(self, input)
| 177 | ) |
| 178 | |
| 179 | def forward(self, input): |
| 180 | if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: |
| 181 | input = input.view(-1, self.nz) |
| 182 | fc1 = nn.parallel.data_parallel(self.fc1, input, range(self.ngpu)) |
| 183 | fc1 = fc1.view(-1, 384, 1, 1) |
| 184 | tconv2 = nn.parallel.data_parallel(self.tconv2, fc1, range(self.ngpu)) |
| 185 | tconv3 = nn.parallel.data_parallel(self.tconv3, tconv2, range(self.ngpu)) |
| 186 | tconv4 = nn.parallel.data_parallel(self.tconv4, tconv3, range(self.ngpu)) |
| 187 | tconv5 = nn.parallel.data_parallel(self.tconv5, tconv4, range(self.ngpu)) |
| 188 | output = tconv5 |
| 189 | else: |
| 190 | input = input.view(-1, self.nz) |
| 191 | fc1 = self.fc1(input) |
| 192 | fc1 = fc1.view(-1, 384, 1, 1) |
| 193 | tconv2 = self.tconv2(fc1) |
| 194 | tconv3 = self.tconv3(tconv2) |
| 195 | tconv4 = self.tconv4(tconv3) |
| 196 | tconv5 = self.tconv5(tconv4) |
| 197 | output = tconv5 |
| 198 | return output |
| 199 | |
| 200 | |
| 201 | class _netD_CIFAR10(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected