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RKNN support

This tutorial is based on Linux systems like Ubuntu-18.04 and Rockchip NPU like rk3588.

Installation

It is recommended to create a virtual environment for the project.

get RKNN-Toolkit2 through:

git clone https://github.com/rockchip-linux/rknn-toolkit2

install RKNN python package following official doc. In our testing, we used the rknn-toolkit 1.2.0 with commit id 834ba0b0a1ab8ee27024443d77b02b5ba48b67fc.
reinstall MMDeploy from source following the instructions. Note that there are conflicts between the pip dependencies of MMDeploy and RKNN. Here is the suggested packages versions for python 3.6:
```
protobuf==3.19.4
onnx==1.8.0
onnxruntime==1.8.0
torch==1.8.0
torchvision==0.9.0
```

To work with models from MMDetection, you may need to install it additionally.

Usage

Example:

python tools/deploy.py \
    configs/mmdet/detection/detection_rknn_static.py \
    /mmdetection_dir/mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
    /tmp/snapshots/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
    tests/data/tiger.jpeg \
    --work-dir ../deploy_result \
    --device cpu

Deployment config

With the deployment config, you can modify the backend_config for your preference. An example backend_config of mmclassification is shown as below:

backend_config = dict(
    type='rknn',
    common_config=dict(
        mean_values=None,
        std_values=None,
        target_platform='rk3588',
        optimization_level=3),
    quantization_config=dict(do_quantization=False, dataset=None),
    input_size_list=[[3, 224, 224]])

The contents of common_config are for rknn.config(). The contents of quantization_config are used to control rknn.build().

Troubleshooting

Quantization fails.

Empirically, RKNN require the inputs not normalized if do_quantization is set to False. Please modify the settings of Normalize in the model_cfg from
```
img_norm_cfg = dict(
  mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
```
to
```
img_norm_cfg = dict(
  mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
```
Besides, the mean_values and std_values of deploy_cfg should be replaced with original normalization settings of model_cfg. Let mean_values=[123.675, 116.28, 103.53] and std_values=[58.395, 57.12, 57.375].

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