options = Options()
opts = options.args
torch.manual_seed(opts.torch_seed)
device = torch.device('cuda:{}'.format(opts.gpu) if torch.cuda.is_available() else torch.device('cpu'))
print('device: {}'.format(device))
# initial mesh
mesh = Mesh(opts.initial_mesh, device=device, hold_history=True)
# input point cloud
input_xyz, input_normals = utils.read_pts(opts.input_pc)
# normalize point cloud based on initial mesh
input_xyz /= mesh.scale
input_xyz += mesh.translations[None, :]
input_xyz = torch.Tensor(input_xyz).type(options.dtype()).to(device)[None, :, :]
input_normals = torch.Tensor(input_normals).type(options.dtype()).to(device)[None, :, :]
part_mesh = PartMesh(mesh, num_parts=options.get_num_parts(len(mesh.faces)), bfs_depth=opts.overlap)
print(f'number of parts {part_mesh.n_submeshes}')
net, optimizer, rand_verts, scheduler = init_net(mesh, part_mesh, device, opts)
for i in range(opts.iterations):
num_samples = options.get_num_samples(i % opts.upsamp)
if opts.global_step:
optimizer.zero_grad()
start_time = time.time()
for part_i, est_verts in enumerate(net(rand_verts, part_mesh)):
if not opts.global_step:
optimizer.zero_grad()
part_mesh.update_verts(est_verts[0], part_i)
torch.manual_seed(opts.torch_seed)
device = torch.device('cuda:{}'.format(opts.gpu) if torch.cuda.is_available(
) else torch.device('cpu'))
print('device: {}'.format(device))
# initial mesh
mesh = Mesh(opts.initial_mesh, device=device, hold_history=True)
# input point cloud
input_xyz, input_normals = utils.read_pts(opts.input_pc)
# normalize point cloud based on initial mesh
input_xyz /= mesh.scale
input_xyz += mesh.translations[None, :]
input_xyz = torch.Tensor(input_xyz).type(
options.dtype()).to(device)[None, :, :]
input_normals = torch.Tensor(input_normals).type(
options.dtype()).to(device)[None, :, :]
# Split the mesh into parts
part_mesh = PartMesh(mesh,
num_parts=options.get_num_parts(len(mesh.faces)),
bfs_depth=opts.overlap)
print(f'number of parts {part_mesh.n_submeshes}')
# Initialize displacement network
net, optimizer, rand_verts, scheduler = init_net(mesh, part_mesh, device, opts)
# Create beamgap loss (how far is this from the surface?)
beamgap_loss = BeamGapLoss(device)
