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visualize.py
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# coding:utf8
from itertools import chain
import visdom
import torch
import time
import torchvision as tv
import numpy as np
class Visualizer():
"""
封装了visdom的基本操作,但是你仍然可以通过`self.vis.function`
调用原生的visdom接口
"""
def __init__(self, env='default', **kwargs):
import visdom
self.vis = visdom.Visdom(env=env, use_incoming_socket=False,**kwargs)
# 画的第几个数,相当于横座标
# 保存(’loss',23) 即loss的第23个点
self.index = {}
self.log_text = ''
def reinit(self, env='default', **kwargs):
"""
修改visdom的配置
"""
self.vis = visdom.Visdom(env=env,use_incoming_socket=False, **kwargs)
return self
def plot_many(self, d):
"""
一次plot多个
@params d: dict (name,value) i.e. ('loss',0.11)
"""
for k, v in d.items():
self.plot(k, v)
def img_many(self, d):
for k, v in d.items():
self.img(k, v)
def plot(self, name, y):
"""
self.plot('loss',1.00)
"""
x = self.index.get(name, 0)
self.vis.line(Y=np.array([y]), X=np.array([x]),
win=(name),
opts=dict(title=name),
update=None if x == 0 else 'append'
)
self.index[name] = x + 1
def img(self, name, img_):
"""
self.img('input_img',t.Tensor(64,64))
"""
if len(img_.size()) < 3:
img_ = img_.cpu().unsqueeze(0)
self.vis.image(img_.cpu(),
win=(name),
opts=dict(title=name)
)
def img_grid_many(self, d):
for k, v in d.items():
self.img_grid(k, v)
def img_grid(self, name, input_3d):
"""
一个batch的图片转成一个网格图,i.e. input(36,64,64)
会变成 6*6 的网格图,每个格子大小64*64
"""
self.img(name, tv.utils.make_grid(
input_3d.cpu()[0].unsqueeze(1).clamp(max=1, min=0)))
def log(self, info, win='log_text'):
"""
self.log({'loss':1,'lr':0.0001})
"""
self.log_text += ('[{time}] {info} <br>'.format(
time=time.strftime('%m%d_%H%M%S'),
info=info))
self.vis.text(self.log_text, win=win)
def __getattr__(self, name):
return getattr(self.vis, name)