + '_z2_' + str(args.z2_size)
print(args)
with open('vae_experiment_log.txt', 'a') as f:
print(args, file=f)
# DIRECTORY FOR SAVING
snapshots_path = 'snapshots/'
dir = snapshots_path + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
# LOAD DATA=========================================================================================================
print('load data')
if args.dataset_name == 'dynamic_mnist':
args.dynamic_binarization = True
else:
args.dynamic_binarization = False
# loading data
train_loader, val_loader, test_loader = load_dataset(args)
# CREATE MODEL======================================================================================================
print('create model')
# importing model
if args.model_name == 'vae':
from models.VAE import VAE
elif args.model_name == 'vae_HF':
from models.VAE_HF import VAE
elif args.model_name == 'vae_ccLinIAF':
from models.VAE_ccLinIAF import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(args)
if args.cuda:
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(args, train_loader, val_loader, test_loader, model, optimizer, dir, model_name = args.model_name)
# ======================================================================================================================
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
with open('vae_experiment_log.txt', 'a') as f:
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n', file=f)