Pytorch 可视化工具

避免自己采坑...(以下是本人安装版本)

1.安装python3.6+pytorch1.0.0

2.安装 torchvision 0.2 

pip install torchvision

3.安装Tensorflow和Tensorboard :清华镜像网站下载

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow
pip install tensorboard==1.7.0

4.安装TensorboardX

pip install  tensorboardX==1.8

5.运行demo.py  编码方式UTF-8

# _*_ coding: utf-8 _*_
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Sequential(     #input_size=(1*28*28)
            nn.Conv2d(1, 6, 5, 1, 2),
            nn.ReLU(),      #(6*28*28)
            nn.MaxPool2d(kernel_size=2, stride=2),  #output_size=(6*14*14)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(6, 16, 5),
            nn.ReLU(),      #(16*10*10)
            nn.MaxPool2d(2, 2)  #output_size=(16*5*5)
        )
        self.fc1 = nn.Sequential(
            nn.Linear(16 * 5 * 5, 120),
            nn.ReLU()
        )
        self.fc2 = nn.Sequential(
            nn.Linear(120, 84),
            nn.ReLU()
        )
        self.fc3 = nn.Linear(84, 10)

    # 定义前向传播过程，输入为x
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # nn.Linear()的输入输出都是维度为一的值，所以要把多维度的tensor展平成一维
        x = x.view(x.size()[0], -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

dummy_input = torch.rand(13, 1, 28, 28) #假设输入13张1*28*28的图片
model = LeNet()
with SummaryWriter(comment='LeNet') as w:
    w.add_graph(model, (dummy_input, ))

6. 运行demo.py,会生成runs文件夹，输入：

tensorboard --logdir runs

 会生成一个网址，复制网址，在谷歌浏览器中打开。

7.可视化的界面

 

踩雷解决

1. AttributeError: 'torch._C.Value' object has no attribute 'debugName'

TensorboardX版本太高，卸载后安装低版本TensorboardX。