TensorboardX----linux服务器远程训练，Windows本地查看日志

     使用pytorch训练大型数据集是常常需要通过loss的下降曲线或者acc准确率的上升情况直观上判断模型的设计是否合理，使用tensorboardX将迭代的loss和acc加入scale中，方便查看中间过程，及时调整模型。

安装：

pip install tensorflow
pip install tensorboardX

定义：

import numpy as np
from tensorboardX import SummaryWriter

writer = SummaryWriter()

 添加scale： 

for n_iter in range(100):
    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)
writer.close()

添加graph：

resnet18 = models.resnet18(False)
dumpty_input = torch.randn(1, 3, 224, 224)
writer.add_graph(resnet18, (dummy_input,))

demo代码：

# 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()

训练后生成的目录文件如下：

linux端：

tensorboard --logdir=./runs

windows端：

ssh -L local_port:127.0.0.1:tensorboard_port username@server_port

浏览器复制地址：

http://localhost:6012/

出现如下界面：

参考代码：tensorboardX_github

来源：CSDN

作者：qq_37172182

链接：https://blog.csdn.net/qq_37172182/article/details/104371497