Python之tensorboardX：tensorboardX库的简介、安装、使用方法之详细攻略

目录

tensorboardX库的简介

tensorboardX库的安装

tensorboardX库的使用方法

1、基础案例

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tensorboardX库的简介

tensorboardX是用简单的函数调用编写TensorBoard事件。

github文档：GitHub - lanpa/tensorboardX: tensorboard for pytorch (and chainer, mxnet, numpy, ...)

tensorboardX库的安装

pip install tensorboardX==1.6

tensorboardX库的使用方法

1、基础案例

 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):
 
    dummy_s1 = torch.rand(1)
    dummy_s2 = torch.rand(1)
     data grouping by `slash`
    writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
    writer.add_scalar('data/scalar2', dummy_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)
 
    dummy_img = torch.rand(32, 3, 64, 64)   output from network
    if n_iter % 10 == 0:
        x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
        writer.add_image('Image', x, n_iter)
 
        dummy_audio = torch.zeros(sample_rate * 2)
        for i in range(x.size(0)):
             amplitude of sound should in [-1, 1]
            dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
        writer.add_audio('myAudio', dummy_audio, 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)
 
         needs tensorboard 0.4RC or later
        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)
 
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()