Pytorch網路結構視覺化

CVer發表於2019-06-17
PyTorch視覺化

作者：田海山

https://zhuanlan.zhihu.com/p/66320870

本文已授權，未經允許，不得二次轉載


安裝

可以透過以下的命令進行安裝

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

本教程基於以下的版本：

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) \n[GCC 7.3.0]'

載入庫

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

網路結構視覺化

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

載入alexnet，draw_model函式需要傳入三個引數，第一個為model，第二個引數為input_shape，第三個引數為orientation，可以選擇'LR'或者'TB'，分別代表左右佈局與上下佈局。

在notebook中，執行完上面的程式碼會顯示如下的圖，將網路的結構及各個層的name和shape進行了視覺化。

Pytorch網路結構視覺化

統計網路引數

可以透過model_stats方法統計各層的引數情況。

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!
Pytorch網路結構視覺化
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)
)

參考

https://github.com/microsoft/tensorwatch

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