print(t, ':', t_var.shape)
elif isinstance(t_var, (list, dict, tuple, str)):
print(t, ':', len(t_var))
if value:
print(t_var)
else:
pass
min_depth = 3
max_depth = stgs.VAE_HPARAMS['max_depth'] # maximum network depth
print(f'Using maximum sequence length of {stgs.VAE_HPARAMS["max_len"]}.')
torch.cuda.empty_cache()
vae = NA_VAE(stgs.VAE_HPARAMS)
vae = vae.float()
vae = vae.cuda()
# vae.load_state_dict(torch.load(f'{checkpoint_path}/weights.pt'))
torch.cuda.empty_cache()
version = datetime.strftime(datetime.fromtimestamp(seed),
'%Y-%m-%d..%H.%M.%S')
logger = TensorBoardLogger(checkpoint_path, version=version)
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
save_top_k=1,
verbose=True,
monitor='loss',
mode='min')
early_stop = EarlyStopping(
monitor='loss',
patience=stgs.VAE_HPARAMS['early_stop_patience'],
verbose=True,
if isinstance(t_var, (torch.Tensor, np.ndarray)):
print(t, ':', t_var.shape)
elif isinstance(t_var, (list, dict, tuple, str)):
print(t, ':', len(t_var))
if value:
print(t_var)
else:
pass
min_depth = 3
max_depth = stgs.VAE_HPARAMS['max_depth'] # maximum network depth
print(f'Using maximum sequence length of {stgs.VAE_HPARAMS["max_len"]}.')
torch.cuda.empty_cache()
vae = NA_VAE(stgs.VAE_HPARAMS)
vae.cuda()
vae = vae.float()
# vae.load_state_dict(torch.load(f'{checkpoint_path}/weights.pt'))
torch.cuda.empty_cache()
version = datetime.strftime(datetime.fromtimestamp(seed), '%Y-%m-%d..%H.%M.%S')
logger = TensorBoardLogger(checkpoint_path, version=version)
checkpoint = ModelCheckpoint(
filepath = checkpoint_path,
save_top_k=1,
verbose = True,
monitor = 'loss',
mode = 'min')
early_stop = EarlyStopping(
monitor = 'loss',
patience = stgs.VAE_HPARAMS['early_stop_patience'],
