| 35 | |
| 36 | |
| 37 | class StackUNet(ME.MinkowskiNetwork): |
| 38 | def __init__(self, in_nchannel, out_nchannel, D): |
| 39 | ME.MinkowskiNetwork.__init__(self, D) |
| 40 | channels = [in_nchannel, 16, 32] |
| 41 | self.net = nn.Sequential( |
| 42 | ME.MinkowskiStackSum( |
| 43 | ME.MinkowskiConvolution( |
| 44 | channels[0], |
| 45 | channels[1], |
| 46 | kernel_size=3, |
| 47 | stride=1, |
| 48 | dimension=D, |
| 49 | ), |
| 50 | nn.Sequential( |
| 51 | ME.MinkowskiConvolution( |
| 52 | channels[0], |
| 53 | channels[1], |
| 54 | kernel_size=3, |
| 55 | stride=2, |
| 56 | dimension=D, |
| 57 | ), |
| 58 | ME.MinkowskiStackSum( |
| 59 | nn.Identity(), |
| 60 | nn.Sequential( |
| 61 | ME.MinkowskiConvolution( |
| 62 | channels[1], |
| 63 | channels[2], |
| 64 | kernel_size=3, |
| 65 | stride=2, |
| 66 | dimension=D, |
| 67 | ), |
| 68 | ME.MinkowskiConvolutionTranspose( |
| 69 | channels[2], |
| 70 | channels[1], |
| 71 | kernel_size=3, |
| 72 | stride=1, |
| 73 | dimension=D, |
| 74 | ), |
| 75 | ME.MinkowskiPoolingTranspose( |
| 76 | kernel_size=2, stride=2, dimension=D |
| 77 | ), |
| 78 | ), |
| 79 | ), |
| 80 | ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D), |
| 81 | ), |
| 82 | ), |
| 83 | ME.MinkowskiToFeature(), |
| 84 | nn.Linear(channels[1], out_nchannel, bias=True), |
| 85 | ) |
| 86 | |
| 87 | def forward(self, x): |
| 88 | return self.net(x) |
| 89 | |
| 90 | |
| 91 | |