nd_to_sample, ed_to_sample)
fake_imgs_tensor = combine_images_maps(gen_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
# Save images
save_image(real_imgs_tensor, "./exps/{}/{}_real.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
save_image(fake_imgs_tensor, "./exps/{}/{}_fake.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
return
# Configure data loader
rooms_path = '/home/nelson/Workspace/autodesk/'
fp_dataset_train = FloorplanGraphDataset(rooms_path,
transforms.Normalize(mean=[0.5],
std=[0.5]),
target_set=opt.target_set)
fp_loader = torch.utils.data.DataLoader(fp_dataset_train,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
collate_fn=floorplan_collate_fn)
fp_dataset_test = FloorplanGraphDataset(rooms_path,
transforms.Normalize(mean=[0.5],
std=[0.5]),
target_set=opt.target_set,
split='eval')
fp_loader_test = torch.utils.data.DataLoader(fp_dataset_test,
batch_size=8,
shuffle=True,
# Initialize generator and discriminator
generator = Generator()
generator.load_state_dict(torch.load(checkpoint), strict=False)
generator = generator.eval()
# Initialize variables
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
rooms_path = '../'
# 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,
num_workers=1)
# Optimizers
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# Generate state
def gen_state(curr_fixed_nodes_state, prev_fixed_nodes_state, sample,
initial_state):
# Create output dir
os.makedirs(opt.out, exist_ok=True)
# Initialize generator and discriminator
model = Generator()
model.load_state_dict(torch.load(opt.checkpoint), strict=True)
model = model.eval()
# Initialize variables
if torch.cuda.is_available():
model.cuda()
# initialize dataset iterator
fp_dataset_test = FloorplanGraphDataset(opt.data_path,
transforms.Normalize(mean=[0.5],
std=[0.5]),
split='test')
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 torch.cuda.is_available(
) else torch.FloatTensor
# run inference
def _infer(graph, model, prev_state=None):
# configure input to the network
z, given_masks_in, given_nds, given_eds = _init_input(graph, prev_state)