PyTorch项目应用实例(转载)

原文链接： https://blog.csdn.net/weixin_36474809/article/details/90030682

PyTorch项目应用实例(八)固定权重|顺序训练网络

原 PyTorch项目应用实例(七)模型添加中继loss | 中继监督优化

原 PyTorch项目应用实例(六)并行化|分组运算|张量乘|常用神经网络层

原 PyTorch项目应用实例(五)加载模型验证并将所有结果写入文件

原 coco再分组与网络按照分组进行训练

原 PyTorch项目应用实例(四)设置learning_rate的decay

原 PyTorch项目应用实例(三)通用的图像分类模型实现图像分类(附代码与操作方法)

原 PyTorch项目应用实例(二)ResNet | SENet实现coco多标签分类

原 PyTorch项目应用实例(一)加载(本地|官方)预训练模型

# -*- coding: utf-8 -*
"""
Created by Xingxiangrui on 2019.5.9
This code is to :
    1. copy image from source_image_dir to the target_image_dir
    2. And generate .txt file for further training
        in which each line is : image_name.jpg  (tab)  image_label (from 0)
        such as:
            image_01.jpg    0
            iamge_02.jpg    1
            ...
            image_02.jpg    0
"""
 
# import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import os
import random
 
# variables need to be change
source_image_dir="/Users/baidu/Desktop/used/SuZhouRuiTu_dataset/single-poly-defect/poly_OK"
target_image_dir="/Users/baidu/Desktop/used/SuZhouRuiTu_dataset/data_for_resnet_classification"
txt_file_dir="/Users/baidu/Desktop/used/SuZhouRuiTu_dataset/data_for_resnet_classification/TxtFile"
prefix="poly_OK"
class_label=1
# label 0: single_OK ; label_1: poly_OK ; label 2: poly_defect
 
print("Program Start......")
print("-"*20)
print("-"*20)
print("-"*20)
 
# load image list in the source dir
source_image_list = os.listdir(source_image_dir)
for idx in range(len(source_image_list)):
    if '.png' in source_image_list[idx-1]:
        continue
    elif '.jpg' in source_image_list[idx-1]:
        continue
    else:
        del source_image_list[idx-1]
 
# shuffle image list
print("initial list:")
print source_image_list
random.shuffle(source_image_list)
print("shuffled list:")
print source_image_list
 
# train list and val list
source_train_list=[]
source_val_list=[]
for idx in range(len(source_image_list)):
    if idx