700字范文 > OpenVINO 部署 YOLOv5 转换IR文件

OpenVINO 部署 YOLOv5 转换IR文件

时间：2024-05-25 01:29:48

环境：

Windows：10YOLOv5：3.1Python 3.7.10 torch：1.7.0+cu101torchvision：0.8.1+cu101 OpenVINO：openvino_.2.185Anaconda：2.0.4

运行以下脚本临时设置 OpenVINO 环境和变量：

cd C:\Program Files (x86)\Intel\openvino_.2.185\bin

setupvars.bat

(Python37) C:\Program Files (x86)\Intel\openvino_.2.185\bin>setupvars.bat
Python 3.7.10
[setupvars.bat] OpenVINO environment initialized

运行以下命令来生成YOLOv5模型的IR：

cd C:\Program Files (x86)\Intel\openvino_.2.185\deployment_tools\model_optimizer

python mo.py --input_model M:\yolov5-3.1\yolov5-v3\best.onnx --model_name M:\yolov5-3.1\best -s 255 --reverse_input_channels --output Conv_487,Conv_471,Conv_455

注：mo.py文件在 \openvino_[版本号]\deployment_tools\model_optimizer 路径下

–input_model 定义了预训练的模型–model_name 是生成的 IR 和输出 .xml/.bin 文件中的网络名称-s 表示来自原始网络输入的所有输入值将除以 this value，–reverse_input_channels 用于将输入通道顺序从RGB 切换到BGR（反之亦然）–output 表示模型的输出操作的名称。

(pytorch) C:\Program Files (x86)\Intel\openvino_.2.185\deployment_tools\model_optimizer>python mo.py --input_model M:\yolov5-3.1\yolov5-v3\best.onnx --model_name M:\yolov5-3.1\best -s 255 --reverse_input_channels --output Conv_487,Conv_471,Conv_455
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: M:\yolov5-3.1\yolov5-v3\best.onnx
- Path for generated IR: C:\Program Files (x86)\Intel\openvino_.2.185\deployment_tools\model_optimizer.
- IR output name: M:\yolov5-3.1\best
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Conv_487,Conv_471,Conv_455
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: 255.0
- Precision of IR: FP32
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: None
- Reverse input channels: True
ONNX specific parameters:
Model Optimizer version: .2.0-1877-176bdf51370-releases//2
[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: M:\yolov5-3.1\best.xml
[ SUCCESS ] BIN file: M:\yolov5-3.1\best.bin
[ SUCCESS ] Total execution time: 37.53 seconds.
It’s been a while, check for a new version of Intel® Distribution of OpenVINO™ toolkit here /content/www/us/en/develop/tools/openvino-toolkit/choose-download.html?cid=other&source=Prod&campid=ww__bu_IOTG&content=upg_pro&medium=organic_uid_agjj or on the GitHub*

对转换的IR文件使用OpenVINO .2 进行测试

python yolov5_OV.2.py -i img.jpg -m best.xml

yolov5_OV.2.py 文件部分代码

#!/usr/bin/env pythonfrom __future__ import print_function, divisionimport loggingimport osimport sysfrom argparse import ArgumentParser, SUPPRESSfrom math import exp as expfrom time import timeimport numpy as npimport cv2from openvino.inference_engine import IENetwork, IECorelogging.basicConfig(format="[ %(levelname)s ] %(message)s", level=logging.INFO, stream=sys.stdout)log = logging.getLogger()def build_argparser():parser = ArgumentParser(add_help=False)args = parser.add_argument_group('Options')args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",required=True, type=str)args.add_argument("-i", "--input", help="Required. Path to an image/video file. (Specify 'cam' to work with ""camera)", required=True, type=str)args.add_argument("-l", "--cpu_extension",help="Optional. Required for CPU custom layers. Absolute path to a shared library with ""the kernels implementations.", type=str, default=None)args.add_argument("-d", "--device",help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is"" acceptable. The sample will look for a suitable plugin for device specified. ""Default value is CPU", default="CPU", type=str)args.add_argument("--labels", help="Optional. Labels mapping file", default=None, type=str)args.add_argument("-t", "--prob_threshold", help="Optional. Probability threshold for detections filtering",default=0.5, type=float)args.add_argument("-iout", "--iou_threshold", help="Optional. Intersection over union threshold for overlapping ""detections filtering", default=0.4, type=float)args.add_argument("-ni", "--number_iter", help="Optional. Number of inference iterations", default=1, type=int)args.add_argument("-pc", "--perf_counts", help="Optional. Report performance counters", default=False,action="store_true")args.add_argument("-r", "--raw_output_message", help="Optional. Output inference results raw values showing",default=False, action="store_true")args.add_argument("--no_show", help="Optional. Don't show output", action='store_true')return parser

如果需要将yolov5s.pt转换为IR文件使用OpenVINO 部署以下是yolov5s.pt的下载链接：

/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt

自定义训练生成权重文件：

python train.py --img 640 --batch-size 2 --epoch 5 --data ./hand_data/hand.yaml --cfg ./models/yolov5s.yaml --weights '' --workers 0 --nosave --cache --device cpu

–img：输入图片分辨率大小，nargs=’+'表示参数可设置一个或多个–batch-size：批次大小，一次训练所选取的样本数，显卡不行的话，就调小点–epoch：训练总轮次，1个epoch等于使用训练集中的全部样本训练一次，值越大模型越精确，训练时间也越长。–data：数据集配置文件，数据集路径，类名等，使用数据集方面的coco.yaml文件–cfg：模型配置文件，网络结构，使用修改好的yolov5m.yaml文件–weights：选用训练的权重，可用根目录下的yolov5s.pt，也可用runs/train/exp/weights/best.pt，不选择的话，从头开始训练。–workers：dataloader的最大worker数量，建议为0–nosave：不保存模型，默认False–cache：是否提前缓存图片到内存，以加快训练速度，默认False–device：训练的设备，cpu；0(表示一个gpu设备cuda:0)；0,1,2,3(多个gpu设备)。值为空时，训练时默认使用计算机自带的显卡或CPU

以下为测试自定义训练生成权重文件使用