yoloV5模型训练教程并进行量化
yoloV5模型训练教程
数据标注
数据标注我们要用labelimg
pip install labelimg
百度爬虫爬取图像
import os
import re
import sys
import urllib
import json
import socket
import urllib.request
import urllib.parse
import urllib.error
# 设置超时
from random import randint
import time
timeout = 5
socket.setdefaulttimeout(timeout)
class Crawler:
# 睡眠时长
__time_sleep = 0.1
__amount = 0
__start_amount = 0
__counter = 0
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
__per_page = 30
# 获取图片url内容等
# t 下载图片时间间隔
def __init__(self, t=0.1):
self.time_sleep = t
# 获取后缀名
@staticmethod
def get_suffix(name):
m = re.search(r'\.[^\.]*$', name)
if m.group(0) and len(m.group(0)) <= 5:
return m.group(0)
else:
return '.jpeg'
# 保存图片
def save_image(self, rsp_data, word):
if not os.path.exists("./" + word):
os.mkdir("./" + word)
# 判断名字是否重复，获取图片长度
self.__counter = len(os.listdir('./' + word)) + 1
for image_info in rsp_data['data']:
try:
if 'replaceUrl' not in image_info or len(image_info['replaceUrl']) < 1:
continue
obj_url = image_info['replaceUrl'][0]['ObjUrl']
thumb_url = image_info['thumbURL']
url = 'https://image.baidu.com/search/down?tn=download&ipn=dwnl&word=download&ie=utf8&fr=result&url=%s&thumburl=%s' % (
urllib.parse.quote(obj_url), urllib.parse.quote(thumb_url))
time.sleep(self.time_sleep)
suffix = self.get_suffix(obj_url)
# 指定UA和referrer，减少403
opener = urllib.request.build_opener()
opener.addheaders = [
('User-agent', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36'),
]
urllib.request.install_opener(opener)
# 保存图片
filepath = './{}/PME_{}_A{}'.format(word, randint(
1000000, 500000000), str(self.__counter) + str(suffix))
for _ in range(5):
urllib.request.urlretrieve(url, filepath)
if os.path.getsize(filepath) >= 5:
break
if os.path.getsize(filepath) < 5:
print("下载到了空文件，跳过!")
os.unlink(filepath)
continue
except urllib.error.HTTPError as urllib_err:
print(urllib_err)
continue
except Exception as err:
time.sleep(1)
print(err)
print("产生未知错误，放弃保存")
continue
else:
print("图+1,已有" + str(self.__counter) + "张图")
self.__counter += 1
return
# 开始获取
def get_images(self, word):
search = urllib.parse.quote(word)
# pn int 图片数
pn = self.__start_amount
while pn < self.__amount:
url = 'https://image.baidu.com/search/acjson?tn=resultjson_com&ipn=rj&ct=201326592&is=&fp=result&queryWord=%s&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=&st=-1&z=&ic=&hd=&latest=©right=&word=%s&s=&se=&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&expermode=&force=&pn=%s&rn=%d&gsm=1e&1594447993172=' % (
search, search, str(pn), self.__per_page)
# 设置header防403
try:
time.sleep(self.time_sleep)
req = urllib.request.Request(url=url, headers=self.headers)
page = urllib.request.urlopen(req)
rsp = page.read()
except UnicodeDecodeError as e:
print(e)
print('-----UnicodeDecodeErrorurl:', url)
except urllib.error.URLError as e:
print(e)
print("-----urlErrorurl:", url)
except socket.timeout as e:
print(e)
print("-----socket timout:", url)
else:
# 解析json
try:
rsp_data = json.loads(rsp)
self.save_image(rsp_data, word)
# 读取下一页
print("下载下一页")
pn += 60
except Exception as e:
continue
finally:
page.close()
print("下载任务结束")
return
def start(self, word, total_page=2, start_page=1, per_page=30):
"""
爬虫入口
:param word: 抓取的关键词
:param total_page: 需要抓取数据页数 总抓取图片数量为 页数 x per_page
:param start_page:起始页码
:param per_page: 每页数量
:return:
"""
self.__per_page = per_page
self.__start_amount = (start_page - 1) * self.__per_page
self.__amount = total_page * self.__per_page + self.__start_amount
self.get_images(word)
if __name__ == '__main__':
crawler = Crawler(0.05) # 抓取延迟为 0.05
crawler.start('玩手机')
标注完成后，每张图像会生成对应的xml标注文件
数据预处理
创建convert_data.py文件，内容如下：
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
from tqdm import tqdm
import os
from os import getcwd
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(image_id):
# try:
in_file = open('VOCData/images/{}.xml'.format(image_id), encoding='utf-8')
out_file = open('VOCData/labels/{}.txt'.format(image_id),
'w', encoding='utf-8')
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))
b1, b2, b3, b4 = b
# 标注越界修正
if b2 > w:
b2 = w
if b4 > h:
b4 = h
b = (b1, b2, b3, b4)
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " +
" ".join([str(a) for a in bb]) + '\n')
# except Exception as e:
# print(e, image_id)
if __name__ == '__main__':
sets = ['train', 'val']
image_ids = [v.split('.')[0]
