(self, x, nce_layers=[], encode_only=False)
| 705 | self.output_dim = dim |
| 706 | |
| 707 | def forward(self, x, nce_layers=[], encode_only=False): |
| 708 | if len(nce_layers) > 0: |
| 709 | feat = x |
| 710 | feats = [] |
| 711 | for layer_id, layer in enumerate(self.model): |
| 712 | feat = layer(feat) |
| 713 | if layer_id in nce_layers: |
| 714 | feats.append(feat) |
| 715 | if layer_id == nce_layers[-1] and encode_only: |
| 716 | return None, feats |
| 717 | return feat, feats |
| 718 | else: |
| 719 | return self.model(x), None |
| 720 | |
| 721 | class Decoder_all(nn.Module): |
| 722 | def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0): |
nothing calls this directly
no outgoing calls
no test coverage detected