翻譯自
https://pytorch.org/docs/stable/torchvision/models.html
主要講解了torchvision.models的使用
torchvision.models
torchvision.models中包含了如下模型
- AlexNet
- VGG
- ResNet
- SqueezeNet
- DenseNet
- Inception v3
隨機(jī)初始化模型
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()
使用預(yù)訓(xùn)練好的參數(shù)
pytorch提供了預(yù)訓(xùn)練的模型，使用torch.utils.model_zoo ，通過讓參數(shù)pretrained =True來構(gòu)建訓(xùn)練好的模型
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)
實(shí)例化一個(gè)預(yù)訓(xùn)練好的模型會(huì)自動(dòng)下載權(quán)重到緩存目錄，這個(gè)權(quán)重存儲(chǔ)路徑可以通過環(huán)境變量TORCH_MODEL_ZOO來指定，詳細(xì)的參考torch.utils.model_zoo.load_url() 這個(gè)函數(shù)
有的模型試驗(yàn)了不同的訓(xùn)練和評(píng)估，例如batch normalization。使用model.train()和model.eval()來轉(zhuǎn)換，查看train() or eval() 來了解更多細(xì)節(jié)
所有的預(yù)訓(xùn)練網(wǎng)絡(luò)希望使用相同的方式進(jìn)行歸一化，例如圖片是mini-batch形式的3通道RGB圖片(3HW),H和W最少是244,。 圖像必須加載到[0,1]范圍內(nèi)，然后使用均值=[0.485,0.456,0.406]和std =[0.229, 0.224, 0.225]進(jìn)行歸一化。
您可以使用以下轉(zhuǎn)換來normalzie:
normalize = trainform.Normalize9mean = [0.485,0.456,0.406],std = [0.229,0.224,0.225])
在這里我們可以找到一個(gè)在Imagenet上的這樣的例子
https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101
目前這些模型的效果如下
下面是模型源碼的具體實(shí)現(xiàn)，具體實(shí)現(xiàn)大家可以閱讀源碼
###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
以上就是Pytorch教程內(nèi)置模型源碼實(shí)現(xiàn)的詳細(xì)內(nèi)容，更多關(guān)于Pytorch內(nèi)置模型的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！
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