return
# Initialize generator and discriminator
generator = Generator()
generator.load_state_dict(torch.load(opt.checkpoint))
generator.eval()
# Initialize variables
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
# Configure data loader
rooms_path = '/local-scratch4/nnauata/autodesk/FloorplanDataset/'
fp_dataset = FloorplanGraphDataset(rooms_path, transforms.Normalize(mean=[0.5], std=[0.5]), \
target_set=opt.target_set, split='eval')
fp_loader = torch.utils.data.DataLoader(fp_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=0,
collate_fn=floorplan_collate_fn)
fp_iter = tqdm(fp_loader, total=len(fp_dataset) // opt.batch_size + 1)
# Optimizers
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# Generate samples
graphs = []
for i, batch in enumerate(fp_iter):
# Unpack batch
os.makedirs(opt.exp_folder, exist_ok=True)
# Initialize generator and discriminator
generator = Generator()
generator.load_state_dict(torch.load(checkpoint))
# Initialize variables
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
rooms_path = '/local-scratch4/nnauata/autodesk/FloorplanDataset/'
# Initialize dataset iterator
fp_dataset_test = FloorplanGraphDataset(rooms_path,
transforms.Normalize(mean=[0.5],
std=[0.5]),
target_set=target_set,
split=phase)
fp_loader = torch.utils.data.DataLoader(fp_dataset_test,
batch_size=opt.batch_size,
shuffle=False,
collate_fn=floorplan_collate_fn)
# Optimizers
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# ------------
# Vectorize
# ------------
globalIndex = 0
final_images = []
target_graph = [6]