(self,x)
| 26 | if discriminator else torch.nn.PReLU(num_parameters=out_channels) |
| 27 | ) |
| 28 | def forward(self,x): |
| 29 | out = self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) |
| 30 | return out |
| 31 | |
| 32 | #采用PixelShuffle进行上采样 |
| 33 | class UpsampleBlock(torch.nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected