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FastDeploy/tutorials/multi_thread/cpp/single_model/README.md
huangjianhui 6c4a08e416 [Other] PPOCR models support model clone function (#1072) 
* 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

* Update VERSION_NUMBER

* Update requirements.txt

* Update README.md

* update version in doc:

* [Serving]Update Dockerfile (#1037)

Update Dockerfile

* Add license notice for RVM onnx model file (#1060)

* [Model] Add GPL-3.0 license (#1065)

Add GPL-3.0 license

* PPOCR model support model clone

* Update README.md

* Update PPOCRv2 && PPOCRv3 clone code

* Update PPOCR python __init__

* Add multi thread ocr example code

* Update README.md

* Update README.md

* Update ResNet50_vd_infer multi process code

* Add PPOCR multi process && thread example

* Update README.md

* Update README.md

* Update multi-thread docs

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
Co-authored-by: heliqi <1101791222@qq.com>
Co-authored-by: WJJ1995 <wjjisloser@163.com>
2023-01-17 15:16:41 +08:00

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Example of PaddleClas models Python Deployment

This directory provides example file multi_thread.cc to fast deploy PaddleClas models on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation.

Taking ResNet50_vd inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

mkdir build
cd build
# # Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` mentioned above
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# 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 multi-thread inference
./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
# GPU multi-thread inference
./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
# TensorRT multi-inference inference on GPU
./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1

Notice: the last number in above command is thread number

The above command works for Linux or MacOS. For SDK in Windows, refer to:

The result returned after running is as follows

Thread Id: 0
ClassifyResult(
label_ids: 153,
scores: 0.686229,
)