本文主要是介绍pytorch实用工具：torchsummary、torchsnooper，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一. torchsummmary工具：
其主要是用来计算网络的计算参数等信息的，其大概的流程为：
- 安装指令：
pip install torchsummary
- 使用方法：
import torch, torchvisionmodel = torchvision.models.vggmodel = torchvision.models.vgg16()from torchsummary import summarysummary(model, (3, 224, 224))
其输出信息为：
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv2d-1 [-1, 64, 224, 224] 1,792ReLU-2 [-1, 64, 224, 224] 0Conv2d-3 [-1, 64, 224, 224] 36,928ReLU-4 [-1, 64, 224, 224] 0MaxPool2d-5 [-1, 64, 112, 112] 0Conv2d-6 [-1, 128, 112, 112] 73,856ReLU-7 [-1, 128, 112, 112] 0Conv2d-8 [-1, 128, 112, 112] 147,584ReLU-9 [-1, 128, 112, 112] 0MaxPool2d-10 [-1, 128, 56, 56] 0Conv2d-11 [-1, 256, 56, 56] 295,168ReLU-12 [-1, 256, 56, 56] 0Conv2d-13 [-1, 256, 56, 56] 590,080ReLU-14 [-1, 256, 56, 56] 0Conv2d-15 [-1, 256, 56, 56] 590,080ReLU-16 [-1, 256, 56, 56] 0MaxPool2d-17 [-1, 256, 28, 28] 0Conv2d-18 [-1, 512, 28, 28] 1,180,160ReLU-19 [-1, 512, 28, 28] 0Conv2d-20 [-1, 512, 28, 28] 2,359,808ReLU-21 [-1, 512, 28, 28] 0Conv2d-22 [-1, 512, 28, 28] 2,359,808ReLU-23 [-1, 512, 28, 28] 0MaxPool2d-24 [-1, 512, 14, 14] 0Conv2d-25 [-1, 512, 14, 14] 2,359,808ReLU-26 [-1, 512, 14, 14] 0Conv2d-27 [-1, 512, 14, 14] 2,359,808ReLU-28 [-1, 512, 14, 14] 0Conv2d-29 [-1, 512, 14, 14] 2,359,808ReLU-30 [-1, 512, 14, 14] 0MaxPool2d-31 [-1, 512, 7, 7] 0Linear-32 [-1, 4096] 102,764,544ReLU-33 [-1, 4096] 0Dropout-34 [-1, 4096] 0Linear-35 [-1, 4096] 16,781,312ReLU-36 [-1, 4096] 0Dropout-37 [-1, 4096] 0Linear-38 [-1, 1000] 4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.59
Params size (MB): 527.79
Estimated Total Size (MB): 746.96
----------------------------------------------------------------
二、Pytorch代码调试工具–torchsnooper
Pytorch有一个十分好用的工具–torchsnooper，在可能出现bug的函数前加一个声明，即可在运行过程中输出这个函数每行代码的所有信息。
## 安装：pip install torchsnooper## 还有另一个同样的工具-- snoop。pip install snoop
这篇关于pytorch实用工具：torchsummary、torchsnooper的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
