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97 lines
4.5 KiB
Markdown
Executable File
97 lines
4.5 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleSeg C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of Unet 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/cn/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/cn/build_and_install/download_prebuilt_libraries.md)
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【Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
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Taking the inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.0 or above (x.x.x>=1.0.0) is required to support this model.
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```bash
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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 Unet model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
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tar -xvf Unet_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# CPU inference
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./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
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# GPU inference
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./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
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# TensorRT inference on GPU
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./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
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# kunlunxin XPU inference
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./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
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```
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The visualized result after running is as follows
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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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/cn/faq/use_sdk_on_windows.md)
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## PaddleSeg C++ Interface
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### PaddleSeg Class
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```c++
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fastdeploy::vision::segmentation::PaddleSegModel(
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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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PaddleSegModel model loading and initialization, among which model_file is the exported Paddle model format.
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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): Inference deployment configuration file
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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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> PaddleSegModel::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 detection results.
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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**: The segmentation result, including the predicted label of the segmentation and the corresponding probability of the label. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of SegmentationResult
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### Class Member Variable
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait, height greater than a width, by setting this parameter to`true`
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#### Post-processing Parameter
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> > * **apply_softmax**(bool): The `apply_softmax` parameter is not specified when the model is exported. Set this parameter to `true` to normalize the probability result (score_map) of the predicted output segmentation label (label_map)
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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/cn/faq/how_to_change_backend.md)
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