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examples/vision/detection/yolov7/quantize/cpp/README.md
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# YOLOv7量化模型 C++部署示例
English | [简体中文](README_CN.md)
# YOLOv7 Quantification Model C++ Deployment Example
本目录下提供的`infer.cc`,可以帮助用户快速完成YOLOv7量化模型在CPU/GPU上的部署推理加速.
This directory provides examples that `infer.cc` fast finishes the deployment of YOLOv7 quantification models on CPU/GPU.
## 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](../../../../../../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)
## Prepare the deployment
### FastDeploy Environment Preparation
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
### 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.
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
### Prepare the quantification model
- 1. Users can directly deploy quantized models provided by FastDeploy.
- 2. Or users can use the [One-click auto-compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to automatically conduct quantification model for deployment.
## 以量化后的YOLOv7模型为例, 进行部署
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
## Example: quantized YOLOv7 model
The compilation and deployment 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.
```bash
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
# Download the 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
#下载FastDeloy提供的yolov7量化模型文件和测试图片
# Download yolov7 quantification model files and test images provided by FastDeploy
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_quant.tar
tar -xvf yolov7_quant.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 在CPU上使用ONNX Runtime推理量化模型
# Use ONNX Runtime quantification model on CPU
./infer_demo yolov7_quant 000000014439.jpg 0
# 在GPU上使用TensorRT推理量化模型
# Use TensorRT quantification model on GPU
./infer_demo yolov7_quant 000000014439.jpg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
# Use Paddle-TensorRT quantification model on GPU
./infer_demo yolov7_quant 000000014439.jpg 2
```

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[English](README.md) | 简体中文
# YOLOv7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YOLOv7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](../../../../../../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)
### 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.
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
## 以量化后的YOLOv7模型为例, 进行部署
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
```bash
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yolov7量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_quant.tar
tar -xvf yolov7_quant.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 在CPU上使用ONNX Runtime推理量化模型
./infer_demo yolov7_quant 000000014439.jpg 0
# 在GPU上使用TensorRT推理量化模型
./infer_demo yolov7_quant 000000014439.jpg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
./infer_demo yolov7_quant 000000014439.jpg 2
```