赞
踩
今天,带大家利用RT-DETR(我们可以换成任意一个模型)+Flask来实现一个目标检测平台小案例,其实现效果如下:
目标检测案例
这个案例很简单,就是让我们上传一张图像,随后选择一下置信度,即可检测出图像中的目标,那么具体该如何实现呢?
在先前的学习过程中,博主对RT-DETR进行来了简要的介绍,作为百度提出的实时性目标检测模型,其无论是速度还是精度均取得了较为理想的效果,今天则主要介绍一下RT-DETR的推理过程,与先前使用DETR
中使用pth
权重与网络结构相结合的推理方式不同,RT-DETR中使用的是onnx这种权重文件,因此,我们需要先对onnx文件进行一个简单了解:
import onnx
# 加载模型
model = onnx.load('onnx_model.onnx')
# 检查模型格式是否完整及正确
onnx.checker.check_model(model)
# 获取输出层,包含层名称、维度信息
output = self.model.graph.output
print(output)
在原本的DETR类目标检测算法中,推理是采用权重文件与模型结构代码相结合的方式,而在RT-DETR中,则采用onnx模型文件来进行推理,即只需要该模型文件即可。
首先是将pth文件与模型结构进行匹配,从而导出onnx模型文件
"""by lyuwenyu
"""
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
import argparse
import numpy as np
from src.core import YAMLConfig
import torch
import torch.nn as nn
def main(args, ):
"""main
"""
cfg = YAMLConfig(args.config, resume=args.resume)
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
if 'ema' in checkpoint:
state = checkpoint['ema']['module']
else:
state = checkpoint['model']
else:
raise AttributeError('only support resume to load model.state_dict by now.')
# NOTE load train mode state -> convert to deploy mode
cfg.model.load_state_dict(state)
class Model(nn.Module):
def __init__(self, ) -> None:
super().__init__()
self.model = cfg.model.deploy()
self.postprocessor = cfg.postprocessor.deploy()
print(self.postprocessor.deploy_mode)
def forward(self, images, orig_target_sizes):
outputs = self.model(images)
return self.postprocessor(outputs, orig_target_sizes)
model = Model()
dynamic_axes = {
'images': {0: 'N', },
'orig_target_sizes': {0: 'N'}
}
data = torch.rand(1, 3, 640, 640)
size = torch.tensor([[640, 640]])
torch.onnx.export(
model,
(data, size),
args.file_name,
input_names=['images', 'orig_target_sizes'],
output_names=['labels', 'boxes', 'scores'],
dynamic_axes=dynamic_axes,
opset_version=16,
verbose=False
)
if args.check:
import onnx
onnx_model = onnx.load(args.file_name)
onnx.checker.check_model(onnx_model)
print('Check export onnx model done...')
if args.simplify:
import onnxsim
dynamic = True
input_shapes = {'images': data.shape, 'orig_target_sizes': size.shape} if dynamic else None
onnx_model_simplify, check = onnxsim.simplify(args.file_name, input_shapes=input_shapes, dynamic_input_shape=dynamic)
onnx.save(onnx_model_simplify, args.file_name)
print(f'Simplify onnx model {check}...')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', default="D:\graduate\programs\RT-DETR-main\RT-DETR-main//rtdetr_pytorch\configs/rtdetr/rtdetr_r18vd_6x_coco.yml",type=str, )
parser.add_argument('--resume', '-r', default="D:\graduate\programs\RT-DETR-main\RT-DETR-main/rtdetr_pytorch/tools\output/rtdetr_r18vd_6x_coco\checkpoint0024.pth",type=str, )
parser.add_argument('--file-name', '-f', type=str, default='model.onnx')
parser.add_argument('--check', action='store_true', default=False,)
parser.add_argument('--simplify', action='store_true', default=False,)
args = parser.parse_args()
main(args)
随后,便是利用onnx模型文件进行目标检测推理过程了
onnx也有自己的一套流程:
关于onnx的前向推理,onnx使用了onnxruntime计算引擎。
onnx runtime是一个用于onnx模型的推理引擎。微软联合Facebook等在2017年搞了个深度学习以及机器学习模型的格式标准–ONNX,顺路提供了一个专门用于ONNX模型推理的引擎(onnxruntime)。
import onnxruntime
# 创建一个InferenceSession的实例,并将模型的地址传递给该实例
sess = onnxruntime.InferenceSession('onnxmodel.onnx')
# 调用实例sess的润方法进行推理
outputs = sess.run(output_layers_name, {input_layers_name: x})
推理代码如下:
import torch
import onnxruntime as ort
from PIL import Image, ImageDraw
from torchvision.transforms import ToTensor
