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81 lines
3.8 KiB
Markdown
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81 lines
3.8 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# Example of PaddleClas models Python Deployment
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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 the FastDeploy Python whl package. Please 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 ResNet50_vd on CPU/GPU and GPU accelerated by TensorRT. 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/classification/paddleclas/python
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# Download the ResNet50_vd model file and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# CPU inference
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
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# GPU inference
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
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# Use 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 ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
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# IPU inference（Attention: It is somewhat time-consuming for the operation of model serialization when running IPU inference for the first time. Please be patient.）
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
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# XPU inference
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device xpu --topk 1
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```
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The result returned after running is as follows
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```bash
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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## PaddleClasModel Python Interface
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```python
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fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleClas model loading and initialization, where model_file and params_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA) 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. (use 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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> PaddleClasModel.predict(input_image, topk=1)
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> ```
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>
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> Model prediction interface. Input images and output classification topk results directly.
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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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> > * **topk**(int): Return the topk classification results with the highest prediction probability. Default 1
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> **Return**
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>
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> > Return `fastdeploy.vision.ClassifyResult` structure. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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## Other documents
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- [PaddleClas Model Description](..)
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- [PaddleClas 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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