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products:sbc:edge2:npu:npu-convert

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products:sbc:edge2:npu:npu-convert [2023/08/22 05:56]
louis 		products:sbc:edge2:npu:npu-convert [2024/06/06 21:57] (current)
nick
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-====== NPU Model Convert ======+{{indexmenu_n>3}} 
 + 
 +====== Edge2 NPU Model Convert ======
  
 ===== Build Virtual Environment ===== ===== Build Virtual Environment =====
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 ===== Get Convert Tool ===== ===== Get Convert Tool =====
  
-Download Tool from [[https://github.com/rockchip-linux/rknn-toolkit2.git|Rockchip Github]].+Download Tool from [[gh>rockchip-linux/rknn-toolkit2]].
  
 ```shell ```shell
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 ===== Convert Model ===== ===== 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.+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. 
  
-```shell+```python
 # Create RKNN object # Create RKNN object
 rknn = RKNN(verbose=True)  rknn = RKNN(verbose=True) 
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 Pre-process config. Pre-process config.
  
-```shell+```python
 # pre-process config # pre-process config
 print('--> Config model') print('--> Config model')
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   * **std_values** - The variance of normalization parameter.   * **std_values** - The variance of normalization parameter.
 model input = (image – mean_values) / std_values model input = (image – mean_values) / std_values
-  * **target_platform** - Chooses ‘’rk3588’’.+  * **target_platform** - Chooses ''rk3588''.
  
 Load model. Load model.
  
-```shell+```python
 # Load ONNX model # Load ONNX model
 print('--> Loading model') print('--> Loading model')
-ret = rknn.load_onnx(model=’./yolov5.onnx’)+ret = rknn.load_onnx(model='./yolov5.onnx')
 if ret != 0: if ret != 0:
     print('Load model failed!')     print('Load model failed!')
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 Load other platform model. Load other platform model.
  
-```shell+```python
 # Load pytorch model # Load pytorch model
 print('--> Loading model') print('--> Loading model')
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 ``` ```
  
-  * **inputs/outputs** - Only use in ‘’tensorflow’’ model. It is the name of inputs/outputs.+  * **inputs/outputs** - Only use in tensorflow model. It is the name of inputs/outputs.
   * **input_size_list** - The size and channels of input.   * **input_size_list** - The size and channels of input.
  
 Build model Build model
  
-```shell+```python
 # Build model # Build model
 print('--> Building model') print('--> Building model')
-ret = rknn.build(do_quantization=True, dataset=”./dataset.txt”)+ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
 if ret != 0: if ret != 0:
     print('Build model failed!')     print('Build model failed!')
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 Export RKNN model Export RKNN model
  
-```shell+```python
 # Export RKNN model # Export RKNN model
 print('--> Export rknn model') print('--> Export rknn model')
-ret = rknn.export_rknn(export_path=“./yolov5_int8.rknn”)+ret = rknn.export_rknn(export_path='./yolov5_int8.rknn')
 if ret != 0: if ret != 0:
     print('Export rknn model failed!')     print('Export rknn model failed!')
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   * **export_path** - The path of rknn model.   * **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.+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.
  
 ```diff patch ```diff patch
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 ``` ```
  
-Run test.py to generate rknn model.+Run ''test.py'' to generate rknn model.
  
 ```shell ```shell
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 ===== See Also ===== ===== See Also =====
  
-For more usage, please refer to the related documents under ‘’doc’’.+For more usage, please refer to the related documents under ''doc''.
  
  
Last modified: 2023/08/22 05:56 by louis

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