def forward(self, xyz, points):
"""
Input:
xyz: input points position data, [B, C, N]
points: input points data, [B, D, N]
Return:
new_xyz: sampled points position data, [B, C, S]
new_points_concat: sample points feature data, [B, D', S]
"""
device = xyz.device
B, C, N = xyz.shape
xyz_t = xyz.permute(0, 2, 1).contiguous() # [B, N, C]
if self.group_all == False:
fps_idx = pointutils.furthest_point_sample(
xyz_t, self.npoint) # [B, npoint]
new_xyz = pointutils.gather_operation(xyz,
fps_idx) # [B, C, npoint]
else:
new_xyz = xyz
new_points = self.queryandgroup(xyz_t,
new_xyz.transpose(2, 1).contiguous(),
points) # [B, D+C, npoint, nsample]
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
new_points = F.relu(bn(
conv(new_points))) # [B, channel, npoint, nsample]
new_points = torch.max(new_points, -1)[0] # [B, channel, nsample]
return new_xyz, new_points
def forward(self, xyz, features, npoint):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
B = Batch, N = Number of Points
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
C是特征维数
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
self.npoint = npoint # 聚合后的质心数量
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
# contiguous：view只能用在内存占用连续的数据块的tensor上
# 如果在view之前用了transpose, permute等
# 需要用contiguous()来返回一个contiguous copy
# (B, 3, N)
new_xyz = (
pointnet2_utils.gather_operation(
#xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
xyz_flipped,
torch.arange(self.npoint).repeat(xyz.size(0),
1).int().cuda()).transpose(
1, 2).contiguous()
) # 聚合后得到的centroids，B * npoint * 3
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features) # (B, C, npoint, nsample)
# (batch, channel, feature_num, in_group_num)
new_features = self.mlps[i](new_features)
# (B, mlp[-1], npoint, nsample)
# (batch, mlp最后一层输出维度, feature_num, in_group_num)
new_features = F.max_pool2d(new_features,
kernel_size=[
1, new_features.size(3)
]) # (B, mlp[-1], npoint, 1)
# (batch, mlp最后一层输出维度, feature_num, 1)
# 全局max_pool
new_features = new_features.squeeze(-1)
# (B, mlp[-1], npoint)
# (batch, mlp最后一层输出维度, feature_num)
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1)
def forward(self, xyz, features, npoint, score):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
self.npoint = npoint
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = (
pointnet2_utils.gather_operation(
#xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
xyz_flipped,
torch.arange(self.npoint).repeat(xyz.size(0),
1).int().cuda()).transpose(
1, 2).contiguous())
for i in range(len(self.groupers)):
new_features, score_id = self.groupers[i](
xyz, new_xyz, score, features) # (B, C, npoint, nsample)
#score_id = new_features[:,3,:,:].sum(dim = 2).argmax(dim = 1)
#B
#new_features_cpu = new_features.squeeze(0).detach().cpu().numpy()
#np.savetxt('vote4.txt',new_features_cpu[0:4,i,:])
idx = torch.arange(new_features.size(0))
new_features = new_features[idx, :, score_id, :]
#B*C*nsample
new_features = new_features.unsqueeze(2)
#B*C*1*nsample
new_xyz = new_xyz[idx, score_id, :]
#B*3
new_features = self.mlps[i](
new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(new_features,
kernel_size=[
1, new_features.size(3)
]) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1).squeeze(-1) # (B, mlp[-1])
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1)
def get_emd_loss(pred, gt, pcd_radius):
idx, _ = auction_match(pred, gt)
matched_out = pn2_utils.gather_operation(
gt.transpose(1, 2).contiguous(), idx)
matched_out = matched_out.transpose(1, 2).contiguous()
dist2 = (pred - matched_out)**2
dist2 = dist2.view(dist2.shape[0], -1) # <-- ???
dist2 = torch.mean(dist2, dim=1, keepdims=True) # B,
dist2 /= pcd_radius
return torch.mean(dist2)
def forward(self, xyz, feature=None):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, C, N) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = (
pointnet2_utils.gather_operation(
xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
)
.transpose(1, 2)
.contiguous()
if self.npoint is not None
else None
)
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, feature
) # (B, C, npoint, nsample)
B, _, npoint, nsample = new_features.shape
new_features = new_features.permute(0, 2, 3, 1) # B npoint nsample C
new_features = self.mlps[i](new_features) # B, npoint, nsample, C
new_features = new_features.permute(0, 3, 1, 2) # B, C, npoint, nsample
new_features = F.max_pool2d(
new_features, kernel_size=[1, new_features.size(3)]
) # (B, mlp[-1], npoint, 1)
new_features_list.append(new_features.reshape(B, 4, -1, npoint))
if self.npoint is not None:
out_npoint = self.npoint
else:
out_npoint = 1
new_features = torch.cat(new_features_list, dim=2).reshape(B, -1, out_npoint)
return new_xyz, new_features
def gather(p: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
"""Point cloud gathering by indices.
