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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
After creating the virtual environment, start the virtual environment and start the next step.
Clone SDK from Rockchip Github.
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
Choose RK3588S
,
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')
At present, we support load and convert Caffe, TensorFlow, TensorFlow Lite, ONNX, DarkNet, PyTorch model. Each platform has its own interface.
Caffe
ret = rknn.load_caffe(model='./mobilenet_v2.prototxt', blobs='./mobilenet_v2.caffemodel')
Tensorflow
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]])
TensorFlow Lite
ret = rknn.load_tflite(model = './mobilenet_v1.tflite')
Onnx
ret = rknn.load_onnx(model = './arcface.onnx')
Darknet
ret = rknn.load_darknet(model = './yolov3-tiny.cfg', weight= './yolov3.weights')
Pytorch
ret = rknn. load_pytorch(model = './resnet18.pt', input_size_list=[[1,3,224,224]])
Convert yolov5
as a example,
cd examples/onnx/yolov5/ python3 test.py
After convert, it will generate the rknn file yolov5s.rknn
.
$ ls
bus.jpg dataset.txt onnx_yolov5_0.npy onnx_yolov5_1.npy onnx_yolov5_2.npy test.py yolov5s.onnx yolov5s.rknn
For more usage, please refer to the related documents under doc
.