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README.md

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YOLOX Python Deployment Example

Two steps before deployment

This directory provides examples that infer.py fast finishes the deployment of YOLOX 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/detection/yolox/python/

# Download YOLOX model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device cpu
# GPU inference
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu
# TensorRT inference on GPU (TensorRT in SDK. No need Separate installation)
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu --use_trt True

The visualized result after running is as follows

YOLOX Python Interface

fastdeploy.vision.detection.YOLOX(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

YOLOX 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

YOLOX.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)

Model prediction interface. Input images and output results

Parameter

  • image_data(np.ndarray): Input data in HWC or BGR format
  • conf_threshold(float): Filtering threshold of detection box confidence
  • nms_iou_threshold(float): iou threshold during NMS processing

Return

Return fastdeploy.vision.DetectionResult 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 [640, 640]
  • padding_value(list[float]): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default [114, 114, 114]
  • is_decode_exported(bool): The default value is is_decode_exported=False. The official default export does not have the decoded part. If you export the model with the decoded part, please set this parameter to true

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