(self, x)
| 402 | in_channels = base_channels * 2**i |
| 403 | |
| 404 | def forward(self, x): |
| 405 | self._check_input_divisible(x) |
| 406 | enc_outs = [] |
| 407 | for enc in self.encoder: |
| 408 | x = enc(x) |
| 409 | enc_outs.append(x) |
| 410 | dec_outs = [x] |
| 411 | for i in reversed(range(len(self.decoder))): |
| 412 | x = self.decoder[i](enc_outs[i], x) |
| 413 | dec_outs.append(x) |
| 414 | |
| 415 | return dec_outs |
| 416 | |
| 417 | def train(self, mode=True): |
| 418 | """Convert the model into training mode while keep normalization layer |
nothing calls this directly
no test coverage detected