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126 lines
5.8 KiB
Markdown
Executable File
126 lines
5.8 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# AdaFace Python Deployment Example
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This directory provides examples that `infer_xxx.py` fast finishes the deployment of AdaFace 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 AdaFace as an example, we demonstrate how `infer.py` fast finishes the deployment of AdaFace 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/adaface/python/
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# Download AdaFace model files and test images
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# Download test images
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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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# Run the following code if the model is in Paddle format
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wget https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz
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tar zxvf mobilefacenet_adaface.tgz -C ./
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# CPU inference
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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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.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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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.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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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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# KunlunXin XPU inference
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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--face test_lite_focal_arcface_0.JPG \
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--face_positive test_lite_focal_arcface_1.JPG \
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--face_negative test_lite_focal_arcface_2.JPG \
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--device kunlunxin
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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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FaceRecognitionResult: [Dim(512), Min(-0.133213), Max(0.148838), Mean(0.000293)]
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FaceRecognitionResult: [Dim(512), Min(-0.102777), Max(0.120130), Mean(0.000615)]
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FaceRecognitionResult: [Dim(512), Min(-0.116685), Max(0.142919), Mean(0.001595)]
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Cosine 01: 0.7483505506964364
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Cosine 02: -0.09605773855893639
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```
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## AdaFace Python Interface
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```python
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fastdeploy.vision.faceid.AdaFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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AdaFace model loading and initialization, among which model_file is the exported ONNX model format or PADDLE static graph 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. Paddle format by default
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### predict function
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> ```python
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> AdaFace.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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The 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 alpha, and calculated as `x'=x*alpha+beta`. beta defaults to [-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): Whether to convert BGR to RGB in pre-processing. Default true
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#### Member variables of AdaFacePostprocessor
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The 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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- [AdaFace Model Description](..)
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- [AdaFace 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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