| 63 | |
| 64 | |
| 65 | class DecoderMLPSkipConcat(nn.Module): |
| 66 | def __init__(self, in_channels, out_channels, hidden_channels, num_hidden_layers, posenc=0) -> None: |
| 67 | super().__init__() |
| 68 | self.posenc = posenc |
| 69 | if posenc > 0: |
| 70 | self.PE = SinusoidalEncoder(in_channels, 0, posenc, use_identity=True) |
| 71 | in_channels = self.PE.latent_dim |
| 72 | first_layer_list = [nn.Linear(in_channels, hidden_channels), nn.ReLU()] |
| 73 | for _ in range(num_hidden_layers // 2): |
| 74 | first_layer_list.append(nn.Linear(hidden_channels, hidden_channels)) |
| 75 | first_layer_list.append(nn.ReLU()) |
| 76 | self.first_layers = nn.Sequential(*first_layer_list) |
| 77 | |
| 78 | second_layer_list = [nn.Linear(in_channels + hidden_channels, hidden_channels), nn.ReLU()] |
| 79 | for _ in range(num_hidden_layers // 2 - 1): |
| 80 | second_layer_list.append(nn.Linear(hidden_channels, hidden_channels)) |
| 81 | second_layer_list.append(nn.ReLU()) |
| 82 | second_layer_list.append(nn.Linear(hidden_channels, out_channels)) |
| 83 | self.second_layers = nn.Sequential(*second_layer_list) |
| 84 | |
| 85 | def forward(self, x): |
| 86 | if self.posenc > 0: |
| 87 | x = self.PE(x) |
| 88 | h = self.first_layers(x) |
| 89 | h = torch.cat([x, h], dim=-1) |
| 90 | h = self.second_layers(h) |
| 91 | return h |
| 92 | |
| 93 | |
| 94 | class SiLU(nn.Module): |