FastDeploy/examples/vision/segmentation/paddleseg/cpu-gpu/python

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PaddleSeg Python Deployment Example

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Install FastDeploy Python whl package. Refer to FastDeploy Python Installation

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

This directory provides examples that infer.py fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python

# 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
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
# GPU inference
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
# kunlunxin XPU inference
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin

The visualized result after running is as follows

PaddleSegModel Python Interface

fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)

PaddleSeg model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to Model Export for more information

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(input_image)

Model prediction interface. Input images and output detection results.

Parameter

• input_image(np.ndarray): Input data in HWC or BGR format

Return

Return fastdeploy.vision.SegmentationResult structure. Refer to Vision Model Prediction Results for the description of the structure.

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 with height greater than width by setting this parameter to true

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) in softmax

Other Documents

• PaddleSeg Model Description
• PaddleSeg C++ Deployment
• Model Prediction Results
• How to switch the model inference backend engine