Pytorch内置模型 Pytorch教程内置模型源码实现(IT技术)

#Pytorch内置模型 Pytorch教程内置模型源码实现(IT技术)| 来源: 网络整理| 查看: 265

想了解Pytorch教程内置模型源码实现的相关内容吗，xz1308579340在本文为您仔细讲解Pytorch内置模型的相关知识和一些Code实例，欢迎阅读和指正，我们先划重点：Pytorch教程,Pytorch内置模型，下面大家一起来学习吧。

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 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

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:

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 https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101

目前这些模型的效果如下

在这里插入图片描述

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] 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

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