mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
e4b1581593
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST * Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg * Add multi thread demo * Add python model clone function * Add multi thread python && C++ example * Fix bug * Update python && cpp multi_thread examples * Add cpp && python directory * Add README.md for examples * Delete redundant code * Create README_CN.md * Rename README_CN.md to README.md * Update README.md * Update README.md Co-authored-by: Jason <jiangjiajun@baidu.com>
50 lines
2.8 KiB
Markdown
50 lines
2.8 KiB
Markdown
# PaddleClas模型 Python多线程/进程部署示例
|
||
|
||
在部署前，需确认以下两个步骤
|
||
|
||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||
|
||
本目录下提供`multi_thread_process.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的多线程/进程示例。执行如下脚本即可完成
|
||
|
||
|
||
```bash
|
||
#下载部署示例代码
|
||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||
cd FastDeploy/tutorials/multi_thread/python
|
||
|
||
# 下载ResNet50_vd模型文件和测试图片
|
||
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多线程推理
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device cpu --topk 1 --thread_num 1
|
||
# CPU多进程推理
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device cpu --topk 1 --use_multi_process True --process_num 1
|
||
|
||
# GPU多线程推理
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --topk 1 --thread_num 1
|
||
# GPU多进程推理
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --topk 1 --use_multi_process True --process_num 1
|
||
|
||
# GPU上使用TensorRT多线程推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1 --thread_num 1
|
||
# GPU上使用TensorRT多进程推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1 --use_multi_process True --process_num 1
|
||
|
||
# IPU多线程推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device ipu --topk 1 --thread_num 1
|
||
# IPU多进程推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
|
||
python infer.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device ipu --topk 1 --use_multi_process True --process_num 1
|
||
```
|
||
>> **注意**: `--image_path` 可以输入图片文件夹的路径
|
||
|
||
运行完成后返回结果如下所示
|
||
```bash
|
||
ClassifyResult(
|
||
label_ids: 153,
|
||
scores: 0.686229,
|
||
)
|
||
``` |