Pytorch教程内置模型源码实现
翻译自
https://pytorch.org/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
- VGG
- ResNet
- SqueezeNet
- DenseNet
- Inception v3
随机初始化模型
1 2 3 4 5 6 7 | 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
1 2 3 4 5 6 | 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batch normalization。使用model.train()和model.eval()来转换,查看train() or eval() 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:
在这里我们可以找到一个在Imagenet上的这样的例子
https://github.com/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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ###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 |
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