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114 lines
5.4 KiB
Markdown
114 lines
5.4 KiB
Markdown
English | [简体中文](README_CN.md)
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# InsightFace Python Deployment Example
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This directory provides examples that `infer_xxx.py` fast finishes the deployment of InsighFace, including ArcFace\CosFace\VPL\Partial_FC on CPU/GPU and GPU accelerated by TensorRT.
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Before deployment, two steps require confirmation
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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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Taking ArcFace as an example, we demonstrate how `infer_arcface.py` fast finishes the deployment of ArcFace 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/faceid/insightface/python/
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# Download ArcFace model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
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unzip face_demo.zip
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# CPU inference
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python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
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--face face_0.jpg \
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--face_positive face_1.jpg \
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--face_negative face_2.jpg \
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--device cpu
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# GPU inference
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python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
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--face face_0.jpg \
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--face_positive face_1.jpg \
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--face_negative face_2.jpg \
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--device gpu
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# TensorRT inference on GPU
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python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
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--face face_0.jpg \
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--face_positive face_1.jpg \
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--face_negative face_2.jpg \
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--device gpu \
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--use_trt True
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```
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The visualized result after running is as follows
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<div width="700">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
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</div>
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```bash
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Prediction Done!
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--- [Face 0]:FaceRecognitionResult: [Dim(512), Min(-2.309220), Max(2.372197), Mean(0.016987)]
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--- [Face 1]:FaceRecognitionResult: [Dim(512), Min(-2.288258), Max(1.995104), Mean(-0.003400)]
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--- [Face 2]:FaceRecognitionResult: [Dim(512), Min(-3.243411), Max(3.875866), Mean(-0.030682)]
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Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388
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```
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## InsightFace Python Interface
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```python
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fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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fastdeploy.vision.faceid.CosFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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fastdeploy.vision.faceid.VPL(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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ArcFace 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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> ArcFace.predict(image_data)
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> ```
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>
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> Model prediction interface. Input images and output detection results.
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>
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> **Parameter**
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>
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> > * **image_data**(np.ndarray): Input data in HWC or BGR format
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> **Return**
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>
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> > Return `fastdeploy.vision.FaceRecognitionResult` 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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#### Member variables of AdaFacePreprocessor
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Member variables of AdaFacePreprocessor are as follows
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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 [112, 112]
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> > * **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]
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> > * **beta**(list[float]): Preprocess normalized beta, and calculated as `x'=x*alpha+beta`，beta defaults to [-1.f, -1.f, -1.f]
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#### Member variables of AdaFacePostprocessor
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Member variables of AdaFacePostprocessor are as follows
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> > * **l2_normalize**(bool): Whether to perform l2 normalization before outputting the face vector. Default False.
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## Other Documents
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- [InsightFace Model Description](..)
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- [InsightFace 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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