args = parser.parse_args()
try:
args = parser.parse_args()
except:
args = parser.parse_known_args()[0]
net = None
if args.model == 'ShapeNet32Vox':
net = model.ShapeNet32Vox()
elif args.model == 'ShapeNet128Vox':
net = model.ShapeNet128Vox()
elif args.model == 'ShapeNetPoints':
net = model.ShapeNetPoints()
elif args.model == 'SVR':
net = model.SVR()
# dataset = voxelized_data.VoxelizedDataset(
# args.mode, voxelized_pointcloud=args.pointcloud, pointcloud_samples=args.pc_samples, res=args.res,
# sample_distribution=args.sample_distribution, sample_sigmas=args.sample_sigmas,
# num_sample_points=100, batch_size=1, num_workers=0)
num_sample_points = 100
sample_distribution = np.array(args.sample_distribution)
sample_sigmas = np.array(args.sample_sigmas)
num_samples = np.rint(sample_distribution * num_sample_points).astype(np.uint32)
data_sample = load_data(args.datapath, args.res, args.pc_samples, num_samples, sample_sigmas,
voxelized_pointcloud=args.pointcloud)
# print('Size of inputs {}'.format(data_sample['inputs'].size()))
# print('Size of grid_coords {}'.format(data_sample['grid_coords'].size()))
# print('Size of occupancies {}'.format(data_sample['occupancies'].size()))
DeviceList, MainGPUID = ptUtils.setupGPUs(args.gpu)
print('[ INFO ]: Using {} GPUs with IDs {}'.format(len(DeviceList),
DeviceList))
Device = ptUtils.setDevice(MainGPUID)
if args.model == 'ShapeNet32Vox':
net = model.ShapeNet32Vox()
if args.model == 'ShapeNet128Vox':
net = model.ShapeNet128Vox()
if args.model == 'ShapeNetPoints':
net = model.ShapeNetPoints()
if args.model == 'SVR':
net = model.SVR(gpu_idx=MainGPUID)
train_dataset = voxelized_data.VoxelizedDataset(
'train',
voxelized_pointcloud=args.pointcloud,
pointcloud_samples=args.pc_samples,
data_path=args.input_dir_train,
res=args.res,
sample_distribution=args.sample_distribution,
sample_sigmas=args.sample_sigmas,
num_sample_points=50000,
batch_size=args.batch_size,
num_workers=30)
val_dataset = voxelized_data.VoxelizedDataset(
'val',