| 90 | |
| 91 | |
| 92 | class ADAINEncoderBlock(nn.Module): |
| 93 | def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False): |
| 94 | super(ADAINEncoderBlock, self).__init__() |
| 95 | kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1} |
| 96 | kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1} |
| 97 | |
| 98 | self.conv_0 = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_down), use_spect) |
| 99 | self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect) |
| 100 | |
| 101 | self.norm_0 = ADAIN(input_nc, feature_nc) |
| 102 | self.norm_1 = ADAIN(output_nc, feature_nc) |
| 103 | self.actvn = nonlinearity |
| 104 | |
| 105 | def forward(self, x, z): |
| 106 | x = self.conv_0(self.actvn(self.norm_0(x, z))) |
| 107 | x = self.conv_1(self.actvn(self.norm_1(x, z))) |
| 108 | return x |
| 109 | |
| 110 | |
| 111 | class ADAINDecoderBlock(nn.Module): |