Pytorch教程内置模型源码实现

第Pytorch教程内置模型源码实现/docs/stable/torchvision/models.html

主要讲解了torchvision.models的使用

torchvision.models

torchvision.models中包含了如下模型

AlexNet

ResNet

SqueezeNet

DenseNet

Inceptionv3

随机初始化模型

importtorchvision.modelsasmodels

resnet18=models.resnet18()

alexnet=models.alexnet()

vgg16=models.vgg16()

squeezenet=models.squeezenet1_0()

desnet=models.densenet161()

inception=models.inception_v3()

使用预训练好的参数

pytorch提供了预训练的模型，使用torch.utils.model_zoo，通过让参数pretrained=True来构建训练好的模型

方法如下

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)

实例化一个预训练好的模型会自动下载权重到缓存目录，这个权重存储路径可以通过环境变量TORCH_MODEL_ZOO来指定，详细的参考torch.utils.model_zoo.load_url()这个函数

有的模型试验了不同的训练和评估，例如batchnormalization。使用model.train()和model.eval()来转换，查看train()oreval()来了解更多细节

所有的预训练网络希望使用相同的方式进行归一化，例如图片是mini-batch形式的3通道RGB图片(3HW),H和W最少是244,。图像必须加载到[0,1]范围内，然后使用均值=[0.485,0.456,0.406]和std=[0.229,0.224,0.225]进行归一化。

您可以使用以下转换来normalzie:

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

在这里我们可以找到一个在Imagenet上的这样的例子

/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101

目前这些模型的效果如下

下面是模型源码的具体实现，具体实现大家可以阅读源码

###ALEXNET

torchvision.models.alexnet(pretrained=False,**kwargs)[SOURCE]

AlexNetmodelarchitecturefromthe“Oneweirdtrick…”paper.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

###VGG

torchvision.models.vgg11(pretrained=False,**kwargs)[SOURCE]

VGG11-layermodel(configuration“A”)

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg11_bn(pretrained=False,**kwargs)[SOURCE]

VGG11-layermodel(configuration“A”)withbatchnormalization

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg13(pretrained=False,**kwargs)[SOURCE]

VGG13-layermodel(configuration“B”)

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg13_bn(pretrained=False,**kwargs)[SOURCE]

VGG13-layermodel(configuration“B”)withbatchnormalization

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg16(pretrained=False,**kwargs)[SOURCE]

VGG16-layermodel(configuration“D”)

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg16_bn(pretrained=False,**kwargs)[SOURCE]

VGG16-layermodel(configuration“D”)withbatchnormalization

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg19(pretrained=False,**kwargs)[SOURCE]

VGG19-layermodel(configuration“E”)

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.vgg19_bn(pretrained=False,**kwargs)[SOURCE]

VGG19-layermodel(configuration‘E')withbatchnormalization

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

RESNET

torchvision.models.resnet18(pretrained=False,**kwargs)[SOURCE]

ConstructsaResNet-18model.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.resnet34(pretrained=False,**kwargs)[SOURCE]

ConstructsaResNet-34model.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.resnet50(pretrained=False,**kwargs)[SOURCE]

ConstructsaResNet-50model.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.resnet101(pretrained=False,**kwargs)[SOURCE]

ConstructsaResNet-101model.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.resnet152(pretrained=False,**kwargs)[SOURCE]

ConstructsaResNet-152model.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

SQUEEZENET

torchvision.models.squeezenet1_0(pretrained=False,**kwargs)[SOURCE]

SqueezeNetmodelarchitecturefromthe“SqueezeNet:AlexNet-levelaccuracywith50xfewerparametersand0.5MBmodelsize”paper.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.squeezenet1_1(pretrained=False,**kwargs)[SOURCE]

SqueezeNet1.1modelfromtheofficialSqueezeNetrepo.SqueezeNet1.1has2.4xlesscomputationandslightlyfewerparametersthanSqueezeNet1.0,withoutsacrificingaccuracy.

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

DENSENET

torchvision.models.densenet121(pretrained=False,**kwargs)[SOURCE]

Densenet-121modelfrom“DenselyConnectedConvolutionalNetworks”

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.densenet169(pretrained=False,**kwargs)[SOURCE]

Densenet-169modelfrom“DenselyConnectedConvolutionalNetworks”

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.densenet161(pretrained=False,**kwargs)[SOURCE]

Densenet-161modelfrom“DenselyConnectedConvolutionalNetworks”

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

torchvision.models.densenet201(pretrained=False,**kwargs)[SOURCE]

Densenet-201modelfrom“DenselyConnectedConvolutionalNetworks”

Parameters:pretrained(bool)–IfTrue,returnsamodelpre-trainedonImageNet

INCEPTIONV3

torchvisi