mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
cbf88a46fa
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md * Update english version of serving/docs/ * Update title of readme * Update some links * Modify a title * Update some links * Update en version of java android README * Modify some titles * Modify some titles * Modify some titles * modify article to document * update some english version of documents in examples * Add english version of documents in examples/visions * Sync to current branch * Add english version of documents in examples * Add english version of documents in examples * Add english version of documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples * Update some documents in examples
81 lines
3.8 KiB
Markdown
Executable File
81 lines
3.8 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
|
||
# Example of PaddleClas models Python Deployment
|
||
|
||
Before deployment, two steps require confirmation.
|
||
|
||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
|
||
- 2. Install the FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
|
||
|
||
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
|
||
|
||
```bash
|
||
# Download deployment example code
|
||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||
cd FastDeploy/examples/vision/classification/paddleclas/python
|
||
|
||
# Download the ResNet50_vd model file and test images
|
||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
|
||
tar -xvf ResNet50_vd_infer.tgz
|
||
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||
|
||
# CPU inference
|
||
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
|
||
# GPU inference
|
||
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
|
||
# 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.）
|
||
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
|
||
# 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.）
|
||
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
|
||
# XPU inference
|
||
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device xpu --topk 1
|
||
```
|
||
|
||
The result returned after running is as follows
|
||
```bash
|
||
ClassifyResult(
|
||
label_ids: 153,
|
||
scores: 0.686229,
|
||
)
|
||
```
|
||
|
||
## PaddleClasModel Python Interface
|
||
|
||
```python
|
||
fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||
```
|
||
|
||
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
|
||
|
||
**Parameter**
|
||
|
||
> * **model_file**(str): Model file path
|
||
> * **params_file**(str): Parameter file path
|
||
> * **config_file**(str): Inference deployment configuration file
|
||
> * **runtime_option**(RuntimeOption): Backend Inference configuration. None by default. (use the default configuration)
|
||
> * **model_format**(ModelFormat): Model format. Paddle format by default
|
||
|
||
### predict function
|
||
|
||
> ```python
|
||
> PaddleClasModel.predict(input_image, topk=1)
|
||
> ```
|
||
>
|
||
> Model prediction interface. Input images and output classification topk results directly.
|
||
>
|
||
> **Parameter**
|
||
>
|
||
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
|
||
> > * **topk**(int): Return the topk classification results with the highest prediction probability. Default 1
|
||
|
||
> **Return**
|
||
>
|
||
> > Return `fastdeploy.vision.ClassifyResult` structure. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
|
||
|
||
|
||
## Other documents
|
||
|
||
- [PaddleClas Model Description](..)
|
||
- [PaddleClas C++ Deployment](../cpp)
|
||
- [Model prediction results](../../../../../docs/api/vision_results/)
|
||
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
|