代码：https://github.com/hustvl/YOLOP
论文：https://arxiv.org/abs/2108.11250
目录
一.数据处理代码
二. 修改YOLOP代码位置
1）默认参数的修改
2）网络参数的训练设置
3）loss的计算
4）数据加载代码的处理
5）测试代码的修改
6）预训练模型的加载
7）修改YOLOP的网络结构
三、训练报错
1） t = tuple(x / seen * 1E3 for x in (t_inf, t_nms, t_inf + t_nms)) + (imgsz, imgsz, batch_size) # tupleZeroDivisionError: float division by zero
2）单独训练检测模型性能差
使用YOLOP跑通目标检测和分割：进行2类（美工刀+打火机）目标检测和三类的语义分割（背景类+美工刀+打火机）
YOLOP改多类效果差：https://github.com/hustvl/YOLOP/issues/70
一.数据处理代码将yolov5 多边形训练标签格式转换为yolop训练格式
我的图片放置路径：
下面是处理代码：（注意yolov5的标签是归一化的多边形标签：label x1 y1 x2 y2 x3 y3，不是归一化的label cx cy w y，换成你自己的标签读取方式；）
'''
将yolov5 label convert yolovp label
yolov5 label: .txt label x1 y1 x2 y2 x3 y3
demo.txt ：1 0.295082 0.479784 0.473361 0.482480 0.469262 0.547170 0.288934 0.533693
yolovp label:
{
"frames": {
"objects": [
{
"category": "lighter",
"box2d": {
"x1": 25.9999,
"y1": 225.999922,
"x2": 42.99992,
"y2": 321.99991
}
}
]
}
}
'''
import os
import cv2
import json
from tqdm import tqdm
from PIL import Image
from PIL import ImageDraw
import numpy as np
dict_label = {'0': 'utility_knife', '1': 'lighter'}
def mkdir_dir(path):
if not os.path.exists(path):
os.mkdir(path)
# else:
# files = os.listdir(path)
# for file in files:
# file_path = os.path.join(path, file)
# os.remove(file_path)
def mkdir_dir1(path):
if not os.path.exists(path):
os.mkdir(path)
else:
files = os.listdir(path)
for file in files:
file_path = os.path.join(path, file)
os.remove(file_path)
def mkdir_dir2(path):
paths_list = []
paths_list.append(path)
while not os.path.exists(path):
path_list = path.split('/')
part_name = '/'+path_list[-1]
path = path.replace(part_name, '')
paths_list.append(path)
paths_list.reverse()
for path in paths_list:
mkdir_dir(path)
# mkdir_dir1(paths_list[-1])
if __name__ == '__main__':
# dir_names = ['2004', '2005', '503', '517', '525', '526', '527', '528', '529', '530', '531', '532',
# '541', '543', '544', '555', '556', '557', '558', '650', '651',
# '849', '1552', '1553', '1646', '1675', '1676', '1719', '1720', '1721']
dir_names = ['541']
src_dir = '/media/fxp/7292a4b1-2584-4296-8caf-eb9788c2ffb9/data/xray/危险品检测/process_ok/20220428'
save_dir = '/media/fxp/7292a4b1-2584-4296-8caf-eb9788c2ffb9/data/xray/危险品检测/process_ok/yolop_label'
for name in dir_names:
print(name)
dir_img = os.path.join(src_dir, name, 'images')
dir_txt = os.path.join(src_dir, name, 'labels')
dir_save_imgs = os.path.join(save_dir, name, 'images', 'train')
mkdir_dir2(dir_save_imgs)
dir_save_det = os.path.join(save_dir, name, 'det_labels', 'train')
mkdir_dir2(dir_save_det)
dir_save_seg = os.path.join(save_dir, name, 'seg_labels', 'train')
mkdir_dir2(dir_save_seg)
txts = os.listdir(dir_txt)
num = 0
for txt in tqdm(txts):
if 1: #num < 1:
result_dict = {}
result_1_list = {}
txt_path = os.path.join(dir_txt, txt)
save_json = os.path.join(dir_save_det,txt[:-4]+'.json')
save_seg_img = os.path.join(dir_save_seg, txt[:-4]+'.png')
img_path = os.path.join(dir_img, txt[:-4]+'.jpg')
