pytorch版yolov3训练自己数据集

1. 环境搭建

1. 将github库download下来。

git clone https://github.com/ultralytics/yolov3.git

1. 建议在linux环境下使用anaconda进行搭建

conda create -n yolov3 python=3.7

1. 安装需要的软件

pip install -r requirements.txt

环境要求：

• python >= 3.7
• pytorch >= 1.1
• numpy
• tqdm
• opencv-python

其中只需要注意pytorch的安装：

到https://pytorch.org/中根据操作系统，python版本，cuda版本等选择命令即可。

关于深度学习环境搭建请参看：https://www.cnblogs.com/pprp/p/9463974.html

anaconda常用用法：https://www.cnblogs.com/pprp/p/9463124.html

2. 数据集构建

1. xml文件生成需要Labelimg软件

在Windows下使用LabelImg软件进行标注，能在网上下载，或者通过github搜索得到。

• 使用快捷键：

Ctrl + u  加载目录中的所有图像，鼠标点击Open dir同功能
Ctrl + r  更改默认注释目标目录(xml文件保存的地址) 
Ctrl + s  保存
Ctrl + d  复制当前标签和矩形框
space     将当前图像标记为已验证
w         创建一个矩形框
d         下一张图片
a         上一张图片
del       删除选定的矩形框
Ctrl++    放大
Ctrl--    缩小
↑→↓←        键盘箭头移动选定的矩形框

2. VOC2007 数据集格式

-data
    - VOCdevkit2007
        - VOC2007
            - Annotations (标签XML文件，用对应的图片处理工具人工生成的)
            - ImageSets (生成的方法是用sh或者MATLAB语言生成)
                - Main
                    - test.txt
                    - train.txt
                    - trainval.txt
                    - val.txt
            - JPEGImages(原始文件)
            - labels (xml文件对应的txt文件)

通过以上软件主要构造好JPEGImages和Annotations文件夹中内容,Main文件夹中的txt文件可以通过python脚本生成：

import os  
import random  
  
trainval_percent = 0.8
train_percent = 0.8  
xmlfilepath = 'Annotations'  
txtsavepath = 'ImageSets\Main'  
total_xml = os.listdir(xmlfilepath)  
  
num=len(total_xml)  
list=range(num)  
tv=int(num*trainval_percent)  
tr=int(tv*train_percent)  
trainval= random.sample(list,tv)  
train=random.sample(trainval,tr)  
  
ftrainval = open('ImageSets/Main/trainval.txt', 'w')  
ftest = open('ImageSets/Main/test.txt', 'w')  
ftrain = open('ImageSets/Main/train.txt', 'w')  
fval = open('ImageSets/Main/val.txt', 'w')  
  
for i  in list:  
    name=total_xml[i][:-4]+'\n'  
    if i in trainval:  
        ftrainval.write(name)  
        if i in train:  
            ftrain.write(name)  
        else:  
            fval.write(name)  
    else:  
        ftest.write(name)  
  
ftrainval.close()  
ftrain.close()  
fval.close()  
ftest.close()

生成labels文件，voc_label.py文件具体内容如下：

# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 11:42:13 2018
将本文件放到VOC2007目录下，然后就可以直接运行
需要修改的地方：
1. sets中替换为自己的数据集
2. classes中替换为自己的类别
3. 将本文件放到VOC2007目录下
4. 直接开始运行
"""

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]  #替换为自己的数据集
classes = ["head", "eye", "nose"]     #修改为自己的类别
#classes = ["eye", "nose"]

def convert(size, box):
    dw = 1./(size[0])
    dh = 1./(size[1])
    x = (box[0] + box[1])/2.0 - 1
    y = (box[2] + box[3])/2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)
def convert_annotation(year, image_id):
    in_file = open('VOC%s/Annotations/%s.xml'%(year, image_id))  #将数据集放于当前目录下
    out_file = open('VOC%s/labels/%s.txt'%(year, image_id), 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)
    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult)==1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
    if not os.path.exists('VOC%s/labels/'%(year)):
        os.makedirs('VOC%s/labels/'%(year))
    image_ids = open('VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
    list_file = open('%s_%s.txt'%(year, image_set), 'w')
    for image_id in image_ids:
        list_file.write('VOC%s/JPEGImages/%s.jpg\n'%(year, image_id))
        convert_annotation(year, image_id)
    list_file.close()   
#os.system("cat 2007_train.txt 2007_val.txt > train.txt")     #修改为自己的数据集用作训练

到底为止，VOC格式数据集构造完毕，但是还需要继续构造符合darknet格式的数据集(coco)。

需要说明的是：如果打算使用coco评价标准，需要构造coco中json格式，如果要求不高，只需要VOC格式即可，使用作者写的mAP计算程序即可。

voc的xml转coco的json文件脚本：xml2json.py

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 15:01:03 2018
需要改动xml_path and json_path
"""
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @Description: xml转换到coco数据集json格式
 
import os, sys, json,xmltodict
 
from xml.etree.ElementTree import ElementTree, Element
from collections import OrderedDict
 
