(self, x, x_mask, g=None)
| 118 | self.norms_2.append(LayerNorm(channels)) |
| 119 | |
| 120 | def forward(self, x, x_mask, g=None): |
| 121 | if g is not None: |
| 122 | x = x + g |
| 123 | for i in range(self.n_layers): |
| 124 | y = self.convs_sep[i](x * x_mask) |
| 125 | y = self.norms_1[i](y) |
| 126 | y = F.gelu(y) |
| 127 | y = self.convs_1x1[i](y) |
| 128 | y = self.norms_2[i](y) |
| 129 | y = F.gelu(y) |
| 130 | y = self.drop(y) |
| 131 | x = x + y |
| 132 | return x * x_mask |
| 133 | |
| 134 | |
| 135 | class WN(torch.nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected