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[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)
* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
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[English](README.md) | 简体中文
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# PaddleSeg在算能（Sophgo）硬件上通过FastDeploy部署模型
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## 1. 说明
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PaddleSeg支持部署的支持如下型号的Sophgo芯片的部署
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- Sophgo 1684X
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PaddleSeg支持通过FastDeploy在算能TPU上部署相关Segmentation模型
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## 2. 预导出的推理模型
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为了方便开发者的测试，下面提供了PaddleSeg导出的部分推理模型，开发者可直接下载使用。
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PaddleSeg训练模型导出为推理模型，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/model_export_cn.md)
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| 模型 | 参数文件大小 |输入Shape | mIoU | mIoU (flip) | mIoU (ms+flip) |
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|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- | :----- |
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| [PP-LiteSeg-T(STDC1)-cityscapes-without-argmax](https://bj.bcebos.com/fastdeploy/models/rk1/ppliteseg.tar.gz)| 31MB | 1024x512 | 77.04% | 77.73% | 77.46% |
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## 3. 自行导出算能硬件支持的PaddleSeg模型
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### 3.1 模型版本
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支持PaddleSeg高于2.6版本的Segmentation模型，目前FastDeploy测试过可在算能TPU支持的模型如下：
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- [PP-LiteSeg系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/configs/pp_liteseg/README.md)
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### 3.2 将PaddleSeg推理模型转换为bmodel模型步骤
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SOPHGO-TPU部署模型前需要将Paddle模型转换成bmodel模型，具体步骤如下:
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- 下载Paddle模型[PP-LiteSeg-B(STDC2)-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz)
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- Paddle模型转换为ONNX模型，请参考[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)
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- ONNX模型转换bmodel模型的过程，请参考[TPU-MLIR](https://github.com/sophgo/tpu-mlir)
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### 3.3 bmode模型转换example
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下面以[PP-LiteSeg-B(STDC2)-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz)为例,教大家如何转换Paddle模型到SOPHGO-TPU支持的bmodel模型
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- 下载PP-LiteSeg-B(STDC2)-cityscapes-without-argmax模型,并转换为ONNX模型
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```shell
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# 下载Paddle2ONNX仓库
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git clone https://github.com/PaddlePaddle/Paddle2ONNX
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# 下载Paddle静态图模型并为Paddle静态图模型固定输入shape
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## 进入为Paddle静态图模型固定输入shape的目录
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cd Paddle2ONNX/tools/paddle
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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tar xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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# 修改PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer模型的输入shape，由动态输入变成固定输入
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python paddle_infer_shape.py --model_dir PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--save_dir pp_liteseg_fix \
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--input_shape_dict="{'x':[1,3,512,512]}"
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#将固定输入的Paddle模型转换成ONNX模型
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paddle2onnx --model_dir pp_liteseg_fix \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--save_file pp_liteseg.onnx \
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--enable_dev_version True
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```
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- 导出bmodel模型
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以转换BM1684x的bmodel模型为例子，我们需要下载[TPU-MLIR](https://github.com/sophgo/tpu-mlir)工程，安装过程具体参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
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#### 1. 安装
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``` shell
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docker pull sophgo/tpuc_dev:latest
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# myname1234是一个示例，也可以设置其他名字
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docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest
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source ./envsetup.sh
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./build.sh
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```
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#### 2. ONNX模型转换为bmodel模型
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``` shell
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mkdir pp_liteseg && cd pp_liteseg
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#在该文件中放入测试图片，同时将上一步转换的pp_liteseg.onnx放入该文件夹中
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cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
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cp -rf ${REGRESSION_PATH}/image .
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#放入onnx模型文件pp_liteseg.onnx
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mkdir workspace && cd workspace
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#将ONNX模型转换为mlir模型，其中参数--output_names可以通过NETRON查看
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model_transform.py \
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--model_name pp_liteseg \
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--model_def ../pp_liteseg.onnx \
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--input_shapes [[1,3,512,512]] \
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--mean 0.0,0.0,0.0 \
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--scale 0.0039216,0.0039216,0.0039216 \
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--keep_aspect_ratio \
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--pixel_format rgb \
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--output_names bilinear_interp_v2_6.tmp_0 \
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--test_input ../image/dog.jpg \
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--test_result pp_liteseg_top_outputs.npz \
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--mlir pp_liteseg.mlir
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#将mlir模型转换为BM1684x的F32 bmodel模型
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model_deploy.py \
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--mlir pp_liteseg.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input pp_liteseg_in_f32.npz \
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--test_reference pp_liteseg_top_outputs.npz \
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--model pp_liteseg_1684x_f32.bmodel
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```
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最终获得可以在BM1684x上能够运行的bmodel模型pp_liteseg_1684x_f32.bmodel。如果需要进一步对模型进行加速，可以将ONNX模型转换为INT8 bmodel，具体步骤参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
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## 4. 详细的部署示例
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- [C++部署](./cpp)
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- [Python部署](./python)
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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set(ENABLE_LITE_BACKEND OFF)
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#set(FDLIB ${FASTDEPLOY_INSTALL_DIR})
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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include_directories(${FastDeploy_INCLUDE_DIRS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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[English](README.md) | 简体中文
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# PaddleSeg 算能 C++ 部署示例
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本目录下提供`infer.cc`快速完成PP-LiteSeg在SOPHGO BM1684x板子上加速部署的示例。
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## 1. 部署环境准备
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在部署前，需自行编译基于算能硬件的预测库，参考文档[算能硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
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## 2. 部署模型准备
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)。
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## 3. 生成基本目录文件
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该例程由以下几个部分组成
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```text
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.