save_img = os.path.join(dir_save_imgs, txt[:-4] + '.jpg')
img = cv2.imread(img_path)
h, w = img.shape[:2]
size = (h, w, 3) # ( annotation.imgWidth , annotation.imgHeight )
# labelImg = Image.new("L", size, 0)
labelImg = np.zeros(size, np.uint8)
labelImg[:, :, 0] = 200
labelImg[:, :, 1] = 0
labelImg[:, :, 2] = 0
# drawer = ImageDraw.Draw(labelImg)
lines = open(txt_path, 'r', encoding='utf-8').readlines()
save_label = False
result_list = []
for line in lines:
dict_result = {}
# print(line, img[:-4]+'.txt')
# point = list(map(int, line.strip().split(' ')))
label = line.strip().split(' ')[0]
label_name = dict_label[label]
dict_result["category"] = label_name
points = list(map(float, line.strip().split(' ')[1:])) # 读取中点，w，h
widths = [x * w for x in points[::2]]
heights = [y * h for y in points[1::2]]
polygon = []
for i_ in range(len(widths)):
ptStart = [widths[i_], heights[i_]]
polygon.append(ptStart)
points = np.array(polygon, dtype=np.int32)
if label == '0':
# drawer.polygon(polygon, fill=100)
# labelImg[:, :, 1] = 2
cv2.fillPoly(labelImg, [points], color=(0, 2, 0))
elif label == '1':
# drawer.polygon(polygon, fill=200)
# labelImg[:, :, 2] = 2
cv2.fillPoly(labelImg, [points], color=(0, 0, 2))
else:
print('label is error:', label)
x1 = min(widths)
x2 = max(widths)
y1 = min(heights)
y2 = max(heights)
dict_box = {}
dict_box["x1"] = x1
dict_box["y1"] = y1
dict_box["x2"] = x2
dict_box["y2"] = y2
dict_result["box2d"] = dict_box
result_list.append(dict_result)
# result_dict["frames"] = {"objects":result_list}
result_1_list["objects"] = result_list
result_dict["frames"] = result_1_list
# data2 = json.dumps(result_list, sort_keys=True, indent=4, separators=(',', ': '))
with open(save_json, 'w') as file_obj:
json.dump(result_dict, file_obj, indent=4, separators=(',', ': '))
# labelImg.save(save_seg_img)
cv2.imwrite(save_seg_img, labelImg)
cv2.imwrite(save_img, img)
num += 1
将车道线相关的代码删掉，具体可查看上传的工程：YOLOP-main: 用于美工刀和打火机的语义分割和目标检测
1）默认参数的修改YOLOP-main/lib/default.py
_C.GPUS 根据你实际的显卡数进行修改
_C.WORKERS 由cpu的数量确认worker是的数量，直接影响数据加载速度
Dataloader的num_worker设置多少才合适，这个问题是很难有一个推荐的值。有以下几个建议：
num_workers=0表示只有主进程去加载batch数据，这个可能会是一个瓶颈。
num_workers = 1表示只有一个worker进程用来加载batch数据，而主进程是不参与数据加载的。这样速度也会很慢。
num_workers>0 表示只有指定数量的worker进程去加载数据，主进程不参与。增加num_works也同时会增加cpu内存的消耗。所以num_workers的值依赖于 batch size和机器性能。
一般开始是将num_workers设置为等于计算机上的CPU数量
最好的办法是缓慢增加num_workers，直到训练速度不再提高，就停止增加num_workers的值。
————————————————
版权声明：本文为CSDN博主「龙南希」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_28057379/article/details/115427052
_C.num_seg_class 修改为3 ，作者进行行驶区域分割为2，自己数据使用时设置为3；这里会影响标签处理；
_C.MODEL.PRETRAINED 加载预训练模型的路径
_C.DATASET.DATAROOT 保留了数据加载的基础路径，因为数据量大，批次多，所以修改了数据读取的方式；
_C.DATASET.NAME_LIST = ['2004'] 自己加入，按照子文件夹来读取图片和标签；
_C.TEST.NMS_CONF_THRESHOLD 将阈值从0.001提升至0.4；这个修改可参看yoloP 的issue；
2）网络参数的训练设置（哪些参数可以不用更新）
YOLOP-main/tools/train.py中第240行下面加入：
if cfg.TRAIN.DET_SEG_ONLY:
logger.info('freeze Ll_Seg heads...')
