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Export Model
Introduction
Fastdeploy has simply integrated the onnx->rknn conversion process. In this instruction, we first write yaml configuration files, then export models in tools/export.py.
Before you start the conversion, please check if the environment is installed successfully referring to RKNN-Toolkit2 Installation.
Configuration Parameter in export.py
| Parameter | Whether it can be NULL | Parameter Role |
|---|---|---|
| verbose | Y(DEFAULT=TRUE) | Decide whether to output specific information when converting |
| config_path | N | Path to configuration file |
Config File Introduction
Module of config yaml file
model_path: ./portrait_pp_humansegv2_lite_256x144_pretrained.onnx
output_folder: ./
target_platform: RK3588
normalize:
mean: [[0.5,0.5,0.5]]
std: [[0.5,0.5,0.5]]
outputs: None
Config parameters
- model_path: Model saving path.
- output_folder: Model saving folder name.
- target_platform: The device model runs on, only RK3588 or RK3568 can be chosen.
- normalize: Configure the normalize operation on NPU with two parameters std and mean.
- std: If you do the normalize operation externally, please configure to [1/255,1/255,1/255].
- mean: If you do the normalize operation externally, please configure to [0,0,0].
- outputs: Output node list, if you use default output node, please configure to None.
How to convert model
Run the line in the root directory:
python tools/export.py --config_path=./config.yaml
Things to note in Model Export
- Please don't export models with softmax or argmax, calculate them externally instead.