30_visdom可视化、TensorboardX及其案例、安装visdom、使用visdom的案例

本是古典何须时尚 2022-10-29 10:25 332阅读 0赞

## 1.25.visdom可视化 ##

### 1.25.1.TensorboardX ###

Pytorch也能用的tensorboard，此外Pytorch还有visdom可视化。

首先上项目地址：https://github.com/lanpa/tensorboardX

### 1.25.2.安装 ###

(base) C:\Users\toto>pip install tensorboardX
    Collecting tensorboardX
      Downloading tensorboardX-2.1-py2.py3-none-any.whl (308 kB)
         |████████████████████████████████| 308 kB 726 kB/s
    Requirement already satisfied: six in d:\installed\anaconda3\lib\site-packages (from tensorboardX) (1.12.0)
    Requirement already satisfied: protobuf>=3.8.0 in d:\installed\anaconda3\lib\site-packages (from tensorboardX) (3.12.1)
    Requirement already satisfied: numpy in d:\installed\anaconda3\lib\site-packages (from tensorboardX) (1.20.1)
    Requirement already satisfied: setuptools in d:\installed\anaconda3\lib\site-packages (from protobuf>=3.8.0->tensorboardX) (41.4.0)
    Installing collected packages: tensorboardX
    Successfully installed tensorboardX-2.1
    
    (base) C:\Users\toto>

**或者从源码中build**

pip install 'git+https://github.com/lanpa/tensorboardX'

您可以选择安装crc32c来加速。

(base) C:\Users\toto>pip install crc32c
    Collecting crc32c
      Downloading crc32c-2.2-cp37-cp37m-win_amd64.whl (31 kB)
    Installing collected packages: crc32c
    Successfully installed crc32c-2.2
    
    (base) C:\Users\toto>

从tensorboardX 2.1开始，你需要为add\_audio()函数安装soundfile(200倍加速)。

(base) C:\Users\toto>pip install soundfile
    Collecting soundfile
      Downloading SoundFile-0.10.3.post1-py2.py3.cp26.cp27.cp32.cp33.cp34.cp35.cp36.pp27.pp32.pp33-none-win_amd64.whl (689 kB)
         |████████████████████████████████| 689 kB 117 kB/s
    Requirement already satisfied: cffi>=1.0 in d:\installed\anaconda3\lib\site-packages (from soundfile) (1.12.3)
    Requirement already satisfied: pycparser in d:\installed\anaconda3\lib\site-packages (from cffi>=1.0->soundfile) (2.19)
    Installing collected packages: soundfile
    Successfully installed soundfile-0.10.3.post1
    
    (base) C:\Users\toto>

### 1.25.3.例子 ###

克隆文件：https://github.com/lanpa/tensorboardX/tree/master/examples  
运行demo脚本：python examples/demo.py  
运行TensorBoard可以用：tensorboard --logdir runs

在下面可能运行：

(base) C:\Users\toto>tensorboard --logdir runs
    2021-02-12 13:35:13.345643: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
    Traceback (most recent call last):
    ValueError: Duplicate plugins for name projector

**例子：**

# -*- coding: UTF-8 -*-
    
    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()

