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* [RKNPU2]support rknpu2 ClasModel #957 * [RKNPU2]support rknpu2 ClasModel #957 * [RKNPU2]support rknpu2 add Resnet50_vd example #957 * [RKNPU2]support rknpu2 add Resnet50_vd example #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957 * [RKNPU2]support rknpu2, improve doc #957
35 lines
1.3 KiB
Markdown
35 lines
1.3 KiB
Markdown
# PaddleClas Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/rknpu2.md)
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本目录下提供`infer.py`快速完成 ResNet50_vd 在RKNPU上部署的示例。执行如下脚本即可完成
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/classification/paddleclas/rknpu2/python
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# 下载图片
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# 推理
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python3 infer.py --model_file ./ResNet50_vd_infer/ResNet50_vd_infer_rk3588.rknn --config_file ResNet50_vd_infer/inference_cls.yaml --image ILSVRC2012_val_00000010.jpeg
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# 运行完成后返回结果如下所示
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ClassifyResult(
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label_ids: 153,
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scores: 0.684570,
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)
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```
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## 注意事项
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RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，
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DisablePermute(C++)或`disable_permute(Python)，在预处理阶段禁用数据格式的转换。
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## 其它文档
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- [ResNet50_vd C++部署](../cpp)
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- [模型预测结果说明](../../../../../../docs/api/vision_results/)
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- [转换ResNet50_vd RKNN模型文档](../README.md) |