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81 lines
3.7 KiB
Markdown
81 lines
3.7 KiB
Markdown
English | [简体中文](README_CN.md)
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# YOLOX Python Deployment Example
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Two steps before deployment
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolox/python/
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# Download YOLOX model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU inference
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python infer.py --model yolox_s.onnx --image 000000014439.jpg --device cpu
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# GPU inference
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python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu
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# TensorRT inference on GPU (TensorRT in SDK. No need Separate installation)
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python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu --use_trt True
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```
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The visualized result after running is as follows
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<img width="640" src="https://user-images.githubusercontent.com/67993288/184301746-04595d76-454a-4f07-8c7d-6f41418f8ae3.jpg">
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## YOLOX Python Interface
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```python
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fastdeploy.vision.detection.YOLOX(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOX model loading and initialization, among which model_file is the exported ONNX model format
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. ONNX format by default
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### predict function
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> ```python
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> YOLOX.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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> ```
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> Model prediction interface. Input images and output results
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>
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> **Parameter**
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> > * **image_data**(np.ndarray): Input data in HWC or BGR format
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> > * **conf_threshold**(float): Filtering threshold of detection box confidence
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> > * **nms_iou_threshold**(float): iou threshold during NMS processing
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> **Return**
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>
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> > Return `fastdeploy.vision.DetectionResult` structure, refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description
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### Class Member Property
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> >* **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]
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> >* **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]
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> >* **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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## Other Documents
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- [YOLOX Model Description](..)
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- [YOLOX C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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