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
