| 43 | |
| 44 | |
| 45 | class DecoderMLP(nn.Module): |
| 46 | def __init__(self, in_channels, out_channels, hidden_channels, num_hidden_layers, posenc=0) -> None: |
| 47 | super().__init__() |
| 48 | self.posenc = posenc |
| 49 | if posenc > 0: |
| 50 | self.PE = SinusoidalEncoder(in_channels, 0, posenc, use_identity=True) |
| 51 | in_channels = self.PE.latent_dim |
| 52 | layer_list = [nn.Linear(in_channels, hidden_channels), nn.ReLU()] |
| 53 | for _ in range(num_hidden_layers): |
| 54 | layer_list.append(nn.Linear(hidden_channels, hidden_channels)) |
| 55 | layer_list.append(nn.ReLU()) |
| 56 | layer_list.append(nn.Linear(hidden_channels, out_channels)) |
| 57 | self.layers = nn.Sequential(*layer_list) |
| 58 | |
| 59 | def forward(self, x): |
| 60 | if self.posenc > 0: |
| 61 | x = self.PE(x) |
| 62 | return self.layers(x) |
| 63 | |
| 64 | |
| 65 | class DecoderMLPSkipConcat(nn.Module): |