• 企业400电话
  • 微网小程序
  • AI电话机器人
  • 电商代运营
  • 全 部 栏 目

    企业400电话 网络优化推广 AI电话机器人 呼叫中心 网站建设 商标✡知产 微网小程序 电商运营 彩铃•短信 增值拓展业务
    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

    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

    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 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教程内置模型源码实现的详细内容,更多关于Pytorch内置模型的资料请关注脚本之家其它相关文章!

    您可能感兴趣的文章:
    • pytorch教程之Tensor的值及操作使用学习
    • 使用Pytorch搭建模型的步骤
    • 如何使用Pytorch搭建模型
    • pytorch构建网络模型的4种方法
    上一篇:pytorch教程之Tensor的值及操作使用学习
    下一篇:pytorch教程实现mnist手写数字识别代码示例
  • 相关文章
  • 

    © 2016-2020 巨人网络通讯 版权所有

    《增值电信业务经营许可证》 苏ICP备15040257号-8

    Pytorch教程内置模型源码实现 Pytorch,教程,内置,模型,源码,