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83 lines
4.2 KiB
Markdown
Executable File
83 lines
4.2 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleSeg 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/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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【Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
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This directory provides examples that `infer.py` fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```bash
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# Download the deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python
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# Download Unet model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
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tar -xvf Unet_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# CPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
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# GPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
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# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
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# kunlunxin XPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
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```
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The visualized result after running is as follows
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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## PaddleSegModel Python Interface
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```python
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fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleSeg model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/model_export_cn.md) for more information
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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
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> * **config_file**(str): Inference deployment configuration file
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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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> PaddleSegModel.predict(input_image)
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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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> > * **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.SegmentationResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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### Class Member Variable
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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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> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait with height greater than width by setting this parameter to `true`
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#### Post-processing Parameter
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> > * **apply_softmax**(bool): The `apply_softmax` parameter is not specified when the model is exported. Set this parameter to `true` to normalize the probability result (score_map) of the predicted output segmentation label (label_map) in softmax
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## Other Documents
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- [PaddleSeg Model Description](..)
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- [PaddleSeg 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/cn/faq/how_to_change_backend.md)
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