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├── CMakeLists.txt
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├── fastdeploy-sophgo # 编译文件夹
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├── image # 存放图片的文件夹
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├── infer.cc
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└── model # 存放模型文件的文件夹
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```
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## 4. 运行部署示例
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### 4.1 编译FastDeploy
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请参考[SOPHGO部署库编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/sophgo.md)编译SDK，编译完成后，将在build目录下生成fastdeploy-sophgo目录。拷贝fastdeploy-sophgo至当前目录
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### 4.2 下载部署示例代码
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/semantic_segmentation/sophgo/cpp
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# # 如果您希望从PaddleSeg下载示例代码，请运行
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# git clone https://github.com/PaddlePaddle/PaddleSeg.git
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# # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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# # git checkout develop
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# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/sophgo/cpp
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```
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### 4.3 拷贝模型文件，以及配置文件至model文件夹
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将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README.md)
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将转换后的SOPHGO bmodel模型文件拷贝至model中
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### 4.4 准备测试图片至image文件夹
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```bash
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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cp cityscapes_demo.png ./images
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```
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### 4.5 编译example
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```bash
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cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-sophgo
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make
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```
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### 4.6 运行例程
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```bash
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./infer_demo model images/cityscapes_demo.png
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```
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## 5. 更多指南
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- [PaddleSeg C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1segmentation.html)
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- [FastDeploy部署PaddleSeg模型概览](../../)
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- [Python部署](../python)
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- [模型转换](../README.md)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <iostream>
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#include <string>
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#include "fastdeploy/vision.h"
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void SophgoInfer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/pp_liteseg_1684x_f32.bmodel";
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std::string params_file;
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std::string config_file = model_dir + "/deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseSophgo();
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auto model_format = fastdeploy::ModelFormat::SOPHGO;
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option, model_format);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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// model.GetPreprocessor().DisableNormalizeAndPermute();
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im_org = cv::imread(image_file);
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// the input of bmodel should be fixed
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int new_width = 512;
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int new_height = 512;
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cv::Mat im;
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cv::resize(im_org, im, cv::Size(new_width, new_height), cv::INTER_LINEAR);
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fastdeploy::vision::SegmentationResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
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tc.End();
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tc.PrintInfo("PPSeg in Sophgo");
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cv::imwrite("infer_sophgo.jpg", vis_im);
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std::cout << "Visualized result saved in ./infer_sophgo.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 3) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
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"e.g ./infer_model ./bmodel ./test.jpeg"
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<< std::endl;
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return -1;
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}
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SophgoInfer(argv[1], argv[2]);
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return 0;
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}
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[English](README.md) | 简体中文
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# PaddleSeg 算能 Python部署示例
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## 1. 部署环境准备
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在部署前，需自行编译基于算能硬件的FastDeploy python wheel包并安装，参考文档[算能硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
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本目录下提供`infer.py`快速完成 pp_liteseg 在SOPHGO TPU上部署的示例。执行如下脚本即可完成
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## 2. 部署模型准备
|
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)。
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## 3. 运行部署示例
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/semantic_segmentation/sophgo/python
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# # 如果您希望从PaddleSeg下载示例代码，请运行
|
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# git clone https://github.com/PaddlePaddle/PaddleSeg.git
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# # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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# # git checkout develop
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# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/sophgo/python
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# 下载图片
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# PaddleSeg模型转换为bmodel模型
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将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README_CN.md)
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# 推理
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python3 infer.py --model_file ./bmodel/pp_liteseg_1684x_f32.bmodel --config_file ./bmodel/deploy.yaml --image cityscapes_demo.png
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# 运行完成后返回结果如下所示
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运行结果保存在sophgo_img.png中
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```
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## 4. 更多指南
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- [PP-LiteSeg SOPHGO C++部署](../cpp)
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- [转换 PP-LiteSeg SOPHGO模型文档](../README.md)
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## 5. 常见问题
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- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True, help="Path of model.")
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parser.add_argument(
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"--config_file", required=True, help="Path of config file.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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model_file = args.model
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params_file = ""
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config_file = args.config_file
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model = fd.vision.segmentation.PaddleSegModel(
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model_file,
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params_file,
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config_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.SOPHGO)
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# 预测图片分类结果
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im_org = cv2.imread(args.image)
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#bmodel 是静态模型，模型输入固定，这里设置为[512, 512]
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im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR)
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result = model.predict(im)
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print(result)
|
||||
|
||||
# 预测结果可视化
|
||||
vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
|
||||
cv2.imwrite("sophgo_img.png", vis_im)
|
||||
Reference in New Issue
Block a user