FastDeploy/examples/vision/matting/modnet/python/README.md

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MODNet 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

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

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

# Download modnet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic_portrait_matting.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg

# CPU inference
python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
# GPU inference
python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
# TensorRT inference on GPU 
python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True

The visualized result after running is as follows

MODNet Python Interface

fastdeploy.vision.matting.MODNet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

MODNet model loading and initialization, among which model_file is the exported ONNX model format

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path. No need to set when the model is in ONNX format
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. ONNX format by default

predict function

MODNet.predict(image_data)

Model prediction interface. Input images and output matting results.

Parameter

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

Return

Return fastdeploy.vision.MattingResult structure. Refer to Vision Model Prediction Results for its description.

Class Member Property

Pre-processing Parameter

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

• size(list[int]): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [256, 256]
• alpha(list[float]): Preprocess normalized alpha, and calculated as x'=x*alpha+beta. alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
• beta(list[float]): Preprocess normalized beta, and calculated as x'=x*alpha+beta. beta defaults to [-1.f, -1.f, -1.f]
• swap_rb(bool): Whether to convert BGR to RGB in pre-processing. Default True

Other Documents

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