Unet项目解析(1): run_training.py

项目GitHub主页：https://github.com/orobix/retina-unet

参考论文：Retina blood vessel segmentation with a convolution neural network (U-net) Retina blood vessel segmentation with a convolution neural network (U-net)

1. run_training.py解析

import os, sys # os模块中主要用于处理文件和目录 
import configparser # Python 3.6中 configparser全使用小写

#config file to read from
config = configparser.RawConfigParser() 
config.readfp(open(r'./configuration.txt')) # 建议使用 config.read('configuration.txt') #'configuration.txt'的内容见下面
#===========================================
#name of the experiment
name_experiment = config.get('experiment name', 'name')
nohup = config.getboolean('training settings', 'nohup')   #std output on log file?

run_GPU = '' if sys.platform == 'win32' else ' THEANO_FLAGS=device=gpu,floatX=float32 ' #是否用GPU进行训练

#create a folder for the results 创建文件夹用于保存结果
result_dir = name_experiment
print ("\n 1. Create directory for the results (if not already existing)")
if os.path.exists(result_dir):
    print ("Dir already existing")  # 用于保存结果的test文件夹如果存在就没有必要创建
elif sys.platform=='win32':
    os.system('mkdir ' + result_dir)
else:
    os.system('mkdir -p ' +result_dir) # 需要时创建上层目录，如目录早已存在则不当作错误

print ("copy the configuration file in the results folder")
if sys.platform=='win32':
    os.system('copy configuration.txt .\\' +name_experiment+'\\'+name_experiment+'_configuration.txt')
else:
    os.system('cp configuration.txt ./' +name_experiment+'/'+name_experiment+'_configuration.txt')

# run the experiment
if nohup: #作者采用不挂断的方式运行命令
    print ("\n2. Run the training with nohup, no GPU ")
    os.system(' nohup python -u ./src/retinaNN_training.py > ' +'./'+name_experiment+'/'+name_experiment+'_training.nohup') #运行retina_training.py文件
else:
    print ("\n2. Run the training(no nohup), no GPU")
    os.system(' python ./src/retinaNN_training.py') # 采用挂起的形式运行命令

配置文件：configuration.txt (使用的是section-option方法，可以利用字符串匹配进行参数解析)

[data paths] #数据路径 以及 训练集 测试集的名字
path_local =  ./DRIVE_datasets_training_testing/         #封装好的训练集图像+金标准  和  测试集图像+金标准
train_imgs_original = DRIVE_dataset_imgs_train.hdf5      #封装好的训练集图像
train_groundTruth = DRIVE_dataset_groundTruth_train.hdf5 #封装好的训练集金标准

train_border_masks = DRIVE_dataset_borderMasks_train.hdf5sks #封装好的训练集掩膜test_imgs_original = DRIVE_dataset_imgs_test.hdf5 #封装好的测试集图像test_groundTruth = DRIVE_dataset_groundTruth_test.hdf5 #封装好的测试集金标准test_border_masks = DRIVE_dataset_borderMasks_test.hdf5 #封装好的测试集掩膜[experiment name]name = test[data attributes]# 作者的训练集不多 所以作者采用了分块进行训练的方式，从图像中裁剪的图像块大小为 patch_height*patch_widthpatch_height = 48 patch_width = 48[training settings]#number of total patches:N_subimgs = 190000#if patches are extracted only inside the field of view:inside_FOV = False#Number of training epochsN_epochs = 150batch_size = 32#if running with nohup #不挂断地运行命令nohup = True[testing settings]#Choose the model to test: best==epoch with min loss, last==last epochbest_last = best#number of full images for the test (max 20)full_images_to_test = 20#How many original-groundTruth-prediction images are visualized in each imageN_group_visual = 1#Compute average in the prediction, improve results but require more patches to be predictedaverage_mode = True#Only if average_mode==True. Stride for patch extraction, lower value require more patches to be predictedstride_height = 5stride_width = 5#if running with nohupnohup = False