一、项目地址
二、label制作
将训练、验证、测试数据的分类信息转换入.csv文件中,运行如下脚本即可:
import os
import csv
#要读取的训练、验证、测试文件的目录,该文件下保存着以各个类别命名的文件夹和对应的分类图片
root_path=r'/media/*********************/train'
#类别种类
classes=['cls1','cls2']
def get_Write_file_infos(path):
# 文件信息列表
file_infos_list=[]
typeclothes=os.listdir(path)
for ii in typeclothes:
everyfile=os.path.join(path , ii)
for root, dirnames, filenames in os.walk(everyfile):
for filename in filenames:
file_infos = {}
dirname=root
#根据自己的需求更改路径地址
filename1 ='train/'+ii+'/'+ filename#.split('.jpg')[0]
flag = filename1[-1]
file_infos["ImageId"] = filename1
file_infos["Flag"] = classes.index(ii)
#将数据追加字典到列表中
file_infos_list.append(file_infos)
return file_infos_list
#写入csv文件
def write_csv(file_infos_list):
with open('train_label.csv','a+',newline='') as csv_file_train:
csv_writer = csv.DictWriter(csv_file_train,fieldnames=['ImageId','Flag'])
csv_writer.writeheader()
for each in file_infos_list:
print(each)
csv_writer.writerow(each)
def main():
file_infos_list =get_Write_file_infos(root_path)
write_csv(file_infos_list)
if __name__ == '__main__':
main()
print('The End!')
生成情况如下:
三、运行程序
(1)修改参数文件 utils/config.py
# -*- coding: utf-8 -*-
class DefaultConfig(object):
net_work = 'ResUnNet50'
num_classes = 2
num_epochs = 100
batch_size = 256
validation_step = 1
root = "/media/code/"
train_file = "train_label.csv"
val_file = "val_label.csv"
test_file = "test_label.csv"
lr = 1e-4
lr_mode = 'poly'
momentum = 0.9
weight_decay = 1e-4
save_model_path = './Model_Saved'.format(net_work,lr)
log_dirs = './Logs_Adam_0304'
pretrained =True# False
pretrained_model_path ='/media/code/UIOS-master/Trained/archive/data/99843712' #None
cuda = 0
num_workers = 4
use_gpu = True
trained_model_path = ''
predict_fold = 'predict_mask'
(2)运行
命令:
python train.py
(3)运行界面
四、踩坑记录
问题原因:ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.
解决方法:
(1)网上看了很多:
方法1:添加 try-except
try:
epoch_train_auc = metrics.roc_auc_score(labels, outputs)
writer.add_scalar('Train/train_auc', float(epoch_train_auc),
epoch)
print('loss for train : {},{}'.format(loss_train_mean,round(epoch_train_auc,6)))
except ValueError:
pass
方法2:DataLoader的参数设置shuffle=True
train_loader = DataLoader(DatasetCFP(
root=args.root,
mode='train',
data_file=args.train_file,
),
batch_size=args.batch_size, shuffle=True, pin_memory=True)
val_loader = DataLoader(DatasetCFP(
root=args.root,
mode='val',
data_file=args.val_file,
),
batch_size=args.batch_size, shuffle=True, pin_memory=True)
test_loader = DataLoader(DatasetCFP(
root=args.root,
mode='test',
data_file=args.test_file,
),
batch_size=args.batch_size, shuffle=True, pin_memory=True)
方法3:增大batch_size
(2)我的方法:
其实是我马虎大意
修改好config.py中的num_classes参数就行了,
见谅(不好意思~( ̄▽ ̄)~*)