XML_PATH = "/home/learner/datasets/VOCdevkit2007/VOC2007/Annotations/test"
JSON_PATH = "./test.json"
json_obj = {}
images = []
annotations = []
categories = []
categories_list = []
annotation_id = 1
 
def read_xml(in_path):
    '''读取并解析xml文件'''
    tree = ElementTree()
    tree.parse(in_path)
    return tree
 
def if_match(node, kv_map):
    '''判断某个节点是否包含所有传入参数属性
      node: 节点
      kv_map: 属性及属性值组成的map'''
    for key in kv_map:
        if node.get(key) != kv_map.get(key):
            return False
    return True
 
def get_node_by_keyvalue(nodelist, kv_map):
    '''根据属性及属性值定位符合的节点，返回节点
      nodelist: 节点列表
      kv_map: 匹配属性及属性值map'''
    result_nodes = []
    for node in nodelist:
        if if_match(node, kv_map):
            result_nodes.append(node)
    return result_nodes
 
def find_nodes(tree, path):
    '''查找某个路径匹配的所有节点
      tree: xml树
      path: 节点路径'''
    return tree.findall(path)
 
print ("-----------------Start------------------")
xml_names = []
for xml in os.listdir(XML_PATH):
    #os.path.splitext(xml)
    #xml=xml.replace('Cow_','')
    xml_names.append(xml)
    

'''xml_path_list=os.listdir(XML_PATH)
os.path.split
xml_path_list.sort(key=len)'''
xml_names.sort(key=lambda x:int(x[:-4]))
new_xml_names = []
for i in xml_names:
    j = 'Cow_' + i
    new_xml_names.append(j)

#print xml_names
#print new_xml_names
for xml in new_xml_names:
    tree = read_xml(XML_PATH + "/" + xml)
    object_nodes = get_node_by_keyvalue(find_nodes(tree, "object"), {})
    if len(object_nodes) == 0:
        print (xml, "no object")
        continue
    else:
        image = OrderedDict()
        file_name = os.path.splitext(xml)[0];  # 文件名
        para1 = file_name + ".jpg"
        height_nodes = get_node_by_keyvalue(find_nodes(tree, "size/height"), {})
        para2 = int(height_nodes[0].text)
        width_nodes = get_node_by_keyvalue(find_nodes(tree, "size/width"), {})
        para3 = int(width_nodes[0].text)
        
        fname=file_name[4:]
        para4 = int(fname)
        
        
                
        for f,i in [("file_name",para1),("height",para2),("width",para3),("id",para4)]:
            image.setdefault(f,i)

            #print(image)
        images.append(image)    #构建images
          
     
        name_nodes = get_node_by_keyvalue(find_nodes(tree, "object/name"), {})
        xmin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmin"), {})
        ymin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymin"), {})
        xmax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmax"), {})
        ymax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymax"), {})
        for index, node in enumerate(object_nodes):
            annotation = {}
            segmentation = []
            bbox = []
            seg_coordinate = []     #坐标
            seg_coordinate.append(int(xmin_nodes[index].text))
            seg_coordinate.append(int(ymin_nodes[index].text))
            seg_coordinate.append(int(xmin_nodes[index].text))
            seg_coordinate.append(int(ymax_nodes[index].text))
            seg_coordinate.append(int(xmax_nodes[index].text))
            seg_coordinate.append(int(ymax_nodes[index].text))
            seg_coordinate.append(int(xmax_nodes[index].text))
            seg_coordinate.append(int(ymin_nodes[index].text))
            segmentation.append(seg_coordinate)
            width = int(xmax_nodes[index].text) - int(xmin_nodes[index].text)
            height = int(ymax_nodes[index].text) - int(ymin_nodes[index].text)
            area = width * height
            bbox.append(int(xmin_nodes[index].text))
            bbox.append(int(ymin_nodes[index].text))
            bbox.append(width)
            bbox.append(height)
     
            annotation["segmentation"] = segmentation
            annotation["area"] = area
            annotation["iscrowd"] = 0
            fname=file_name[4:]
            annotation["image_id"] = int(fname)
            annotation["bbox"] = bbox
            cate=name_nodes[index].text
            if cate=='head':
                category_id=1
            elif cate=='eye':
                category_id=2
            elif cate=='nose':
                category_id=3
            annotation["category_id"] = category_id
            annotation["id"] = annotation_id
            annotation_id += 1
            annotation["ignore"] = 0
            annotations.append(annotation)
     
            if category_id in categories_list:
                pass
            else:
                categories_list.append(category_id)
                categorie = {}
                categorie["supercategory"] = "none"
                categorie["id"] = category_id
                categorie["name"] = name_nodes[index].text
                categories.append(categorie)
     
json_obj["images"] = images
json_obj["type"] = "instances"
json_obj["annotations"] = annotations
json_obj["categories"] = categories
 
f = open(JSON_PATH, "w")
#json.dump(json_obj, f)
json_str = json.dumps(json_obj)
f.write(json_str)
print ("------------------End-------------------")

