(self, features)
| 50 | init.constant_(m.bias, 0) |
| 51 | |
| 52 | def forward(self, features): |
| 53 | x_32x = self.conv(features[3]) # 1/32 |
| 54 | x_32 = self.conv1(x_32x) |
| 55 | x_16 = self.upsample(x_32) # 1/16 |
| 56 | |
| 57 | x_8 = self.ffm2(features[2], x_16) # 1/8 |
| 58 | x_4 = self.ffm1(features[1], x_8) # 1/4 |
| 59 | x_2 = self.ffm0(features[0], x_4) # 1/2 |
| 60 | #----------------------------------------- |
| 61 | x = self.outconv(x_2) # original size |
| 62 | return x |
| 63 | |
| 64 | class DepthNet(nn.Module): |
| 65 | __factory = { |