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[Hackathon 181] Add TVM support for FastDeploy on macOS (#1969)
* update for tvm backend * update third_party * update third_party * update * update * update * update * update * update * update * update --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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[English](README.md) | 简体中文
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# PaddleDetection C++部署示例
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本目录下提供`infer_ppyoloe_demo.cc`快速完成PPDetection模型使用TVM加速部署的示例。
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## 转换模型并运行
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```bash
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# build example
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mkdir build
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cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=/path/to/fastdeploy-sdk
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make -j
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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./infer_ppyoloe_demo ../tvm_save 000000014439.jpg
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```
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## PaddleDetection C++接口
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### 模型类
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PaddleDetection目前支持6种模型系列,类名分别为`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`,`SSD`,`PaddleYOLOv5`,`PaddleYOLOv6`,`PaddleYOLOv7`,`RTMDet`,`CascadeRCNN`,`PSSDet`,`RetinaNet`,`PPYOLOESOD`,`FCOS`,`TTFNet`,`TOOD`,`GFL`所有类名的构造函数和预测函数在参数上完全一致,本文档以PPYOLOE为例讲解API
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```c++
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fastdeploy::vision::detection::PPYOLOE(
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const string& model_file,
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const string& params_file,
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const string& config_file
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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PaddleDetection PPYOLOE模型加载和初始化,其中model_file为导出的ONNX模型格式。
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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): 配置文件路径,即PaddleDetection导出的部署yaml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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> ```c++
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> PPYOLOE::Predict(cv::Mat* im, DetectionResult* result)
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> ```
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>
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> 模型预测接口,输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像,注意需为HWC,BGR格式
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> > * **result**: 检测结果,包括检测框,各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [模型介绍](../../)
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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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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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