for v in os.listdir('VOCData/images/') if v.endswith('.xml')]
split_num = int(0.95 * len(image_ids))
classes = ['face', 'normal', 'phone', 'write',
'smoke', 'eat', 'computer', 'sleep']
if not os.path.exists('VOCData/labels/'):
os.makedirs('VOCData/labels/')
list_file = open('train.txt', 'w')
for image_id in tqdm(image_ids[:split_num]):
list_file.write('VOCData/images/{}.jpg\n'.format(image_id))
convert_annotation(image_id)
list_file.close()
list_file = open('val.txt', 'w')
for image_id in tqdm(image_ids[split_num:]):
list_file.write('VOCData/images/{}.jpg\n'.format(image_id))
convert_annotation(image_id)
list_file.close()
运行结束后，可以看到VOCData/labels下生成了对应的txt文件
在data文件夹下创建myvoc.yaml文件
内容如下：
train: train.txt
val: val.txt
# number of classes
nc: 8
# class names
names: ["face", "normal", "phone", "write", "smoke", "eat", "computer", "sleep"]
下载预训练模型
我训练yolov5m这个模型，因此将它的预训练模型下载到weights文件夹下：
模型训练
修改models/yolov5m.yaml下的类别数：
python train.py --img 640 --batch 4 --epoch 300 --data ./data/myvoc.yaml --cfg ./models/yolov5m.yaml --weights weights/yolov5m.pt --workers 0
模型推理测试
训练结束后在 run/train/exp/weights 文件夹下会生成训练好的两个模型文件，我们将 last.pt 取出放到根目录下，然后运行：
python detect.py --source data/images --weights last.pt --conf 0.25
模型量化
这时我们注意到，训练好的 last.pt 有172MB，而官方给出的 yolov5m.pt 只有 40MB，这时候我们需要导出半精度模型重新保存,创建slim.py文件
python slim.py --in_weights last.pt --out_weights slim_model.pt --device 0
slim.py
import os
import torch
import torch
import torch.nn as nn
from tqdm import tqdm
def autopad(k, p=None):
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
class Conv(nn.Module):
# Standard convolution
# ch_in, ch_out, kernel, stride, padding, groups
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p),
groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
self.act = nn.Hardswish() if act else nn.Identity()
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
return self.act(self.conv(x))
class Ensemble(nn.ModuleList):
# Ensemble of models
def __init__(self):
super(Ensemble, self).__init__()
def forward(self, x, augment=False):
y = []
for module in self:
y.append(module(x, augment)[0])
# y = torch.stack(y).max(0)[0] # max ensemble
# y = torch.cat(y, 1) # nms ensemble
y = torch.stack(y).mean(0) # mean ensemble
return y, None # inference, train output
def attempt_load(weights, map_location=None):
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
# load FP32 model
model.append(torch.load(
w, map_location=map_location)['model'].float().fuse().eval())
# Compatibility updates
for m in tqdm(model.modules()):
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
m.inplace = True # pytorch 1.7.0 compatibility
elif type(m) is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if len(model) == 1:
return model[-1] # return model
else:
print('Ensemble created with %s\n' % weights)
for k in ['names', 'stride']:
setattr(model, k, getattr(model[-1], k))
return model # return ensemble
def select_device(device='', batch_size=None):
# device = 'cpu' or '0' or '0,1,2,3'
cpu_request = device.lower() == 'cpu'
if device and not cpu_request: # if device requested other than 'cpu'
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable
assert torch.cuda.is_available(
), 'CUDA unavailable, invalid device %s requested' % device # check availablity
cuda = False if cpu_request else torch.cuda.is_available()
if cuda:
c = 1024 ** 2 # bytes to MB
ng = torch.cuda.device_count()
if ng > 1 and batch_size: # check that batch_size is compatible with device_count
assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (
batch_size, ng)
x = [torch.cuda.get_device_properties(i) for i in range(ng)]
s = f'Using torch {torch.__version__} '
for i in range(0, ng):
if i == 1:
s = ' ' * len(s)
return torch.device('cuda:0' if cuda else 'cpu')
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--in_weights', type=str,
default='last.pt', help='initial weights path')
parser.add_argument('--out_weights', type=str,
default='slim_model.pt', help='output weights path')
parser.add_argument('--device', type=str, default='0', help='device')
opt = parser.parse_args()
device = select_device(opt.device)
model = attempt_load(opt.in_weights, map_location=device)
model.to(device).eval()
model.half()
torch.save(model, opt.out_weights)
print('done.')
print('-[INFO] before: {} kb, after: {} kb'.format(
os.path.getsize(opt.in_weights), os.path.getsize(opt.out_weights)))