pytorch如何查看网络参数显存占用量

这篇文章主要介绍pytorch如何查看网络参数显存占用量，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！

1.使用torchstat

pip install torchstat from torchstat import statimport torchvision.models as modelsmodel = models.resnet152()stat(model, (3, 224, 224))

关于stat函数的参数，第一个应该是模型，第二个则是输入尺寸，3为通道数。我没有调研该函数的详细参数，也不知道为什么使用的时候并不提示相应的参数。

2.使用torchsummary

pip install torchsummary from torchsummary import summarysummary(model.cuda(),input_size=(3,32,32),batch_size=-1)

使用该函数直接对参数进行提示，可以发现直接有显式输入batch_size的地方，我自己的感觉好像该函数更好一些。但是！！！不知道为什么，该函数在我的机器上一直报错！！！

TypeError: can't convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Update：经过论坛咨询，报错的原因找到了，只需要把

pip install torchsummary

修改为

pip install torch-summary

补充：Pytorch查看模型参数并计算模型参数量与可训练参数量

查看模型参数(以AlexNet为例）

import torchimport torch.nn as nnimport torchvisionclass AlexNet(nn.Module):    def __init__(self,num_classes=1000):        super(AlexNet,self).__init__()        self.feature_extraction = nn.Sequential(            nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2,bias=False),            nn.ReLU(inplace=True),            nn.MaxPool2d(kernel_size=3,stride=2,padding=0),            nn.Conv2d(in_channels=96,out_channels=192,kernel_size=5,stride=1,padding=2,bias=False),            nn.ReLU(inplace=True),            nn.MaxPool2d(kernel_size=3,stride=2,padding=0),            nn.Conv2d(in_channels=192,out_channels=384,kernel_size=3,stride=1,padding=1,bias=False),            nn.ReLU(inplace=True),            nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False),            nn.ReLU(inplace=True),            nn.Conv2d(in_channels=256,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False),            nn.ReLU(inplace=True),            nn.MaxPool2d(kernel_size=3, stride=2, padding=0),        )        self.classifier = nn.Sequential(            nn.Dropout(p=0.5),            nn.Linear(in_features=256*6*6,out_features=4096),            nn.ReLU(inplace=True),            nn.Dropout(p=0.5),            nn.Linear(in_features=4096, out_features=4096),            nn.ReLU(inplace=True),            nn.Linear(in_features=4096, out_features=num_classes),        )    def forward(self,x):        x = self.feature_extraction(x)        x = x.view(x.size(0),256*6*6)        x = self.classifier(x)        return xif __name__ =='__main__':    # model = torchvision.models.AlexNet()    model = AlexNet()        # 打印模型参数    #for param in model.parameters():        #print(param)        #打印模型名称与shape    for name,parameters in model.named_parameters():        print(name,':',parameters.size())

feature_extraction.0.weight : torch.Size([96, 3, 11, 11])feature_extraction.3.weight : torch.Size([192, 96, 5, 5])feature_extraction.6.weight : torch.Size([384, 192, 3, 3])feature_extraction.8.weight : torch.Size([256, 384, 3, 3])feature_extraction.10.weight : torch.Size([256, 256, 3, 3])classifier.1.weight : torch.Size([4096, 9216])classifier.1.bias : torch.Size([4096])classifier.4.weight : torch.Size([4096, 4096])classifier.4.bias : torch.Size([4096])classifier.6.weight : torch.Size([1000, 4096])classifier.6.bias : torch.Size([1000])

计算参数量与可训练参数量

def get_parameter_number(model):    total_num = sum(p.numel() for p in model.parameters())    trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)    return {'Total': total_num, 'Trainable': trainable_num}

第三方工具

from torchstat import statimport torchvision.models as modelsmodel = models.alexnet()stat(model, (3, 224, 224))

from torchvision.models import alexnetimport torchfrom thop import profilemodel = alexnet()input = torch.randn(1, 3, 224, 224)flops, params = profile(model, inputs=(input, ))print(flops, params)

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