# Dataset
# This is for DTU when we parallelise over images
# we do not want to treat different images from same object as
# different objects
cfg['data']['split_model_for_images'] = False
dataset = config.get_dataset(cfg, mode='test', return_idx=True)
# Model
model = config.get_model(cfg, device=device, len_dataset=len(dataset))
checkpoint_io = CheckpointIO(out_dir, model=model)
checkpoint_io.load(cfg['test']['model_file'])
# Generator
generator = config.get_generator(model, cfg, device=device)
torch.manual_seed(0)
# Loader
test_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=True)
# Statistics
time_dicts = []
vis_file_dict = {}
# Generate
model.eval()
batch_size=1,
shuffle=False,
repeat_count=1,
epoch=1)
# Loader
dataloader = dataset.loader()
model = config.get_model(cfg, dataset=dataset)
dummy_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-08)
checkpoint_io = CheckpointIO(model, dummy_optimizer, checkpoint_dir=out_dir)
checkpoint_io.load(cfg['test']['model_file'])
# Generator
generator = config.get_generator(model, cfg)
# Determine what to generate
generate_mesh = cfg["generation"]["generate_mesh"]
generate_pointcloud = cfg["generation"]["generate_pointcloud"]
if generate_mesh and not hasattr(generator, "generate_mesh"):
generate_mesh = False
print("Warning: generator does not support mesh generation.")
if generate_pointcloud and not hasattr(generator, "generate_pointcloud"):
generate_pointcloud = False
print("Warning: generator does not support pointcloud generation.")
# Statistics
time_dicts = []