Pytorch网络结构可视化：PyTorch是使用GPU和CPU优化的深度学习张量库

 

安装

可以通过以下的命令进行安装

conda install pytorch-nightly -c pytorch
 
conda install graphviz
 
conda install torchvision
 
conda install tensorwatch

 基于以下的版本：

torchvision.__version__   '0.2.1'
 
torch.__version__         '1.2.0.dev20190610'
 
sys.version               '3.6.8 |Anaconda custom (64-bit)| (default, Dec 30 2018, 01:22:34)
 
[GCC 7.3.0]'

载入库

import sys
 
import torch
 
import tensorwatch as tw
 
import torchvision.models

 网络结构可视化

alexnet_model = torchvision.models.alexnet()
 
tw.draw_model(alexnet_model, [1, 3, 224, 224])

 载入alexnet，draw_model函数需要传入三个参数，第一个为model，第二个参数为input_shape，第三个参数为orientation，可以选择'LR'或者'TB'，分别代表左右布局与上下布局。

在notebook中，执行完上面的代码会显示如下的图，将网络的结构及各个层的name和shape进行了可视化。

 

统计网络参数

可以通过model_stats方法统计各层的参数情况。

tw.model_stats(alexnet_model, [1, 3, 224, 224])
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
[MAdd]: Dropout is not supported!
 
[Flops]: Dropout is not supported!
 
[Memory]: Dropout is not supported!
 
alexnet_model.features
 
Sequential(
 
  (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
 
  (1): ReLU(inplace=True)
 
  (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
 
  (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
 
  (4): ReLU(inplace=True)
 
  (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
 
  (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 
  (7): ReLU(inplace=True)
 
  (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 
  (9): ReLU(inplace=True)
 
  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
 
  (11): ReLU(inplace=True)
 
  (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
 
)
 
alexnet_model.classifier
 
Sequential(
 
  (0): Dropout(p=0.5)
 
  (1): Linear(in_features=9216, out_features=4096, bias=True)
 
  (2): ReLU(inplace=True)
 
  (3): Dropout(p=0.5)
 
  (4): Linear(in_features=4096, out_features=4096, bias=True)
 
  (5): ReLU(inplace=True)
 
  (6): Linear(in_features=4096, out_features=1000, bias=True)
 
)