mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-30 11:26:39 +08:00
[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 * 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
This commit is contained in:
charl-u
@@ -1,45 +1,46 @@
|
||||
# YOLOv5 量化模型 C++ 部署示例
|
||||
English | [简体中文](README_CN.md)
|
||||
# YOLOv5 Quantitative Model C++ Deployment Example
|
||||
|
||||
本目录下提供的 `infer.cc`，可以帮助用户快速完成 YOLOv5 量化模型在 A311D 上的部署推理加速。
|
||||
`infer.cc` in this directory can help you quickly complete the inference acceleration of YOLOv5 quantization model deployment on A311D.
|
||||
|
||||
## 部署准备
|
||||
### FastDeploy 交叉编译环境准备
|
||||
1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/a311d.md#交叉编译环境搭建)
|
||||
## Deployment Preparations
|
||||
### FastDeploy Cross-compile Environment Preparations
|
||||
1. For the software and hardware environment, and the cross-compile environment, please refer to [FastDeploy Cross-compile environment](../../../../../../docs/en/build_and_install/a311d.md#Cross-compilation-environment-construction).
|
||||
|
||||
### 量化模型准备
|
||||
可以直接使用由 FastDeploy 提供的量化模型进行部署，也可以按照如下步骤准备量化模型：
|
||||
1. 按照 [YOLOv5](https://github.com/ultralytics/yolov5/releases/tag/v6.1) 官方导出方式导出 ONNX 模型，或者直接使用如下命令下载
|
||||
### Model Preparations
|
||||
The quantified model can be deployed directly using the model provided by FastDeploy, or you can prepare it as follows:
|
||||
1. Export ONNX model according to the official [YOLOv5](https://github.com/ultralytics/yolov5/releases/tag/v6.1) export method, or you can download it directly with the following command:
|
||||
```bash
|
||||
wget https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx
|
||||
```
|
||||
2. 准备 300 张左右量化用的图片，也可以使用如下命令下载我们准备好的数据。
|
||||
2. Prepare about 300 images for quantification, or you can use the following command to download the data we have prepared.
|
||||
```bash
|
||||
wget https://bj.bcebos.com/fastdeploy/models/COCO_val_320.tar.gz
|
||||
tar -xf COCO_val_320.tar.gz
|
||||
```
|
||||
3. 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。
|
||||
3. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.
|
||||
```bash
|
||||
fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model_new/'
|
||||
```
|
||||
4. YOLOv5 模型需要异构计算，异构计算文件可以参考：[异构计算](./../../../../../../docs/cn/faq/heterogeneous_computing_on_timvx_npu.md)，由于 FastDeploy 已经提供了 YOLOv5 模型，可以先测试我们提供的异构文件，验证精度是否符合要求。
|
||||
4. The model requires heterogeneous computation. Please refer to: [Heterogeneous Computation](./../../../../../../docs/en/faq/heterogeneous_computing_on_timvx_npu.md). Since the YOLOv5 model is already provided, you can test the heterogeneous file we provide first to verify whether the accuracy meets the requirements.
|
||||
```bash
|
||||
# 先下载我们提供的模型，解压后将其中的 subgraph.txt 文件拷贝到新量化的模型目录中
|
||||
# First download the model we provide, unzip it and copy the subgraph.txt file to the newly quantized model directory.
|
||||
wget https://bj.bcebos.com/fastdeploy/models/yolov5s_ptq_model.tar.gz
|
||||
tar -xvf yolov5s_ptq_model.tar.gz
|
||||
```
|
||||
|
||||
更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||
For more information, please refer to [Model Quantization](../../quantize/README.md)
|
||||
|
||||
## 在 A311D 上部署量化后的 YOLOv5 检测模型
|
||||
请按照以下步骤完成在 A311D 上部署 YOLOv5 量化模型：
|
||||
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/a311d.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||
## Deploying the Quantized YOLOv5 Detection model on A311D
|
||||
Please follow these steps to complete the deployment of the YOLOv5 quantization model on A311D.
|
||||
1. Cross-compile the FastDeploy library as described in [Cross-compile FastDeploy](../../../../../../docs/en/build_and_install/a311d.md#FastDeploy-cross-compilation-library-compilation-based-on-Paddle-Lite)
