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utils.py
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utils.py
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# -*- coding: utf-8 -*-
# @Time : 2023/3/22 16:08
# @Author : Karry Ren
""" The util functions. """
import random
from typing import Optional
import torch.optim
import matplotlib.pyplot as plt
def print_log(log_info: str, log_file=None) -> None:
""" Print the log information to console and log_file.
:param log_info: the information to log
:param log_file: the file to log
"""
# ---- Step 1. Print the log_info to the log_file ---- #
if log_file is not None:
# use print function
print(log_info, file=log_file)
# flush the file
if random.randint(0, 20) < 3:
log_file.flush()
# ---- Step 2. Print the log_info to the console ---- #
print(log_info)
def adjust_lr(optimizer: torch.optim.Optimizer, base_lr: float, step: int, epoch: int, whole_steps: int,
base_steps: int, power: float, change_epoch: Optional[int] = 30) -> None:
""" Adjust the learning rate.
:param optimizer: the optimizer
:param base_lr: the initial learning rate
:param step: the current step
:param epoch: the current epoch
:param whole_steps: the whole steps
:param base_steps: the base train steps
:param power: lr down power
:param change_epoch: the change epoch
Returns:
the new lr for one step
"""
# ---- Change the lr based on the steps ---- #
if change_epoch is not None: # have the change epoch
if epoch >= change_epoch: # after the change epoch, start changing (be smaller)
new_lr = base_lr * ((1 - float(step - base_steps) / (whole_steps - base_steps)) ** power)
else: # before the change epoch, keep lr
new_lr = base_lr
else: # not have the change epoch, keep changing
new_lr = base_lr * ((1 - float(step) / whole_steps) ** power)
# ---- Bound lr it to 1e-5 ---- #
if new_lr <= 1e-5:
new_lr = 1e-5
# ---- Set the lr to optimizer ---- #
optimizer.param_groups[0]["lr"] = new_lr
def draw_curves(data_list, label_list, color_list, linestyle_list=None, filename='training_curve.png'):
plt.figure()
for i in range(len(data_list)):
data = data_list[i]
label = label_list[i]
color = color_list[i]
if linestyle_list == None:
line_style = '-'
else:
line_style = linestyle_list[i]
plt.plot(data, label=label, color=color, linestyle=line_style)
plt.legend(loc='best')
plt.savefig(filename)
plt.clf()
plt.close()
plt.show()
plt.close('all')