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)
train_dataset,
batch_size=train_args.batch_size,
shuffle=True,
num_workers=train_args.num_workers,
pin_memory=True,
)
net = VAE(
label="vgg",
image_size=train_args.image_size,
channel_num=3,
kernel_num=128,
z_size=train_args.z_dim,
)
if cuda:
net.cuda()
# run a test or a training process.
train_model(
net,
dataset="vgg",
epochs=train_args.epochs,
batch_size=train_args.batch_size,
sample_size=32,
lr=3e-05,
weight_decay=1e-06,
checkpoint_dir=train_args.ckpt_dir,
loss_log_interval=train_args.log_interval,
image_log_interval=500,
resume=False,
cuda=cuda,
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(args, kwargs):
# LOAD DATA=========================================================================================================
print('load data')
checkpoints_dir, results_dir = create_dirNames(args)
# 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
model = VAE(args)
elif args.model_name == 'conv_vae':
from models.conv_vae import VAE
model = VAE(args)
elif args.model_name == 'hvae_2level':
from models.HVAE_2level import VAE
model = VAE(args)
elif args.model_name == 'convhvae_2level':
from models.convHVAE_2level import VAE
model = VAE(args)
elif args.model_name == 'convhvae_2level-smim':
from models.convHVAE_2level import SymMIM as VAE
model = VAE(args)
elif args.model_name == 'MLP_wae':
from models.MLP_wae import WAE
model = WAE(args)
elif args.model_name == 'conv_wae':
from models.conv_wae import WAE
model = WAE(args)
else:
raise Exception('Wrong name of the model!')
#model = VAE(args)
if args.cuda:
model.cuda()
optimizer = AdamNormGrad(model.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
# ======================================================================================================================
print(args)
# ======================================================================================================================
print('perform experiment')
if args.trainflag:
if args.model_name == 'MLP_wae' or args.model_name == 'conv_wae' or args.model_name == 'Pixel_wae' or args.model_name == 'conv_wae_2level':
train_wae(args, train_loader, val_loader, model, optimizer,
checkpoints_dir, results_dir)
else:
train_vae(args, train_loader, val_loader, test_loader, model,
optimizer, checkpoints_dir, results_dir)
if args.testflag:
test(args, train_loader, test_loader, model, checkpoints_dir,
results_dir)
print('accuracy {}'.format(acc.item()))
if not os.path.exists('joint_models/'):
os.mkdir('joint_models/')
torch.save(
classifier.state_dict(), 'joint_models/joint_classifier_' +
arguments.dataset_name + 'accuracy_{}'.format(acc) + '.t')
pdb.set_trace()
#
### generator
model = VAE(arguments)
if arguments.cuda:
model = model.cuda()
if 0 & os.path.exists(model_path):
print('loading model...')
model.load_state_dict(torch.load(model_path))
model = model.cuda()
else:
print('training model...')
optimizer = AdamNormGrad(model.parameters(), lr=arguments.lr)
tr.experiment_vae(arguments, train_loader, val_loader, test_loader, model,
optimizer, dr, arguments.model_name)
results = ev.evaluate_vae(arguments, model, train_loader, test_loader, 0,
results_path, 'test')
pickle.dump(results, open(results_path + results_name + '.pk', 'wb'))
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)