if __name__ == "__main__":
##################
classes = ['car','truck',"bus"]
##################
# print(onnx.helper.printable_graph(mm.graph))
#############
img_path = "1.jpg"
#############
im = Image.open(img_path).convert('RGB')
im = im.resize((640, 640))
im_data = ToTensor()(im)[None]
print(im_data.shape)
size = torch.tensor([[640, 640]])
sess = ort.InferenceSession("model.onnx")
import time
start = time.time()
output = sess.run(
output_names=['labels', 'boxes', 'scores'],
#output_names=None,
input_feed={'images': im_data.data.numpy(), "orig_target_sizes": size.data.numpy()}
)
end = time.time()
fps = 1.0 / (end - start)
print(fps)
# print(type(output))
# print([out.shape for out in output])
labels, boxes, scores = output
draw = ImageDraw.Draw(im)
thrh = 0.6
for i in range(im_data.shape[0]):
scr = scores[i]
lab = labels[i][scr > thrh]
box = boxes[i][scr > thrh]
print(i, sum(scr > thrh))
#print(lab)
print(f'box:{box}')
for l, b in zip(lab, box):
draw.rectangle(list(b), outline='red',)
print(l.item())
draw.text((b[0], b[1] - 10), text=str(classes[l.item()]), fill='blue', )
#############
im.save('2.jpg')
#############
前端代码包含两部分,一个是上传页面,一个是显示页面
上传页面如下:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
<title></title>
<script src="http://www.jq22.com/jquery/jquery-1.10.2.js"></script>
<style>
#addCommodityIndex {
text-align: center;
width: 300px;
height: 340px;
position: absolute;
left: 50%;
top: 50%;
margin: -200px 0 0 -200px;
border: solid #ccc 1px;
padding: 35px;
}
#imghead {
cursor: pointer;
}
.btn {
width: 100%;
height: 40px;
text-align: center;
}
</style>
<link rel="stylesheet" href="../static/css/bootstrap.min.css" crossorigin="anonymous">
</head>
<body>
<div id="addCommodityIndex">
<h2>目标检测</h2>
<div class="form-group row">
<form id="upload" action="/upload" enctype="multipart/form-data" method="POST">
<img src="">
<div class="form-group row">
<label>上传图像</label>
<input type="file" class="form-control" name='file'>
</div>
<div class="form-group row">
<label>选择置信度</label>
<select class="form-control" name="score" id="exampleFormControlSelect1">
<option value="0.5">0.5</option>
<option value="0.6">0.6</option>
<option value="0.7">0.7</option>
<option value="0.8">0.8</option>
<option value="0.9">0.9</option>
</select>
</div>
<div class="form-group row">
<div class="btn"><input type="submit" class="btn btn-success" value="提交图像" /></div>
</div>
</form>
</div>
</div>
</body>
</html>
显示页面:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
<title></title>
<script src="http://www.jq22.com/jquery/jquery-1.10.2.js"></script>
<style>
#addCommodityIndex {
text-align: center;
position: absolute;
left: 40%;
top: 50%;
margin: -200px 0 0 -200px;
border: solid #ccc 1px;
}
#imghead {
cursor: pointer;
}
.result {
width: 100%;
height: 100%;
text-align: center;
}
</style>
<link rel="stylesheet" href="../static/css/bootstrap.min.css" crossorigin="anonymous">
</head>
<body>
<div id="addCommodityIndex">
<div class="card mb-3" style="max-width: 680px;">
<div class="row no-gutters">
<div class="col-md-5">
<img src="../static/img/result.jpg" class="result">
</div>
<div class="col-md-5">
<div class="card-body">
<h5 class="card-title">检测结果</h5>
<p class="card-text">目标数量:{{num}}</p>
<p class="card-text">检测速度:{{fps}} 帧/秒</p>
<a href="/home" class="btn btn-success">继续提交</a>
</div>
</div>
</div>
</div>
</div>
</body>
</html>
Flask框架代码:
# -*- coding: utf-8 -*-
from flask import Flask,request,render_template
