(self,x)
| 62 | torch.nn.Tanh() |
| 63 | ) |
| 64 | def forward(self,x): |
| 65 | d1 = self.initial_down(x) |
| 66 | d2 = self.down1(d1) |
| 67 | d3 = self.down2(d2) |
| 68 | d4 = self.down3(d3) |
| 69 | d5 = self.down4(d4) |
| 70 | d6 = self.down5(d5) |
| 71 | d7 = self.down6(d6) |
| 72 | |
| 73 | bottleneck = self.bottleneck(d7) |
| 74 | |
| 75 | u1 = self.up1(bottleneck) |
| 76 | u2 = self.up2(torch.cat([u1,d7],dim=1)) |
| 77 | u3 = self.up3(torch.cat([u2,d6],dim=1)) |
| 78 | u4 = self.up4(torch.cat([u3,d5],dim=1)) |
| 79 | u5 = self.up5(torch.cat([u4,d4],dim=1)) |
| 80 | u6 = self.up6(torch.cat([u5,d3],dim=1)) |
| 81 | u7 = self.up7(torch.cat([u6,d2],dim=1)) |
| 82 | |
| 83 | final_up = self.final_up(torch.cat([u7,d1],dim=1)) |
| 84 | return final_up |
| 85 | |
| 86 | |
| 87 | if __name__ == '__main__': |
nothing calls this directly
no outgoing calls
no test coverage detected