YOLOv5

• • • • YOLOv5下載與測試運行
      • 導(dǎo)出ONNX格式文件
      • ONNX轉(zhuǎn)為為IR中間格式

環(huán)境：

• Windows 10
• Anaconda 2.0.4
• OpenVINO 工具包 2021.2
• Python 3.6.13
• torch 1.9.0
• onnx 1.10.1
• YOLOv5

YOLOv5下載與測試運行

YOLOv5是第二個非官方的YOLO對象檢測版本，也是第一個Pytorch實現(xiàn)的YOLO對象檢測版本。Github地址：https://github.com/ultralytics/yolov5

克隆到本地

git clone https://github.com/ultralytics/yolov5.git

安裝YOLOv5所有依賴

pip install -r requirements.txt

導(dǎo)出ONNX格式文件

• OpenVINO 工具包 2021.2 可以直接讀取ONNX格式文件，所以我們既可以通過腳本直接導(dǎo)出onnx格式文件，直接給OpenVINO調(diào)用，也可以對得到ONNX文件通過OpenVINO的模型轉(zhuǎn)換生成IR中間格式(.bin文件與.xml文件)。

Pytorch的YOLOv5項目本身已經(jīng)提供了轉(zhuǎn)換腳本，命令行運行方式如下：

(python37) C:\Program Files (x86)\Intel\openvino_2021.2.185\bin> setupvars.bat Python 3.7.10

[setupvars.bat] OpenVINO environment initialized

cd 到Y(jié)OLOv5項目所在目錄下

使用 YOLOv5 提供的 export.py 將 yolov5s.pt 轉(zhuǎn)換為 ONNX。

python models/export.py --weights yolov5s.pt --img 640 --batch 1

(python37) M:\python\OpenCV\yolov5\yolov5-master>python models/export.py --weights yolov5s.pt --img 640 --batch 1
Namespace(batch_size=1, device=‘cpu’, dynamic=False, half=False, img_size=[640, 640], include=[‘torchscript’, ‘onnx’, ‘coreml’], inplace=False, opset_version=12, optimize=False, simplify=False, train=False, weights=‘yolov5s.pt’)
YOLOv5 2021-5-18 torch 1.8.1+cpu CPU
Fusing layers…
Model Summary: 224 layers, 7266973 parameters, 0 gradients
PyTorch: starting from yolov5s.pt (14.8 MB)
TorchScript: starting export with torch 1.8.1+cpu…
M:\python\OpenCV\yolov5\yolov5-master\models\yolo.py:51: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can’t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
TorchScript: export success, saved as yolov5s.torchscript.pt (29.4 MB)
ONNX: starting export with onnx 1.10.1…
ONNX: export success, saved as yolov5s.onnx (29.2 MB)
CoreML: starting export with coremltools 4.1…
Tuple detected at graph output. This will be flattened in the converted model.
Converting graph.
Adding op ‘1’ of type const
Adding op ‘2’ of type const
Adding op ‘3’ of type const
…
…
…
Adding op ‘729’ of type add
Converting op 730 : select
Converting Frontend ==> MIL Ops: 87%|██████████████████████████████████████▏ | 604/695 [00:01<00:00, 557.66 ops/s]
CoreML: export failure:
Export complete (10.18s). Visualize with https://github.com/lutzroeder/netron.

生成的yolov5s.onnx文件在YOLOv5目錄下。

ONNX轉(zhuǎn)為為IR中間格式

Windows 10 下 torch模型轉(zhuǎn)換為 OpenVINO需要的IR文件：https://blog.csdn.net/qq_44989881/article/details/119488209

使用 OpenVINO 工具包提供的 mo_onnx.py文件，對模型進行轉(zhuǎn)換。

管理員模式打開Anaconda，啟動虛擬環(huán)境的終端，cd進openVINO轉(zhuǎn)換工具目錄，執(zhí)行轉(zhuǎn)換代碼：

cd C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\model_optimizer python mo_onnx.py --input_model M:\python\OpenCV\yolov5\yolov5-master\yolov5s.onnx

