/
logger.py
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/
logger.py
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import argparse
import sys
if sys.version_info[0] < 3:
import cPickle as pickle
else:
import _pickle as pickle
import os
import shutil
import numpy as np
import matplotlib.pyplot as plt
import time
def copy_script_to_folder(caller_path, folder):
'''copy script'''
script_filename = caller_path.split('/')[-1]
script_relative_path = os.path.join(folder, script_filename)
shutil.copy(caller_path, script_relative_path)
def time_string():
'''convert time format'''
ISOTIMEFORMAT = '%Y-%m-%d %X'
string = '[{}]'.format(time.strftime(ISOTIMEFORMAT, time.gmtime(time.time())))
return string
def convert_secs2time(epoch_time):
need_hour = int(epoch_time / 3600)
need_mins = int((epoch_time - 3600 * need_hour) / 60)
need_secs = int(epoch_time - 3600 * need_hour - 60 * need_mins)
return need_hour, need_mins, need_secs
class RecorderMeter(object):
"""Computes and stores the minimum loss value and its epoch index"""
def __init__(self, total_epoch):
self.reset(total_epoch)
def reset(self, total_epoch):
assert total_epoch > 0
self.total_epoch = total_epoch
self.current_epoch = 0
self.epoch_losses = np.zeros((self.total_epoch, 2), dtype=np.float32) # [epoch, train/val]
self.epoch_losses = self.epoch_losses - 1
self.epoch_accuracy = np.zeros((self.total_epoch, 2),
dtype=np.float32) # [epoch, train/val]
self.epoch_accuracy = self.epoch_accuracy
self.epoch_best_accuracy = np.zeros((self.total_epoch),
dtype=np.float32) # [epoch, best_acc]
self.epoch_time = np.zeros((self.total_epoch),
dtype=np.float32) # [epoch, epoch_time]
self.mix_time = np.zeros((self.total_epoch),
dtype=np.float32) # [epoch, epoch_time]
# def update(self, idx, train_loss, train_acc, val_loss, val_acc, best_acc, epoch_time, mix_time):
def update(self, idx, train_loss, train_acc, val_loss, val_acc, best_acc, epoch_time):
assert idx >= 0 and idx < self.total_epoch, 'total_epoch : {} , but update with the {} index'.format(
self.total_epoch, idx)
self.epoch_losses[idx, 0] = train_loss
self.epoch_losses[idx, 1] = val_loss
self.epoch_accuracy[idx, 0] = train_acc
self.epoch_accuracy[idx, 1] = val_acc
self.epoch_best_accuracy[idx] = best_acc
self.epoch_time[idx] = epoch_time
# self.mix_time[idx] = mix_time
self.current_epoch = idx + 1
return self.max_accuracy(False) == val_acc
def max_accuracy(self, istrain):
if self.current_epoch <= 0: return 0
if istrain: return self.epoch_accuracy[:self.current_epoch, 0].max()
else: return self.epoch_accuracy[:self.current_epoch, 1].max()
def plot_curve(self, save_path):
title = 'the accuracy/loss curve of train/val'
dpi = 80
width, height = 1200, 800
legend_fontsize = 10
scale_distance = 48.8
figsize = width / float(dpi), height / float(dpi)
fig = plt.figure(figsize=figsize)
x_axis = np.array([i for i in range(self.total_epoch)]) # epochs
y_axis = np.zeros(self.total_epoch)
plt.xlim(0, self.total_epoch)
plt.ylim(0, 100)
interval_y = 5
interval_x = 5
plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x))
plt.yticks(np.arange(0, 100 + interval_y, interval_y))
plt.grid()
plt.title(title, fontsize=20)
plt.xlabel('the training epoch', fontsize=16)
plt.ylabel('accuracy', fontsize=16)
y_axis[:] = self.epoch_accuracy[:, 0]
plt.plot(x_axis, y_axis, color='g', linestyle='-', label='train-accuracy', lw=2)
plt.legend(loc=4, fontsize=legend_fontsize)
y_axis[:] = self.epoch_accuracy[:, 1]
plt.plot(x_axis, y_axis, color='y', linestyle='-', label='valid-accuracy', lw=2)
plt.legend(loc=4, fontsize=legend_fontsize)
y_axis[:] = self.epoch_losses[:, 0]
plt.plot(x_axis, y_axis * 50, color='g', linestyle=':', label='train-loss-x50', lw=2)
plt.legend(loc=4, fontsize=legend_fontsize)
y_axis[:] = self.epoch_losses[:, 1]
plt.plot(x_axis, y_axis * 50, color='y', linestyle=':', label='valid-loss-x50', lw=2)
plt.legend(loc=4, fontsize=legend_fontsize)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches='tight')
print('---- save figure {} into {}'.format(title, save_path))
plt.close(fig)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def plotting(exp_dir):
# Load the training log dictionary:
train_dict = pickle.load(open(os.path.join(exp_dir, 'log.pkl'), 'rb'))
###########################################################
### Make the vanilla train and test loss per epoch plot ###
###########################################################
plt.plot(np.asarray(train_dict['train_loss']), label='train_loss')
plt.plot(np.asarray(train_dict['test_loss']), label='test_loss')
#plt.ylim(0,2000)
plt.xlabel('evaluation step')
plt.ylabel('metrics')
plt.tight_layout()
plt.legend(loc='upper right')
plt.savefig(os.path.join(exp_dir, 'loss.png'))
plt.clf()
## accuracy###
plt.plot(np.asarray(train_dict['train_acc']), label='train_acc')
plt.plot(np.asarray(train_dict['test_acc']), label='test_acc')
#plt.ylim(0,2000)
plt.xlabel('evaluation step')
plt.ylabel('metrics')
plt.tight_layout()
plt.legend(loc='upper right')
plt.savefig(os.path.join(exp_dir, 'acc.png'))
plt.clf()
if __name__ == '__main__':
plotting('temop')