import json
import os
import time
app = Flask(__name__)
import infer
@app.route('/home',methods=['GET'])
def home():
return render_template('upload.html')
@app.route('/upload',methods=['GET','POST'])
def upload():
if request.method == 'POST':
f = request.files['file'] #获取数据流
rootPath = os.path.dirname(os.path.abspath(__file__)) #根目录路径
#创建存储文件的文件夹,使用时间戳防止重名覆盖
file_path = 'static/upload/' + str(int(time.time()))
absolute_path = os.path.join(rootPath,file_path).replace('\\','/') #存储文件的绝对路径,window路径显示\\要转化/
if not os.path.exists(absolute_path): #不存在改目录则会自动创建
os.makedirs(absolute_path)
save_file_name = os.path.join(absolute_path,f.filename).replace('\\','/') #文件存储路径(包含文件名)
f.save(save_file_name)
score=request.values.to_dict().get("score")
num,fps=infer.inference(save_file_name,score)
#return json.dumps({'code':200,'url':url_path},ensure_ascii=False)
return render_template("show.html",num=num,fps=fps)
app.run(port='5000',debug=True)
上述项目博主已经上传到github上
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/pengxiang1998/rt-detr.git
git push -u origin main
在使用onnx时,安装了onnxruntime后,出现了下面的错误:
ImportError: cannot import name 'create_and_register_allocator_v2' from 'onnxruntime.capi._pybind_state'
这是由于onnxruntime-gpu版本与CUDA、CuDNN版本不匹配导致的,可以查看下面的网址来查看匹配版本
https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html
随后又出现错误:
> This ORT build has ['TensorrtExecutionProvider',
> 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled. Since ORT
> 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example,
> onnxruntime.InferenceSession(...,
> providers=['TensorrtExecutionProvider',
这是由于InferenceSession中没有提供对应的provider,修改代码如下:
if torch.cuda.is_available():
print("GPU")
sess = ort.InferenceSession("model.onnx", None, providers=["CUDAExecutionProvider"])
else:
print("CPU")
sess= ort.InferenceSession("model.onnx", None)
随后运行,发现安装了onnxruntime-gpu后的速度竟然满了下来,fps仅为0.2,而原本使用onnxruntime的fps则为7左右,这到底是怎么回事呢?
而在YOLO集成的RT-DETR项目中,训练得到的权重 文件为.pt,在推理时需要与RT-DETR搭配使用,从而实现推理过程:
需要注意的是,由于YOLO里面集成了多种模型,因此为了具有适配性,其代码都具有通用性
from ultralytics.models import RTDETR
if __name__ == '__main__':
model=RTDETR("weights/best.pt")
model.predict(source="images/1.mp4",save=True,conf=0.6)
随后执行predict
,代码如下:
def predict(
self,
source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False,
predictor=None,
**kwargs,
) -> list:
if source is None:
source = ASSETS
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
is_cli = (ARGV[0].endswith("yolo") or ARGV[0].endswith("ultralytics")) and any(
x in ARGV for x in ("predict", "track", "mode=predict", "mode=track")
)
custom = {"conf": 0.25, "batch": 1, "save": is_cli, "mode": "predict"} # method defaults
args = {**self.overrides, **custom, **kwargs} # highest priority args on the right
prompts = args.pop("prompts", None) # for SAM-type models
if not self.predictor:
self.predictor = predictor or self._smart_load("predictor")(overrides=args, _callbacks=self.callbacks)
self.predictor.setup_model(model=self.model, verbose=is_cli)
else: # only update args if predictor is already setup
self.predictor.args = get_cfg(self.predictor.args, args)
if "project" in args or "name" in args:
self.predictor.save_dir = get_save_dir(self.predictor.args)
if prompts and hasattr(self.predictor, "set_prompts"): # for SAM-type models
self.predictor.set_prompts(prompts)
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
这部分代码在功能上具有复用性,因此在理解上存在一定难度。
Copyright © 2003-2013 www.wpsshop.cn 版权所有,并保留所有权利。