Pytorch 网络结构可视化

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2021-06-01 19:42

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文自:深度学习这件小事

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

本教程基于以下的版本:

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)
)

考:

https://github.com/microsoft/tensorwatch

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