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if k.split(".")[1] in Encoder_para_idx + Ll_Seg_Head_para_idx:
print('freezing %s' % k)
v.requires_grad = FalseEncoder_para_idx参数固定后将不会训练主干，要训练主干可将其打开；
3）loss的计算YOLOP-main/lib/core/loss.py中将有关车道线的代码置0；
默认都是 nn.BCEWithLogitsLoss，修改default.py中的loss params，可修改loss参数；
4）数据加载代码的处理（包括数据制作）
数据读取：YOLOP-main/lib/dataset/AutoDriveDataset.py
修改AutoDriveDataset类的初始化为：
def get_filelist(path):
Filelist = []
for home, dirs, files in os.walk(path):
for filename in files:
# 文件名列表，包含完整路径
Filelist.append(os.path.join(home, filename))
# file_ = os.path.join(home, filename)[::-1].split('/')[1][::-1]
# # 文件名列表，只包含文件名
# Filelist.append( filename)
return Filelist
class AutoDriveDataset(Dataset):
"""
A general Dataset for some common function
"""
def __init__(self, cfg, is_train, inputsize=640, transform=None):
"""
initial all the characteristic
Inputs:
-cfg: configurations
-is_train(bool): whether train set or not
-transform: ToTensor and Normalize
Returns:
None
"""
self.is_train = is_train
self.cfg = cfg
self.transform = transform
self.inputsize = inputsize
self.Tensor = transforms.ToTensor()
self.img_root = Path(cfg.DATASET.DATAROOT)
# label_root = Path(cfg.DATASET.LABELROOT)
# mask_root = Path(cfg.DATASET.MASKROOT)
# lane_root = Path(cfg.DATASET.LANEROOT)
if is_train:
self.indicator = cfg.DATASET.TRAIN_SET
else:
self.indicator = cfg.DATASET.TEST_SET
self.name_list = cfg.DATASET.NAME_LIST
mask_root_list = []
self.mask_list = []
for name in self.name_list:
# self.img_root_list.append(self.img_root / name / 'images' / self.indicator)
mask_root_list.append(self.img_root / name / 'seg_labels' / self.indicator)
# self.det_root_list.append(self.img_root / name / 'det_labels' / self.indicator)
for mask_root in mask_root_list:
# result_ = mask_root.iterdir()
result_ = get_filelist(mask_root)
self.mask_list += result_ # 可以获取直接下级文件和文件夹
# for name in self.name_list:
# self.img_root_list.append(img_root / name / self.indicator)
# self.img_root = img_root / indicator
# self.label_root = label_root / indicator
# self.mask_root = mask_root / indicator
# self.lane_root = lane_root / indicator
# self.label_list = self.label_root.iterdir()
# self.mask_list = self.mask_root.iterdir() # 可以获取直接下级文件和文件夹
self.db = []
self.data_format = cfg.DATASET.DATA_FORMAT
self.scale_factor = cfg.DATASET.SCALE_FACTOR
self.rotation_factor = cfg.DATASET.ROT_FACTOR
self.flip = cfg.DATASET.FLIP
self.color_rgb = cfg.DATASET.COLOR_RGB
# self.target_type = cfg.MODEL.TARGET_TYPE
# self.shapes = np.array(cfg.DATASET.ORG_IMG_SIZE)YOLOP-main/lib/dataset/bdd.py中的修改内容：
single_cls： False # just detect vehicle
其中遍历数据的内容修改为：
for mask in tqdm(self.mask_list):
mask_path = str(mask)
# label_path = mask_path.replace(str(self.mask_root), str(self.label_root)).replace(".png", ".json")
# image_path = mask_path.replace(str(self.mask_root), str(self.img_root)).replace(".png", ".jpg")
label_path = mask_path.replace(".png", ".json").replace('/seg_labels/', '/det_labels/')
image_path = mask_path.replace(".png", ".jpg").replace('/seg_labels/', '/images/')
assert (os.path.exists(label_path), 'label_path is not exist!')
