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charl-u cbf88a46fa [Doc]Update English version of some documents (#1083 )

* 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

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* Standardize one writing

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* 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

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* Finish translation of demo.md

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* Update en version of java android README

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* 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

2023-01-09 10:08:19 +08:00

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English | 简体中文

PaddleClas 量化模型 C++部署示例

本目录下提供的infer.cc,可以帮助用户快速完成PaddleClas量化模型在CPU/GPU上的部署推理加速.

部署准备

FastDeploy环境准备

1. 软硬件环境满足要求，参考FastDeploy环境要求
1. FastDeploy Python whl包安装，参考FastDeploy Python安装

量化模型准备

1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
1. 用户可以使用FastDeploy提供的一键模型自动化压缩工具,自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)

以量化后的ResNet50_Vd模型为例, 进行部署，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)

在本目录执行如下命令即可完成编译,以及量化模型部署.

mkdir build
cd build
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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

#下载FastDeloy提供的ResNet50_Vd量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
tar -xvf resnet50_vd_ptq.tar
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# 在CPU上使用ONNX Runtime推理量化模型
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 0
# 在GPU上使用TensorRT推理量化模型
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 2

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