dataset_files = sample(os.listdir(dataset_dir), 10000)
train_dataset = CelebaDataset(dataset_dir, dataset_files, WT=False)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=10,
shuffle=True)
sample_dataset = Subset(train_dataset, sample(range(len(train_dataset)),
8))
sample_loader = DataLoader(sample_dataset, batch_size=8, shuffle=False)
if args.device >= 0:
device = 'cuda:{}'.format(args.device)
else:
device = 'cpu'
print('Device: {}'.format(device))
filters = create_filters(device=device, wt_fn='haar')
wt_model = WT(wt=wt_haar, num_wt=args.num_wt)
wt_model.set_filters(filters)
wt_model = wt_model.to(device)
wt_model.set_device(device)
model = AE_Mask_64(z_dim=args.z_dim)
model.set_device(device)
model = model.to(device)
train_losses = []
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = None
if args.loss == 'l1':
criterion = torch.nn.L1Loss()
# Create training and sample dataset (to test out model and save images for)
dataset_dir = os.path.join(args.root_dir, 'data/celebaHQ512')
dataset_files = sample(os.listdir(dataset_dir), 10000)
train_dataset = CelebaDataset(dataset_dir, dataset_files, WT=False)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=10, shuffle=True)
sample_dataset = Subset(train_dataset, sample(range(len(train_dataset)), 8))
sample_loader = DataLoader(sample_dataset, batch_size=8, shuffle=False)
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
# Setting up WT & IWT filters
filters = create_filters(device=device)
# Create model, set filters for WT (calculating loss), and set device
wt_model = WTVAE_512_2(z_dim=args.z_dim, num_wt=args.num_iwt)
wt_model = wt_model.to(device)
wt_model.set_filters(filters)
wt_model.set_device(device)
train_losses = []
optimizer = optim.Adam(wt_model.parameters(), lr=args.lr)
# Create output directories
img_output_dir = os.path.join(args.root_dir, 'wtvae_results/image_samples/wtvae512_{}'.format(args.config))
model_dir = os.path.join(args.root_dir, 'wtvae_results/models/wtvae512_{}/'.format(args.config))
try:
WT=False)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=10,
shuffle=True)
sample_dataset = Subset(train_dataset, sample(range(len(train_dataset)),
8))
sample_loader = DataLoader(sample_dataset, batch_size=8, shuffle=False)
if torch.cuda.is_available() and args.device >= 0:
device = 'cuda:{}'.format(args.device)
else:
device = 'cpu'
# Setting up WT & IWT filters
filters = create_filters(device=device, wt_fn='bior2.2')
# Create model, set filters for WT (calculating loss), and set device
wt_model = WTVAE_64_1(z_dim=args.z_dim, num_wt=args.num_wt)
wt_model = wt_model.to(device)
wt_model.set_filters(filters)
wt_model.set_device(device)
train_losses = []
optimizer = optim.Adam(wt_model.parameters(), lr=args.lr)
# Create output and log directories
img_output_dir = os.path.join(
args.root_dir,
'wtvae_results/image_samples/wtvae64_{}'.format(args.config))
model_dir = os.path.join(