### 1.25.4.安装visdom ###

(base) C:\Users\toto>pip install visdom
    Collecting visdom
      Downloading visdom-0.1.8.9.tar.gz (676 kB)
         |████████████████████████████████| 676 kB 27 kB/s
    Requirement already satisfied: numpy>=1.8 in d:\installed\anaconda3\lib\site-packages (from visdom) (1.18.5)
    Requirement already satisfied: scipy in d:\installed\anaconda3\lib\site-packages (from visdom) (1.4.1)
    Requirement already satisfied: requests in d:\installed\anaconda3\lib\site-packages (from visdom) (2.22.0)
    Requirement already satisfied: tornado in d:\installed\anaconda3\lib\site-packages (from visdom) (6.0.3)
    Requirement already satisfied: pyzmq in d:\installed\anaconda3\lib\site-packages (from visdom) (18.1.0)
    Requirement already satisfied: six in d:\installed\anaconda3\lib\site-packages (from visdom) (1.15.0)
    Collecting jsonpatch
      Downloading jsonpatch-1.28-py2.py3-none-any.whl (12 kB)
    Collecting torchfile
      Downloading torchfile-0.1.0.tar.gz (5.2 kB)
    Collecting websocket-client
      Downloading websocket_client-0.57.0-py2.py3-none-any.whl (200 kB)
         |████████████████████████████████| 200 kB 20 kB/s
    Requirement already satisfied: pillow in d:\installed\anaconda3\lib\site-packages (from visdom) (6.2.0)
    Requirement already satisfied: chardet<3.1.0,>=3.0.2 in d:\installed\anaconda3\lib\site-packages (from requests->visdom) (3.0.4)
    Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in d:\installed\anaconda3\lib\site-packages (from requests->visdom) (1.24.2)
    Requirement already satisfied: idna<2.9,>=2.5 in d:\installed\anaconda3\lib\site-packages (from requests->visdom) (2.8)
    Requirement already satisfied: certifi>=2017.4.17 in d:\installed\anaconda3\lib\site-packages (from requests->visdom) (2019.9.11)
    Collecting jsonpointer>=1.9
      Downloading jsonpointer-2.0-py2.py3-none-any.whl (7.6 kB)
    Building wheels for collected packages: visdom, torchfile
      Building wheel for visdom (setup.py) ... done
      Created wheel for visdom: filename=visdom-0.1.8.9-py3-none-any.whl size=655249 sha256=aa110012ea279783c86dcbc7778c90e8d4c021e5d9ac1a98f68c3134b7c52627
      Stored in directory: c:\users\toto\appdata\local\pip\cache\wheels\2d\d1\9b\cde923274eac9cbb6ff0d8c7c72fe30a3da9095a38fd50bbf1
      Building wheel for torchfile (setup.py) ... done
      Created wheel for torchfile: filename=torchfile-0.1.0-py3-none-any.whl size=5711 sha256=ac722c7bbb17ae1c36bef8db4b20cd6978462f240d76c5b6b0a4dfc6719db2b4
      Stored in directory: c:\users\toto\appdata\local\pip\cache\wheels\ac\5c\3a\a80e1c65880945c71fd833408cd1e9a8cb7e2f8f37620bb75b
    Successfully built visdom torchfile
    Installing collected packages: jsonpointer, jsonpatch, torchfile, websocket-client, visdom
    Successfully installed jsonpatch-1.28 jsonpointer-2.0 torchfile-0.1.0 visdom-0.1.8.9 websocket-client-0.57.0
    
    (base) C:\Users\toto>

### 1.25.5.后台运行visdom.server ###

(base) C:\Users\toto>pip install visdom  -i https://pypi.tuna.tsinghua.edu.cn/simple
    Collecting visdom
      Downloading visdom-0.1.8.9.tar.gz (676 kB)
         |████████████████████████████████| 676 kB 27 kB/s
    Requirement already satisfied: numpy>=1.8 in d:\installed\anaconda3\lib\site-packages (from visdom) (1.18.5)
    Requirement already satisfied: scipy in d:\installed\anaconda3\lib\site-packages (from visdom) (1.4.1)
    Requirement already satisfied: requests in d:\installed\anaconda3\lib\site-packages (from visdom) (2.22.0)
    Requirement already satisfied: tornado in d:\installed\anaconda3\lib\site-packages (from visdom) (6.0.3)
    Requirement already satisfied: pyzmq in d:\installed\anaconda3\lib\site-packages (from visdom) (18.1.0)
    Requirement already satisfied: six in d:\installed\anaconda3\lib\site-packages (from visdom) (1.15.0)
    Collecting jsonpatch
      Downloading jsonpatch-1.28-py2.py3-none-any.whl (12 kB)
    Collecting torchfile
      Downloading torchfile-0.1.0.tar.gz (5.2 kB)
    Collecting websocket-client
    xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    Installing collected packages: jsonpointer, jsonpatch, torchfile, websocket-client, visdom
    Successfully installed jsonpatch-1.28 jsonpointer-2.0 torchfile-0.1.0 visdom-0.1.8.9 websocket-client-0.57.0
    
