30_visdom可视化、TensorboardX及其案例、安装visdom、使用visdom的案例
作者：互联网
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
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
(base) C:\Users\toto>
(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.
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')
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')
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'))
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)))
标签：visdom,TensorboardX,lib,site,installed,案例,packages,data 来源： https://blog.csdn.net/toto1297488504/article/details/113795817