def empirical_estimate(points, num_neighbors):
ps, batch = points.permute(1, 0).cat_tensors()
N = ps.shape[0]
val = knn(ps.contiguous(),
ps.contiguous(),
batch_x=batch,
batch_y=batch,
k=num_neighbors)
A = ps[val[1, :]].reshape(N, num_neighbors, 3)
A = A - A.mean(dim=1, keepdim=True)
Asqr = A.permute(0, 2, 1).bmm(A)
sigma, _ = Asqr.cpu().symeig()
w = (sigma[:, 2] * sigma[:, 1]).sqrt()
val = val[1, :].reshape(N, num_neighbors)
w, _ = torch.median(w[val].to(ps.device), dim=1, keepdim=True)
weights = TensorListList()
bi = 0
for point_list in points:
ww = TensorList()
for p in point_list:
ww.append(w[batch == bi])
bi = bi + 1
weights.append(ww)
return weights
def GARPointList(t1_list, t2_list, R1_list, R2_list, V, c, alpha):
err = TensorListList()
for t1, t2, R1, R2, Vb in zip(t1_list, t2_list, R1_list, R2_list, V):
M = len(t1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
Rtest = R1[ind1].permute(1, 0) @ R1[ind2]
# estimated relative translation from ind2 to ind1
test = R1[ind1].permute(1, 0) @ t1[ind2] - R1[ind1].permute(
1, 0) @ t1[ind1]
Rgt = R2[ind1].permute(1, 0) @ R2[ind2]
# ground truth translation from ind2 to ind1
tgt = R2[ind1].permute(1, 0) @ t2[ind2] - R2[ind1].permute(
1, 0) @ t2[ind1]
diff = Rtest @ Vb[ind2] + test - (Rgt @ Vb[ind2] + tgt)
err_i = (diff * diff).sum(dim=-2).sqrt()
err1.append(lossfun(err_i, alpha=alpha, scale=c).mean())
err.append(err1)
return err
def initialize_mu(self, K, features):
X = TensorList()
for TV in features:
Xi = np.random.randn(TV[0].shape[0], K).astype(np.float32)
Xi = torch.from_numpy(Xi).to(TV[0].device)
Xi = Xi / torch.norm(Xi, dim=0, keepdim=True)
X.append(Xi.permute(1, 0))
return X
def L2sqrList(R1_list, R2_list):
err = TensorListList()
for R1, R2 in zip(R1_list, R2_list):
M = len(R1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
Rdiff = R1[ind1].permute(1, 0) @ R1[ind2] - R2[ind1].permute(
1, 0) @ R2[ind2]
err1.append(
(Rdiff * Rdiff).sum(dim=-1).sum(dim=-1).unsqueeze(0))
err.append(err1)
return err
def forward(self, x):
coords = x['coordinates'].clone()
t_tot = time.time()
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
pcd_down, f = self.preprocess_point_cloud(
coords_s, self.params.voxel_size)
features_L.append(f)
coords_ds_L.append(pcd_down)
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
x = dict()
x['features'] = self.cluster_features(
features_LL,
self.params.feature_distr_parameters.num_feature_clusters)
x['att'] = att_LL
x['coordinates_ds'] = coords_ds_LL.to(self.params.device)
x['indices'] = inds_LL
out = self.registration(x)
tot_time = time.time() - t_tot
print("tot time: %.1f ms" % (tot_time * 1000))
out["time"] = tot_time
out["features"] = features_LL
out["indices"] = inds_LL
out["coordinates_ds"] = coords_ds_LL
return out
def L2sqrTransList(t1_list, t2_list, R1_list, R2_list):
err = TensorListList()
for t1, t2, R1, R2 in zip(t1_list, t2_list, R1_list, R2_list):
M = len(t1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
# estimated relative translation from ind2 to ind1
test = R1[ind1].permute(1, 0) @ t1[ind2] - R1[ind1].permute(
1, 0) @ t1[ind1]
# ground truth translation from ind2 to ind1
tgt = R2[ind1].permute(1, 0) @ t2[ind2] - R2[ind1].permute(
1, 0) @ t2[ind1]
diff = test - tgt
err1.append((diff * diff).sum(dim=-2))
err.append(err1)
return err
def cluster_features(self, features, num_clusters):
feature_labels_LL = TensorListList()
for f in features:
feature_labels_L = TensorList()
fcat = torch.cat([fi for fi in f])
fcat = fcat.to("cpu").numpy()
kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(fcat)
labels = torch.from_numpy(kmeans.labels_)
onehot = torch.nn.functional.one_hot(
labels.long(), num_clusters).to(self.params.device)
cnt = 0
for fi in f:
feature_labels_L.append(onehot[cnt:cnt + fi.shape[0]])
cnt += fi.shape[0]
feature_labels_LL.append(feature_labels_L.permute(1, 0).float())
return feature_labels_LL
def initialize(self, K, features):
init_method = self.params.get("init_method", "dirichlet")
if init_method == "uniform":
init_distr_list = []
for i in range(len(features)):
init_distr_list.append(
torch.ones(K, self.params.num_feature_clusters).float() /
self.params.num_feature_clusters)
elif init_method == "dirichlet":
init_distr_list = TensorList()
relative_distr = features.sum(dim=1).sum_list()
relative_distr = relative_distr / relative_distr.sum()
for r in relative_distr:
dir = torch.distributions.dirichlet.Dirichlet(r)
init_distr_list.append(dir.sample((K, )))
else:
init_distr_list = []
for i in range(len(features)):
init_distr_list.append(
torch.ones(K, self.params.num_feature_clusters).float() /
self.params.num_feature_clusters)
self.distr = TensorListList(init_distr_list, repeat=self.repeat_list)
def extract_features(self, x):
coords = x['coordinates']
sinput, inds_list = self.create_sparse_tensors(coords)
features_dict = self.feature_extractor(sinput)
features = features_dict["features"]
if "attention" in features_dict.keys():
att = features_dict["attention"]
batch_indices = list(features.coords_man.get_batch_indices())
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
mask = features.C[:, 0] == batch_indices[ind_cnt]
# hacky way of finding batch channel dimension
if mask.int().sum() != inds_list[ind_cnt].shape[0]:
mask = features.C[:, -1] == batch_indices[ind_cnt]
f = features.F[mask]
assert f.shape[0] == inds_list[ind_cnt].shape[0]
if "attention" in features_dict.keys():
a = att.F[mask]
assert a.shape[0] == inds_list[ind_cnt].shape[0]
att_L.append(a)
features_L.append(f.permute(1, 0))
coords_ds_L.append(coords_s[:, inds_list[ind_cnt]])
inds_L.append(inds_list[ind_cnt])
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
out = dict()
out['features'] = features_LL
out['att'] = att_LL
out['coordinates_ds'] = coords_ds_LL.to(self.params.device)
out['indices'] = inds_LL
out['coordinates'] = x['coordinates']
return out
def register_point_sets(self, x):
Vs = x["coordinates_ds"]
features = x["features"]
features_w = x["att"]
repeat_list = [len(V) for V in Vs]
init_R, init_t = get_init_transformation_list(
Vs, self.params.get("mean_init", True))
TVs = init_R @ Vs + init_t
X = TensorList()
Q = TensorList()
mu = TensorList()
for TV, Fs in zip(TVs, features):
if self.params.cluster_init == "box":
Xi = get_randn_box_cluster_means_list(TV, self.params.K)
else:
Xi = get_randn_sphere_cluster_means_list(
TV, self.params.K,
self.params.get("cluster_mean_scale", 1.0))
Q.append(
get_scaled_cluster_precisions_list(
TV, Xi, self.params.cluster_precision_scale))
X.append(Xi.T)
feature_distr = feature_models.MultinomialModel(
self.params.feature_distr_parameters,
self.params.K,
features,
repeat_list=repeat_list)
feature_distr.to(self.params.device)
X = TensorListList(X, repeat=repeat_list)
self.betas = get_default_beta(Q, self.params.gamma)
Vs = Vs
TVs = TVs
# Compute the observation weights
if self.params.use_dare_weighting:
observation_weights = empirical_estimate(Vs, self.params.ow_args)
ow_reg_factor = 8.0
ow_mean = observation_weights.mean(dim=0, keepdim=True)
for idx in range(len(observation_weights)):
for idxx in range(len(observation_weights[idx])):
observation_weights[idx][idxx][observation_weights[idx][idxx] > ow_reg_factor * ow_mean[idx][idxx]] \
= ow_reg_factor * ow_mean[idx][idxx]
else:
observation_weights = 1.0
ds = TVs.permute(1, 0).sqe(X).permute(1, 0)
if self.params.debug:
self.visdom.register(
dict(pcds=Vs[0].cpu(), X=X[0][0].cpu(), c=None),
'point_clouds', 2, 'init')
time.sleep(1)
Rs = init_R.to(self.params.device)
ts = init_t.to(self.params.device)
self.betas = TensorListList(self.betas, repeat=repeat_list)
QL = TensorListList(Q, repeat=repeat_list)
Riter = TensorListList()
titer = TensorListList()
TVs_iter = TensorListList()
for i in range(self.params.num_iters):
Qt = QL.permute(1, 0)
ap = (-0.5 * ds * QL).exp() * QL.pow(1.5)
if i < 1000:
pyz_feature = feature_distr.posteriors(features)
else:
pyz_feature = 1.0
a = ap * pyz_feature
ac_den = a.sum(dim=-1, keepdim=True) + self.betas
a = a / ac_den # normalize row-wise
a = a * observation_weights
L = a.sum(dim=-2, keepdim=True).permute(1, 0)
W = (Vs @ a) * QL
b = L * Qt # weights, b
mW = W.sum(dim=-1, keepdim=True)
mX = (b.permute(1, 0) @ X).permute(1, 0)
z = L.permute(1, 0) @ Qt
P = (W @ X).permute(1, 0) - mX @ mW.permute(1, 0) / z
# Compute R and t
svd_list_list = P.cpu().svd()
Rs = TensorListList()
for svd_list in svd_list_list:
Rs_list = TensorList()
for svd in svd_list:
uu, vv = svd.U, svd.V
vvt = vv.permute(1, 0)
detuvt = uu @ vvt
detuvt = detuvt.det()
S = torch.ones(1, 3)
S[:, -1] = detuvt
Rs_list.append((uu * S) @ vvt)
Rs.append(Rs_list)
Rs = Rs.to(self.params.device)
Riter.append(Rs)
ts = (mX - Rs @ mW) / z
titer.append(ts)
TVs = Rs @ Vs + ts
TVs_iter.append(TVs.clone())
if self.params.debug:
self.visdom.register(
dict(pcds=TVs[0].cpu(), X=X[0][0].cpu(), c=None),
'point_clouds', 2, 'registration-iter')
time.sleep(0.2)
# Update X
den = L.sum_list()
if self.params.fix_cluster_pos_iter < i:
X = (TVs @ a).permute(1, 0)
X = TensorListList(X.sum_list() / den, repeat_list)
# Update Q
ds = TVs.permute(1, 0).sqe(X).permute(1, 0)
wn = (a * ds).sum(dim=-2, keepdim=True).sum_list()
Q = (3 * den /
(wn.permute(1, 0) + 3 * den * self.params.epsilon)).permute(
1, 0)
QL = TensorListList(Q, repeat=repeat_list)
feature_distr.maximize(ap=ap,
ow=observation_weights,
y=features,
den=ac_den)
out = dict(Rs=Rs,
ts=ts,
X=X,
Riter=Riter[:-1],
titer=titer[:-1],
Vs=TVs,
Vs_iter=TVs_iter[:-1],
ow=observation_weights)
return out
def forward(self, x):
coords = x['coordinates'].clone()
t_tot = time.time()
sinput, inds_list = self.create_sparse_tensors(coords)
features_dict = self.feature_extractor(sinput)
features = features_dict["features"]
if "attention" in features_dict.keys():
att = features_dict["attention"]
if torch.isnan(features.feats).any():
print("nans in features!")