assert (os.path.exists(image_path), 'image_path is not exist!')YOLOP-main/lib/dataset/convert.py中的内容修改：
id_dict = {'utility_knife': 0, 'lighter': 1}其中'utility_knife', 'lighter是我自己训练样本的类别；
5）测试代码的修改YOLOP-main/lib/core/function.py
将：
da_seg_mask = torch.nn.functional.interpolate(da_seg_mask, scale_factor=int(1/ratio), mode='bilinear')替换为：
ori_h, ori_w = img_test.shape[:2]
da_seg_mask = torch.nn.functional.interpolate(da_seg_mask, size=[ori_h, ori_w],
mode='bilinear') # 将图片上/下采样到指定的大小
将：
da_gt_mask = torch.nn.functional.interpolate(da_gt_mask, scale_factor=int(1/ratio), mode='bilinear')替换为：
da_gt_mask = torch.nn.functional.interpolate(da_gt_mask, size=[ori_h, ori_w],
mode='bilinear')YOLOP-main/lib/utils/plot.py
将
img = cv2.resize(img, (1280,720), interpolation=cv2.INTER_LINEAR)屏蔽掉；
x修改原因：本人的训练数据原始尺寸各不相同，没有一个统一的原始尺寸；
6）预训练模型的加载原代码：
if os.path.exists(cfg.MODEL.PRETRAINED):
logger.info("=> loading model '{}'".format(cfg.MODEL.PRETRAINED))
checkpoint = torch.load(cfg.MODEL.PRETRAINED)
begin_epoch = checkpoint['epoch']
# best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])修改为：
checkpoint1 = checkpoint['state_dict'] #分割类别变化了，会影响模型参数加载
# -------begin
model_state_dict = model.state_dict()
for k in list(checkpoint1.keys()):
if k in model_state_dict:
shape_model = tuple(model_state_dict[k].shape)
shape_checkpoint = tuple(checkpoint1[k].shape)
if shape_model != shape_checkpoint:
# incorrect_shapes.append((k, shape_checkpoint, shape_model))
checkpoint1.pop(k)
print(k, shape_model, shape_checkpoint)
else:
print(k, ' layer is missing!')
model.load_state_dict(checkpoint1, strict=False)其他的细节请查看修改后的工程；
7）修改YOLOP的网络结构检测的检测头detect的参数类别是2（美工刀和打火机），之前为1；区域分割的输出通道数也要修改为3;这和分割数据类别（default.py）参数‘_C.num_seg_class = 3 #2’是对应的；
YOLOP = [
[24, 33, 42], #Det_out_idx, Da_Segout_idx, LL_Segout_idx
[ -1, Focus, [3, 32, 3]], #0
[ -1, Conv, [32, 64, 3, 2]], #1
[ -1, BottleneckCSP, [64, 64, 1]], #2
[ -1, Conv, [64, 128, 3, 2]], #3
[ -1, BottleneckCSP, [128, 128, 3]], #4
[ -1, Conv, [128, 256, 3, 2]], #5
[ -1, BottleneckCSP, [256, 256, 3]], #6
[ -1, Conv, [256, 512, 3, 2]], #7
[ -1, SPP, [512, 512, [5, 9, 13]]], #8
[ -1, BottleneckCSP, [512, 512, 1, False]], #9
[ -1, Conv,[512, 256, 1, 1]], #10
[ -1, Upsample, [None, 2, 'nearest']], #11
[ [-1, 6], Concat, [1]], #12
[ -1, BottleneckCSP, [512, 256, 1, False]], #13
[ -1, Conv, [256, 128, 1, 1]], #14
[ -1, Upsample, [None, 2, 'nearest']], #15
[ [-1,4], Concat, [1]], #16 #Encoder
