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helper.py
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helper.py
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import datetime
import os
from tensorboardX import SummaryWriter
from collections import OrderedDict
import datetime
import numpy as np
import json
import matplotlib.pyplot as plt
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.__sum = 0
self.__count = 0
def update(self, val, n=1):
self.val = val
self.__sum += val * n
self.__count += n
@property
def avg(self):
if self.__count == 0:
return 0.
return self.__sum / self.__count
class Logger(object):
def __init__(self, log_dir, label, titles):
"""
log_dir : str, directory where all the logs will be written.
label : str, root filename for the logs. It shouldn't contain an extension, such as .txt
titles : list, title for each log attribute.
"""
self.log_dir = log_dir
self.label = label
self.titles = titles
self.logs = {} # all title-log pairs that will be traced for this instance
self.meters = {}
for t in titles:
self.logs[t] = []
self.meters[t] = AverageMeter()
if not os.path.exists(self.log_dir): os.makedirs(self.log_dir)
self.tb_logger = SummaryWriter(self.log_dir)
def save_logs(self):
"""
Saves raw log values in both numpy arrays and matplotlib plots.
"""
self.save_as_arrays()
self.save_as_figures()
def close(self):
"""
"""
self.save_logs()
self.tb_logger.close()
def update_meters(self, titles, values, n=1):
"""
Updates average meter of each title in titles.
If step is multiple of append_steps, then self.append is called.
titles : list, entries must be in self.titles.
values : list, must be of the same size as self.titles.
n : number of samples whose values are aggregated into values.
"""
assert len(titles) == len(values)
for t, v in zip(titles, values):
self.meters[t].update(v, n)
def flush_meters(self, step):
"""
Calls self.append with meters whose average value is non-zero. The function also
resets values of the meters.
"""
titles = []
values = []
for t, m in self.meters.items():
if m.avg != 0:
titles.append(t)
values.append(m.avg)
m.reset()
self.append(titles, values, step)
def append(self, titles, values, step):
"""
Adds a new log value for each title in titles.
titles : list, entries must be in self.titles.
values : list, value for each title in titles.
step : int, a step number for log summary
"""
if titles is None: titles = self.titles
assert len(titles) == len(values)
step_log = OrderedDict()
step_log['step'] = str(step)
step_log['time'] = datetime.datetime.now().strftime("%y-%m-%d %H:%M:%S")
for t, v in zip(titles, values):
self.logs[t].append(v)
step_log[t] = v
self.tb_logger.add_scalar(t, v, step)
f_txt = open(os.path.join(self.log_dir, '{}.txt'.format(self.label)), 'a')
json.dump(step_log, f_txt, indent=4)
f_txt.write('\n')
f_txt.flush()
f_txt.close()
def save_as_arrays(self):
"""
Converts all logs to numpy arrays and saves them into self.log_dir.
"""
arrays = {}
for t, v in self.logs.items():
if len(v) > 0:
v = np.array(v)
arrays[t] = v
if len(arrays) > 0:
np.savez(
os.path.join(self.log_dir, '{}.npz'.format(self.label)), **arrays)
def save_as_figures(self):
"""
First, converts all logs to numpy arrays, then plots them using matplotlib. Finally, saves the plots into self.log_dir.
"""
for t, v in self.logs.items():
if len(v) > 0:
v = np.array(v)
fig = plt.figure(dpi=400)
ax = fig.add_subplot(111)
ax.plot(v)
ax.set_title(t)
ax.grid(True)
fig.savefig(
os.path.join(self.log_dir, '{}_{}.png'.format(self.label, t.replace('/', '_'))),
bbox_inches='tight' )
plt.close()
def get_logger(log_dir):
training_log_titles = [
'ZSL/acc',
'R/loss',
'R/G_loss_R',
'criticD/GP_att',
'criticD/lambda1',
'criticD/WGAN',
'criticD2/lambda2',
'criticD2/WGAN',
'criticD2/GP_att',
'G/vae_loss',
'G/fakeG_loss',
'G/Trans_fakeG_loss',
'VAE/R_loss',
'criticR/GP_att',
'criticR/WD_unseen',
'Visualization/seen_norm',
'Visualization/unseen_norm',
]
training_logger = Logger(
log_dir,
'training_log',
training_log_titles)
return training_logger