ms = MeanShift()
dataset = Dataset(config.batch_size,
config.num_train,
config.num_val,
config.num_test,
normals=True,
primitives=True,
if_train_data=False,
prefix=userspace)
get_test_data = dataset.get_test(align_canonical=True,
anisotropic=False,
if_normal_noise=True)
loader = generator_iter(get_test_data, int(1e10))
get_test_data = iter(
DataLoader(
loader,
batch_size=1,
shuffle=False,
collate_fn=lambda x: x,
num_workers=0,
pin_memory=False,
))
os.makedirs(userspace +
"logs/results/{}/results/".format(config.pretrain_model_path),
exist_ok=True)
evaluation = Evaluation()
dataset = DataSetControlPointsPoisson(
config.dataset_path,
config.batch_size,
splits=split_dict,
size_v=config.grid_size,
size_u=config.grid_size)
get_train_data = dataset.load_train_data(
if_regular_points=True, align_canonical=align_canonical, anisotropic=anisotropic, if_augment=if_augment
)
get_val_data = dataset.load_val_data(
if_regular_points=True, align_canonical=align_canonical, anisotropic=anisotropic
)
loader = generator_iter(get_train_data, int(1e10))
get_train_data = iter(
DataLoader(
loader,
batch_size=1,
shuffle=False,
collate_fn=lambda x: x,
num_workers=0,
pin_memory=False,
)
)
loader = generator_iter(get_val_data, int(1e10))
get_val_data = iter(
DataLoader(
loader,