[ -1, BottleneckCSP, [256, 128, 1, False]], #17
[ -1, Conv, [128, 128, 3, 2]], #18
[ [-1, 14], Concat, [1]], #19
[ -1, BottleneckCSP, [256, 256, 1, False]], #20
[ -1, Conv, [256, 256, 3, 2]], #21
[ [-1, 10], Concat, [1]], #22
[ -1, BottleneckCSP, [512, 512, 1, False]], #23
[ [17, 20, 23], Detect, [2, [[3,9,5,11,4,20], [7,18,6,39,12,31], [19,50,38,81,68,157]], [128, 256, 512]]], #Detection head 24
[ 16, Conv, [256, 128, 3, 1]], #25
[ -1, Upsample, [None, 2, 'nearest']], #26
[ -1, BottleneckCSP, [128, 64, 1, False]], #27
[ -1, Conv, [64, 32, 3, 1]], #28
[ -1, Upsample, [None, 2, 'nearest']], #29
[ -1, Conv, [32, 16, 3, 1]], #30
[ -1, BottleneckCSP, [16, 8, 1, False]], #31
[ -1, Upsample, [None, 2, 'nearest']], #32
# [ -1, Conv, [8, 2, 3, 1]], #33 Driving area segmentation head
[ -1, Conv, [8, 3, 3, 1]], #33 Driving area segmentation head
[ 16, Conv, [256, 128, 3, 1]], #34
[ -1, Upsample, [None, 2, 'nearest']], #35
[ -1, BottleneckCSP, [128, 64, 1, False]], #36
[ -1, Conv, [64, 32, 3, 1]], #37
[ -1, Upsample, [None, 2, 'nearest']], #38
[ -1, Conv, [32, 16, 3, 1]], #39
[ -1, BottleneckCSP, [16, 8, 1, False]], #40
[ -1, Upsample, [None, 2, 'nearest']], #41
[ -1, Conv, [8, 2, 3, 1]] #42 Lane line segmentation head
]0it [00:00, ?it/s]
Traceback (most recent call last):
File "/media/fuxueping/7292a4b1-2584-4296-8caf-eb9788c2ffb9/code/码云/yolop-main/tools/train.py", line 445, in
main()
File "/media/fuxueping/7292a4b1-2584-4296-8caf-eb9788c2ffb9/code/码云/yolop-main/tools/train.py", line 375, in main
logger, device, rank
File "/media/fuxueping/7292a4b1-2584-4296-8caf-eb9788c2ffb9/code/码云/yolop-main/lib/core/function.py", line 452, in validate
all 0 0 0 0 0 0
t = tuple(x / seen * 1E3 for x in (t_inf, t_nms, t_inf + t_nms)) + (imgsz, imgsz, batch_size) # tuple
File "/media/fuxueping/7292a4b1-2584-4296-8caf-eb9788c2ffb9/code/码云/yolop-main/lib/core/function.py", line 452, in
t = tuple(x / seen * 1E3 for x in (t_inf, t_nms, t_inf + t_nms)) + (imgsz, imgsz, batch_size) # tuple
ZeroDivisionError: float division by zero 问题原因：没有验证集合；yoloP工程中一定需要验证集合，没有验证集合会报错；
解决办法：加入一定量的验证数据；或者代码功底好，可以修改相应的代码；
2）单独训练检测模型性能差单独训练检测模型，有的数据集合可以得出检测结果，有的训练样本一直没有检测结果或者是出现乱框；
类似：
原因：目前官网issue也有人问这个问题：https://github.com/hustvl/YOLOP/issues/70
得到的解答是所有的类别应该归为一类，多类训练效果是比较差 ；然后自己使用了多类（2个类别：美工刀+打火机）数据集和单个类别数据集（分别使用美工刀，打火机）训练对比实验，发现单类别效果是比较好，多类别效果起不来，不仅漏框，还会瞎框；
解决办法： 目前没找到原因；
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