~~tag> DenseNet NPU Edge2 RK3588~~

====== DenseNet CTC ONNX Keras Edge2 Demo - 3 ======

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===== Get Source Code =====

We will use a DenseNet model based on [[gh>YCG09/chinese_ocr]].

```shell
git clone https://github.com/YCG09/chinese_ocr.git
```

===== Convert Model =====

==== 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.

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

Follow this docs to install [[https://conda.io/projects/conda/en/stable/user-guide/install/linux.html | conda]].

Then create a virtual environment.

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

==== Get convert tool ====

Download Tool from [[gh>rockchip-linux/rknn-toolkit2]].

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

Install dependences and RKNN toolkit2 packages.

```shell
$ 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 ====

After training model, run the codes as follows to modify net input and output and convert model to onnx.

<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.
</WRAP>


```python export.py
import onnx
from keras.models import *
import keras
import keras2onnx
from train import get_model
import densenet

basemodel, model = get_model(32, 88) # input height, classes number
basemodel.load_weights("models/weights_densenet-32-0.40.h5")
onnx_model = keras2onnx.convert_keras(basemodel, basemodel.name, target_opset=12)
onnx_model.graph.input[0].type.tensor_type.shape.dim[0].dim_value = int(1)
onnx_model.graph.input[0].type.tensor_type.shape.dim[1].dim_value = int(1)
onnx_model.graph.input[0].type.tensor_type.shape.dim[2].dim_value = int(32)
onnx_model.graph.input[0].type.tensor_type.shape.dim[3].dim_value = int(280)
onnx_model.graph.output[0].type.tensor_type.shape.dim[0].dim_value = int(1)
onnx_model.graph.node.remove(onnx_model.graph.node[0])
onnx_model.graph.node[0].input[0] = "the_input"
onnx.save_model(onnx_model, "./densenet_ctc.onnx")
```

Enter ''rknn-toolkit2/examples/onnx/yolov5'' and modify ''test.py'' as follows.

```python test.py
# Create RKNN object
rknn = RKNN(verbose=True)

# pre-process config
print('--> Config model')
rknn.config(mean_values=[0], std_values=[255], target_platform='rk3588')
print('done')

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

# 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')

# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn('./densenet_ctc.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')
```

Run ''test.py'' to generate rknn model.

```shell
$ python3 test.py
```

===== Run NPU =====

==== Get source code ====

Clone the source code from our [[gh>khadas/edge2-npu]].

```shell
$ git clone https://github.com/khadas/edge2-npu
```

==== Install dependencies ====

```shell
$ sudo apt update
$ sudo apt install cmake libopencv-dev
```

==== Compile and run ====

=== Picture input demo ===

Put ''densenet_ctc.rknn'' in ''edge2-npu/C++/densenet_ctc/data/model''.

```shell
# Compile
$ bash build.sh

# Run
$ cd install/densenet_ctc
$ ./densenet_ctc data/model/densenet_ctc.rknn data/img/KhadasTeam.png
```

<WRAP tip >
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>