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50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
English | [中文](../../../cn/faq/rknpu2/export.md)
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# Export Model
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## Introduction
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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`.
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Before you start the conversion, please check if the environment is installed successfully referring to [RKNN-Toolkit2 Installation](./install_rknn_toolkit2.md).
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## Configuration Parameter in export.py
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| Parameter | Whether it can be NULL | Parameter Role |
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|-----------------|------------|--------------------|
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| verbose | Y(DEFAULT=TRUE) | Decide whether to output specific information when converting |
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| config_path | N | Path to configuration file |
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## Config File Introduction
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### Module of config yaml file
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```yaml
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model_path: ./portrait_pp_humansegv2_lite_256x144_pretrained.onnx
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output_folder: ./
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target_platform: RK3588
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normalize:
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mean: [[0.5,0.5,0.5]]
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std: [[0.5,0.5,0.5]]
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outputs: None
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```
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### Config parameters
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* model_path: Model saving path.
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* output_folder: Model saving folder name.
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* target_platform: The device model runs on, only RK3588 or RK3568 can be chosen.
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* normalize: Configure the normalize operation on NPU with two parameters std and mean.
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* std: If you do the normalize operation externally, please configure to [1/255,1/255,1/255].
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* mean: If you do the normalize operation externally, please configure to [0,0,0].
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* outputs: Output node list, if you use default output node, please configure to None.
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## How to convert model
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Run the line in the root directory:
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```bash
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python tools/export.py --config_path=./config.yaml
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```
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## Things to note in Model Export
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* Please don't export models with softmax or argmax, calculate them externally instead. |