(self, x: torch.Tensor)
| 51 | return (x - self.mean) / self.std |
| 52 | |
| 53 | def forward(self, x: torch.Tensor): |
| 54 | x = self.z_score(x) |
| 55 | |
| 56 | output = [] |
| 57 | for i, (_, layer) in enumerate(self.layers._modules.items(), 1): |
| 58 | x = layer(x) |
| 59 | if i in self.target_layers: |
| 60 | output.append(normalize_activation(x)) |
| 61 | if len(output) == len(self.target_layers): |
| 62 | break |
| 63 | return output |
| 64 | |
| 65 | |
| 66 | class SqueezeNet(BaseNet): |
nothing calls this directly
no test coverage detected