Args:
p: Reference point cloud of shape [batch_size, dim, num_point].
idx: Indices tensor of shape [batch_size, num_query].
Returns:
Point cloud tensor of shape [batch_size, dim, num_query].
"""
p = p.contiguous()
return _PU.gather_operation(p, idx)
def forward(self, xyz, features=None, return_critical_index=False):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
new_features_list = []
new_indices_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = (
pointnet2_utils.gather_operation(
xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
)
.transpose(1, 2)
.contiguous()
if self.npoint is not None
else None
)
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features
) # (B, C, npoint, nsample)
new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample)
new_features, new_indices = F.max_pool2d(
new_features, kernel_size=[1, new_features.size(3)], return_indices=True
) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_indices = new_indices.squeeze(-1)
new_features_list.append(new_features)
new_indices_list.append(new_indices)
if return_critical_index is False:
return new_xyz, torch.cat(new_features_list, dim=1)
else:
return new_xyz, torch.cat(new_features_list, dim=1), torch.cat(new_indices_list, dim=1)
def forward(self, xyz, points):
"""
Input:
xyz: input points position data, [B, C, N]
points: input points data, [B, D, N]
Return:
new_xyz: sampled points position data, [B, C, S]
new_points_concat: sample points feature data, [B, D', S]
"""
device = xyz.device
B, C, N = xyz.shape
xyz_t = xyz.permute(0, 2, 1).contiguous() # [B, N, C]
fps_idx = pointutils.furthest_point_sample(xyz_t,
self.npoint) # [B, npoint]
new_xyz = pointutils.gather_operation(xyz, fps_idx) # [B, 3, npoint]
new_xyz_t = new_xyz.permute(0, 2, 1).contiguous()
_, idx = pointutils.knn(self.nsample, new_xyz_t,
xyz_t) # [B, npoint, nsample]
neighbors = pointutils.grouping_operation(
xyz, idx) # [B, 3, npoint, nsample]
centers = new_xyz.view(B, -1, self.npoint, 1).repeat(
1, 1, 1, self.nsample) # [B, 3, npoint, nsample]
pos_diff = centers - neighbors # [B, 3, npoint, nsample]
distances = torch.norm(pos_diff, p=2, dim=1,
keepdim=True) # [B, 1, npoint, nsample]
h_xi_xj = torch.cat([distances, pos_diff, centers, neighbors],
dim=1) # [B, 1+3+3+3, npoint, nsample]
x = pointutils.grouping_operation(points,
idx) # [B, D, npoint, nsample]
x = torch.cat([neighbors, x], dim=1) # [B, D+3, npoint, nsample]
h_xi_xj = self.mapping_func2(
F.relu(self.bn_mapping(
self.mapping_func1(h_xi_xj)))) # [B, c_in, npoint, nsample]
if self.first_layer:
x = F.relu(self.bn_xyz_raising(
self.xyz_raising(x))) # [B, c_in, npoint, nsample]
x = F.relu(self.bn_rsconv(torch.mul(h_xi_xj,
x))) # (B, c_in, npoint, nsample)
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
x = F.relu(bn(conv(x))) # [B, c_out, npoint, nsample]
x = torch.max(x, -1)[0] # [B, c_out, npoint]
# x = F.relu(self.bn_channel_raising(self.cr_mapping(x))) # [B, c_out, npoint]
return new_xyz, x
def get_emd_loss(self, pred, gt, radius=1.0):
'''
pred and gt is B N 3
'''
idx, _ = auction_match(pred.contiguous(), gt.contiguous())
#gather operation has to be B 3 N
#print(gt.transpose(1,2).shape)
matched_out = pn2_utils.gather_operation(
gt.transpose(1, 2).contiguous(), idx)
matched_out = matched_out.transpose(1, 2).contiguous()
dist2 = (pred - matched_out)**2
dist2 = dist2.view(dist2.shape[0], -1) # <-- ???
