def vgg19(pretrained=False, **kwargs): """VGG 19-layer model (configuration "E") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG_FCN(make_layers(cfg['E']), **kwargs) if pretrained: pretrained_dict = model_zoo.load_url(model_urls['vgg19']) model = model_helper.get_not_fc_para(model, pretrained_dict) # model.load_state_dict(model_zoo.load_url(model_urls['vgg19'])) return model
def resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) if pretrained: pretrained_dict = model_zoo.load_url(model_urls['resnet152']) model = model_helper.get_not_fc_para(model, pretrained_dict) # model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) return model
def vgg13_bn(pretrained=False, **kwargs): """VGG 13-layer model (configuration "B") with batch normalization Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs) if pretrained: pretrained_dict = model_zoo.load_url(model_urls['vgg13_bn']) model = model_helper.get_not_fc_para(model, pretrained_dict) # model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn'])) return model
def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) if pretrained: pretrained_dict = model_zoo.load_url(model_urls['resnet18']) model = model_helper.get_not_fc_para(model, pretrained_dict) # model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model
def inception_v3(pretrained=False, **kwargs): r"""Inception v3 model architecture from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ if pretrained: if 'transform_input' not in kwargs: kwargs['transform_input'] = True model = Inception3(**kwargs) pretrained_dict = model_zoo.load_url(model_urls['inception_v3_google']) model = model_helper.get_not_fc_para(model, pretrained_dict) # model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google'])) return model return Inception3(**kwargs)