1.25.visdom可視化

1.25.1.TensorboardX

Pytorch也能用的tensorboard，此外Pytorch還有visdom可視化。

首先上項目地址：https://github.com/lanpa/tensorboardX

1.25.2.安裝

(base) C:\Users\toto>pip install tensorboardX Collecting tensorboardXDownloading 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>

或者從源碼中build

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

您可以選擇安裝crc32c來加速。

(base) C:\Users\toto>pip install crc32c Collecting crc32cDownloading crc32c-2.2-cp37-cp37m-win_amd64.whl (31 kB) Installing collected packages: crc32c Successfully installed crc32c-2.2(base) C:\Users\toto>

從tensorboardX 2.1開始，你需要為add_audio()函數安裝soundfile(200倍加速)。

(base) C:\Users\toto>pip install soundfile Collecting soundfileDownloading 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
運行demo腳本：python examples/demo.py
運行TensorBoard可以用：tensorboard --logdir runs

在下面可能運行：

(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 SummaryWriterresnet18 = 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 networkif 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 laterwriter.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.安裝visdom

(base) C:\Users\toto>pip install visdom Collecting visdomDownloading 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 jsonpatchDownloading jsonpatch-1.28-py2.py3-none-any.whl (12 kB) Collecting torchfileDownloading torchfile-0.1.0.tar.gz (5.2 kB) Collecting websocket-clientDownloading 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>=1.9Downloading jsonpointer-2.0-py2.py3-none-any.whl (7.6 kB) Building wheels for collected packages: visdom, torchfileBuilding wheel for visdom (setup.py) ... doneCreated wheel for visdom: filename=visdom-0.1.8.9-py3-none-any.whl size=655249 sha256=aa110012ea279783c86dcbc7778c90e8d4c021e5d9ac1a98f68c3134b7c52627Stored in directory: c:\users\toto\appdata\local\pip\cache\wheels\2d\d1\9b\cde923274eac9cbb6ff0d8c7c72fe30a3da9095a38fd50bbf1Building wheel for torchfile (setup.py) ... doneCreated wheel for torchfile: filename=torchfile-0.1.0-py3-none-any.whl size=5711 sha256=ac722c7bbb17ae1c36bef8db4b20cd6978462f240d76c5b6b0a4dfc6719db2b4Stored 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.后臺運行visdom.server

(base) C:\Users\toto>pip install visdom -i https://pypi.tuna.tsinghua.edu.cn/simple Collecting visdomDownloading 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 jsonpatchDownloading jsonpatch-1.28-py2.py3-none-any.whl (12 kB) Collecting torchfileDownloading 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安裝并更新，不更新不能正常使用，會卡在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.

如果在啟動過程中一直報：Downloading scripts, this may take a little while
一直卡著不動，解決辦法是：

• 找到visdom安裝位置，我的路徑為D:\Anaconda3\Lib\site-packages\visdom，打開sever.py。
• 修改server.py文件，在函數download_scripts_and_run下注釋掉download_scripts()，第1917行。
  
  然后再執行，出現如下錯誤：

(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_mainreturn _run_code(code, main_globals, None,File "D:\installed\anaconda3\lib\runpy.py", line 87, in _run_codeexec(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_runmain()File "D:\installed\anaconda3\lib\site-packages\visdom\server.py", line 1910, in mainstart_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_serverapp.listen(port, max_buffer_size=1024 ** 3)File "D:\installed\anaconda3\lib\site-packages\tornado\web.py", line 2116, in listenserver.listen(port, address)File "D:\installed\anaconda3\lib\site-packages\tornado\tcpserver.py", line 152, in listenself.add_sockets(sockets)File "D:\installed\anaconda3\lib\site-packages\tornado\tcpserver.py", line 165, in add_socketsself._handlers[sock.fileno()] = add_accept_handler(File "D:\installed\anaconda3\lib\site-packages\tornado\netutil.py", line 279, in add_accept_handlerio_loop.add_handler(sock, accept_handler, IOLoop.READ)File "D:\installed\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 100, in add_handlerself.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)File "D:\installed\anaconda3\lib\asyncio\events.py", line 501, in add_readerraise NotImplementedError NotImplementedError(base) C:\Users\toto>

大概的意思Python3.8的asyncio改變了循環方式，因為這種方式在windows上不支持相應的add_reader APIs，就會拋NotImplementedError。

解決辦法是： 找到這個項目使用的python環境的lib\site-packages,做下面的修改，在\Lib\site-packages\tornado\platform\asyncio.py開頭添加代碼： import sys #此處如果已經引入過了，不用再引入了。if sys.platform == 'win32':asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

然后在執行命令：

如果期間出現：

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.

解決辦法：

vis = visdom.Visdom(use_incoming_socket=False)

示例：

在這里插入代碼片

lines: single trace

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

lines: multi-traces

from visdom import Visdomviz = 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')

visual X

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

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, transformsfrom visdom import Visdombatch_size=200 learning_rate=0.01 epochs=10train_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 xdevice = 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 = 0for 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 += 1viz.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 = 0correct = 0for 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)))

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