def train_one_epoch(train_loader, model, loss_fn, optimizer):
losses = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
for ref_cloud, src_cloud, gtR, gtt in tqdm(train_loader):
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
optimizer.zero_grad()
R, t, pred_ref_clouds = model(
src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
loss = compute_loss(ref_cloud, pred_ref_clouds, loss_fn)
loss.backward()
optimizer.step()
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
losses.append(loss.item())
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
results = {
'loss': np.mean(losses),
'r_mse': r_mse,
'r_mae': r_mae,
't_mse': t_mse,
't_mae': t_mae,
'r_isotropic': r_isotropic,
't_isotropic': t_isotropic
}
return results
def test_one_epoch(test_loader, model, loss_fn):
model.eval()
losses = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
with torch.no_grad():
for ref_cloud, src_cloud, gtR, gtt in tqdm(test_loader):
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
R, t, pred_ref_clouds = model(
src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
loss = compute_loss(ref_cloud, pred_ref_clouds, loss_fn)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
losses.append(loss.item())
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
model.train()
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
results = {
'loss': np.mean(losses),
'r_mse': r_mse,
'r_mae': r_mae,
't_mse': t_mse,
't_mae': t_mae,
'r_isotropic': r_isotropic,
't_isotropic': t_isotropic
}
return results
def evaluate_benchmark_icp(args, test_loader):
in_dim = 6 if args.normal else 3
model = IterativeBenchmark(in_dim=in_dim, niters=args.niters, gn=args.gn)
if args.cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(
torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
with torch.no_grad():
for i, (ref_cloud, src_cloud, gtR,
gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
tic = time.time()
R1, t1, pred_ref_cloud = model(
src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud_tmp = torch.squeeze(pred_ref_cloud[-1]).cpu().numpy()
R2, t2, pred_ref_cloud = icp(npy2pcd(src_cloud_tmp),
npy2pcd(ref_cloud))
R2, t2 = torch.from_numpy(R2)[None, ...].to(R1), \
torch.from_numpy(t2)[None, ...].to(R1)
R, t = R2 @ R1, torch.squeeze(R2 @ t1[:, :, None], dim=-1) + t2
toc = time.time()
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = pred_ref_cloud
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
def evaluate_benchmark(args, test_loader):
model = IterativeBenchmark(in_dim=args.in_dim,
niters=args.niters,
gn=args.gn)
if args.cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
with torch.no_grad():
for i, (ref_cloud, src_cloud, gtR, gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
tic = time.time()
R, t, pred_ref_cloud = model(src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
toc = time.time()
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
pred_ref_cloud = torch.squeeze(pred_ref_cloud[-1]).cpu().numpy()
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = npy2pcd(pred_ref_cloud, 2)
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
def evaluate_fgr(args, test_loader):
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
for i, (ref_cloud, src_cloud, gtR, gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
ref_points = torch.squeeze(ref_cloud).cpu().numpy()[:, :3]
src_points = torch.squeeze(src_cloud).cpu().numpy()[:, :3]
ref_normals = torch.squeeze(ref_cloud).cpu().numpy()[:, 3:]
src_normals = torch.squeeze(src_cloud).cpu().numpy()[:, 3:]
tic = time.time()
R, t, pred_ref_cloud = fgr(source=npy2pcd(src_points),
target=npy2pcd(ref_points),
src_normals=src_normals,
tgt_normals=ref_normals)
toc = time.time()
R = torch.from_numpy(np.expand_dims(R, 0)).to(gtR)
t = torch.from_numpy(np.expand_dims(t, 0)).to(gtt)
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
print(cur_t_error.item(), cur_R_error.item(),
cur_degree_error.item())
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = pred_ref_cloud
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
def evaluate_icp(args, test_loader):
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
for i, (ref_cloud, src_cloud, gtR, gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
tic = time.time()
R, t, pred_ref_cloud = icp(npy2pcd(src_cloud), npy2pcd(ref_cloud))
toc = time.time()
R = torch.from_numpy(np.expand_dims(R, 0)).to(gtR)
t = torch.from_numpy(np.expand_dims(t, 0)).to(gtt)
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = pred_ref_cloud
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