(pytorch) C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\model_optimizer>python mo_onnx.py --input_model M:\python\OpenCV\yolov5\yolov5-master\yolov5s.onnx
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: M:\python\OpenCV\yolov5\yolov5-master\yolov5s.onnx
- Path for generated IR: C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\model_optimizer.
- IR output name: yolov5s
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP32
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: None
- Reverse input channels: False
ONNX specific parameters:
Model Optimizer version: 2021.2.0-1877-176bdf51370-releases/2021/2
[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\model_optimizer.\yolov5s.xml
[ SUCCESS ] BIN file: C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\model_optimizer.\yolov5s.bin
[ SUCCESS ] Total execution time: 26.05 seconds.
It’s been a while, check for a new version of Intel? Distribution of OpenVINO? toolkit here https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/choose-download.html?cid=other&source=Prod&campid=ww_2021_bu_IOTG&content=upg_pro&medium=organic_uid_agjj or on the GitHub*

轉(zhuǎn)換成功后，在轉(zhuǎn)換工具 model_optimizer 目錄下生成了bin和xml文件，然后就可以用 OpenVINO部署了。

.xml - 描述網(wǎng)絡(luò)拓撲
.bin - 包含權(quán)重和偏差二進制數(shù)據(jù)。

遇到的問題：
缺少 onnx 庫 和 coremltools庫

(Python37) M:\python\OpenCV\yolov5\yolov5-master>python models/export.py --weights yolov5s.pt --img 640 --batch 1
Namespace(batch_size=1, device=‘cpu’, dynamic=False, half=False, img_size=[640, 640], include=[‘torchscript’, ‘onnx’, ‘coreml’], inplace=False, opset_version=12, optimize=False, simplify=False, train=False, weights=‘yolov5s.pt’)
YOLOv5 2021-5-18 torch 1.8.1+cpu CPU
Fusing layers…
Model Summary: 224 layers, 7266973 parameters, 0 gradients
PyTorch: starting from yolov5s.pt (14.8 MB)
TorchScript: starting export with torch 1.8.1+cpu…
M:\python\OpenCV\yolov5\yolov5-master\models\yolo.py:51: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can’t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
TorchScript: export success, saved as yolov5s.torchscript.pt (29.4 MB)
ONNX: export failure: No module named ‘onnx’
CoreML: export failure: No module named ‘coremltools’
Export complete (5.22s). Visualize with https://github.com/lutzroeder/netron.

安裝：onnx

pip install onnx

(python37) C:\Program Files (x86)\Intel\openvino_2021.2.185\bin>pip install onnx
Collecting onnx
Downloading onnx-1.10.1-cp37-cp37m-win_amd64.whl (11.4 MB)
|████████████████████████████████| 11.4 MB 1.1 MB/s
Requirement already satisfied: typing-extensions>=3.6.2.1 in h:\anacondanavigator\anaconda\envs\python37\lib\site-packages (from onnx) (3.7.4.3)
Requirement already satisfied: six in h:\anacondanavigator\anaconda\envs\python37\lib\site-packages (from onnx) (1.16.0)
Requirement already satisfied: numpy>=1.16.6 in h:\anacondanavigator\anaconda\envs\python37\lib\site-packages (from onnx) (1.20.2)
Requirement already satisfied: protobuf in h:\anacondanavigator\anaconda\envs\python37\lib\site-packages (from onnx) (3.17.0)
Installing collected packages: onnx
Successfully installed onnx-1.10.1

安裝：coremltools

pip install coremltools

(python37) C:\Program Files (x86)\Intel\openvino_2021.2.185\bin>pip install coremltools
Collecting coremltools
Downloading coremltools-4.1.tar.gz (783 kB)
|████████████████████████████████| 783 kB 726 kB/s
Collecting numpy<1.20,>=1.14.5
Downloading numpy-1.19.5-cp37-cp37m-win_amd64.whl (13.2 MB)
|████████████████████████████████| 13.2 MB 1.3 MB/s
Requirement already satisfied: protobuf>=3.1.0 in h:\anacondanavigator\anaconda\envs\python37\lib\site-packages (from coremltools) (3.17.0)
Requirement already satisfied: six>=1.10.0 in
…
…
…
installed. This behaviour is the source of the following dependency conflicts.
imgaug 0.4.0 requires opencv-python-headless, which is not installed.
labelme 4.5.7 requires matplotlib<3.3, but you have matplotlib 3.4.2 which is incompatible.
Successfully installed attr-0.3.1 coremltools-4.1 mpmath-1.2.1 numpy-1.19.5 sympy-1.8

總結(jié)

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