PaddleSeg Python Deployment Example

Before deployment, two steps require confirmation

1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
1. 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

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