log_util.py

import os, sys, shutil, time, random
import argparse
import torch
import torch.backends.cudnn as cudnn
import torchvision.datasets as dset
import torchvision.transforms as transforms
from utils import AverageMeter, RecorderMeter, time_string, convert_secs2time, clustering_loss, change_quan_bitwidth
import models
import torch.nn.functional as F
import copy

import pandas as pd
import numpy as np



def print_log(print_string, log):
    print("{}".format(print_string))
    log.write("{}\n".format(print_string))
    log.flush()

def accuracy(output, target, topk=(1, )):
    """Computes the precision@k for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))
        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def accuracy_logger(base_dir, epoch, train_accuracy, test_accuracy):
    file_name = 'accuracy.txt'
    file_path = "%s/%s" % (base_dir, file_name)
    # create and format the log file if it does not exists
    if not os.path.exists(file_path):
        create_log = open(file_path, 'w')
        create_log.write('epochs train test\n')
        create_log.close()

    recorder = {}
    recorder['epoch'] = epoch
    recorder['train'] = train_accuracy
    recorder['test'] = test_accuracy
    # append the epoch index, train accuracy and test accuracy:
    with open(file_path, 'a') as accuracy_log:
        accuracy_log.write(
            '{epoch}       {train}    {test}\n'.format(**recorder))