utils.py

from __future__ import print_function
import numpy as np
import math
np.set_printoptions(suppress=True)
import os
import time
import torch
from importlib import reload

def makePath(path):
    if not os.path.isdir(path):
        os.makedirs(path)
    return path


def monitor(process, multiple, second):
    while True:
        sum = 0
        for ps in process:
            if ps.is_alive():
                sum += 1
        if sum < multiple:
            break
        else:
            time.sleep(second)

def save_load_name(args, name=''):
    name = name if len(name) > 0 else 'default_model'
    return name

def save_model(args, model, name=''):
    name = save_load_name(args, name)
    torch.save(model, f'./pre_trained_models/{name}.pt')


def load_model(args, name=''):
    name = save_load_name(args, name)
    model = torch.load(f'./pre_trained_models/{name}.pt')
    return model

def get_logger(name, log_path, length):
    import logging
    reload(logging)

    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    logfile = makePath(log_path) + "/Train"+str(length)+"s_" + name + ".log"
    fh = logging.FileHandler(logfile, mode='w')
    fh.setLevel(logging.DEBUG)

    formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s")
    fh.setFormatter(formatter)

    logger.addHandler(fh)

    if log_path == "./result/test":
        ch = logging.StreamHandler()
        ch.setLevel(logging.INFO)
        ch.setFormatter(formatter)
        logger.addHandler(ch)

    return logger