| 6 | |
| 7 | class STN3d(nn.Module): |
| 8 | def __init__(self, channel): |
| 9 | super(STN3d, self).__init__() |
| 10 | self.conv1 = torch.nn.Conv1d(channel, 64, 1) |
| 11 | self.conv2 = torch.nn.Conv1d(64, 128, 1) |
| 12 | self.conv3 = torch.nn.Conv1d(128, 1024, 1) |
| 13 | self.fc1 = nn.Linear(1024, 512) |
| 14 | self.fc2 = nn.Linear(512, 256) |
| 15 | self.fc3 = nn.Linear(256, 9) |
| 16 | self.relu = nn.ReLU() |
| 17 | |
| 18 | self.bn1 = nn.BatchNorm1d(64) |
| 19 | self.bn2 = nn.BatchNorm1d(128) |
| 20 | self.bn3 = nn.BatchNorm1d(1024) |
| 21 | self.bn4 = nn.BatchNorm1d(512) |
| 22 | self.bn5 = nn.BatchNorm1d(256) |
| 23 | |
| 24 | def forward(self, x): |
| 25 | batchsize = x.size()[0] |