89 lines
2.5 KiB
Markdown
89 lines
2.5 KiB
Markdown
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# PaddleClas 模型在RKNPU2上部署方案-FastDeploy
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## 1. 说明
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PaddleClas支持通过FastDeploy在RKNPU2上部署相关模型.
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## 2. 转换模型
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下面以 ResNet50_vd为例子，教大家如何转换分类模型到RKNN模型.
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目前FastDeploy只在PaddleClas测试过ResNet50_vd模型, 欢迎用户尝试其他的PaddleClas模型进行部署.
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### 2.1 导出ONNX模型
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```bash
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# 安装 paddle2onnx
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pip install paddle2onnx
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# 下载ResNet50_vd模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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# 静态图转ONNX模型，注意，这里的save_file请和压缩包名对齐
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paddle2onnx --model_dir ResNet50_vd_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ResNet50_vd_infer/ResNet50_vd_infer.onnx \
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--enable_dev_version True \
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--opset_version 10 \
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--enable_onnx_checker True
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# 固定shape，注意这里的inputs得对应netron.app展示的 inputs 的 name，有可能是image 或者 x
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python -m paddle2onnx.optimize --input_model ResNet50_vd_infer/ResNet50_vd_infer.onnx \
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--output_model ResNet50_vd_infer/ResNet50_vd_infer.onnx \
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--input_shape_dict "{'inputs':[1,3,224,224]}"
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```
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### 2.2 编写模型导出配置文件
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以转化RK3588的RKNN模型为例子，我们需要编辑./rknpu2_tools/config/ResNet50_vd_infer_rknn.yaml，来转换ONNX模型到RKNN模型。
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如果你需要在NPU上执行normalize操作，请根据你的模型配置normalize参数，例如:
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```yaml
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model_path: ./ResNet50_vd_infer/ResNet50_vd_infer.onnx
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output_folder: ./ResNet50_vd_infer
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mean:
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-
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- 123.675
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- 116.28
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- 103.53
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std:
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-
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- 58.395
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- 57.12
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- 57.375
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outputs_nodes:
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do_quantization: False
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dataset: "./ResNet50_vd_infer/dataset.txt"
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```
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**在CPU上做normalize**可以参考以下yaml：
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```yaml
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model_path: ./ResNet50_vd_infer/ResNet50_vd_infer.onnx
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output_folder: ./ResNet50_vd_infer
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mean:
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-
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- 0
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- 0
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- 0
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std:
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-
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- 1
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- 1
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- 1
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outputs_nodes:
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do_quantization: False
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dataset: "./ResNet50_vd_infer/dataset.txt"
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```
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这里我们选择在NPU上执行normalize操作.
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### 2.3 ONNX模型转RKNN模型
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```shell
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python ./rknpu2_tools/export.py \
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--config_path ./rknpu2_tools/config/ResNet50_vd_infer_rknn.yaml \
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--target_platform rk3588
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```
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## 3. 其他链接
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- [Cpp部署](./cpp)
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- [Python部署](./python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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