batch_indices = list(features.coords_man.get_batch_indices())
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
mask = features.C[:, 0] == batch_indices[ind_cnt]
if mask.int().sum() != inds_list[ind_cnt].shape[0]:
mask = features.C[:, -1] == batch_indices[ind_cnt]
f = features.F[mask]
assert f.shape[0] == inds_list[ind_cnt].shape[0]
if "attention" in features_dict.keys():
a = att.F[mask]
assert a.shape[0] == inds_list[ind_cnt].shape[0]
att_L.append(a)
features_L.append(f)
coords_ds_L.append(coords_s[:, inds_list[ind_cnt]])
inds_L.append(inds_list[ind_cnt])
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
x = dict()
x['features'] = self.cluster_features(
features_LL,
self.params.feature_distr_parameters.num_feature_clusters)
x['att'] = att_LL
x['coordinates_ds'] = coords_ds_LL.to(self.params.device)
x['indices'] = inds_LL
out = self.registration(x)
tot_time = time.time() - t_tot
print("tot time: %.1f ms" % (tot_time * 1000))
out["time"] = tot_time
out["features"] = features_LL
out["indices"] = inds_LL
out["coordinates_ds"] = coords_ds_LL
return out
def forward(self, x_in):
coords = x_in['coordinates'].clone()
t_tot = time.time()
sinput, inds_list = self.create_sparse_tensors(coords)
resample_time = time.time() - t_tot
print("resample time: %.1f ms" % ((resample_time) * 1000))
if not self.params.feature_distr_parameters.model=='none':
features_dict = self.feature_extractor(sinput)
features = features_dict["features"]
if "attention" in features_dict.keys():
att = features_dict["attention"]
extract_time=time.time()-t_tot
print("extract time: %.1f ms" % ((extract_time) * 1000))
batch_indices = list(features.coords_man.get_batch_indices())
else:
features_dict=None
extract_time=0
time_preprocess = time.time()
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
if not features_dict is None:
mask = features.C[:, 0] == batch_indices[ind_cnt]
# hacky way of finding batch channel dimension
if mask.int().sum() != inds_list[ind_cnt].shape[0]:
mask = features.C[:, -1] == batch_indices[ind_cnt]
f = features.F[mask]
assert f.shape[0] == inds_list[ind_cnt].shape[0]
if "attention" in features_dict.keys():
a = att.F[mask]
assert a.shape[0] == inds_list[ind_cnt].shape[0]
att_L.append(a)
features_L.append(f.permute(1, 0))
coords_ds_L.append(coords_s[:, inds_list[ind_cnt]])
inds_L.append(inds_list[ind_cnt])
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
x = dict()
x['features'] = features_LL
x['att'] = att_LL
x['coordinates_ds'] = coords_ds_LL.to(self.params.device)
x['indices'] = inds_LL
x['coordinates'] = x_in['coordinates']
print("preprocess time: %.1f ms" % ((time.time()-time_preprocess) * 1000))
reg_time=time.time()
out = self.registration(x)
reg_time2 = time.time() - reg_time
print("reg time: %.1f ms" % ((time.time() - reg_time) * 1000))
tot_time = time.time() - t_tot
print("tot time: %.1f ms" % (tot_time * 1000))
out["time"] = tot_time
out["reg_time"] = reg_time2
out["extract_time"] =extract_time
out["resample_time"] = resample_time
out["coordinates_ds"] = coords_ds_LL
return out
