DETR训练自己的数据集-实践笔记
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DETR(Detection with TRansformers)训练自己的数据集-实践笔记&问题总结
DETR(Detection with TRansformers)是基于transformer的端对端目标检测，无NMS后处理步骤，无anchor。
实现使用NWPUVHR10数据集训练DETR.
NWPU数据集总共包含十种类别目标，包含650个正样本，150个负样本（没有用到）。
NWPU_CATEGORIES=['airplane','ship','storage tank','baseball diamond','tennis court',\\
'basketball court','ground track field','harbor','bridge','vehicle']
代码：https://github.com/facebookresearch/detr
文章目录
一.训练
1.数据集准备
DETR需要的数据集格式为coco格式，图片和标签文件保存于训练集、测试集、验证集、标签文件四个文件夹中，其中annotations中存放json格式的标签文件
下面的代码包含了几种数据集RSOD、NWPU、DIOR、YOLO数据集标签文件转换json功能。新建py文件tojson.py，使用如下代码生成需要的json文件。
生成instances_train2017.json
（a）修改29行image_path默认路径为train2017的路径；
（b）修改31行annotation_path默认路径为标签文件路径（train和val的标签都放在这个文件夹下，所以生成instances_val2017.json时就不需要再修改这个路径了）；
（c）修改33行dataset为自己的数据集名称NWPU
（d）.修改34行save的默认路径为json文件的保存路径…/NWPUVHR-10/annotations/instances_train2017.json
import os
import cv2
import json
import argparse
from tqdm import tqdm
import xml.etree.ElementTree as ET
COCO_DICT=['images','annotations','categories']
IMAGES_DICT=['file_name','height','width','id']
ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id']
CATEGORIES_DICT=['id','name']
## 'supercategory': 'person', 'id': 1, 'name': 'person'
## 'supercategory': 'vehicle', 'id': 2, 'name': 'bicycle'
YOLO_CATEGORIES=['person']
RSOD_CATEGORIES=['aircraft','playground','overpass','oiltank']
NWPU_CATEGORIES=['airplane','ship','storage tank','baseball diamond','tennis court',\\
'basketball court','ground track field','harbor','bridge','vehicle']
VOC_CATEGORIES=['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow',\\ 'diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
DIOR_CATEGORIES=['golffield','Expressway-toll-station','vehicle','trainstation','chimney','storagetank',\\
'ship','harbor','airplane','groundtrackfield','tenniscourt','dam','basketballcourt',\\
'Expressway-Service-area','stadium','airport','baseballfield','bridge','windmill','overpass']
parser=argparse.ArgumentParser(description='2COCO')
#parser.add_argument('--image_path',type=str,default=r'T:/shujuji/DIOR/JPEGImages-trainval/',help='config file')
parser.add_argument('--image_path',type=str,default=r'G:/NWPU VHR-10 dataset/positive image set/',help='config file')
#parser.add_argument('--annotation_path',type=str,default=r'T:/shujuji/DIOR/Annotations/',help='config file')
parser.add_argument('--annotation_path',type=str,default=r'G:/NWPU VHR-10 dataset/ground truth/',help='config file')
parser.add_argument('--dataset',type=str,default='NWPU',help='config file')
parser.add_argument('--save',type=str,default='G:/NWPU VHR-10 dataset/instances_train2017.json',help='config file')
args=parser.parse_args()
def load_json(path):
with open(path,'r') as f:
json_dict=json.load(f)
for i in json_dict:
print(i)
print(json_dict['annotations'])
def save_json(dict,path):
print('SAVE_JSON...')
with open(path,'w') as f:
json.dump(dict,f)
print('SUCCESSFUL_SAVE_JSON:',path)
def load_image(path):
img=cv2.imread(path)
return img.shape[0],img.shape[1]
def generate_categories_dict(category): #ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id']
print('GENERATE_CATEGORIES_DICT...')
return [CATEGORIES_DICT[0]:category.index(x)+1,CATEGORIES_DICT[1]:x for x in category] #CATEGORIES_DICT=['id','name']
def generate_images_dict(imagelist,image_path,start_image_id=11725): #IMAGES_DICT=['file_name','height','width','id']
print('GENERATE_IMAGES_DICT...')
images_dict=[]
with tqdm(total=len(imagelist)) as load_bar:
for x in imagelist: #x就是图片的名称
#print(start_image_id)
dict=IMAGES_DICT[0]:x,IMAGES_DICT[1]:load_image(image_path+x)[0],\\
IMAGES_DICT[2]:load_image(image_path+x)[1],IMAGES_DICT[3]:imagelist.index(x)+start_image_id
load_bar.update(1)
images_dict.append(dict)
return images_dict
def DIOR_Dataset(image_path,annotation_path,start_image_id=11725,start_id=0):
categories_dict=generate_categories_dict(DIOR_CATEGORIES) #CATEGORIES_DICT=['id'：，1'name'：golffield......] id从1开始
imgname=os.listdir(image_path)
images_dict=generate_images_dict(imgname,image_path,start_image_id) #IMAGES_DICT=['file_name','height','width','id'] id从0开始的
print('GENERATE_ANNOTATIONS_DICT...') #生成cooc的注记 ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id']
annotations_dict=[]
id=start_id
for i in images_dict:
image_id=i['id']
print(image_id)
image_name=i['file_name']
annotation_xml=annotation_path+image_name.split('.')[0]+'.xml'
tree=ET.parse(annotation_xml)
root=tree.getroot()
for j in root.findall('object'):
category=j.find('name').text
category_id=DIOR_CATEGORIES.index(category) #字典的索引，是从1开始的
x_min=float(j.find('bndbox').find('xmin').text)
y_min=float(j.find('bndbox').find('ymin').text)
w=float(j.find('bndbox').find('xmax').text)-x_min
h=float(j.find('bndbox').find('ymax').text)-y_min
area = w * h
bbox = [x_min, y_min, w, h]
dict = 'image_id': image_id, 'iscrowd': 0, 'area': area, 'bbox': bbox, 'category_id': category_id,
'id': id
annotations_dict.append(dict)
id=id+1
print('SUCCESSFUL_GENERATE_DIOR_JSON')
return COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict
def NWPU_Dataset(image_path,annotation_path,start_image_id=0,start_id=0):
categories_dict=generate_categories_dict(NWPU_CATEGORIES)
imgname=os.listdir(image_path)
images_dict=generate_images_dict(imgname,image_path,start_image_id)
print('GENERATE_ANNOTATIONS_DICT...')
