| 253 | |
| 254 | class SPADE(nn.Module): |
| 255 | def __init__(self, norm_nc, label_nc): |
| 256 | super().__init__() |
| 257 | |
| 258 | self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) |
| 259 | nhidden = 128 |
| 260 | |
| 261 | self.mlp_shared = nn.Sequential( |
| 262 | nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), |
| 263 | nn.ReLU()) |
| 264 | self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) |
| 265 | self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) |
| 266 | |
| 267 | def forward(self, x, segmap): |
| 268 | normalized = self.param_free_norm(x) |