(self, x, x_mask, g=None)
| 162 | self.cond = nn.Conv1d(gin_channels, in_channels, 1) |
| 163 | |
| 164 | def forward(self, x, x_mask, g=None): |
| 165 | x = torch.detach(x) |
| 166 | if g is not None: |
| 167 | g = torch.detach(g) |
| 168 | x = x + self.cond(g) |
| 169 | x = self.conv_1(x * x_mask) |
| 170 | x = torch.relu(x) |
| 171 | x = self.norm_1(x) |
| 172 | x = self.drop(x) |
| 173 | x = self.conv_2(x * x_mask) |
| 174 | x = torch.relu(x) |
| 175 | x = self.norm_2(x) |
| 176 | x = self.drop(x) |
| 177 | x = self.proj(x * x_mask) |
| 178 | return x * x_mask |
| 179 | |
| 180 | |
| 181 | class TextEncoder(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected