pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model def resnet50(num_classes=1_000, pretrained=False, with_se=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 6, 3], with_se=with_se) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model def se_resnet50(num_classes=1_000, pretrained=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: #model.load_state_dict(model_zoo.load_url("https://www.dropbox.com/s/xpq8ne7rwa4kg4c/seresnet50-60a8950a85b2b.pkl")) model.load_state_dict( torch.load(
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model def se_resnet50(num_classes=1_000, pretrained=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: model.load_state_dict( load_state_dict_from_url( "https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl" )) return model def se_resnet101(num_classes=1_000): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model