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pytorch_YOLO_OpenVINO_demo

Integration of Pytorch YOLO models (YOLO-V3 / YOLO-V4 / Scaled-YOLO-V4 / YOLO-V5) for OpenVINO inference.
Windows 10 and Ubuntu 18.04 are validated to use. (Scaled-YOLO-V4 is only available on Ubuntu)

Convert Weights to ONNX File

The following components are required.

OpenVINO 2021.2
Python >= 3.6
ONNX >= 1.8.0
Pytorch >= 1.7.0
Netron 4.4.3
See 'requirements.txt' for other required components

Download Pytorch Weights

About YOLOV3
This document uses a darknet YOLOV3 model, because the author has not found a public pytorch version.
https://github.com/zldrobit/onnx_tflite_yolov3

About YOLOV4
Taken from the repository released by one of the authors of YOLOV4, KinYiu Wong.
https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u3_preview
Take two models as examples from the repository; all the activation of convolutional layer of yolov4-pacsp uses leaky, and yolov4-pacsp-mish uses mish at activation.

About Scaled-YOLOV4
Taken from the repository released by one of the authors of Scaled-YOLOV4, KinYiu Wong.
https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large#installation
There are three scaled models (p5, p6, p7) with different backbone.

About YOLOV5
There are four tag in YOLOv5 repository so far. And YOLOv5 includes YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x due to different backbone. Here we use models from tag v4.0 for inference.
https://github.com/ultralytics/yolov5

Model	Test Size	Weights	AP<sub>50</sub><sup>OpenVINO Accuracy Checker Tool</sup>

YOLOv3	416	weights	60.23%

YOLOv4<sub>pacsp</sub>	672	weights	65.71%
YOLOv4<sub>pacsp-mish</sub>	672	weights	65.41%

Scaled-YOLOv4-p5	896	weights	69.06%
Scaled-YOLOv4-p6	1280	weights	71.39%
Scaled-YOLOv4-p7	1536	weights	73.11%

YOLOv5s	640	weights	50.85%
YOLOv5m	640	weights	59.45%
YOLOv5l	640	weights	62.85%
YOLOv5x	640	weights	64.79%

Convert Pytorch Weights to ONNX Weights

If YOLOV3: You can following https://github.com/zldrobit/onnx_tflite_yolov3 to convert model to onnx.

  Edited detect.py (optional) to obtain the weight with higher accuracy.  
  line 48 'opset_version=9' -> 'opset_version=12'
  
$ python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights

If YOLOV4 / Scaled-YOLOV4 / YOLOV5:

The repository provides a script models/export.py to export Pytorch weights with extensions *.pt to ONNX weights with extensions *.onnx.

Intall mish-cuda if you want to convert Scaled-YOLOV4. (refer to https://github.com/DataXujing/ScaledYOLOv4)

$ git clone https://github.com/thomasbrandon/mish-cuda mc
$ cd mc

  change all of name which is mish_cuda to mish_mish and build.
  1. mc/src/mish_cuda -> mc/src/mish_mish
  2. mc/csrc/mish_cuda.cpp -> mc/csrc/mish_mish.cpp
  3. in mc/setup.py
    3.1 line 5 -> 'csrc/mish_mish.cpp'
    3.2 line 11 -> name='mish_mish'
    3.3 line 20 -> 'mish_mish._C'

$ python3 setup.py build
  rename mc/build/lib.xxx folder to mc/build/lib

Next, edit models/yolo.py and models/common.py according to the model to be converted.
There is one part that needs to be edited at line.46 in models/yolo.py,

# comment out only if yolov4, (scaled-yolov4 and yolov5 do not need)
            x[i] = self.m[i](x[i])  # conv

and there are two at line.42-line.45 and line.80-line.83 in models/common.py.

# if yolov4
        #self.act = Mish() if act else nn.Identity()
# if yolov5
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

# if yolov4
        #self.act = Mish()
# if yolov5
        self.act = nn.LeakyReLU(0.1, inplace=True)

At last, run models/export.py to generate .onnx file.
Here take yolov5s.pt (default) as an example, run the following command:

$ python3 models/export.py  --weights yolov5s.pt  --img-size 640

Then we can get yolov5s.onnx.

