~~tag>NPU VGG16 Edge2 TensorFlow Keras ~~

====== VGG16 TensorFlow Keras Edge2 Demo - 4 ======

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

```shell
git clone https://github.com/Daipuwei/Mini-VGG-CIFAR10
```

===== 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 ====

Keras model can convert rknn model directly. But this demo is to convert tensorflow model(.pb) first, and convert tnesorflow model to rknn model. We use this tool to convert.

```shell
git clone https://github.com/amir-abdi/keras_to_tensorflow.git
```

Enter ''rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1'' 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=[0.0, 0.0, 0.0], std_values=[255.0, 255.0, 255.0], target_platform='rk3588')
print('done')

# Load model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb='./vgg16.pb',
                            inputs=['image_input'],
                            outputs=['dense_2/Softmax'],
                            input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]])
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('./vgg16.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 ''vgg16.rknn'' in ''edge2-npu/C++/vgg16/data/model''.

```shell
# Compile
$ bash build.sh

# Run
$ cd install/vgg16
$ ./vgg16 data/model/vgg16.rknn data/img/airplane.jpeg
```

<WRAP tip >
If your **VGG16** model classes is not the same as **cifar10**, please change ''data/vgg16_class.txt'' and the ''OBJ_CLASS_NUM'' in ''include/postprocess.h''.
</WRAP>