| 4 | |
| 5 | class _netG(nn.Module): |
| 6 | def __init__(self, ngpu, nz): |
| 7 | super(_netG, self).__init__() |
| 8 | self.ngpu = ngpu |
| 9 | self.nz = nz |
| 10 | |
| 11 | # first linear layer |
| 12 | self.fc1 = nn.Linear(110, 768) |
| 13 | # Transposed Convolution 2 |
| 14 | self.tconv2 = nn.Sequential( |
| 15 | nn.ConvTranspose2d(768, 384, 5, 2, 0, bias=False), |
| 16 | nn.BatchNorm2d(384), |
| 17 | nn.ReLU(True), |
| 18 | ) |
| 19 | # Transposed Convolution 3 |
| 20 | self.tconv3 = nn.Sequential( |
| 21 | nn.ConvTranspose2d(384, 256, 5, 2, 0, bias=False), |
| 22 | nn.BatchNorm2d(256), |
| 23 | nn.ReLU(True), |
| 24 | ) |
| 25 | # Transposed Convolution 4 |
| 26 | self.tconv4 = nn.Sequential( |
| 27 | nn.ConvTranspose2d(256, 192, 5, 2, 0, bias=False), |
| 28 | nn.BatchNorm2d(192), |
| 29 | nn.ReLU(True), |
| 30 | ) |
| 31 | # Transposed Convolution 5 |
| 32 | self.tconv5 = nn.Sequential( |
| 33 | nn.ConvTranspose2d(192, 64, 5, 2, 0, bias=False), |
| 34 | nn.BatchNorm2d(64), |
| 35 | nn.ReLU(True), |
| 36 | ) |
| 37 | # Transposed Convolution 5 |
| 38 | self.tconv6 = nn.Sequential( |
| 39 | nn.ConvTranspose2d(64, 3, 8, 2, 0, bias=False), |
| 40 | nn.Tanh(), |
| 41 | ) |
| 42 | |
| 43 | def forward(self, input): |
| 44 | if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: |