def VGG19Templet(input_channel=3, pretrained=False, **kwargs):
"""VGG 19-layer model (configuration "E")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGGTemplet(make_layers(cfg['E'], input_channel), **kwargs)
if pretrained:
model_dict = LoadPretrainedModel(
model, model_zoo.load_url(model_urls['vgg19']))
model.load_state_dict(model_dict)
return model
def Resnet152Templet(input_channel, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNetTemplet(Bottleneck, [3, 8, 36, 3], input_channel, **kwargs)
if pretrained:
model_dict = LoadPretrainedModel(
model, model_zoo.load_url(model_urls['resnet152']))
model.load_state_dict(model_dict)
return model
def VGG16BNTemplet(input_channel=3, pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGGTemplet(make_layers(cfg['D'], input_channel, batch_norm=True),
**kwargs)
if pretrained:
model_dict = LoadPretrainedModel(
model, model_zoo.load_url(model_urls['vgg16_bn']))
model.load_state_dict(model_dict)
return model