def forward(self, pointcloud: torch.cuda.FloatTensor, return_all=False):
pointcloud = pointcloud.transpose(1, 2).contiguous()
li_xyz, li_features = self._break_up_pc(pointcloud)
# B,C,N
# l_xyz, l_features = [xyz], [li_features]
l_xyz, l_features = [], []
for i in range(len(self.SA_modules)):
# Pointnetmodule + MLP + maxpool
li_xyz, li_features = self.SA_modules[i](li_xyz, li_features)
li_features_post = self.postSA_mlp[i](li_features)
l_xyz.append(li_xyz)
l_features.append(li_features_post)
# max pool (B,4*#SA,1) all SAmodules
# exclude the first None features
global_code = torch.cat(
[torch.max(l_feat, dim=-1)[0] for l_feat in l_features], dim=1)
l_features.append(global_code)
l_xyz.append(None)
if return_all:
return l_features, l_xyz
else:
return global_code
def forward(self, pointcloud: torch.cuda.FloatTensor):
r"""
Forward pass of the network
Parameters
----------
pointcloud: Variable(torch.cuda.FloatTensor)
(B, N, 3 + input_channels) tensor
Point cloud to run predicts on
Each point in the point-cloud MUST
be formated as (x, y, z, features...)
"""
pointcloud = pointcloud.transpose(1, 2)
xyz, features = self._break_up_pc(pointcloud)
cls = torch.zeros(pointcloud.size(0), 16).cuda()
l_xyz, l_features = [xyz], [features]
for i in range(len(self.SA_modules)):
if i < 5:
li_xyz, li_features = self.SA_modules[i](l_xyz[i],
l_features[i])
if li_xyz is not None:
random_index = np.arange(li_xyz.size()[1])
np.random.shuffle(random_index)
li_xyz = li_xyz[:, random_index, :]
li_features = li_features[:, :, random_index]
l_xyz.append(li_xyz)
l_features.append(li_features)
_, global_out2_feat = self.SA_modules[5](l_xyz[3], l_features[3])
for i in range(-1, -(len(self.FP_modules) + 1), -1):
l_features[i - 1 - 1] = self.FP_modules[i](l_xyz[i - 1 - 1],
l_xyz[i - 1],
l_features[i - 1 - 1],
l_features[i - 1])
cls = cls.view(-1, 16, 1).repeat(
1, 1, l_features[0].size()[2]) # object class one-hot-vector
l_features[0] = torch.cat(
(l_features[0], l_features[-1].repeat(1, 1,
l_features[0].size()[2]),
global_out2_feat.repeat(1, 1, l_features[0].size()[2]), cls), 1)
return self.FC_layer(l_features[0]).transpose(1, 2).contiguous()
def forward(self, pointcloud: torch.cuda.FloatTensor):
r"""
Forward pass of the network
Parameters
----------
pointcloud: Variable(torch.cuda.FloatTensor)
(B, N, 3 + input_channels) tensor
Point cloud to run predicts on
Each point in the point-cloud MUST
be formated as (x, y, z, features...)
"""
pointcloud = pointcloud.transpose(1, 2)
xyz, features = self._break_up_pc(pointcloud)
for module in self.SA_modules:
xyz, features = module(xyz, features)
logits = self.FC_layer(features.squeeze(-1))
#print(logits)
return logits, None