(self, use_sigmoid=True, use_spectral_norm=True, init_weights=True, in_channels=None)
| 429 | |
| 430 | class Discriminator(BaseNetwork): |
| 431 | def __init__(self, use_sigmoid=True, use_spectral_norm=True, init_weights=True, in_channels=None): |
| 432 | super(Discriminator, self).__init__() |
| 433 | self.use_sigmoid = use_sigmoid |
| 434 | self.conv1 = self.features = nn.Sequential( |
| 435 | spectral_norm(nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm), |
| 436 | nn.LeakyReLU(0.2, inplace=True), |
| 437 | ) |
| 438 | |
| 439 | self.conv2 = nn.Sequential( |
| 440 | spectral_norm(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm), |
| 441 | nn.LeakyReLU(0.2, inplace=True), |
| 442 | ) |
| 443 | |
| 444 | self.conv3 = nn.Sequential( |
| 445 | spectral_norm(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm), |
| 446 | nn.LeakyReLU(0.2, inplace=True), |
| 447 | ) |
| 448 | |
| 449 | self.conv4 = nn.Sequential( |
| 450 | spectral_norm(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=1, padding=1, bias=not use_spectral_norm), use_spectral_norm), |
| 451 | nn.LeakyReLU(0.2, inplace=True), |
| 452 | ) |
| 453 | |
| 454 | self.conv5 = nn.Sequential( |
| 455 | spectral_norm(nn.Conv2d(in_channels=512, out_channels=1, kernel_size=4, stride=1, padding=1, bias=not use_spectral_norm), use_spectral_norm), |
| 456 | ) |
| 457 | |
| 458 | if init_weights: |
| 459 | self.init_weights() |
| 460 | |
| 461 | def forward(self, x): |
| 462 | conv1 = self.conv1(x) |
nothing calls this directly
no test coverage detected