| 10 | class MYNET(nn.Module): |
| 11 | |
| 12 | def __init__(self, args, mode=None): |
| 13 | super().__init__() |
| 14 | |
| 15 | self.mode = mode |
| 16 | self.args = args |
| 17 | # self.num_features = 512 |
| 18 | if self.args.dataset in ['cifar100','manyshotcifar']: |
| 19 | self.encoder = resnet20() |
| 20 | self.num_features = 64 |
| 21 | if self.args.dataset in ['mini_imagenet','manyshotmini','imagenet100','imagenet1000', 'mini_imagenet_withpath']: |
| 22 | self.encoder = resnet18(False, args) # pretrained=False |
| 23 | self.num_features = 512 |
| 24 | if self.args.dataset in ['cub200','manyshotcub']: |
| 25 | self.encoder = resnet18(True, args) # pretrained=True follow TOPIC, models for cub is imagenet pre-trained. https://github.com/xyutao/fscil/issues/11#issuecomment-687548790 |
| 26 | self.num_features = 512 |
| 27 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 28 | |
| 29 | self.fc = nn.Linear(self.num_features, self.args.num_classes, bias=False) |
| 30 | |
| 31 | def forward_metric(self, x): |
| 32 | x = self.encode(x) |