~~tag> NPU RetinaFace Edge2 PyTorch~~

====== RetinaFace PyTorch Edge2 Demo - 5 ======

{{indexmenu_n>5}}

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

The codes we use [[gh>bubbliiiing/retinaface-pytorch]].

```shell
git clone https://github.com/bubbliiiing/retinaface-pytorch
```

Before training, modify ''retinaface-pytorch/utils/utils.py'' as follows.

```diff
diff --git a/utils/utils.py b/utils/utils.py
index 87bb528..4a22f2a 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -25,5 +25,6 @@ def get_lr(optimizer):
         return param_group['lr']
 
 def preprocess_input(image):
-    image -= np.array((104, 117, 123),np.float32)
+    image = image / 255.0
     return image
```

===== 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, we should convert pytorch model to onnx model. Create a python file written as follows and run.

```python export.py
import torch
import numpy as np
from nets.retinaface import RetinaFace
from utils.config import cfg_mnet, cfg_re50

model_path = "logs/Epoch150-Total_Loss6.2802.pth"
net = RetinaFace(cfg=cfg_mnet, mode='eval').eval()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net.load_state_dict(torch.load(model_path, map_location=device))

img = torch.zeros(1, 3, 640, 640)
torch.onnx.export(net, img, "./retinaface.onnx", verbose=False, opset_version=12, input_names=['images'])
```

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, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
print('done')

# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./retinaface.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('./retinaface.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 ''retinaface.rknn'' in ''edge2-npu/C++/retinaface/data/model''.

```shell
# Compile
$ bash build.sh

# Run
$ cd install/retinaface
$ ./retinaface data/model/retinaface.rknn data/img/timg.jpg
```

=== Camera input demo ===

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

```shell
# Compile
$ bash build.sh

# Run
$ cd install/retinaface_cap
$ ./retinaface_cap data/model/retinaface.rknn 33
```

''33'' is camera device index.