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在原本代码中额外添加如下几行即可实现查看模型结构#xff1a; from tensorboardX import SummaryWriter # 用于进行可视化# 1. 来用tensorflow进行可视化with SummaryWriter(./log, commentsample_model_visualization) as sw: …总结
在原本代码中额外添加如下几行即可实现查看模型结构 from tensorboardX import SummaryWriter # 用于进行可视化# 1. 来用tensorflow进行可视化with SummaryWriter(./log, commentsample_model_visualization) as sw: sw.add_graph(modelviz, sampledata) 操作步骤如下
安装完torch之后再安装tensorboardX
pip install tensorboardX -i https://pypi.tuna.tsinghua.edu.cn/simple
运行下面代码
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter # 用于进行可视化 class modelViz(nn.Module):def __init__(self):super(modelViz, self).__init__()self.conv1 nn.Conv2d(3, 16, 3, 1, padding1)self.bn1 nn.BatchNorm2d(16)self.conv2 nn.Conv2d(16, 64, 3, 1, padding1)self.bn2 nn.BatchNorm2d(64)self.conv3 nn.Conv2d(64, 10, 3, 1, padding1)self.bn3 nn.BatchNorm2d(10)def forward(self, x):x self.bn1(self.conv1(x))x F.relu(x)x self.bn2(self.conv2(x))x F.relu(x)x self.bn3(self.conv3(x))x F.relu(x)return xif __name__ __main__:# 首先来搭建一个模型modelviz modelViz()# 创建输入sampledata torch.rand(1, 3, 4, 4)# 看看输出结果对不对out modelviz(sampledata)print(out) # 测试有输出网络没有问题# 1. 来用tensorflow进行可视化with SummaryWriter(./log, commentsample_model_visualization) as sw:sw.add_graph(modelviz, sampledata)# # 2. 保存成pt文件后进行可视化# torch.save(modelviz, ./log/modelviz.pt)
运行代码后会在./log路径下生成一个tfevents文件在终端中进入代码的主目录下执行命令
tensorboard --logdir./ 然后会输出
(base) jiedell:~/桌面/fno_task$ tensorboard --logdir./
TensorFlow installation not found - running with reduced feature set.NOTE: Using experimental fast data loading logic. To disable, pass--load_fastfalse and report issues on GitHub. More details:https://github.com/tensorflow/tensorboard/issues/4784Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.16.2 at http://localhost:6006/ (Press CTRLC to quit)http://localhost:6006/
然后按照提示打开浏览器输入上面这个网址就可以看到我们搭建的网络结构了如下图所示可以双击打开每一个节点查看其内容。也可以查看详细的结构以及每一层的输入输出shape。通过双击模型的组件实现展示网络细节和收起细节。 结束
官网详细和介绍使用链接https://www.tensorflow.org/tensorboard/graphs?hlzh-cn tips:tensorboard是适用于tensorflow而tensorboardX可以适用pytorch
tips: 如果你在虚拟环境cd到log的上一级文件夹那么按照上面的路径就得不到你想要的可视化结果路径不正确应该输入
tensorboard --logdir./log/ 参考链接https://blog.csdn.net/Vertira/article/details/127326470
