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Hu Chuqi
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[English](README.md) | 简体中文
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# PaddleClas模型 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/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。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/python
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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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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# CPU推理
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
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# GPU推理
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
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# IPU推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
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# 昆仑芯XPU推理
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device kunlunxin --topk 1
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# 华为昇腾NPU推理
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ascend --topk 1
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```
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运行完成后返回结果如下所示
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```bash
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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## PaddleClasModel Python接口
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```python
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fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleClas模型加载和初始化，其中model_file, params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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### predict函数
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> ```python
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> PaddleClasModel.predict(input_image, topk=1)
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> ```
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>
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> 模型预测结口，输入图像直接输出分类topk结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1
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> **返回**
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>
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> > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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## 其它文档
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- [PaddleClas 模型介绍](..)
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- [PaddleClas C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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