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yeliang2258
a509dd8ec1
* add smoke model * add 3d vis * update code * update doc * mv paddle3d from detection to perception * update result for velocity * update code for CI * add set input data for TRT backend * add serving support for smoke model * update code * update code * update code --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
68 lines
2.5 KiB
Markdown
Executable File
68 lines
2.5 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# Smoke 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 Smoke 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/smoke/python
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wget https://bj.bcebos.com/fastdeploy/models/smoke.tar.gz
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tar -xf smoke.tar.gz
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wget https://bj.bcebos.com/fastdeploy/models/smoke_test.png
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# CPU reasoning
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python infer.py --model smoke --image smoke_test.png --device cpu
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# GPU inference
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python infer.py --model smoke --image smoke_test.png --device gpu
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```
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The visual result after running is shown in the figure below
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<img width="640" src="https://user-images.githubusercontent.com/30516196/230387825-53ac0a09-4137-4e49-9564-197cbc30ff08.png">
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## Smoke Python interface
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```python
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fastdeploy.vision.detection.Smoke(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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Smoke 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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> Smoke. 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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>
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> **parameters**
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>
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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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- [Smoke Model Introduction](..)
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- [Smoke 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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