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import torch.nn as nn
from modules.third_party.pointnet2.pointnet2_modules import PointnetSAModule
def break_up_pc(pc):
"""
Split the pointcloud into xyz positions and features tensors.
This method is taken from VoteNet codebase (https://github.com/facebookresearch/votenet)
@param pc: pointcloud [N, 3 + C]
:return: the xyz tensor and the feature tensor
"""
xyz = pc[..., 0:3].contiguous()
features = (
pc[..., 3:].transpose(1, 2).contiguous()
if pc.size(-1) > 3 else None
)
return xyz, features
class PointNetPP(nn.Module):
"""
Pointnet++ encoder.
For the hyper parameters please advise the paper (https://arxiv.org/abs/1706.02413)
"""
def __init__(self, sa_n_points: list,
sa_n_samples: list,
sa_radii: list,
sa_mlps: list,
bn=True,
use_xyz=True):
super().__init__()
n_sa = len(sa_n_points)
if not (n_sa == len(sa_n_samples) == len(sa_radii) == len(sa_mlps)):
raise ValueError('Lens of given hyper-params are not compatible')
self.encoder = nn.ModuleList()
for i in range(n_sa):
self.encoder.append(PointnetSAModule(
npoint=sa_n_points[i],
nsample=sa_n_samples[i],
radius=sa_radii[i],
mlp=sa_mlps[i],
bn=bn,
use_xyz=use_xyz,
))
out_n_points = sa_n_points[-1] if sa_n_points[-1] is not None else 1
self.fc = nn.Linear(out_n_points * sa_mlps[-1][-1], sa_mlps[-1][-1])
def forward(self, features):
"""
@param features: B x N_objects x N_Points x 3 + C
"""
xyz, features = break_up_pc(features)
for i in range(len(self.encoder)):
xyz, features = self.encoder[i](xyz, features)
return self.fc(features.view(features.size(0), -1)) |