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* Support PETR v2 * make petrv2 precision equal with the origin repo * delete extra func * modify review problem * delete visualize * Update README_CN.md * Update README.md * Update README_CN.md * fix build problem * delete external variable and function --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
64 lines
2.3 KiB
Markdown
Executable File
64 lines
2.3 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# Petr Python Deployment Example
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Before deployment, the following two steps need to be confirmed
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- 1. The hardware and software environment meets the requirements, refer to [FastDeploy environment requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl package installation, refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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This directory provides an example of `infer.py` to quickly complete the deployment of Petr on CPU/GPU. Execute the following script to complete
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```bash
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#Download deployment sample code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/vision/paddle3d/petr/python
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wget https://bj.bcebos.com/fastdeploy/models/petr.tar.gz
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tar -xf petr.tar.gz
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wget https://bj.bcebos.com/fastdeploy/models/petr_test.png
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# CPU reasoning
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python infer.py --model petr --image petr_test.png --device cpu
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# GPU inference
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python infer.py --model petr --image petr_test.png --device gpu
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```
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## Petr Python interface
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```python
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fastdeploy.vision.detection.Petr(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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Petr model loading and initialization.
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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): configuration file path
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> * **runtime_option**(RuntimeOption): Backend reasoning configuration, the default is None, that is, the default configuration is used
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> * **model_format**(ModelFormat): model format, the default is Paddle format
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### predict function
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> ```python
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> Petr. predict(image_data)
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> ```
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> Model prediction interface, the input image directly outputs the detection result.
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> **parameters**
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> > * **image_data**(np.ndarray): input data, note that it must be in HWC, BGR format
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> **Back**
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> > Return the `fastdeploy.vision.PerceptionResult` structure, structure description reference document [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
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## Other documents
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- [Petr Model Introduction](..)
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- [Petr C++ deployment](../cpp)
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- [Description of model prediction results](../../../../../docs/api/vision_results/)
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- [How to switch model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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