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products:sbc:edge2:npu:demos:densenet-ctc

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products:sbc:edge2:npu:demos:densenet-ctc [2023/08/22 06:05]
louis created 		products:sbc:edge2:npu:demos:densenet-ctc [2023/09/20 03:13] (current)
louis
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-====== Demo3 densenet_ctc ======+~~tag> DenseNet NPU Edge2 RK3588~~ 
 + 
 +====== DenseNet CTC ONNX Keras Edge2 Demo - 3 ====== 
 + 
 +{{indexmenu_n>3}}
  
 ===== Get Source Code ===== ===== Get Source Code =====
  
-The codes we use.+We will use a DenseNet model based on [[gh>YCG09/chinese_ocr]].
  
 ```shell ```shell
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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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 ``` ```
  
-Install dependences and RKNN toolkit2 packages,+Install dependences and RKNN toolkit2 packages.
  
 ```shell ```shell
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 ``` ```
  
-==== convert ====+==== Convert ====
  
 After training model, run the codes as follows to modify net input and output and convert model to onnx. After training model, run the codes as follows to modify net input and output and convert model to onnx.
  
 <WRAP tip > <WRAP tip >
-Keras model(.h5) can convert rknn model directly. If you want to convert keras model, please use ‘’model.save’’ to save model with weight and network structure.+Keras model(.h5) can convert rknn model directly. If you want to convert keras model, please use ''model.save'' to save model with weight and network structure.
 </WRAP> </WRAP>
  
  
-```shell+```python export.py
 import onnx import onnx
 from keras.models import * from keras.models import *
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 ``` ```
  
-Enter rknn-toolkit2/examples/onnx/yolov5 and modify test.py as follows.+Enter ''rknn-toolkit2/examples/onnx/yolov5'' and modify ''test.py'' as follows.
  
-```shell+```python test.py
 # Create RKNN object # Create RKNN object
 rknn = RKNN(verbose=True) rknn = RKNN(verbose=True)
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 # Load ONNX model # Load ONNX model
 print('--> Loading model') print('--> Loading model')
-ret = rknn.load_onnx(model=”./densenet_ctc.onnx”)+ret = rknn.load_onnx(model='./densenet_ctc.onnx')
 if ret != 0: if ret != 0:
     print('Load model failed!')     print('Load model failed!')
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 # 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
 print('--> Export rknn model') print('--> Export rknn model')
-ret = rknn.export_rknn(“./densenet_ctc.rknn”)+ret = rknn.export_rknn('./densenet_ctc.rknn')
 if ret != 0: if ret != 0:
     print('Export rknn model failed!')     print('Export rknn model failed!')
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 ``` ```
  
-Run test.py to generate rknn model.+Run ''test.py'' to generate rknn model.
  
 ```shell ```shell
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 ==== Get source code ==== ==== Get source code ====
  
-Clone the source code form our [[https://github.com/khadas/edge2-npu|Github]].+Clone the source code from our [[gh>khadas/edge2-npu]].
  
 ```shell ```shell
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 === Picture input demo === === Picture input demo ===
  
-Put densenet_ctc.rknn in edge2-npu/C++/densenet_ctc/data/model.+Put ''densenet_ctc.rknn'' in ''edge2-npu/C++/densenet_ctc/data/model''.
  
 ```shell ```shell
-// compile+# Compile
 $ bash build.sh $ bash build.sh
  
-// run+# Run
 $ cd install/densenet_ctc $ cd install/densenet_ctc
 $ ./densenet_ctc data/model/densenet_ctc.rknn data/img/KhadasTeam.png $ ./densenet_ctc data/model/densenet_ctc.rknn data/img/KhadasTeam.png
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 <WRAP tip > <WRAP tip >
-If your yolov8n model classes is not the same as coco, please change ‘’data/class_str.txt’’ and the ‘’OBJ_CLASS_NUM’’ in ‘’include/postprocess.h’’.+If your **DenseNet CTC** model classes is not the same as mine, please change ''data/class_str.txt'' and the ''OBJ_CLASS_NUM'' in ''include/postprocess.h''.
 </WRAP> </WRAP>
  
Last modified: 2023/08/22 06:05 by louis

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