Pytorch版deeplabv3+环境配置训练自己的数据集

这个很不错：https://blog.csdn.net/qq_39056987/article/details/106455828 【windows10】使用pytorch版本deeplabv3+训练自己数据集

参考：https://blog.csdn.net/qq_36766560/article/details/110009622?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522161594607816780266214828%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=161594607816780266214828&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_v2~rank_v29-1-110009622.first_rank_v2_pc_rank_v29_10&utm_term=pytorch+deplabv3%2B

开发环境
数据集准备
- 1.VOC数据集格式
- - `JPEGImages`里面放原图
  - `SegmentationClass`里面放对应的mask图片png格式，注意要和`JPEGImages`里的图片一一对应
  - `ImageSets/ Segmentation`的txt文件中放去掉后缀的图片名
- 2.json转换mask图片
- 3.提取出所有文件夹中的`label.png`并改成对应的名字放在指定目录中
修改代码
- 1.在`mypath.py`中添加自己的数据集名称与路径
- 2.在同级目录中修改`train.py`约185行添加自己数据集的名称(可以设置为默认)
- 3.在dataloaders目录下修改__init__.py
- 4. 修改dateloaders目录下`utils.py`
- 5.在dataloaders/datasets目录下添加文件
运行并测试
- 1.开始训练
- 2.测试

开发环境

我使用的是实验室服务器的环境：

pytorch-gpu1.7.1
python3.8.5

代码在这：链接

VOC数据集：链接：https://pan.baidu.com/s/1ncWVIVgntas3cJBFMqnfFg 提取码：64pp

预训练模型：链接：https://pan.baidu.com/s/1d58JnOtxa4JuJKO3tiZYqw 提取码：po5m

数据集准备

1.VOC数据集格式

文件安排如下：

- ImageSets- Segmentation- train.txt- trainval.txt- val.txt
- JPEGImages
- SegmentationClass

JPEGImages里面放原图

SegmentationClass里面放对应的mask图片png格式，注意要和JPEGImages里的图片一一对应

ImageSets/ Segmentation的txt文件中放去掉后缀的图片名

2.json转换mask图片，(没做，制作自己的数据集时用到)

数据转换参考的这里

用下面的代码替换labelme环境下的对应文件Anaconda3\envs\labelme\Lib\site-packages\labelme\cli（注意替换掉path的路径，换上自己的json文件路径）

import argparse
import base64
import json
import os
import os.path as ospimport PIL.Image
import yamlfrom labelme.logger import logger
from labelme import utilspath = "G:/Seg552VOC/seg552Json"
dirs = os.listdir(path)def label(json_file, out_dir, label_name_to_value):data = json.load(open(json_file))if data['imageData']:imageData = data['imageData']else:imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])with open(imagePath, 'rb') as f:imageData = f.read()imageData = base64.b64encode(imageData).decode('utf-8')img = utils.img_b64_to_arr(imageData)for shape in sorted(data['shapes'], key=lambda x: x['label']):label_name = shape['label']if label_name in label_name_to_value:label_value = label_name_to_value[label_name]else:label_value = len(label_name_to_value)label_name_to_value[label_name] = label_valuelbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)label_names = [None] * (max(label_name_to_value.values()) + 1)for name, value in label_name_to_value.items():label_names[value] = namelbl_viz = utils.draw_label(lbl, img, label_names)PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))utils.lblsave(osp.join(out_dir, 'label.png'), lbl)PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:for lbl_name in label_names:f.write(lbl_name + '\n')logger.warning('info.yaml is being replaced by label_names.txt')info = dict(label_names=label_names)with open(osp.join(out_dir, 'info.yaml'), 'w') as f:yaml.safe_dump(info, f, default_flow_style=False)logger.info('Saved to: {}'.format(out_dir))def main():logger.warning('This script is aimed to demonstrate how to convert the''JSON file to a single image dataset, and not to handle''multiple JSON files to generate a real-use dataset.')parser = argparse.ArgumentParser()parser.add_argument('json_file_dir')parser.add_argument('-o', '--out', default=None)args = parser.parse_args()label_name_to_value = {'_background_': 0}for json_file in dirs:if args.out is None:out_dir = osp.basename(json_file).replace('.', '_')out_dir = osp.join(osp.dirname(json_file), out_dir)else:out_dir = args.outif not osp.exists(out_dir):os.mkdir(out_dir)label(json_file, out_dir, label_name_to_value)
if __name__ == '__main__':main()

在conda prompt中启动labelme环境运行labelme_json_to_dataset 输出的目录，得到很多文件夹，每个文件夹里都有四个文件，我们只需要label.png

