(self, x)
| 35 | self.layernorm = nn.LayerNorm(channels) |
| 36 | |
| 37 | def forward(self, x): |
| 38 | x_hat = self.layernorm(x) |
| 39 | bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) |
| 40 | return x_hat - bias |
| 41 | |
| 42 | |
| 43 | class moving_avg(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected