| 33 | |
| 34 | # Encoder |
| 35 | class Encoder(nn.Module): |
| 36 | def __init__(self): |
| 37 | super(Encoder, self).__init__() |
| 38 | |
| 39 | basemodel_name = 'tf_efficientnet_b5_ap' |
| 40 | print('Loading base model ()...'.format(basemodel_name), end='') |
| 41 | basemodel = torch.hub.load('rwightman/gen-efficientnet-pytorch', basemodel_name, pretrained=True) |
| 42 | print('Done.') |
| 43 | |
| 44 | # Remove last layer |
| 45 | print('Removing last two layers (global_pool & classifier).') |
| 46 | basemodel.global_pool = nn.Identity() |
| 47 | basemodel.classifier = nn.Identity() |
| 48 | |
| 49 | self.original_model = basemodel |
| 50 | |
| 51 | def forward(self, x): |
| 52 | features = [x] |
| 53 | for k, v in self.original_model._modules.items(): |
| 54 | if (k == 'blocks'): |
| 55 | for ki, vi in v._modules.items(): |
| 56 | features.append(vi(features[-1])) |
| 57 | else: |
| 58 | features.append(v(features[-1])) |
| 59 | return features |
| 60 | |
| 61 | |
| 62 | # Decoder (no pixel-wise MLP, no uncertainty-guided sampling) |