3.提取出所有文件夹中的`label.png`并改成对应的名字放在指定目录中，(没做，制作自己的数据集时用到)

label.py代码如下：

import os
import shutilinputdir = 'G:/Seg552VOC/datasetDir'
outputdir = 'G:/Seg552VOC/ImageSetNew'for dir in os.listdir(inputdir):# 设置旧文件名（就是路径+文件名）oldname = inputdir + os.sep + dir + os.sep + 'label.png'  # os.sep添加系统分隔符# 设置新文件名newname = outputdir + os.sep + dir.split('_')[0] + '.png'shutil.copyfile(oldname, newname)  # 用os模块中的rename方法对文件改名print(oldname, '======>', newname)

得到下面的文件，就是我们所需要的SegmentationClass

python train.py --backbone mobilenet --lr 0.007 --workers 1 --epochs 50 --batch-size 8 --gpu-ids 0 --checkname deeplab-mobilenet

–backbone mobilenet 指的是使用mobilenet作为backbone
–gpu-ids 0 指定gpu
–checkname deeplab-mobilenet 使用mobilenet预训练模型

2.测试

测试testdemo.py
修改–in-path为数据集的测试图片，最后的结果保存在–in-path中

#
# demo.py
#
import argparse
import os
import numpy as np
import timefrom modeling.deeplab import *
from dataloaders import custom_transforms as tr
from PIL import Image
from torchvision import transforms
from dataloaders.utils import  *
from torchvision.utils import make_grid, save_imagedef main():parser = argparse.ArgumentParser(description="PyTorch DeeplabV3Plus Training")parser.add_argument('--in-path', type=str,  default='/root/home/zyx/Seg552_VOC/test',help='image to test')# parser.add_argument('--out-path', type=str, required=True, help='mask image to save')parser.add_argument('--backbone', type=str, default='resnet',choices=['resnet', 'xception', 'drn', 'mobilenet'],help='backbone name (default: resnet)')parser.add_argument('--ckpt', type=str, default='deeplab-resnet.pth',help='saved model')parser.add_argument('--out-stride', type=int, default=16,help='network output stride (default: 8)')parser.add_argument('--no-cuda', action='store_true', default=False,help='disables CUDA training')parser.add_argument('--gpu-ids', type=str, default='0',help='use which gpu to train, must be a \comma-separated list of integers only (default=0)')parser.add_argument('--dataset', type=str, default='pascal',choices=['pascal', 'coco', 'cityscapes','invoice'],help='dataset name (default: pascal)')parser.add_argument('--crop-size', type=int, default=513,help='crop image size')parser.add_argument('--num_classes', type=int, default=4,help='crop image size')parser.add_argument('--sync-bn', type=bool, default=None,help='whether to use sync bn (default: auto)')parser.add_argument('--freeze-bn', type=bool, default=False,help='whether to freeze bn parameters (default: False)')args = parser.parse_args()args.cuda = not args.no_cuda and torch.cuda.is_available()if args.cuda:try:args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]except ValueError:raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')if args.sync_bn is None:if args.cuda and len(args.gpu_ids) > 1:args.sync_bn = Trueelse:args.sync_bn = Falsemodel_s_time = time.time()model = DeepLab(num_classes=args.num_classes,backbone=args.backbone,output_stride=args.out_stride,sync_bn=args.sync_bn,freeze_bn=args.freeze_bn)ckpt = torch.load(args.ckpt, map_location='cpu')model.load_state_dict(ckpt['state_dict'])model = model.cuda()model_u_time = time.time()model_load_time = model_u_time-model_s_timeprint("model load time is {}".format(model_load_time))composed_transforms = transforms.Compose([tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),tr.ToTensor()])for name in os.listdir(args.in_path):s_time = time.time()image = Image.open(args.in_path+"/"+name).convert('RGB')# image = Image.open(args.in_path).convert('RGB')target = Image.open(args.in_path+"/"+name).convert('L')sample = {'image': image, 'label': target}tensor_in = composed_transforms(sample)['image'].unsqueeze(0)model.eval()if args.cuda:tensor_in = tensor_in.cuda()with torch.no_grad():output = model(tensor_in)grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy()),3, normalize=False, range=(0, 255))save_image(grid_image,args.in_path+"/"+"{}_mask.png".format(name[0:-4]))u_time = time.time()img_time = u_time-s_timeprint("image:{} time: {} ".format(name,img_time))# save_image(grid_image, args.out_path)# print("type(grid) is: ", type(grid_image))# print("grid_image.shape is: ", grid_image.shape)print("image save in in_path.")
if __name__ == "__main__":main()# python demo.py --in-path your_file --out-path your_dst_file

测试命令

 python testdemo.py --ckpt run/Seg552/deeplab-mobilenet/checkpoint.pth.tar --backbone mobilenet

参考博客:
Pytorch 语义分割DeepLabV3+ 训练自己的数据集从数据准备到模型训练
【windows10】使用pytorch版本deeplabv3+训练自己数据集
制作自己的语义分割数据集（仿voc2012数据集）