dist2 = torch.mean(dist2, dim=1, keepdims=True) # B,
dist2 /= radius
return torch.mean(dist2)
def forward(self, xyz, features, npoint):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
self.npoint = npoint
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = (
pointnet2_utils.gather_operation(
#xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
xyz_flipped,
torch.arange(self.npoint).repeat(xyz.size(0),
1).int().cuda()).transpose(
1, 2).contiguous())
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features) # (B, C, npoint, nsample)
new_features = self.mlps[i](
new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(new_features,
kernel_size=[
1, new_features.size(3)
]) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1)
def forward(self, AA, features=None):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
AA : torch.Tensor
(B, N, 32 tensor of the azimuth, distance coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_AA : torch.Tensor
(B, npoint, 2) tensor of the new features' azimuth distance
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
new_features_list = []
AA_flipped = AA.transpose(1, 2).contiguous()
new_AA = (
pointnet2_utils.gather_operation(
AA_flipped, pointnet2_utils.furthest_point_sample(AA, self.npoint)
)
.transpose(1, 2)
.contiguous()
if self.npoint is not None
else None
)
for i in range(len(self.groupers)):
new_features = self.groupers[i](
AA, new_AA, features
) # (B, C, npoint, nsample)
new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(
new_features, kernel_size=[1, new_features.size(3)]
) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return new_AA, torch.cat(new_features_list, dim=1)
def forward(self,
xyz: torch.Tensor,
features: torch.Tensor = None) -> (torch.Tensor, torch.Tensor):
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
"""
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = pointnet2_utils.gather_operation(
xyz_flipped, pointnet2_utils.furthest_point_sample(
xyz, self.npoint)).transpose(
1, 2).contiguous() if self.npoint is not None else None
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features) # (B, C, npoint, nsample)
new_features = self.mlps[i](
new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(new_features,
kernel_size=[
1, new_features.size(3)
]) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1)
def get_uniform_loss(self,
pcd,
percentage=[0.004, 0.006, 0.008, 0.010, 0.012],
radius=1.0):
B, N, C = pcd.shape[0], pcd.shape[1], pcd.shape[2]
npoint = int(N * 0.05)
loss = 0
further_point_idx = pn2_utils.furthest_point_sample(
pcd.permute(0, 2, 1).contiguous(), npoint)
new_xyz = pn2_utils.gather_operation(
pcd.permute(0, 2, 1).contiguous(), further_point_idx) # B,C,N
for p in percentage:
nsample = int(N * p)
r = math.sqrt(p * radius)
disk_area = math.pi * (radius**2) / N
idx = pn2_utils.ball_query(r, nsample, pcd.contiguous(),
new_xyz.permute(
0, 2, 1).contiguous()) #b N nsample
expect_len = math.sqrt(disk_area)
grouped_pcd = pn2_utils.grouping_operation(
pcd.permute(0, 2, 1).contiguous(), idx) #B C N nsample
grouped_pcd = grouped_pcd.permute(0, 2, 3, 1) #B N nsample C
grouped_pcd = torch.cat(torch.unbind(grouped_pcd, dim=1),
dim=0) #B*N nsample C
dist, _ = self.knn_uniform(grouped_pcd, grouped_pcd)
#print(dist.shape)
uniform_dist = dist[:, :, 1:] #B*N nsample 1
uniform_dist = torch.abs(uniform_dist + 1e-8)
uniform_dist = torch.mean(uniform_dist, dim=1)
uniform_dist = (uniform_dist - expect_len)**2 / (expect_len + 1e-8)
mean_loss = torch.mean(uniform_dist)
mean_loss = mean_loss * math.pow(p * 100, 2)
loss += mean_loss
return loss / len(percentage)
def forward(self, xyz, features=None, curvature=None):
# type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, N, C) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
new_features_list = []
if self.npoint is not None:
# =============================================================================
# xyz_pairdistance = torch.topk(torch.norm(xyz,dim=2,keepdim=True),int(xyz.shape[1]*0.4),dim=1,largest=False)[1]
# # xyz_norm = torch.norm(xyz[:,:,0:3],dim=2,keepdim=True)
# # xyz_pairdistance = xyz_norm.lt(0.60).nonzero()[:,1].unsqueeze(0).unsqueeze(-1)
# xyz_inner = torch.gather(xyz,1,xyz_pairdistance.repeat(1,1,3))
# xyz_flipped = xyz_inner.transpose(1, 2).contiguous()
#
# curvature_inner = torch.gather(curvature.unsqueeze(-1),1,xyz_pairdistance)
# curvature_inner = curvature_inner.squeeze(-1)
# xyz_c = torch.stack((xyz_inner[:,:,0],xyz_inner[:,:,1],xyz_inner[:,:,2],curvature_inner.squeeze(-1)),dim=2)
# =============================================================================
xyz_norm = torch.norm(xyz[:, :, 0:3], dim=2, keepdim=True)
# print(xyz_norm.shape)
xyz_pairdistance = xyz_norm.squeeze(-1).gt(0.7).nonzero()
# print(xyz_pairdistance[0:10])
# print(xyz_pairdistance[0][1])
# print(curvature.shape)
# print(curvature[0:2,:])
# print('******')
# print(curvature[xyz_pairdistance[:,0],xyz_pairdistance[:,1]])
# quit()
curvature[xyz_pairdistance[:, 0], xyz_pairdistance[:, 1]] = 0
xyz_flipped = xyz[:, :, 0:3].transpose(1, 2).contiguous()
xyz_c = torch.stack(
(xyz[:, :, 0], xyz[:, :, 1], xyz[:, :,
2], curvature.squeeze(-1)),
dim=2)
idx = pointnet2_utils.furthest_point_sample(
xyz_c.contiguous(), self.npoint)
# idx = idx.detach()
new_xyz = (pointnet2_utils.gather_operation(
xyz_flipped, idx).transpose(1, 2).contiguous())
new_curvature = torch.gather(curvature, dim=1, index=idx.long())
else:
new_xyz = None
new_curvature = None
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features) # (B, C, npoint, nsample)
new_features = self.mlps[i](
new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(new_features,
kernel_size=[
1, new_features.size(3)
]) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1), new_curvature
