hcq_lib.py

更新時間：20180410

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

import time
import datetime
import os
import shutil

## utils --> bar by hy
import sys
import math

import torch.nn as nn
import torch.nn.init as init


def hcq_write(save_info_path, is_write_time, is_print, write_content):
    i = datetime.datetime.now()
    date = str(i.year) + str("%02d"%i.month) + str("%02d"%i.day) + "-" + str("%02d"%i.hour) + str("%02d"%i.minute) + str("%02d"%i.second)
    with open(save_info_path, "a+") as save_info_txt:
        if(is_write_time):
            if(is_print):
                print("[{}] {}".format(date, write_content))
            save_info_txt.writelines("[{}] {}".format(date, write_content) + "\n")
        else:
            if(is_print):
                print(write_content)
            save_info_txt.writelines("{}".format(write_content) + "\n")

def hcq_backup_txt_rename(txt_path):
    i = datetime.datetime.now()
    date = str(i.year) + str("%02d"%i.month) + str("%02d"%i.day) + "-" + str("%02d"%i.hour) + str("%02d"%i.minute) + str("%02d"%i.second)
    if os.path.exists(txt_path):
        new_name = txt_path +".bak" + date
        os.rename(txt_path, new_name)
        # copy(new_name, "./backup/")
        print("copied and deleted file, new_name = {}".format(new_name))

def hcq_backup_txt_rename_index(txt_path, index):
    if os.path.exists(txt_path):
        new_name = txt_path + "_" + str(index)
        os.rename(txt_path, new_name)
        # copy(new_name, "./backup/")
        print("copied and deleted file, new_name = {}".format(new_name))



def hcq_bubble_sort(active_train_samples_list):
    print("hcq_bubble_sort...")
    for i in range(len(active_train_samples_list)-1):
        current_status = False

        for j in range(len(active_train_samples_list) - i -1):

            if active_train_samples_list[j].p_pred_max > active_train_samples_list[j+1].p_pred_max:

                temp = active_train_samples_list[j]
                active_train_samples_list[j] = active_train_samples_list[j+1]
                active_train_samples_list[j+1] = temp

                # active_train_samples_list[j], active_train_samples_list[j+1] = active_train_samples_list[j+1], active_train_samples_list[j]
                
                current_status = True

        if not current_status:
            break

    return active_train_samples_list


# def hcq_random_select(active_train_samples_list):
#     print("hcq_random_select...")
#     active_train_samples_list = 


def hcq_create_dir(dir_path):
    if not os.path.exists(dir_path):
        print("Create dir = {}".format(dir_path))
        os.makedirs(dir_path)

def hcq_move(old_file, new_file):
    shutil.move(old_file, new_file)



#### progress_bar
#### from hy, 20180310

_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)

TOTAL_BAR_LENGTH = 10.
last_time = time.time()
begin_time = last_time

def progress_bar(current, total, msg=None):

    global last_time, begin_time
    if current == 0:
        begin_time = time.time()  # Reset for new bar.

    cur_len = int(TOTAL_BAR_LENGTH*current/total)
    rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1

    sys.stdout.write(' [')
    for i in range(cur_len):
        sys.stdout.write('=')
    sys.stdout.write('>')
    for i in range(rest_len):
        sys.stdout.write('.')
    sys.stdout.write(']')

    cur_time = time.time()
    step_time = cur_time - last_time
    last_time = cur_time
    tot_time = cur_time - begin_time

    L = []

    if msg:
        L.append(' | ' + msg)

    msg = ''.join(L)
    sys.stdout.write(msg)
    for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
        sys.stdout.write(' ')

    # Go back to the center of the bar.
    for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
        sys.stdout.write('\b')
    sys.stdout.write(' %d/%d ' % (current+1, total))

    if current < total-1:
        sys.stdout.write('\r')
    else:
        sys.stdout.write('\n')
    sys.stdout.flush()



### class
### author: houchaoqun
### 2018.03.24

class active_train_samples:

    def __init__(self, image_path, p_pred_max, result):
        self.image_path = image_path
        self.p_pred_max = p_pred_max  ## model predict the p of real lable
        self.result = result

    # def __repr__(self):
    #     return repr((self.name, self.grade, self.age))
 

備份函式：

#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import datetime

import shutil

def backup_txt_rename(txt_path):
	if os.path.exists(txt_path):
		i = datetime.datetime.now()
		date = str(i.year) + str("%02d"%i.month) + str("%02d"%i.day) + str("%02d"%i.hour) + str("%02d"%i.minute) + str("%02d"%i.second)
		new_name = txt_path +".bak" + date
		os.rename(txt_path, new_name)
		print("copied and deleted file, new_name = {}".format(new_name))
 

1）txt 檔案備份，當檔案存在時，先備份再生成相同的檔名；

2）txt_path：檔案路徑，如：

"/home/reserch/documents/deeplearning/alzheimers_disease_DL/pytorch/subject_id/test/file.txt"