| 210 | |
| 211 | # PoseNet (SE(3)) w/ mobilev2 backbone |
| 212 | class PoseNetV2(nn.Module): |
| 213 | def __init__(self, feat_dim=12): |
| 214 | super(PoseNetV2, self).__init__() |
| 215 | self.backbone_net = models.mobilenet_v2(pretrained=True) |
| 216 | self.feature_extractor = self.backbone_net.features |
| 217 | self.avgpool = nn.AdaptiveAvgPool2d(1) |
| 218 | self.fc_pose = nn.Linear(1280, feat_dim) |
| 219 | |
| 220 | def forward(self, Input): |
| 221 | x = self.feature_extractor(Input) |
| 222 | x = self.avgpool(x) |
| 223 | x = x.reshape(x.size(0), -1) |
| 224 | predict = self.fc_pose(x) |
| 225 | # pdb.set_trace() |
| 226 | return predict |
| 227 | |
| 228 | # PoseNet (SE(3)) w/ resnet34 backnone. We found dropout layer is unnecessary, so we set droprate as 0 in reported results. |
| 229 | class PoseNet_res34(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected