def vgg16(pretrained=False, **kwargs): if pretrained: kwargs['init_weights'] = False model = VGG(make_layers(cfg['D']), **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['vgg16'])) return model
def vgg19_bn(pretrained=False, **kwargs): if pretrained: kwargs['init_weights'] = False model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn'])) return model
def inception_v3(pretrained=False, **kwargs): if pretrained: model = Inception3(**kwargs) model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google'])) return model return Inception3(**kwargs)
def resnet152(pretrained=False, **kwargs): model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) return model
def resnet34(pretrained=False, **kwargs): model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) return model
def alexnet(pretrained=False, **kwargs): model = AlexNet(**kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['alexnet'])) return model
def squeezenet1_1(pretrained=False, **kwargs): model = SqueezeNet(version=1.1, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_1'])) return model