annotations_dict=[]
id=start_id
for i in images_dict:
image_id=i['id']
image_name=i['file_name']
annotation_txt=annotation_path+image_name.split('.')[0]+'.txt'
txt=open(annotation_txt,'r')
lines=txt.readlines()
for j in lines:
if j=='\\n':
continue
category_id=int(j.split(',')[4])
category=NWPU_CATEGORIES[category_id-1]
print(category_id,' ',category)
x_min=float(j.split(',')[0].split('(')[1])
y_min=float(j.split(',')[1].split(')')[0])
w=float(j.split(',')[2].split('(')[1])-x_min
h=float(j.split(',')[3].split(')')[0])-y_min
area=w*h
bbox=[x_min,y_min,w,h]
dict = 'image_id': image_id, 'iscrowd': 0, 'area': area, 'bbox': bbox, 'category_id': category_id,
'id': id
id=id+1
annotations_dict.append(dict)
print('SUCCESSFUL_GENERATE_NWPU_JSON')
return COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict
def YOLO_Dataset(image_path,annotation_path,start_image_id=0,start_id=0):
categories_dict=generate_categories_dict(YOLO_CATEGORIES)
imgname=os.listdir(image_path)
images_dict=generate_images_dict(imgname,image_path)
print('GENERATE_ANNOTATIONS_DICT...')
annotations_dict=[]
id=start_id
for i in images_dict:
image_id=i['id']
image_name=i['file_name']
W,H=i['width'],i['height']
annotation_txt=annotation_path+image_name.split('.')[0]+'.txt'
txt=open(annotation_txt,'r')
lines=txt.readlines()
for j in lines:
category_id=int(j.split(' ')[0])+1
category=YOLO_CATEGORIES
x=float(j.split(' ')[1])
y=float(j.split(' ')[2])
w=float(j.split(' ')[3])
h=float(j.split(' ')[4])
x_min=(x-w/2)*W
y_min=(y-h/2)*H
w=w*W
h=h*H
area=w*h
bbox=[x_min,y_min,w,h]
dict='image_id':image_id,'iscrowd':0,'area':area,'bbox':bbox,'category_id':category_id,'id':id
annotations_dict.append(dict)
id=id+1
print('SUCCESSFUL_GENERATE_YOLO_JSON')
return COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict
def RSOD_Dataset(image_path,annotation_path,start_image_id=0,start_id=0):
categories_dict=generate_categories_dict(RSOD_CATEGORIES)
imgname=os.listdir(image_path)
images_dict=generate_images_dict(imgname,image_path,start_image_id)
print('GENERATE_ANNOTATIONS_DICT...')
annotations_dict=[]
id=start_id
for i in images_dict:
image_id=i['id']
image_name=i['file_name']
annotation_txt=annotation_path+image_name.split('.')[0]+'.txt'
txt=open(annotation_txt,'r')
lines=txt.readlines()
for j in lines:
category=j.split('\\t')[1]
category_id=RSOD_CATEGORIES.index(category)+1
x_min=float(j.split('\\t')[2])
y_min=float(j.split('\\t')[3])
w=float(j.split('\\t')[4])-x_min
h=float(j.split('\\t')[5])-y_min
area = w * h
bbox = [x_min, y_min, w, h]
dict = 'image_id': image_id, 'iscrowd': 0, 'area': area, 'bbox': bbox, 'category_id': category_id,
'id': id
annotations_dict.append(dict)
id=id+1
print('SUCCESSFUL_GENERATE_RSOD_JSON')
return COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict
if __name__=='__main__':
dataset=args.dataset #数据集名字
save=args.save #json的保存路径
image_path=args.image_path #对于coco是图片的路径
annotation_path=args.annotation_path #coco的annotation路径
if dataset=='RSOD':
json_dict=RSOD_Dataset(image_path,annotation_path,0)
if dataset=='NWPU':
json_dict=NWPU_Dataset(image_path,annotation_path,0)
if dataset=='DIOR':
json_dict=DIOR_Dataset(image_path,annotation_path,11725)
if dataset=='YOLO':
json_dict=YOLO_Dataset(image_path,annotation_path,0)
save_json(json_dict,save)
运行生成instances_train2017.json，再修改路径生成instances_train2017.json。
（经评论区小伙伴提醒发现有问题，已修改）
如果自己的数据集是voc格式，可以使用参考链接1中的大大给出的代码。
2.环境配置
激活当前项目所在环境，使用如下命令完成环境配置：
pip install -r requirements.txt
3.pth文件生成
先下载预训练文件，官方提供了 DETR 和 DETR-DC5 models,两个模型，选择一个下载
新建py文件，mydataset.py，使用如下代码，修改第5行为自己的类别数+1
import torch
pretrained_weights = torch.load('detr-r50-e632da11.pth')
#NWPU数据集，10类
num_class = 11 #类别数+1，1为背景
pretrained_weights[<以上是关于DETR训练自己的数据集-实践笔记的主要内容，如果未能解决你的问题，请参考以下文章