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82 lines
3.9 KiB
Markdown
Executable File
82 lines
3.9 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleDetection C++ Deployment Example
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This directory provides examples that `infer_xxx.cc` fast finishes the deployment of PaddleDetection models, including PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN/YOLOv5/YOLOv6/YOLOv7/RTMDet on CPU/GPU and GPU accelerated by TensorRT.
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
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```bash
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ppyoloe is taken as an example for inference deployment
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mkdir build
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cd build
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# Download the PPYOLOE model file and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# CPU inference
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 0
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# GPU inference
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
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# TensorRT Inference on GPU
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2
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```
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The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
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## PaddleDetection C++ Interface
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### Model Class
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PaddleDetection currently supports 6 kinds of models, including `PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，`SSD`,`PaddleYOLOv5`,`PaddleYOLOv6`,`PaddleYOLOv7`,`RTMDet`. The constructors and predictors for all 6 kinds are consistent in terms of parameters. This document takes PPYOLOE as an example to introduce its 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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Loading and initializing PaddleDetection PPYOLOE model, where the format of model_file is as the exported ONNX model.
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path
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> * **config_file**(str): • Configuration file path, which is the deployment yaml file exported by PaddleDetection
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. Paddle format by default
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#### Predict Function
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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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> Model prediction interface. Input images and output results directly.
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>
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> **Parameter**
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>
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> > * **im**: Input images in HWC or BGR format
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> > * **result**: Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult
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- [Model Description](../../)
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- [Python Deployment](../python)
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- [Vision Model prediction results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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