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Smoke Python Deployment Example

Before deployment, the following two steps need to be confirmed

• 1. The hardware and software environment meets the requirements, refer to FastDeploy environment requirements
• 2. FastDeploy Python whl package installation, refer to FastDeploy Python Installation

This directory provides an example of infer.py to quickly complete the deployment of Smoke on CPU/GPU. Execute the following script to complete

#Download deployment sample code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/vision/paddle3d/smoke/python

wget https://bj.bcebos.com/fastdeploy/models/smoke.tar.gz
tar -xf smoke.tar.gz
wget https://bj.bcebos.com/fastdeploy/models/smoke_test.png

# CPU reasoning
python infer.py --model smoke --image smoke_test.png --device cpu
# GPU inference
python infer.py --model smoke --image smoke_test.png --device gpu

The visual result after running is shown in the figure below

Smoke Python interface

fastdeploy.vision.detection.Smoke(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)

Smoke model loading and initialization.

parameter

• model_file(str): model file path
• params_file(str): parameter file path
• config_file(str): configuration file path
• runtime_option(RuntimeOption): Backend reasoning configuration, the default is None, that is, the default configuration is used
• model_format(ModelFormat): model format, the default is Paddle format

predict function

Smoke. predict(image_data)

Model prediction interface, the input image directly outputs the detection result.

parameters

• image_data(np.ndarray): input data, note that it must be in HWC, BGR format

Back

Return the fastdeploy.vision.PerceptionResult structure, structure description reference document Vision Model Prediction Results

Other documents

• Smoke Model Introduction
• Smoke C++ deployment
• Description of model prediction results
• How to switch model inference backend engine