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Gao__Jiansheng
Beginner
206 Views

Unable to convert retrained tiny-yolo v3 model from DarkNet to Tensorflow and then to IR

    I convert tiny-yolo v3 model from DarkNet to Tensorflow and the pb file works normally. Then I convert the pb file to IR by the following command:

            sudo python3 mo_tf.py    \

            --input_model frozen_tiny_yolo_v3.pb    \

            --output_dir save_IR    \

            --data_type FP16    \

            --batch 1    \

            --tensorflow_use_custom_operations_config   yolo_v3_tiny.json

The frozen_tiny_yolo_v3.pb  is converted to IR  successfully.

Then I test the IR by the

    " ~/inference_engine_vpu_arm/deployment_tools/inference_engine/samples/python_samples/object_detection_demo_tiny-yolov3.py" file.

(The file is used for yolov3 at first and I changed it to tiny-yolov3).

The command is:

        python3   object_detection_demo_tiny-yolov3.py  -m  frozen_tiny_yolo_v3.xml  -d  MYRIAD  -i  cam

It works unnormally. It prints the following messages:     

            Detected boxes for batch 1:

            [ INFO ] Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR

            [ INFO ] 0 | 0.513803 | 190 | 4 | 197 | 5 | (0, 0, 0)

            [ INFO ] 0 | 0.587425 | 12 | 13 | 20 | 13 | (0, 0, 0)

           [ INFO ] 1 | 0.700414 | 239 | 6 | 239 | 23 | (12, 7, 5)

           [ INFO ] 1 | 0.526423 | 9 | 4 | 14 | 36 | (12, 7, 5)

           [ INFO ] 0 | 0.522735 | 57 | 16 | 75 | 30 | (0, 0, 0)

          [ INFO ] 1 | 0.524174 | 96 | 8 | 100 | 31 | (12, 7, 5)

          [ INFO ] 1 | 0.749252 | 178 | 16 | 178 | 28 | (12, 7, 5) 

         [ INFO ] 0 | 0.513668 | 164 | 7 | 203 | 36 | (0, 0, 0)

         [ INFO ] 1 | 0.588534 | 116 | 22 | 116 | 40 | (12, 7, 5)

       ..................

        [ INFO ] Layer detector/yolo-v3-tiny/Conv_12/BiasAdd/YoloRegion parameters:

       [ INFO ] num : 3

       [ INFO ] coords : 4

       [ INFO ] anchors : [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]

       [ INFO ] classes : 2

       [ INFO ] Layer detector/yolo-v3-tiny/Conv_9/BiasAdd/YoloRegion parameters:

       [ INFO ] num : 3

       [ INFO ] coords : 4

       [ INFO ] anchors : [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]

      [ INFO ] classes : 2

The num of coordinates is about 200 but it shoule be 1. And the XMIN is equals XMAX.  In addition, the class id shoud only be 1.

 

When I use the file to test a normal IR, it shows the correct messages:

             Detected boxes for batch 1:

            [ INFO ] Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR

            [ INFO ] 58 | 0.826624 | 74 | 78 | 129 | 118 | (255, 255, 255)

            [ INFO ] 58 | 0.500350 | 163 | 77 | 218 | 112 | (255, 255, 255)

            [ INFO ] Layer detector/yolo-v3-tiny/Conv_9/BiasAdd/YoloRegion parameters:

            [ INFO ] classes : 80

            [ INFO ] anchors : [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]

            [ INFO ] coords : 4

            [ INFO ] num : 3

            [ INFO ] Layer detector/yolo-v3-tiny/Conv_12/BiasAdd/YoloRegion parameters:

            [ INFO ] classes : 80

            [ INFO ] anchors : [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0] 

            [ INFO ] coords : 4

            [ INFO ] num : 3

            [ INFO ] Layer detector/yolo-v3-tiny/Conv_9/BiasAdd/YoloRegion parameters:

            [ INFO ] classes : 80

            [ INFO ] anchors : [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]

            [ INFO ] coords : 4

            [ INFO ] num : 3

           ..................................

It is normal.  The coordinates are only about the object that is detected and the class id is right.

 

 I would appreciate if anyone can give some methods to solve this problem. 

 

Thany you,

 

Gao.

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4 Replies
Shubha_R_Intel
Employee
206 Views

Dear Jiansheng:

Instead of -i cam, can you try -i the following video :

person-bicycle-car-detction.mp4  found within
https://github.com/intel-iot-devkit/sample-videos

Please report results here. I'm wondering if it's a problem with -i cam only.

Thanks for using OpenVino !

Shubha

Gao__Jiansheng
Beginner
206 Views

Hi Shubha,

I try -i worker-zone-detection.mp4 instead of -i cam to test the IR.  The command is:

    python3 object_detection_demo_tiny-yolov3.py -m frozen_tiny_yolo_v3.xml -d MYRIAD -i worker-zone-detection.mp4

And the result is:

     https://i.loli.net/2019/04/01/5ca1bca3f026d.png

when I use -i cam, the result is:

    https://i.loli.net/2019/04/01/5ca1bd1c1239b.png

And when I use the pb file to test before it is converted to IR, the result is normal:

    https://i.loli.net/2019/04/01/5ca1bd96698f1.jpg

 

The json file used to convert to IR is:

[

    {

        "id": "TFYOLOV3",

        "match_kind": "general",

        "custom_attributes": {

        "classes": 2,

        "coords": 4,

        "num": 6,

        "mask": [0,1,2],

        "jitter":0.3,

        "ignore_thresh":0.7,

        "truth_thresh":1,

         "random":1,

         "anchors":[10,14,23,27,37,58,81,82,135,169,344,319],

        "entry_points": ["detector/yolo-v3-tiny/Reshape","detector/yolo-v3-tiny/Reshape_4"]

        }

    }

]

 

I  wonder if it is a logical issure.

I would appreciate if you can give some suggestions.

 

Thanks.

 

Gao__Jiansheng
Beginner
206 Views

@Shubha

 

Shubha_R_Intel
Employee
206 Views

Dearest Gao, Jiansheng, 

You have definitely performed a valid test, so thank you for that. And I agree that the OpenVino IR converted result looks bad. If the same 12 anchor values are being used between the OpenVino and not-OpenVino then the results shouldn't be different.

When you say this : "And when I use the pb file to test before it is converted to IR, the result is normal"  I assume you are using some other method for inference. Which hardware and which method are you using for inference in this case ?

Gao, Jiansheng can you try this test on the just released 2019 R1 ? Also please try both the C++ version and the Python version of object_detection_demo_yolov3_async ?

Thanks,

Shubha

 

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