python第三⽅库visdom的使⽤⼊门教程概述
Visdom:⼀个灵活的可视化⼯具，可⽤来对于实时，富数据的创建，组织和共享。⽀持Torch和Numpy还有pytorch。
visdom
可以实现远程数据的可视化，对科学实验有很⼤帮助。我们可以远程的发送图⽚和数据，并进⾏在ui界⾯显⽰出来，检查实验结果，或者debug.要⽤这个先要安装，对于python模块⽽⾔，安装都是蛮简单的：
pip install visdom
安装完每次要⽤直接输⼊代码打开：
python -m visdom.server
使⽤⽰例
1. (), vis.image()
import visdom # 添加visdom库
import numpy as np # 添加numpy库
vis = visdom.Visdom(env='test') # 设置环境窗⼝的名称，如果不设置名称就默认为main
<('test', win='main') # 使⽤⽂本输出
vis.s((3, 100, 100))) # 绘制⼀幅尺⼨为3 * 100 * 100的图⽚，图⽚的像素值全部为1
其中:
visdom.Visdom(env=‘命名新环境')
<(‘⽂本', win=‘环境名')
vis.image(‘图⽚'，win=‘环境名')
2. 画直线 .line() ⼀条
import visdom
import numpy as np
vis = visdom.Visdom(env='my_windows') # 设置环境窗⼝的名称，如果不设置名称就默认为main
x = list(range(10))
y = list(range(10))
# 使⽤line函数绘制直线并选择显⽰坐标轴
vis.line(X=np.array(x), Y=np.array(y), opts=dict(showlegend=True))
vis.line([x], [y], opts=dict(showlegend=True)[展⽰说明])
两条
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), lumn_stack((np.array(y), np.array(z))), opts=dict(showlegend=True)) vis.line([x], [lumn_stack((np.array(y),np.array(z),np.array(还可以增加)))]) np.column_stack(a,b), 表⽰两个矩阵按列合并
sin(x)曲线
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))
持续更新图表
import visdom
import numpy as np
vis = visdom.Visdom(env='my_windows')
# 利⽤update更新图像
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')
使⽤“append”追加数据，“replace”使⽤新数据，“remove”⽤于删除“name”中指定的跟踪。
vis.images()
import visdom
import torch
# 新建⼀个连接客户端
# 指定env = 'test1'，默认是'main'，注意在浏览器界⾯做环境的切换
vis = visdom.Visdom(env='test1')
# 绘制正弦函数
x = torch.arange(1, 100, 0.01)
y = torch.sin(x)
vis.line(X=x,Y=y, win='sinx',opts={'title':'y=sin(x)'})
# 绘制36张图⽚随机的彩⾊图⽚
vis.images(torch.randn(36,3,64,64).numpy(),nrow=6, win='imgs',opts={'title':'imgs'})
绘制loss函数的变化趋势
#绘制loss变化趋势，参数⼀为Y轴的值，参数⼆为X轴的值，参数三为窗体名称，参数四为表格名称，参数五为更新选项，从第⼆个点开始可以更新vis.line(Y=np.array([totalloss.item()]), X=np.array([traintim
e]),
win=('train_loss'),
opts=dict(title='train_loss'),
update=None if traintime == 0 else 'append'
)
对于Visdom更详细的代码⽰例详见
更多介绍详见
实际代码
此代码出⾃CycleGAN的 utils.py ⾥⼀个实现
# 记录训练⽇志，显⽰⽣成图，画loss曲线的类
class Logger():
def __init__(self, n_epochs, batches_epoch):
'''
:param n_epochs: 跑多少个epochs
:param batches_epoch: ⼀个epoch有⼏个batches
'''
self.viz = Visdom() # 默认env是main函数
self.n_epochs = n_epochs
self.batches_epoch = batches_epoch
self.epoch = 1 # 当前epoch数
self.batch = 1 # 当前batch数
self.prev_time = time.time()
self.losses = {}
self.loss_windows = {} # 保存loss图的字典集合
self.image_windows = {} # 保存⽣成图的字典集合
def log(self, losses=None, images=None):
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() #这⾥losses[loss_name].data是个tensor（包在值外⾯的数据结构），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))numpy库需要安装吗
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_an_period/batches_done)))
# 显⽰⽣成图
for image_name, tensor in images.items(): # 字典.items()是以list形式返回键值对
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: # ⼀个epoch结束时
# 绘制loss曲线图
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图不断更新
# 每个epoch重置⼀次loss
self.losses[loss_name] = 0.0
# 跑完⼀个epoch，更新⼀下下⾯参数
self.epoch += 1
self.batch = 1
sys.stdout.write('\n')
else:
self.batch += 1
train.py中调⽤代码是
# 绘画Loss图
logger = Logger(opt.n_epochs, len(dataloader))
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
......
# 记录训练⽇志
# Progress report (localhost:8097) 显⽰visdom画图的⽹址
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})
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