TensorboardX：在pytorch上用Tensorboard

自2019年5月后，pytorch官方支持了Tensorboard，可千万pytorch官网查询Tensorboard的文档

TensorboardX

简介

GitHub来源

原理

调用TensorFlow的tensorboard，接口封装成pytorch的格式

安装

安装依赖包

pip install tensorflow

安装方法

由于pip install tensorboardX安装的版本比该法低，许多功能不支持，故
请使用下法安装

pip install git+https://github.com/lanpa/tensorboard-pytorch

使用

创建demo.py

import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter

resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]

for n_iter in range(100):
    s1 = torch.rand(1) # value to keep
    s2 = torch.rand(1)
    writer.add_scalar('data/scalar1', s1[0], n_iter) #data grouping by `slash`
    writer.add_scalar('data/scalar2', s2[0], n_iter)
    writer.add_scalars('data/scalar_group', {"xsinx":n_iter*np.sin(n_iter),
                                             "xcosx":n_iter*np.cos(n_iter),
                                             "arctanx": np.arctan(n_iter)}, n_iter)
    x = torch.rand(32, 3, 64, 64) # output from network
    if n_iter%10==0:
        x = vutils.make_grid(x, normalize=True, scale_each=True)
        writer.add_image('Image', x, n_iter)
        x = torch.zeros(sample_rate*2)
        for i in range(x.size(0)):
            x[i] = np.cos(freqs[n_iter//10]*np.pi*float(i)/float(sample_rate)) # sound amplitude should in [-1, 1]
        writer.add_audio('myAudio', x, n_iter, sample_rate=sample_rate)
        writer.add_text('Text', 'text logged at step:'+str(n_iter), n_iter)
        for name, param in resnet18.named_parameters():
            writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter) #needs tensorboard 0.4RC or later
dataset = datasets.MNIST('mnist', train=False, download=True)
images = dataset.test_data[:100].float()
label = dataset.test_labels[:100]
features = images.view(100, 784)
writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))

# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")

writer.close()

执行目标文件

python demo.py

打开TensorBoard

tensorboard --logdir runs --port=6006

默认端口6006，可更改

本地访问TensorBoard

tb 4 6006

• 4是gpu服务器的编号

• 6006是tensorboard占用的服务器端口，需保持和上面的一致

API

文档