Args: xyz: Tensor, (B, 3, N) points: Tensor, (B, f, N) Returns: new_xyz: Tensor, (B, 3, npoint) new_points: Tensor, (B, mlp[-1], npoint)
(self, xyz, points)
| 193 | self.mlp_conv = nn.Sequential(*self.mlp_conv) |
| 194 | |
| 195 | def forward(self, xyz, points): |
| 196 | """ |
| 197 | Args: |
| 198 | xyz: Tensor, (B, 3, N) |
| 199 | points: Tensor, (B, f, N) |
| 200 | |
| 201 | Returns: |
| 202 | new_xyz: Tensor, (B, 3, npoint) |
| 203 | new_points: Tensor, (B, mlp[-1], npoint) |
| 204 | """ |
| 205 | if self.group_all: |
| 206 | new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, self.use_xyz) |
| 207 | else: |
| 208 | new_xyz, new_points, idx, grouped_xyz = sample_and_group(xyz, points, self.npoint, self.nsample, self.radius, self.use_xyz) |
| 209 | |
| 210 | new_points = self.mlp_conv(new_points) |
| 211 | new_points = torch.max(new_points, 3)[0] |
| 212 | |
| 213 | return new_xyz, new_points |
| 214 | |
| 215 | |
| 216 | class PointNet_FP_Module(nn.Module): |
nothing calls this directly
no test coverage detected