| 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)) |