(self, input)
| 119 | self.sigmoid = nn.Sigmoid() |
| 120 | |
| 121 | def forward(self, input): |
| 122 | if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: |
| 123 | conv1 = nn.parallel.data_parallel(self.conv1, input, range(self.ngpu)) |
| 124 | conv2 = nn.parallel.data_parallel(self.conv2, conv1, range(self.ngpu)) |
| 125 | conv3 = nn.parallel.data_parallel(self.conv3, conv2, range(self.ngpu)) |
| 126 | conv4 = nn.parallel.data_parallel(self.conv4, conv3, range(self.ngpu)) |
| 127 | conv5 = nn.parallel.data_parallel(self.conv5, conv4, range(self.ngpu)) |
| 128 | conv6 = nn.parallel.data_parallel(self.conv6, conv5, range(self.ngpu)) |
| 129 | flat6 = conv6.view(-1, 13*13*512) |
| 130 | fc_dis = nn.parallel.data_parallel(self.fc_dis, flat6, range(self.ngpu)) |
| 131 | fc_aux = nn.parallel.data_parallel(self.fc_aux, flat6, range(self.ngpu)) |
| 132 | else: |
| 133 | conv1 = self.conv1(input) |
| 134 | conv2 = self.conv2(conv1) |
| 135 | conv3 = self.conv3(conv2) |
| 136 | conv4 = self.conv4(conv3) |
| 137 | conv5 = self.conv5(conv4) |
| 138 | conv6 = self.conv6(conv5) |
| 139 | flat6 = conv6.view(-1, 13*13*512) |
| 140 | fc_dis = self.fc_dis(flat6) |
| 141 | fc_aux = self.fc_aux(flat6) |
| 142 | classes = self.softmax(fc_aux) |
| 143 | realfake = self.sigmoid(fc_dis).view(-1, 1).squeeze(1) |
| 144 | return realfake, classes |
| 145 | |
| 146 | |
| 147 | class _netG_CIFAR10(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected