(self, in_channels, out_channels, *args, **kwargs)
| 1240 | |
| 1241 | class SimpleDecoder(nn.Module): |
| 1242 | def __init__(self, in_channels, out_channels, *args, **kwargs): |
| 1243 | super().__init__() |
| 1244 | self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), |
| 1245 | ResnetBlock(in_channels=in_channels, |
| 1246 | out_channels=2 * in_channels, |
| 1247 | temb_channels=0, dropout=0.0), |
| 1248 | ResnetBlock(in_channels=2 * in_channels, |
| 1249 | out_channels=4 * in_channels, |
| 1250 | temb_channels=0, dropout=0.0), |
| 1251 | ResnetBlock(in_channels=4 * in_channels, |
| 1252 | out_channels=2 * in_channels, |
| 1253 | temb_channels=0, dropout=0.0), |
| 1254 | nn.Conv2d(2*in_channels, in_channels, 1), |
| 1255 | Upsample(in_channels, with_conv=True)]) |
| 1256 | # end |
| 1257 | self.norm_out = Normalize(in_channels) |
| 1258 | self.conv_out = torch.nn.Conv2d(in_channels, |
| 1259 | out_channels, |
| 1260 | kernel_size=3, |
| 1261 | stride=1, |
| 1262 | padding=1) |
| 1263 | |
| 1264 | def forward(self, x): |
| 1265 | for i, layer in enumerate(self.model): |
nothing calls this directly
no test coverage detected