How To Transform Yolo Model

The training completed model is based on the corresponding framework. Although most of the frameworks have C / C + + interfaces, even if these interfaces are used, only CPU or GPU can be used. If you want to use NPU acceleration, you need to convert the trained model into NPU accelerated model code through SDK. This document mainly introduces how to adapt our model on SDK.
Note: The following examples take the trained object detection model of khadas as an example.

Apply for SDK

About the trainsform SDK, you need to apply with our website. After filling in the information, it will be sent to your email.

$ cd Tool/acuity-toolkit-binary-5.0.0/conversion_scripts/

How to use Trainsform tool

0_import_model.sh

Parameter setting

  1. NAME is yolovX
  2. --net-input is path to yolovX-voc.cfg file
  3. --weight-input is path to weights file
    example:
    NAME=yolov3
    --net-input /home/khadas/Pictures/VOCdevkit/yolov3-train/yolov3-voc.cfg \
    --weight-input /home/khadas/Pictures/VOCdevkit/yolov3-train/backup/yolov3-voc_final.weights \

1_quantize_model.sh

Parameter setting

  1. NAME is yolovX
  2. --source-file is path to Validation file
  3. --channel-mean-value set to 0 0 0 256
  4. --quantized-dtype set to dynamic_fixed_point-8

example:

NAME=yolov3
--channel-mean-value '0 0 0 256' \
--quantized-dtype dynamic_fixed_point-8 \

2_export_case_code.sh

Parameter setting

  1. NAME is yolovX
  2. --reorder-channel RGB color channel order changed to BGR 2 1 0
  3. --channel-mean-value set ot 0 0 0 256
  4. --export-dtype set to quantized

example:

NAME=yolov3
--reorder-channel '2 1 0' \
--channel-mean-value '0 0 0 256' \
--export-dtype quantized \

Run The trainform shell script

$ cd acuity-toolkit-binary-5.0.0/conversion_scripts/
$ chmod +x 0_import_model.sh
$ chmod +x 1_quantize_model.sh
$ chmod +x 2_export_case_code.sh
$ ./0_import_model.sh
$ ./1_quantize_model.sh
$ ./2_export_case_code.sh

After conversion, In directory acuity-toolkit-binary-5.0.0/conversion_scripts/, a file with the same name as the script will be generated. These files are the basic files after conversion.

Apply your own converted code

Download aml_npu_app and aml_npu_demo_binaries

$ cd $workspace
$ git clone https://gitlab.com/khadas/aml_npu_demo_binaries.git
$ git clone https://gitlab.com/khadas/aml_npu_app.git

After the download is complete, there will be two directories: aml_npu_app and aml_npu_binaries

File replacement

$ cd $workspace/aml_npu_app/DDK_6.3.3.4/detect_library/model_code/detect_yolo_v3/
  1. Copy the yolov3.nb file converted by the conversion tool and replace the yolov3.nb file in the NN data directory
  2. Replace vnn_yolov3.c converted by the conversion tool with vnn_yolov3.c in DDK directory.
  3. Copy the vnn_yolov3.h file converted by the conversion tool and replace the vnn_yolov3.h file under include

edit file

$ vim yolovX_process.c

Modify 3 places

  1. The content of *coco_names[] is modified to the class of your training model. The order should be the same as the names file when you train.
  2. Change the value of num_class to the size of your training class.
  3. change the lastest value of size[3] to (num_class+5)*3

example:

  1. *coco_names[] = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "Edge-V", "Edge", "VIM1", "VIM2", "VIM3", "VIM3L", "Fan3705", "Captain", "Captain+Edge", "ToneBoard", "Heatsink(VIMs)", "Heatsink(Edge)];
  2. num_class=92
  3. int size[3]={nn_width/32, nn_height/32,97*3};

Build the source code

$ cd workspace/aml_npu_app/DDK_6.3.3.4/detect_library/model_code/detect_yolo_v3/
$ ./build_vx.sh $path/to/linux_sdk_dir $path/to/fenix

After compiling the corresponding yolov3 directory, the generated .so file will be found in the bin_r directory.

Replace demo file

$ cd $workspace/aml_npu_demo_binaries/detect_demo/

Replace two places:

  1. Copy the so file generated in the previous step and replace it with lib.
  2. Copy the Nb file in the previous step and replace the file in nn_data.
    $ cp workspace/aml_npu_app/DDK_6.3.3.4/detect_library/model_code/detect_yolo_v3/bin_dir/libnn_yolo_v3.so $workspace/aml_npu_demo_binaries/detect_demo/lib/libnn_yolo_v3.so
    $ cp workspace/aml_npu_app/DDK_6.3.3.4/detect_library/model_code/detect_yolo_v3/nn_data/yolov3_88.nb $workspace/aml_npu_demo_binaries/detect_demo/n_data/yolov3_88.nb

After the demo is completed, you can run your own Model.