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69 lines
2.8 KiB
Markdown
69 lines
2.8 KiB
Markdown
English | [简体中文](README_CN.md)
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# FSANet Python Deployment Example
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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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This directory provides examples that `infer.py` fast finishes the deployment of FSANet on CPU/GPU and GPU accelerated by TensorRT. FastDeploy version 0.6.0 or above is required to support this model. The script is as follows
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```bash
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# Download deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/headpose/fsanet/python
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# Download the FSANet model files and test images
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## Original ONNX Model
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fsanet-var.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/headpose_input.png
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# CPU inference
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device cpu
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# GPU inference
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu
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# TRT inference
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu --backend trt
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```
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The visualized result after running is as follows
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<div width="520">
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<img width="500" height="514" float="left" src="https://user-images.githubusercontent.com/19977378/198279932-3eee424e-98a2-4249-bdeb-0f79127cbc9d.png">
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</div>
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## FSANet Python Interface
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```python
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fd.vision.headpose.FSANet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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FSANet 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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> FSANet.predict(input_image)
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> ```
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> Model prediction interface. Input images and output head pose prediction results.
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>
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> **Parameter**
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>
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> > * **input_image**(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.HeadPoseResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure
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## Other Documents
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- [FSANet Model Description](..)
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- [FSANet 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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