import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
desnet = models.densenet161()
inception =models.inception_v3()

resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet161(pretrained=True)
inception = models.inception_v3(pretrained=True)

normalize = trainform.Normalize9mean = [0.485,0.456,0.406],std = [0.229,0.224,0.225])

###ALEXNET
torchvision.models.alexnet(pretrained=False, **kwargs)[SOURCE]
AlexNet model architecture from the “One weird trick…” paper.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
###VGG
torchvision.models.vgg11(pretrained=False, **kwargs)[SOURCE]
VGG 11-layer model (configuration “A”)
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg11_bn(pretrained=False, **kwargs)[SOURCE]
VGG 11-layer model (configuration “A”) with batch normalization
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg13(pretrained=False, **kwargs)[SOURCE]
VGG 13-layer model (configuration “B”)
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg13_bn(pretrained=False, **kwargs)[SOURCE]
VGG 13-layer model (configuration “B”) with batch normalization
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg16(pretrained=False, **kwargs)[SOURCE]
VGG 16-layer model (configuration “D”)
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg16_bn(pretrained=False, **kwargs)[SOURCE]
VGG 16-layer model (configuration “D”) with batch normalization
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg19(pretrained=False, **kwargs)[SOURCE]
VGG 19-layer model (configuration “E”)
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg19_bn(pretrained=False, **kwargs)[SOURCE]
VGG 19-layer model (configuration ‘E') with batch normalization
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
RESNET
torchvision.models.resnet18(pretrained=False, **kwargs)[SOURCE]
Constructs a ResNet-18 model.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet34(pretrained=False, **kwargs)[SOURCE]
Constructs a ResNet-34 model.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet50(pretrained=False, **kwargs)[SOURCE]
Constructs a ResNet-50 model.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet101(pretrained=False, **kwargs)[SOURCE]
Constructs a ResNet-101 model.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet152(pretrained=False, **kwargs)[SOURCE]
Constructs a ResNet-152 model.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
SQUEEZENET
torchvision.models.squeezenet1_0(pretrained=False, **kwargs)[SOURCE]
SqueezeNet model architecture from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size” paper.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.squeezenet1_1(pretrained=False, **kwargs)[SOURCE]
SqueezeNet 1.1 model from the official SqueezeNet repo. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
DENSENET
torchvision.models.densenet121(pretrained=False, **kwargs)[SOURCE]
Densenet-121 model from “Densely Connected Convolutional Networks”
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.densenet169(pretrained=False, **kwargs)[SOURCE]
Densenet-169 model from “Densely Connected Convolutional Networks”
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.densenet161(pretrained=False, **kwargs)[SOURCE]
Densenet-161 model from “Densely Connected Convolutional Networks”
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.densenet201(pretrained=False, **kwargs)[SOURCE]
Densenet-201 model from “Densely Connected Convolutional Networks”
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet
INCEPTION V3
torchvision.models.inception_v3(pretrained=False, **kwargs)[SOURCE]
Inception v3 model architecture from “Rethinking the Inception Architecture for Computer Vision”.
Parameters:	pretrained (bool) – If True, returns a model pre-trained on ImageNet