pip install visdom

python -m visdom.server

import visdom  # 添加visdom庫(kù)
import numpy as np  # 添加numpy庫(kù)
vis = visdom.Visdom(env='test')  # 設(shè)置環(huán)境窗口的名稱,如果不設(shè)置名稱就默認(rèn)為main
vis.text('test', win='main')  # 使用文本輸出
vis.image(np.ones((3, 100, 100)))  # 繪制一幅尺寸為3 * 100 * 100的圖片，圖片的像素值全部為1

import visdom
import numpy as np
vis = visdom.Visdom(env='my_windows')  # 設(shè)置環(huán)境窗口的名稱,如果不設(shè)置名稱就默認(rèn)為main
x = list(range(10))
y = list(range(10))
# 使用line函數(shù)繪制直線 并選擇顯示坐標(biāo)軸
vis.line(X=np.array(x), Y=np.array(y), opts=dict(showlegend=True))

import visdom
import numpy as np
vis = visdom.Visdom(env='my_windows')
x = list(range(10))
y = list(range(10))
z = list(range(1,11))
vis.line(X=np.array(x), Y=np.column_stack((np.array(y), np.array(z))),  opts=dict(showlegend=True))

import visdom
import torch
vis = visdom.Visdom(env='sin')
x = torch.arange(0, 100, 0.1)
y = torch.sin(x)
vis.line(X=x,Y=y,win='sin(x)',opts=dict(showlegend=True))

import visdom
import numpy as np
vis = visdom.Visdom(env='my_windows')
# 利用update更新圖像
x = 0
y = 0
my_win = vis.line(X=np.array([x]), Y=np.array([y]), opts=dict(title='Update'))
for i in range(10):
    x += 1
    y += i
    vis.line(X=np.array([x]), Y=np.array([y]), win=my_win, update='append')

import visdom
import torch
# 新建一個(gè)連接客戶端
# 指定env = 'test1'，默認(rèn)是'main',注意在瀏覽器界面做環(huán)境的切換
vis = visdom.Visdom(env='test1')
# 繪制正弦函數(shù)
x = torch.arange(1, 100, 0.01)
y = torch.sin(x)
vis.line(X=x,Y=y, win='sinx',opts={'title':'y=sin(x)'})
# 繪制36張圖片隨機(jī)的彩色圖片
vis.images(torch.randn(36,3,64,64).numpy(),nrow=6, win='imgs',opts={'title':'imgs'})

#繪制loss變化趨勢(shì)，參數(shù)一為Y軸的值，參數(shù)二為X軸的值，參數(shù)三為窗體名稱，參數(shù)四為表格名稱，參數(shù)五為更新選項(xiàng)，從第二個(gè)點(diǎn)開(kāi)始可以更新
vis.line(Y=np.array([totalloss.item()]), X=np.array([traintime]),
                win=('train_loss'),
                opts=dict(title='train_loss'),
                update=None if traintime == 0 else 'append'
                )

# 記錄訓(xùn)練日志，顯示生成圖，畫(huà)loss曲線 的類(lèi)
class Logger():
    def __init__(self, n_epochs, batches_epoch):
        '''
        :param n_epochs:  跑多少個(gè)epochs
        :param batches_epoch:  一個(gè)epoch有幾個(gè)batches
        '''
        self.viz = Visdom() # 默認(rèn)env是main函數(shù)
        self.n_epochs = n_epochs
        self.batches_epoch = batches_epoch
        self.epoch = 1 # 當(dāng)前epoch數(shù)
        self.batch = 1 # 當(dāng)前batch數(shù)
        self.prev_time = time.time()
        self.mean_period = 0
        self.losses = {}
        self.loss_windows = {} # 保存loss圖的字典集合
        self.image_windows = {} # 保存生成圖的字典集合

    def log(self, losses=None, images=None):
        self.mean_period += (time.time() - self.prev_time)
        self.prev_time = time.time()

        sys.stdout.write('\rEpoch %03d/%03d [%04d/%04d] -- ' % (self.epoch, self.n_epochs, self.batch, self.batches_epoch))

        for i, loss_name in enumerate(losses.keys()):
            if loss_name not in self.losses:
                self.losses[loss_name] = losses[loss_name].data.item() #這里losses[loss_name].data是個(gè)tensor（包在值外面的數(shù)據(jù)結(jié)構(gòu)），要用item方法取值
            else:
                self.losses[loss_name] = losses[loss_name].data.item()

            if (i + 1) == len(losses.keys()):
                sys.stdout.write('%s: %.4f -- ' % (loss_name, self.losses[loss_name]/self.batch))
            else:
                sys.stdout.write('%s: %.4f | ' % (loss_name, self.losses[loss_name]/self.batch))

        batches_done = self.batches_epoch * (self.epoch - 1) + self.batch
        batches_left = self.batches_epoch * (self.n_epochs - self.epoch) + self.batches_epoch - self.batch
        sys.stdout.write('ETA: %s' % (datetime.timedelta(seconds=batches_left*self.mean_period/batches_done)))

        # 顯示生成圖
        for image_name, tensor in images.items(): # 字典.items()是以list形式返回鍵值對(duì)
            if image_name not in self.image_windows:
                self.image_windows[image_name] = self.viz.image(tensor2image(tensor.data), opts={'title':image_name})
            else:
                self.viz.image(tensor2image(tensor.data), win=self.image_windows[image_name], opts={'title':image_name})

        # End of each epoch
        if (self.batch % self.batches_epoch) == 0: # 一個(gè)epoch結(jié)束時(shí)
            # 繪制loss曲線圖
            for loss_name, loss in self.losses.items():
                if loss_name not in self.loss_windows:
                    self.loss_windows[loss_name] = self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]),
                                                                 opts={'xlabel':'epochs', 'ylabel':loss_name, 'title':loss_name})
                else:
                    self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), win=self.loss_windows[loss_name], update='append') #update='append'可以使loss圖不斷更新
                # 每個(gè)epoch重置一次loss
                self.losses[loss_name] = 0.0
            # 跑完一個(gè)epoch，更新一下下面參數(shù)
            self.epoch += 1
            self.batch = 1
            sys.stdout.write('\n')
        else:
            self.batch += 1

# 繪畫(huà)Loss圖
logger = Logger(opt.n_epochs, len(dataloader))

for epoch in range(opt.epoch, opt.n_epochs):
    for i, batch in enumerate(dataloader):
    	
    	......
		
		# 記錄訓(xùn)練日志
            # Progress report (http://localhost:8097) 顯示visdom畫(huà)圖的網(wǎng)址
            logger.log({'loss_G': loss_G, 'loss_G_identity': (loss_identity_A + loss_identity_B),
                        'loss_G_GAN': (loss_GAN_A2B + loss_GAN_B2A),
                        'loss_G_cycle': (loss_cycle_ABA + loss_cycle_BAB), 'loss_D': (loss_D_A + loss_D_B)},
                       images={'real_A': real_A, 'real_B': real_B, 'fake_A': fake_A, 'fake_B': fake_B})