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products:sbc:edge2:npu:npu-convert

Edge2 NPU Model Convert

Build Virtual Environment

The SDK only supports python3.6 or python3.8, here is an example of creating a virtual environment for python3.8.

Install python packages.

$ sudo apt update
$ sudo apt install python3-dev python3-numpy

Follow this docs to install conda.

Then create a virtual environment.

$ conda create -n npu-env python=3.8
$ conda activate npu-env     #activate
$ conda deactivate           #deactivate

Get Convert Tool

Download Tool from rockchip-linux/rknn-toolkit2.

$ git clone https://github.com/rockchip-linux/rknn-toolkit2.git
$ git checkout 9ad79343fae625f4910242e370035fcbc40cc31a

Install dependences and RKNN toolkit2 packages,

$ cd rknn-toolkit2
$ sudo apt-get install python3 python3-dev python3-pip
$ sudo apt-get install libxslt1-dev zlib1g-dev libglib2.0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc cmake
$ pip3 install -r doc/requirements_cp38-*.txt
$ pip3 install packages/rknn_toolkit2-*-cp38-cp38-linux_x86_64.whl

Convert Model

Converting model has five main steps. Create RKNN object, pre-process config, load model, build model and export RKNN model. Here, take yolov5 onnx as an example.

Create RKNN object.

# Create RKNN object
rknn = RKNN(verbose=True) 

Pre-process config.

# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
print('done')
  • mean_values - The mean of normalization parameter.
  • std_values - The variance of normalization parameter.

model input = (image – mean_values) / std_values

  • target_platform - Chooses rk3588.

Load model.

# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./yolov5.onnx')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
  • model - The path of model.

Load other platform model.

# Load pytorch model
print('--> Loading model')
ret = rknn.load_pytorch(model='./resnet18.pt', input_size_list=[[1, 3, 224, 224]])
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb',
                           inputs=['Preprocessor/sub'],
                           outputs=['concat', 'concat_1'],
                           input_size_list=[[300, 300, 3]])
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load caffe model
print('--> Loading model')
ret = rknn.load_caffe(model='./mobilenet_v2.prototxt', 
                      blobs='./mobilenet_v2.caffemodel')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load tensorflow lite model
print('--> Loading model')
ret = rknn.load_tflite(model='./mobilenet_v1.tflite')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load darknet model
print('--> Loading model')
ret = rknn.load_darknet(model='./yolov3-tiny.cfg',
                        weight='./yolov3.weights')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
  • inputs/outputs - Only use in tensorflow model. It is the name of inputs/outputs.
  • input_size_list - The size and channels of input.

Build model

# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
    print('Build model failed!')
    exit(ret)
print('done')
  • do_quantization - Quantize model or not.
  • dataset - The path of txt file which is written in image path.

Export RKNN model

# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn(export_path='./yolov5_int8.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')
  • export_path - The path of rknn model.

All the above codes can be found in rknn-toolkit2/examples. There are all platforms we support now. Choose rk3588 in rknn-toolkit2/examples/onnx/yolov5/test.py and run the file to convert model.

patch
diff --git a/examples/onnx/yolov5/test.py b/examples/onnx/yolov5/test.py
index a1c9988..f7ce11e 100644
--- a/examples/onnx/yolov5/test.py
+++ b/examples/onnx/yolov5/test.py
@@ -240,7 +240,7 @@ if __name__ == '__main__':
 
     # pre-process config
     print('--> Config model')
-    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]])
+    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
     print('done')

Run test.py to generate rknn model.

$ python3 test.py

In test.py, there are inferring rknn model codes. You can refer it to infer rknn on PC.

See Also

For more usage, please refer to the related documents under doc.

Last modified: 2024/06/06 21:57 by nick