    安装并更新，不更新不能正常使用，会卡在download script。
    (base) C:\Users\toto> pip install --upgrade visdom

(base) C:\Users\toto>python -m visdom.server
    D:\installed\Anaconda3\lib\site-packages\visdom\server.py:39: DeprecationWarning: zmq.eventloop.ioloop is deprecated in pyzmq 17. pyzmq now works with default tornado and asyncio eventloops.
      ioloop.install()  # Needs to happen before any tornado imports!
    Checking for scripts.

如果在启动过程中一直报：Downloading scripts, this may take a little while  
一直卡着不动，解决办法是：

*  找到visdom安装位置，我的路径为D:\\Anaconda3\\Lib\\site-packages\\visdom，打开sever.py。
 *  修改server.py文件，在函数download\_scripts\_and\_run下注释掉download\_scripts()，第1917行。  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]  
    然后再执行，出现如下错误：

(base) C:\Users\toto>python -m visdom.server
    D:\installed\anaconda3\lib\site-packages\visdom\server.py:39: DeprecationWarning: zmq.eventloop.ioloop is deprecated in pyzmq 17. pyzmq now works with default tornado and asyncio eventloops.
      ioloop.install()  # Needs to happen before any tornado imports!
    It's Alive!
    Traceback (most recent call last):
      File "D:\installed\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
        return _run_code(code, main_globals, None,
      File "D:\installed\anaconda3\lib\runpy.py", line 87, in _run_code
        exec(code, run_globals)
      File "D:\installed\anaconda3\lib\site-packages\visdom\server.py", line 1922, in <module>
        download_scripts_and_run()
      File "D:\installed\anaconda3\lib\site-packages\visdom\server.py", line 1918, in download_scripts_and_run
        main()
      File "D:\installed\anaconda3\lib\site-packages\visdom\server.py", line 1910, in main
        start_server(port=FLAGS.port, hostname=FLAGS.hostname, base_url=base_url,
      File "D:\installed\anaconda3\lib\site-packages\visdom\server.py", line 1791, in start_server
        app.listen(port, max_buffer_size=1024 ** 3)
      File "D:\installed\anaconda3\lib\site-packages\tornado\web.py", line 2116, in listen
        server.listen(port, address)
      File "D:\installed\anaconda3\lib\site-packages\tornado\tcpserver.py", line 152, in listen
        self.add_sockets(sockets)
      File "D:\installed\anaconda3\lib\site-packages\tornado\tcpserver.py", line 165, in add_sockets
        self._handlers[sock.fileno()] = add_accept_handler(
      File "D:\installed\anaconda3\lib\site-packages\tornado\netutil.py", line 279, in add_accept_handler
        io_loop.add_handler(sock, accept_handler, IOLoop.READ)
      File "D:\installed\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 100, in add_handler
        self.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)
      File "D:\installed\anaconda3\lib\asyncio\events.py", line 501, in add_reader
        raise NotImplementedError
    NotImplementedError
    
    (base) C:\Users\toto>

大概的意思Python3.8的asyncio改变了循环方式，因为这种方式在windows上不支持相应的add\_reader APIs，就会抛NotImplementedError。

解决办法是：  找到这个项目使用的python环境的lib\\site-packages,做下面的修改，在\\Lib\\site-packages\\tornado\\platform\\asyncio.py开头添加代码：

import sys            #此处如果已经引入过了，不用再引入了。
    
    if sys.platform == 'win32':
        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]  
然后在执行命令：  
![在这里插入图片描述][20210228205802616.png]  
**如果期间出现：**

Visdom python client failed to establish socket to get messages from the server. This feature is optional 
    and can be disabled by initializing Visdom with `use_incoming_socket=False`, which will prevent waiting for this request to timeout.