（运行bash yolov3/data/get_coco_dataset.sh,仿照格式将数据放到其中）

但是这个库还需要其他模型：

3. 创建*.names file,

其中保存的是你的所有的类别，每行一个类别，如data/coco.names：

head
eye
nose

4. 更新data/coco.data,其中保存的是很多配置信息

classes = 3 # 改成你的数据集的类别个数
train = ./data/2007_train.txt # 通过voc_label.py文件生成的txt文件
valid = ./data/2007_test.txt # 通过voc_label.py文件生成的txt文件
names = data/coco.names # 记录类别
backup = backup/ # 记录checkpoint存放位置
eval = coco # 选择map计算方式

5. 更新cfg文件，修改类别相关信息

打开cfg文件夹下的yolov3.cfg文件，大体而言，cfg文件记录的是整个网络的结构，是核心部分，具体内容讲解请见：https://pprp.github.io/2018/09/20/tricks.html

只需要更改每个[yolo]层前边卷积层的filter个数即可：

每一个[region/yolo]层前的最后一个卷积层中的 filters=预测框的个数(mask对应的个数，比如mask=0,1,2, 代表使用了anchors中的前三对，这里预测框个数就应该是3)*(classes+5) ,5的意义是5个坐标，论文中的tx,ty,tw,th,po

举个例子：我有三个类，n = 3, 那么filter = 3 * (n+5) = 24

[convolutional]
size=1
stride=1
pad=1
filters=255 # 改为 24
activation=linear


[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=80 # 改为 3
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

6. 数据集格式说明

- yolov3
    - data
      - 2007_train.txt
      - 2007_test.txt
      - coco.names
      - coco.data
      - annotations(json files)
      - images(将2007_train.txt中的图片放到train2014文件夹中，test同理)
        - train2014
          - 0001.jpg
          - 0002.jpg
        - val2014
          - 0003.jpg
          - 0004.jpg
      - labels（voc_labels.py生成的内容需要重新组织一下）
        - train2014
          - 0001.txt
          - 0002.txt
        - val2014
          - 0003.txt
          - 0004.txt
      - samples(存放待测试图片)

2007_train.txt内容示例：

/home/dpj/yolov3-master/data/images/val2014/Cow_1192.jpg
/home/dpj/yolov3-master/data/images/val2014/Cow_1196.jpg
.....

注意images和labels文件架构一致性，因为txt是通过简单的替换得到的：

images -> labels
.jpg -> .txt

3. 训练模型

预训练模型：

• Darknet *.weights format: https://pjreddie.com/media/files/yolov3.weights
• PyTorch *.pt format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI

开始训练：

python train.py --data data/coco.data --cfg cfg/yolov3.cfg

如果日志正常输出那证明可以运行了

如果中断了，可以恢复训练

python train.py --data data/coco.data --cfg cfg/yolov3.cfg --resume

4. 测试模型

将待测试图片放到data/samples中，然后运行

python detect.py --weights weights/best.pt

5. 评估模型

python test.py --weights weights/latest.pt

如果使用cocoAPI使用以下命令：

git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3
 
python3 test.py --save-json --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
               Class    Images   Targets         P         R       mAP        F1
Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00,  1.71s/it]
                 all     5e+03  3.58e+04     0.109     0.773      0.57     0.186
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.349
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.432
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.458
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620

python3 test.py --save-json --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
               Class    Images   Targets         P         R       mAP        F1
Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00,  1.01it/s]
                 all     5e+03  3.58e+04      0.12      0.81     0.611     0.203
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.366
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.386
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.296
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.464
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618

6. 可视化

可以使用python -c from utils import utils;utils.plot_results()

创建drawLog.py

def plot_results():
    # Plot YOLO training results file 'results.txt'
    import glob
    import numpy as np
    import matplotlib.pyplot as plt
    #import os; os.system('rm -rf results.txt && wget https://storage.googleapis.com/ultralytics/results_v1_0.txt')

    plt.figure(figsize=(16, 8))
    s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
    files = sorted(glob.glob('results.txt'))
    for f in files:
        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 17, 18, 16]).T  # column 16 is mAP
        n = results.shape[1]
        for i in range(10):
            plt.subplot(2, 5, i + 1)
            plt.plot(range(1, n), results[i, 1:], marker='.', label=f)
            plt.title(s[i])
            if i == 0:
                plt.legend()
    plt.savefig('./plot.png')
if __name__ == "__main__":
    plot_results()

7. 高级进阶-网络结构更改

详细cfg文件讲解：https://pprp.github.io/2018/09/20/YOLO%20cfg%E6%96%87%E4%BB%B6%E8%A7%A3%E6%9E%90/

参考资料以及网络更改经验：https://pprp.github.io/2019/06/20/YOLO%E7%BB%8F%E9%AA%8C%E6%80%BB%E7%BB%93/

欢迎在评论区进行讨论，也便于我继续完善该教程。