Convert ONNX File to IR File

After we get ONNX weights file from the last section, we can convert it to IR file with model optimizer.
(refer to https://github.com/violet17/yolov5_demo)
Run the following script to temporarily set OpenVINO environment and variables:

$ source /opt/intel/openvino_2021/bin/setupvars.sh

We need to specify the output node of the IR when we use model optimizer to convert the ONNX weights file.
For example, there are 3 output nodes in yolov5s.onnx that obtained in the previous step. We can use Netron to visualize yolov5s.onnx. Then we find the output nodes by searching the keyword “Transpose” in Netron. After that, we can find the convolution node marked as oval shown in following Figure. After double clicking the convolution node, we can read its name “Conv_245”.

Similarly, we can find the other two output nodes “Conv_261” and “Conv_277”.

we can run the following command to generate the IR of YOLOv5 model:

$ python3 /opt/intel/openvino_2021.1.110/deployment_tools/model_optimizer/mo.py  --input_model yolov5s.onnx -s 255 --reverse_input_channels --output Conv_245,Conv_261,Conv_277

After that, we can get IR of yolov5s in FP32.

OpenVINO Inference Python Demo

After generate IR model, we can use yolo__openvino_demo.py for inference.
The usage is as follows:

usage: yolo__openvino_demo.py [-h] -m MODEL -at
                              {yolov3,yolov4,yolov5,yolov4-p5,yolov4-p6,yolov4-p7}
                              -i INPUT [-l CPU_EXTENSION] [-d DEVICE]
                              [--labels LABELS] [-t PROB_THRESHOLD]
                              [-iout IOU_THRESHOLD] [-ni NUMBER_ITER] [-pc]
                              [-r] [--no_show]

Options:
  -h, --help            Show this help message and exit.
  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model.
  -at {yolov3,yolov4,yolov5,yolov4-p5,yolov4-p6,yolov4-p7}, --architecture_type {yolov3,yolov4,yolov5,yolov4-p5,yolov4-p6,yolov4-p7}
                        Required. Specify model' architecture type.
  -i INPUT, --input INPUT
                        Required. Path to an image/video file. (Specify 'cam'
                        to work with camera)
  -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                        Optional. Required for CPU custom layers. Absolute
                        path to a shared library with the kernels
                        implementations.
  -d DEVICE, --device DEVICE
                        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
  --labels LABELS       Optional. Labels mapping file
  -t PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD
                        Optional. Probability threshold for detections
                        filtering
  -iout IOU_THRESHOLD, --iou_threshold IOU_THRESHOLD
                        Optional. Intersection over union threshold for
                        overlapping detections filtering
  -ni NUMBER_ITER, --number_iter NUMBER_ITER
                        Optional. Number of inference iterations
  -pc, --perf_counts    Optional. Report performance counters
  -r, --raw_output_message
                        Optional. Output inference results raw values showing
  --no_show             Optional. Don't show output

Here we take yolov5s as an example, we can use the following command to run the demo.

$ python3 yolo__openvino_demo.py -m yolov5s.xml -i images/bus.jpg -at yolov5

OpenVINO Accuracy Checker Tool

Installation

Follow the guide of openvinotookit_open_model_zoo.

$ cd /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/accuracy_checker   
$ sudo apt-get install python3 python3-dev python3-setuptools python3-pip  
$ python3 setup.py install

Modify

Copy the contents of tools/accuracy_checker to accuracy checker tool path you installed before.

Added content:

An adapter specifically used for pytorch yolo models: pytorch_yolo.
Non-maximum suppression method used in yolov4: diou_nms.

$ cp -r tools/accuracy_checker /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/

Download Datasets

$ wget http://images.cocodataset.org/zips/val2017.zip  
$ wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip  
$ unzip -d annotations_trainval2017/ annotations_trainval2017.zip    
$ unzip -d annotations_trainval2017/annotations/ val2017.zip

Run Accuracy Checker Tool

Same as before, take yolov5s as an example. Use the command below to run accuracy checker.

$ accuracy_check -c configs/accuracy-check-yolov5.yml -m . --definitions /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/accuracy_checker/dataset_definitions.yml -s annotations_trainval2017/annotations/ -td CPU

References

YOLOV3
https://github.com/zldrobit/onnx_tflite_yolov3

YOLOV4
https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u5_preview
https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u5_mish_preview

Scaled-YOLOV4
https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large#installation
https://github.com/linghu8812/tensorrt_inference/tree/master/ScaledYOLOv4
https://github.com/DataXujing/ScaledYOLOv4

YOLOV5
https://github.com/ultralytics/yolov5
https://github.com/violet17/yolov5_demo

OpenVINO
https://docs.openvinotoolkit.org/
https://github.com/openvinotoolkit/open_model_zoo/tree/master/tools/accuracy_checker