**解决办法：**

vis = visdom.Visdom(use_incoming_socket=False)

示例：

在这里插入代码片

**lines: single trace**

from visdom import Visdom
    
    viz = Visdom()
    
    viz.line([0.],[0.], win='train_loss', opts=dict(title='train loss'))
    
    viz.line([loss.item()], [global_step], win='train_loss', update='append')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]

**lines: multi-traces**

from visdom import Visdom
    
    viz = Visdom()
    
    viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',
                                                       legend=['loss', 'acc.']))
    
    viz.line([[test_loss, correct / len(test_loader.dataset)]],
             [global_step], win='test', update='append')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 3]

**visual X**

from visdom import Visdom
    
    viz = Visdom()
    
    viz.images(data.view(-1, 1, 28, 28), win='x')
    
    viz.text(str(pred.detach().cpu().numpy()), win='pred', opts=dict(title='pred'))

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 4]

### 1.25.6.使用visdom的案例 ###

import  torch
    import  torch.nn as nn
    import  torch.nn.functional as F
    import  torch.optim as optim
    from    torchvision import datasets, transforms
    
    from visdom import Visdom
    
    batch_size=200
    learning_rate=0.01
    epochs=10
    
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           # transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=batch_size, shuffle=True)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.Compose([
            transforms.ToTensor(),
            # transforms.Normalize((0.1307,), (0.3081,))
        ])),
        batch_size=batch_size, shuffle=True)
    
    
    class MLP(nn.Module):
    
        def __init__(self):
            super(MLP, self).__init__()
    
            self.model = nn.Sequential(
                nn.Linear(784, 200),
                nn.LeakyReLU(inplace=True),
                nn.Linear(200, 200),
                nn.LeakyReLU(inplace=True),
                nn.Linear(200, 10),
                nn.LeakyReLU(inplace=True),
            )
    
        def forward(self, x):
            x = self.model(x)
    
            return x
    
    device = torch.device('cuda:0')
    net = MLP().to(device)
    optimizer = optim.SGD(net.parameters(), lr=learning_rate)
    criteon = nn.CrossEntropyLoss().to(device)
    
    viz = Visdom()
    
    viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))
    viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',
                                                       legend=['loss', 'acc.']))
    global_step = 0
    
    for epoch in range(epochs):
    
        for batch_idx, (data, target) in enumerate(train_loader):
            data = data.view(-1, 28*28)
            data, target = data.to(device), target.cuda()
    
            logits = net(data)
            loss = criteon(logits, target)
    
            optimizer.zero_grad()
            loss.backward()
            # print(w1.grad.norm(), w2.grad.norm())
            optimizer.step()
    
            global_step += 1
            viz.line([loss.item()], [global_step], win='train_loss', update='append')
    
            if batch_idx % 100 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                           100. * batch_idx / len(train_loader), loss.item()))
    
    
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = data.view(-1, 28 * 28)
            data, target = data.to(device), target.cuda()
            logits = net(data)
            test_loss += criteon(logits, target).item()
    
            pred = logits.argmax(dim=1)
            correct += pred.eq(target).float().sum().item()
    
        viz.line([[test_loss, correct / len(test_loader.dataset)]],
                 [global_step], win='test', update='append')
        viz.images(data.view(-1, 1, 28, 28), win='x')
        viz.text(str(pred.detach().cpu().numpy()), win='pred',
                 opts=dict(title='pred'))
    
        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]: /images/20221024/bf79f4a0fe9d4a40b5b4d29babd5d4ba.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]: /images/20221024/a3769f9f917343c0bfb377bad405be2c.png
[20210228205802616.png]: /images/20221024/8dca4613ed42401c82c6befcf9060d9c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]: /images/20221024/d3051b0b91e14ff5ac01377d2238d14a.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 3]: /images/20221024/12bd85eb94934baa84215267e3337503.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 4]: /images/20221024/08e86a7723d44a5eb9f845fd6696ebd7.png