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PaddleSeg C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

【Attention】For the deployment of PP-Matting、PP-HumanMatting and ModNet, refer to Matting Model Deployment

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.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download Unet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png


# CPU inference
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
# GPU inference
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
# TensorRT inference on GPU
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
# kunlunxin XPU inference
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

PaddleSeg C++ Interface

PaddleSeg Class

fastdeploy::vision::segmentation::PaddleSegModel(
        const string& model_file,
        const string& params_file = "",
        const string& config_file,
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

PaddleSegModel model loading and initialization, among which model_file is the exported Paddle model format.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): Inference deployment configuration file
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. Paddle format by default

Predict Function

PaddleSegModel::Predict(cv::Mat* im, DetectionResult* result)

Model prediction interface. Input images and output detection results.

Parameter

• im: Input images in HWC or BGR format
• result: The segmentation result, including the predicted label of the segmentation and the corresponding probability of the label. Refer to Vision Model Prediction Results for the description of SegmentationResult

Class Member Variable

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

• is_vertical_screen(bool): For PP-HumanSeg models, the input image is portrait, height greater than a width, by setting this parameter totrue

Post-processing Parameter

• 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)

• Model Description
• Python Deployment
• Vision Model Prediction Results
• How to switch the model inference backend engine