将自有数据集下yolov训练结果（*.weights) 在进行部署

原始的 *.weigths是由darknet训练的（目前也存在tensorflow和其它库的改写版本，这里不做介绍），AlexeyAB版的darknet（ https://github.com/AlexeyAB/darknet）的配进行了较多改进，是我推荐的版本。在他的git中 非常详细地讲解了配置方法，特别关键的是在训练过程中能够直观回显loss和mAP图。

它的中文翻译版本（有所简化）

https://zhuanlan.zhihu.com/p/102628373

关于自有数据下yolo训练，请参考《YOLOv3自有数据集训练（AlexeyAB等）》

最后我们希望能够部署结果，我认为主要有两种方法：

一是基于darkhelp直接部署。由于darknent网络是C语言类库，所以就有 darkhelp ( DarkHelp is  not  Darknet! DarkHelp is a C++ API layer used to call Darknet's original C API.)  这样纯C的部署，目前这个库运行在Linux上，初步发现在部署的过程中存在一些需要修改的部分，目前还需要进一步研究。

二是 在OpenVINO上面，由于OpenVINO只识别自家的IR模型，所以就需要首先将.weights转换为pb,再转换为IR。这个方面的资料以OpenVINO自家的最为全面（ https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html ），简单进行翻译。

一、模型转换主要分为两个步骤。

1、由*.weights转换为*.pb

To dump TensorFlow model out of https://github.com/mystic123/tensorflow-yolo-v3 GitHub repository (commit ed60b90), follow the instructions below:

1. Clone the repository:
   
   git clone https : / /github.com /mystic123 /tensorflow -yolo -v3.git
   cd tensorflow -yolo -v3
2. (Optional) Checkout to the commit that the conversion was tested on:
   
   git checkout ed60b90
3. Download coco.names file from the DarkNet website OR use labels that fit your task.
4. Download the yolov3.weights (for the YOLOv3 model) or yolov3-tiny.weights (for the YOLOv3-tiny model) file OR use your pretrained weights with the same structure
5. Run a converter:

• for YOLO-v3:
  python3 convert_weights_pb.py - -class_names coco.names - -data_format NHWC - -weights_file yolov3.weights
• for YOLOv3-tiny:
  python3 convert_weights_pb.py - -class_names coco.names - -data_format NHWC - -weights_file yolov3 -tiny.weights - -tiny

If you have YOLOv3 weights trained for an input image with the size different from 416 (320, 608 or your own), please provide the --size key with the size of your image specified while running the converter. For example, run the following command for an image with size 608:

python3 convert_weights_pb.py - -class_names coco.names - -data_format NHWC - -weights_file yolov3_608.weights - -size 608

这部分的操作比较简单，只需要注意根据你数据集的情况修改.names等文件就可以。

 2、由*.pb转换为IR(xml+bin)

To solve the problems explained in the YOLOv3 architecture overview section, use the yolo_v3.json or yolo_v3_tiny.json (depending on a model) configuration file with custom operations located in the <OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf repository.

It consists of several attributes:

[
{
"id" : "TFYOLOV3",
"match_kind" : "general",
"custom_attributes" : {
"classes" : 80,
"anchors" : [ 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326],
"coords" : 4,
"num" : 9,
"masks" :[[ 6, 7, 8], [ 3, 4, 5], [ 0, 1, 2]],
"entry_points" : [ "detector/yolo-v3/Reshape", "detector/yolo-v3/Reshape_4", "detector/yolo-v3/Reshape_8"]
}
}
]

where:

• id and match_kind are parameters that you cannot change.
• custom_attributes is a parameter that stores all the YOLOv3 specific attributes:
  • classes, coords, num, and masks are attributes that you should copy from the configuration file file that was used for model training. If you used DarkNet officially shared weights, you can use yolov3.cfg or yolov3-tiny.cfg configuration file from https://github.com/pjreddie/darknet/tree/master/cfg. Replace the default values in custom_attributes with the parameters that follow the [yolo] titles in the configuration file.
  • anchors is an optional parameter that is not used while inference of the model, but it used in a demo to parse Region layer output
  • entry_points is a node name list to cut off the model and append the Region layer with custom attributes specified above.

To generate the IR of the YOLOv3 TensorFlow model, run:

python3 mo_tf.py
--input_model /path /to /yolo_v3.pb
--tensorflow_use_custom_operations_config $MO_ROOT /extensions /front /tf /yolo_v3.json
--batch 1

To generate the IR of the YOLOv3-tiny TensorFlow model, run:

python3 mo_tf.py
--input_model /path /to /yolo_v3_tiny.pb
--tensorflow_use_custom_operations_config $MO_ROOT /extensions /front /tf /yolo_v3_tiny.json
--batch 1

where:

--batch defines shape of model input. In the example, --batch is equal to 1, but you can also specify other integers larger than 1.

--tensorflow_use_custom_operations_config adds missing Region layers to the model. In the IR, the Region layer has name RegionYolo.

这部分可能出现的问题比较多，注意事项：

1、出现：'Graph' object has no attribute 'node '

解决方法： pip install networkx==2.3  

2、*.json文件作为训练参数文件，需要做较多修改。

以上部分，我都是直接在ubuntu中进行的修改。

二、模型部署主要参考官方文档

https://docs.openvinotoolkit.org/latest/_demos_object_detection_demo_yolov3_async_README.html

对于OpenVINO来说，只需要有最后的IR模型就可以。我发现对于自己训练的IR来说，由于类别比较少，能够以较快的速度运行；但是对于原始的80类数据，运转起来速度就会比较慢—甚至无法运行；此外目前只有tiny下是可以运行的，yolo是无法跑的，这里出现一些新的错误，都是由于采用了复杂模型带来的异常，只有能够进一步懂原理，才能够最终灵活运用。

来自为知笔记(Wiz)