|
||||
|
||||
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||
2. Copy the compiled library to the current directory. You can run this line:
|
||||
```bash
|
||||
cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
```
|
||||
|
||||
3. 在当前路径下载部署所需的模型和示例图片：
|
||||
3. Download the model and example images required for deployment in current path.
|
||||
```bash
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
mkdir models && mkdir images
|
||||
@@ -50,26 +51,26 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
|
||||
cp -r 000000014439.jpg images
|
||||
```
|
||||
|
||||
4. 编译部署示例，可使入如下命令：
|
||||
4. Compile the deployment example. You can run the following lines:
|
||||
```bash
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
mkdir build && cd build
|
||||
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
|
||||
make -j8
|
||||
make install
|
||||
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||
# After success, an install folder will be created with a running demo and libraries required for deployment.
|
||||
```
|
||||
|
||||
5. 基于 adb 工具部署 YOLOv5 检测模型到晶晨 A311D
|
||||
5. Deploy the YOLOv5 detection model to A311D based on adb.
|
||||
```bash
|
||||
# 进入 install 目录
|
||||
# Go to the install directory.
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp/build/install/
|
||||
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||
# The following line represents: bash run_with_adb.sh, demo needed to run, model path, image path, DEVICE ID.
|
||||
bash run_with_adb.sh infer_demo yolov5s_ptq_model 000000014439.jpg $DEVICE_ID
|
||||
```
|
||||
|
||||
部署成功后，vis_result.jpg 保存的结果如下：
|
||||
The result vis_result.jpg is saveed as follows:
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/30516196/203706969-dd58493c-6635-4ee7-9421-41c2e0c9524b.png">
|
||||
|
||||
需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||
Please note that the model deployed on A311D needs to be quantized. You can refer to [Model Quantization](../../../../../../docs/en/quantize.md)
|
||||
|
||||
76
examples/vision/detection/yolov5/a311d/cpp/README_CN.md
Normal file
76
examples/vision/detection/yolov5/a311d/cpp/README_CN.md
Normal file
@@ -0,0 +1,76 @@
|
||||
[English](README.md) | 简体中文
|
||||
# YOLOv5 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 YOLOv5 量化模型在 A311D 上的部署推理加速。
|
||||
|
||||
## 部署准备
|
||||
### FastDeploy 交叉编译环境准备
|
||||
1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/a311d.md#交叉编译环境搭建)
|
||||
|
||||
### 量化模型准备
|
||||
可以直接使用由 FastDeploy 提供的量化模型进行部署，也可以按照如下步骤准备量化模型：
|
||||
1. 按照 [YOLOv5](https://github.com/ultralytics/yolov5/releases/tag/v6.1) 官方导出方式导出 ONNX 模型，或者直接使用如下命令下载
|
||||
```bash
|
||||
wget https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx
|
||||
```
|
||||
2. 准备 300 张左右量化用的图片，也可以使用如下命令下载我们准备好的数据。
|
||||
```bash
|
||||
wget https://bj.bcebos.com/fastdeploy/models/COCO_val_320.tar.gz
|
||||
tar -xf COCO_val_320.tar.gz
|
||||
```
|
||||
3. 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。
|
||||
```bash
|
||||
fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model_new/'
|
||||
```
|
||||
4. YOLOv5 模型需要异构计算，异构计算文件可以参考：[异构计算](./../../../../../../docs/cn/faq/heterogeneous_computing_on_timvx_npu.md)，由于 FastDeploy 已经提供了 YOLOv5 模型，可以先测试我们提供的异构文件，验证精度是否符合要求。
|
||||
```bash
|
||||
# 先下载我们提供的模型，解压后将其中的 subgraph.txt 文件拷贝到新量化的模型目录中
|
||||
wget https://bj.bcebos.com/fastdeploy/models/yolov5s_ptq_model.tar.gz
|
||||
tar -xvf yolov5s_ptq_model.tar.gz
|
||||
```
|
||||
|
||||
更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||
|
||||
## 在 A311D 上部署量化后的 YOLOv5 检测模型
|
||||
请按照以下步骤完成在 A311D 上部署 YOLOv5 量化模型：
|
||||
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/a311d.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||
|
||||
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||
```bash
|
||||
cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
```
|
||||
|
||||
3. 在当前路径下载部署所需的模型和示例图片：
|
||||
```bash
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
mkdir models && mkdir images
|
||||
wget https://bj.bcebos.com/fastdeploy/models/yolov5s_ptq_model.tar.gz
|
||||
tar -xvf yolov5s_ptq_model.tar.gz
|
||||
cp -r yolov5s_ptq_model models
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
cp -r 000000014439.jpg images
|
||||
```
|
||||
|
||||
4. 编译部署示例，可使入如下命令：
|
||||
```bash
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp
|
||||
mkdir build && cd build
|
||||
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
|
||||
make -j8
|
||||
make install
|
||||
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||
```
|
||||
|
||||
5. 基于 adb 工具部署 YOLOv5 检测模型到晶晨 A311D
|
||||
```bash
|
||||
# 进入 install 目录
|
||||
cd FastDeploy/examples/vision/detection/yolov5/a311d/cpp/build/install/
|
||||
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||
bash run_with_adb.sh infer_demo yolov5s_ptq_model 000000014439.jpg $DEVICE_ID
|
||||
```
|
||||
|
||||
部署成功后，vis_result.jpg 保存的结果如下：
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/30516196/203706969-dd58493c-6635-4ee7-9421-41c2e0c9524b.png">
|
||||
|
||||
需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||
Reference in New Issue
Block a user