pytorch visdom安装开启及使用方法

安装

conda activate ps 
pip install visdom

激活ps的环境，在指定的ps环境中安装visdom

开启

python -m visdom.server

浏览器输入红框内的网址

使用

1. 简单示例：一条线

from visdom import Visdom

# 创建一个实例
viz=Visdom()

# 创建一个直线，再把最新数据添加到直线上
# y x二维两个轴，win 创建一个小窗口，不指定就默认为大窗口，opts其他信息比如名称
viz.line([1,2,3,4],[1,2,3,4],win="train_loss",opts=dict(title="train_loss"))

# 更一般的情况,因为下面y x数据不存在，只是示例
#  append 添加到原来的后面，不然全部覆盖掉
# viz.line([loss.item()],[global_step],win="train_loss",update="append")

2. 简单示例：2条线

下面主要是[[y1],[y2]],[x] 两条映射，legend就是线条名称

from visdom import Visdom
viz=Visdom()
viz.line([[1,2],[5,6]],[1,2],win="loss_acc",opts=dict(title="train loss & acc",legend=["loss","acc"]))

3. 显示图片

from visdom import Visdom
viz=Visdom()
# data 是一个batch
viz.image(data.view(-1,1,28,28),win="x")
viz.text(str(pred.datach().cpu().numpy()),win="pred",opts=dict(title="pred"))

4. 手写数字示例

动画效果图如下

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}%)]	Loss: {:.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("
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
".format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

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原文链接：https://blog.csdn.net/qq_42146775/article/details/115837929