diff --git a/examples/vision/detection/paddledetection/sophgo/README.md b/examples/vision/detection/paddledetection/sophgo/README.md new file mode 100644 index 000000000..d574275df --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/README.md @@ -0,0 +1,107 @@ +# PaddleDetection SOPHGO部署示例 + +## 支持模型列表 + +目前SOPHGO支持如下模型的部署 +- [PP-YOLOE系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe) +- [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet) + +## 准备PP-YOLOE或者PicoDet部署模型以及转换模型 + +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: +- Paddle动态图模型转换为ONNX模型，请参考[PaddleDetection导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/EXPORT_MODEL.md). +- ONNX模型转换bmodel模型的过程，请参考[TPU-MLIR](https://github.com/sophgo/tpu-mlir) + +## 模型转换example + +PP-YOLOE和PicoDet模型转换过程类似，下面以ppyoloe_crn_s_300e_coco为例子,教大家如何转换Paddle模型到SOPHGO-TPU模型 + +### 导出ONNX模型 +```shell +#导出paddle模型 +python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams + +#paddle模型转ONNX模型 +paddle2onnx --model_dir ppyoloe_crn_s_300e_coco \ + --model_filename model.pdmodel \ + --params_filename model.pdiparams \ + --save_file ppyoloe_crn_s_300e_coco.onnx \ + --enable_dev_version True + +#进入Paddle2ONNX文件夹，固定ONNX模型shape +python -m paddle2onnx.optimize --input_model ppyoloe_crn_s_300e_coco.onnx \ + --output_model ppyoloe_crn_s_300e_coco.onnx \ + --input_shape_dict "{'image':[1,3,640,640]}" + +``` +### 导出bmodel模型 + +以转化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)。 +### 1. 安装 +``` shell +docker pull sophgo/tpuc_dev:latest + +# myname1234是一个示例，也可以设置其他名字 +docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest + +source ./envsetup.sh +./build.sh +``` + +### 2. ONNX模型转换为bmodel模型 +``` shell +mkdir ppyoloe_crn_s_300e_coco && cd ppyoloe_crn_s_300e_coco + +# 下载测试图片，并将图片转换为npz格式 +wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg + +#使用python获得模型转换所需要的npz文件 +im = cv2.imread(im) +im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) +#[640 640]为ppyoloe_crn_s_300e_coco的输入大小 +im_scale_y = 640 / float(im.shape[0]) +im_scale_x = 640 / float(im.shape[1]) +inputs = {} +inputs['image'] = np.array((im, )).astype('float32') +inputs['scale_factor'] = np.array([im_scale_y, im_scale_x]).astype('float32') +np.savez('inputs.npz', image = inputs['image'], scale_factor = inputs['scale_factor']) + +#放入onnx模型文件ppyoloe_crn_s_300e_coco.onnx + +mkdir workspace && cd workspace + +# 将ONNX模型转换为mlir模型 +model_transform.py \ + --model_name ppyoloe_crn_s_300e_coco \ + --model_def ../ppyoloe_crn_s_300e_coco.onnx \ + --input_shapes [[1,3,640,640],[1,2]] \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names p2o.Div.1,p2o.Concat.29 \ + --test_input ../inputs.npz \ + --test_result ppyoloe_crn_s_300e_coco_top_outputs.npz \ + --mlir ppyoloe_crn_s_300e_coco.mlir +``` +### 注意 +**由于TPU-MLIR当前不支持后处理算法，所以需要查看后处理的输入作为网络的输出** +具体方法为：output_names需要通过[NETRO](https://netron.app/) 查看，网页中打开需要转换的ONNX模型，搜索NonMaxSuppression节点 +查看INPUTS中boxes和scores的名字，这个两个名字就是我们所需的output_names +例如使用Netron可视化后，可以得到如下图片 +![](https://user-images.githubusercontent.com/120167928/210939488-a37e6c8b-474c-4948-8362-2066ee7a2ecb.png) +找到蓝色方框标记的NonMaxSuppression节点，可以看到红色方框标记的两个节点名称为p2o.Div.1,p2o.Concat.29 + +``` bash +# 将mlir模型转换为BM1684x的F32 bmodel模型 +model_deploy.py \ + --mlir ppyoloe_crn_s_300e_coco.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ppyoloe_crn_s_300e_coco_in_f32.npz \ + --test_reference ppyoloe_crn_s_300e_coco_top_outputs.npz \ + --model ppyoloe_crn_s_300e_coco_1684x_f32.bmodel +``` +最终获得可以在BM1684x上能够运行的bmodel模型ppyoloe_crn_s_300e_coco_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)。 + +## 其他链接 +- [Cpp部署](./cpp) +- [python部署](./python) diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/CMakeLists.txt b/examples/vision/detection/paddledetection/sophgo/cpp/CMakeLists.txt new file mode 100644 index 000000000..8caaaed25 --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/cpp/CMakeLists.txt @@ -0,0 +1,19 @@ +PROJECT(infer_demo C CXX) +CMAKE_MINIMUM_REQUIRED (VERSION 3.10) +# 指定下载解压后的fastdeploy库路径 +option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") + +set(ENABLE_LITE_BACKEND OFF) +#set(FDLIB ${FASTDEPLOY_INSTALL_DIR}) + +include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) + +# 添加FastDeploy依赖头文件 +include_directories(${FASTDEPLOY_INCS}) +include_directories(${FastDeploy_INCLUDE_DIRS}) + +add_executable(infer_ppyoloe ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc) +add_executable(infer_picodet ${PROJECT_SOURCE_DIR}/infer_picodet.cc) +# 添加FastDeploy库依赖 +target_link_libraries(infer_ppyoloe ${FASTDEPLOY_LIBS}) +target_link_libraries(infer_picodet ${FASTDEPLOY_LIBS}) diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/README.md b/examples/vision/detection/paddledetection/sophgo/cpp/README.md new file mode 100644 index 000000000..224b4b42a --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/cpp/README.md @@ -0,0 +1,61 @@ +# PaddleDetection C++部署示例 + +本目录下提供`infer_ppyoloe.cc`和`infer_picodet.cc`快速完成PP-YOLOE模型和PicoDet模型在SOPHGO BM1684x板子上加速部署的示例。 + +在部署前，需确认以下两个步骤: + +1. 软硬件环境满足要求 +2. 根据开发环境，从头编译FastDeploy仓库 + +以上步骤请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)实现 + +## 生成基本目录文件 + +该例程由以下几个部分组成 +```text +. +├── CMakeLists.txt +├── build # 编译文件夹 +├── image # 存放图片的文件夹 +├── infer_ppyoloe.cc +├── infer_picodet.cc +└── model # 存放模型文件的文件夹 +``` + +## 编译 + +### 编译并拷贝SDK到thirdpartys文件夹 + +请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)仓库编译SDK，编译完成后，将在build目录下生成fastdeploy-0.0.3目录. + +### 拷贝模型文件，以及配置文件至model文件夹 +将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README.md) +将转换后的SOPHGO bmodel模型文件拷贝至model中 + +### 准备测试图片至image文件夹 +```bash +wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg +cp 000000014439.jpg ./images +``` + +### 编译example + +```bash +cd build +cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-0.0.3 +make +``` + +## 运行例程 + +```bash +#ppyoloe推理示例 +./infer_ppyoloe model images/000000014439.jpg + +#picodet推理示例 +./infer_picodet model images/000000014439.jpg +``` + + +- [模型介绍](../../) +- [模型转换](../) diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc b/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc new file mode 100644 index 000000000..7018fd015 --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc @@ -0,0 +1,60 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include + +#include +#include + +#include "fastdeploy/vision.h" + +void SophgoInfer(const std::string& model_dir, const std::string& image_file) { + auto model_file = model_dir + "/picodet_s_416_coco_lcnet_1684x_f32.bmodel"; + auto params_file = ""; + auto config_file = model_dir + "/infer_cfg.yml"; + + auto option = fastdeploy::RuntimeOption(); + option.UseSophgo(); + + auto format = fastdeploy::ModelFormat::SOPHGO; + + auto model = fastdeploy::vision::detection::PicoDet( + model_file, params_file, config_file, option, format); + + model.GetPostprocessor().ApplyDecodeAndNMS(); + + auto im = cv::imread(image_file); + + fastdeploy::vision::DetectionResult res; + if (!model.Predict(&im, &res)) { + std::cerr << "Failed to predict." << std::endl; + return; + } + + std::cout << res.Str() << std::endl; + auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); + cv::imwrite("infer_sophgo.jpg", vis_im); + std::cout << "Visualized result saved in ./infer_sophgo.jpg" << std::endl; +} + +int main(int argc, char* argv[]) { + if (argc < 3) { + std::cout + << "Usage: infer_demo path/to/model_dir path/to/image run_option, " + "e.g ./infer_model ./picodet_model_dir ./test.jpeg" + << std::endl; + return -1; + } + SophgoInfer(argv[1], argv[2]); + return 0; +} diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc b/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc new file mode 100644 index 000000000..5844468cb --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc @@ -0,0 +1,60 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include + +#include +#include + +#include "fastdeploy/vision.h" + +void SophgoInfer(const std::string& model_dir, const std::string& image_file) { + auto model_file = model_dir + "/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel"; + auto params_file = ""; + auto config_file = model_dir + "/infer_cfg.yml"; + + auto option = fastdeploy::RuntimeOption(); + option.UseSophgo(); + + auto format = fastdeploy::ModelFormat::SOPHGO; + + auto model = fastdeploy::vision::detection::PPYOLOE( + model_file, params_file, config_file, option, format); + + model.GetPostprocessor().ApplyDecodeAndNMS(); + + auto im = cv::imread(image_file); + + fastdeploy::vision::DetectionResult res; + if (!model.Predict(&im, &res)) { + std::cerr << "Failed to predict." << std::endl; + return; + } + + std::cout << res.Str() << std::endl; + auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); + cv::imwrite("infer_sophgo.jpg", vis_im); + std::cout << "Visualized result saved in ./infer_sophgo.jpg" << std::endl; +} + +int main(int argc, char* argv[]) { + if (argc < 3) { + std::cout + << "Usage: infer_demo path/to/model_dir path/to/image run_option, " + "e.g ./infer_model ./picodet_model_dir ./test.jpeg" + << std::endl; + return -1; + } + SophgoInfer(argv[1], argv[2]); + return 0; +} diff --git a/examples/vision/detection/paddledetection/sophgo/python/README.md b/examples/vision/detection/paddledetection/sophgo/python/README.md new file mode 100644 index 000000000..ee8c87464 --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/python/README.md @@ -0,0 +1,32 @@ +# PaddleDetection Python部署示例 + +在部署前，需确认以下步骤 + +- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/sophgo.md) + +本目录下提供`infer_ppyoloe.py`和`infer_picodet.py`快速完成 PP-YOLOE 和 PicoDet 在SOPHGO TPU上部署的示例。执行如下脚本即可完成 + +```bash +# 下载部署示例代码 +git clone https://github.com/PaddlePaddle/FastDeploy.git +cd FastDeploy/examples/vision/detection/paddledetection/sophgo/python + +# 下载图片 +wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg + +# 推理 +#ppyoloe推理示例 +python3 infer_ppyoloe.py --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg + +#picodet推理示例 +python3 infer_picodet.py --model_file model/picodet_s_416_coco_lcnet_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg + +# 运行完成后返回结果如下所示 +可视化结果存储在sophgo_result.jpg中 +``` + +## 其它文档 +- [PP-YOLOE C++部署](../cpp) +- [PicoDet C++部署](../cpp) +- [转换PicoDet SOPHGO模型文档](../README.md) +- [转换PP-YOLOE SOPHGO模型文档](../README.md) diff --git a/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py b/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py new file mode 100644 index 000000000..fd079e16c --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import fastdeploy as fd +import cv2 +import os + + +def parse_arguments(): + import argparse + import ast + parser = argparse.ArgumentParser() + parser.add_argument( + "--model_file", required=True, help="Path of sophgo model.") + parser.add_argument("--config_file", required=True, help="Path of config.") + parser.add_argument( + "--image", type=str, required=True, help="Path of test image file.") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_arguments() + + model_file = args.model_file + params_file = "" + config_file = args.config_file + + # 配置runtime，加载模型 + runtime_option = fd.RuntimeOption() + runtime_option.use_sophgo() + + model = fd.vision.detection.PicoDet( + model_file, + params_file, + config_file, + runtime_option=runtime_option, + model_format=fd.ModelFormat.SOPHGO) + + model.postprocessor.apply_decode_and_nms() + + # 预测图片分割结果 + im = cv2.imread(args.image) + result = model.predict(im) + print(result) + + # 可视化结果 + vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5) + cv2.imwrite("sophgo_result.jpg", vis_im) + print("Visualized result save in ./sophgo_result.jpg") diff --git a/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py b/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py new file mode 100644 index 000000000..597a77a5f --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import fastdeploy as fd +import cv2 +import os + + +def parse_arguments(): + import argparse + import ast + parser = argparse.ArgumentParser() + parser.add_argument( + "--model_file", required=True, help="Path of sophgo model.") + parser.add_argument("--config_file", required=True, help="Path of config.") + parser.add_argument( + "--image", type=str, required=True, help="Path of test image file.") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_arguments() + + model_file = args.model_file + params_file = "" + config_file = args.config_file + + # 配置runtime，加载模型 + runtime_option = fd.RuntimeOption() + runtime_option.use_sophgo() + + model = fd.vision.detection.PPYOLOE( + model_file, + params_file, + config_file, + runtime_option=runtime_option, + model_format=fd.ModelFormat.SOPHGO) + + model.postprocessor.apply_decode_and_nms() + + # 预测图片分割结果 + im = cv2.imread(args.image) + result = model.predict(im) + print(result) + + # 可视化结果 + vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5) + cv2.imwrite("sophgo_result.jpg", vis_im) + print("Visualized result save in ./sophgo_result.jpg") diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/README.md b/examples/vision/ocr/PP-OCRv3/sophgo/README.md new file mode 100644 index 000000000..ee3cfc9a9 --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/README.md @@ -0,0 +1,88 @@ +# PPOCRv3 SOPHGO C++部署示例 + +## 支持模型列表 + +- PP-OCRv3部署模型实现来自[PP-OCR系列模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/models_list.md) + +## 准备PPOCRv3部署模型以及转换模型 + +PPOCRv3包括文本框检测模型（ch_PP-OCRv3_det）、方向分类模型（ch_ppocr_mobile_v2.0_cls）、文字识别模型（ch_PP-OCRv3_rec） +SOPHGO-TPU部署模型前需要将以上Paddle模型转换成bmodel模型，我们以ch_PP-OCRv3_det模型为例，具体步骤如下: +- 下载Paddle模型[ch_PP-OCRv3_det](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) +- Pddle模型转换为ONNX模型，请参考[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) +- ONNX模型转换bmodel模型的过程，请参考[TPU-MLIR](https://github.com/sophgo/tpu-mlir) + +## 模型转换example + +### 下载ch_PP-OCRv3_det模型,并转换为ONNX模型 +```shell +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar +tar xvf ch_PP-OCRv3_det_infer.tar + +# 修改ch_PP-OCRv3_det模型的输入shape，由动态输入变成固定输入 +python paddle_infer_shape.py --model_dir ch_PP-OCRv3_det_infer \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_dir ch_PP-OCRv3_det_infer_fix \ + --input_shape_dict="{'x':[1,3,960,608]}" + +#将固定输入的Paddle模型转换成ONNX模型 +paddle2onnx --model_dir ch_PP-OCRv3_det_infer_fix \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_file ch_PP-OCRv3_det_infer_fix.onnx \ + --enable_dev_version True +``` + +### 导出bmodel模型 + +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)。 +### 1. 安装 +``` shell +docker pull sophgo/tpuc_dev:latest + +# myname1234是一个示例，也可以设置其他名字 +docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest + +source ./envsetup.sh +./build.sh +``` + +### 2. ONNX模型转换为bmodel模型 +``` shell +mkdir ch_PP-OCRv3_det && cd ch_PP-OCRv3_det + +#在该文件中放入测试图片，同时将上一步转换的ch_PP-OCRv3_det_infer_fix.onnx放入该文件夹中 +cp -rf ${REGRESSION_PATH}/dataset/COCO2017 . +cp -rf ${REGRESSION_PATH}/image . +#放入onnx模型文件ch_PP-OCRv3_det_infer_fix.onnx + +mkdir workspace && cd workspace + +#将ONNX模型转换为mlir模型，其中参数--output_names可以通过NETRON查看 +model_transform.py \ + --model_name ch_PP-OCRv3_det \ + --model_def ../ch_PP-OCRv3_det_infer_fix.onnx \ + --input_shapes [[1,3,960,608]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names sigmoid_0.tmp_0 \ + --test_input ../image/dog.jpg \ + --test_result ch_PP-OCRv3_det_top_outputs.npz \ + --mlir ch_PP-OCRv3_det.mlir + +#将mlir模型转换为BM1684x的F32 bmodel模型 +model_deploy.py \ + --mlir ch_PP-OCRv3_det.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ch_PP-OCRv3_det_in_f32.npz \ + --test_reference ch_PP-OCRv3_det_top_outputs.npz \ + --model ch_PP-OCRv3_det_1684x_f32.bmodel +``` +最终获得可以在BM1684x上能够运行的bmodel模型ch_PP-OCRv3_det_1684x_f32.bmodel。按照上面同样的方法，可以将ch_ppocr_mobile_v2.0_cls，ch_PP-OCRv3_rec转换为bmodel的格式。如果需要进一步对模型进行加速，可以将ONNX模型转换为INT8 bmodel，具体步骤参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。 + +## 其他链接 +- [Cpp部署](./cpp) diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/cpp/CMakeLists.txt b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/CMakeLists.txt new file mode 100644 index 000000000..b32846afe --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/CMakeLists.txt @@ -0,0 +1,13 @@ +PROJECT(infer_demo C CXX) +CMAKE_MINIMUM_REQUIRED (VERSION 3.10) +# 指定下载解压后的fastdeploy库路径 +option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") + +include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) + +# 添加FastDeploy依赖头文件 +include_directories(${FASTDEPLOY_INCS}) + +add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc) +# 添加FastDeploy库依赖 +target_link_libraries(infer_demo ${FASTDEPLOY_LIBS}) diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/cpp/README.md b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/README.md new file mode 100644 index 000000000..22898e60a --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/README.md @@ -0,0 +1,58 @@ +# PPOCRv3 C++部署示例 + +本目录下提供`infer.cc`快速完成PPOCRv3模型在SOPHGO BM1684x板子上加速部署的示例。 + +在部署前，需确认以下两个步骤: + +1. 软硬件环境满足要求 +2. 根据开发环境，从头编译FastDeploy仓库 + +以上步骤请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)实现 + +## 生成基本目录文件 + +该例程由以下几个部分组成 +```text +. +├── CMakeLists.txt +├── build # 编译文件夹 +├── image # 存放图片的文件夹 +├── infer.cc +└── model # 存放模型文件的文件夹 +``` + +## 编译 + +### 编译并拷贝SDK到thirdpartys文件夹 + +请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)仓库编译SDK，编译完成后，将在build目录下生成fastdeploy-0.0.3目录. + +### 拷贝bmodel模型文至model文件夹 +将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README.md) +将转换后的SOPHGO bmodel模型文件拷贝至model中 + +### 准备测试图片至image文件夹，以及字典文件 +```bash +wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg +cp 12.jpg image/ + +wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt +``` + +### 编译example + +```bash +cd build +cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-0.0.3 +make +``` + +## 运行例程 + +```bash +./infer_demo model ./ppocr_keys_v1.txt image/12.jpeg +``` + + +- [模型介绍](../../../) +- [模型转换](../) diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/cpp/infer.cc b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/infer.cc new file mode 100644 index 000000000..168099b53 --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/cpp/infer.cc @@ -0,0 +1,136 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "fastdeploy/vision.h" +#ifdef WIN32 +const char sep = '\\'; +#else +const char sep = '/'; +#endif + +void InitAndInfer(const std::string& det_model_dir, + const std::string& rec_label_file, + const std::string& image_file, + const fastdeploy::RuntimeOption& option) { + auto det_model_file = + det_model_dir + sep + "ch_PP-OCRv3_det_1684x_f32.bmodel"; + auto det_params_file = det_model_dir + sep + ""; + + auto cls_model_file = + det_model_dir + sep + "ch_ppocr_mobile_v2.0_cls_1684x_f32.bmodel"; + auto cls_params_file = det_model_dir + sep + ""; + + auto rec_model_file = + det_model_dir + sep + "ch_PP-OCRv3_rec_1684x_f32.bmodel"; + auto rec_params_file = det_model_dir + sep + ""; + + auto format = fastdeploy::ModelFormat::SOPHGO; + + auto det_option = option; + auto cls_option = option; + auto rec_option = option; + + // The cls and rec model can inference a batch of images now. + // User could initialize the inference batch size and set them after create + // PPOCR model. + int cls_batch_size = 1; + int rec_batch_size = 1; + + // If use TRT backend, the dynamic shape will be set as follow. + // We recommend that users set the length and height of the detection model to + // a multiple of 32. We also recommend that users set the Trt input shape as + // follow. + det_option.SetTrtInputShape("x", {1, 3, 64, 64}, {1, 3, 640, 640}, + {1, 3, 960, 960}); + cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {cls_batch_size, 3, 48, 320}, + {cls_batch_size, 3, 48, 1024}); + rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {rec_batch_size, 3, 48, 320}, + {rec_batch_size, 3, 48, 2304}); + + // Users could save TRT cache file to disk as follow. + // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt"); + // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt"); + // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt"); + + auto det_model = fastdeploy::vision::ocr::DBDetector( + det_model_file, det_params_file, det_option, format); + auto cls_model = fastdeploy::vision::ocr::Classifier( + cls_model_file, cls_params_file, cls_option, format); + auto rec_model = fastdeploy::vision::ocr::Recognizer( + rec_model_file, rec_params_file, rec_label_file, rec_option, format); + + // Users could enable static shape infer for rec model when deploy PP-OCR on + // hardware which can not support dynamic shape infer well, like Huawei Ascend + // series. + rec_model.GetPreprocessor().SetStaticShapeInfer(true); + rec_model.GetPreprocessor().SetRecImageShape({3, 48, 584}); + + assert(det_model.Initialized()); + assert(cls_model.Initialized()); + assert(rec_model.Initialized()); + + // The classification model is optional, so the PP-OCR can also be connected + // in series as follows auto ppocr_v3 = + // fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model); + auto ppocr_v3 = + fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model); + + // Set inference batch size for cls model and rec model, the value could be -1 + // and 1 to positive infinity. When inference batch size is set to -1, it + // means that the inference batch size of the cls and rec models will be the + // same as the number of boxes detected by the det model. + ppocr_v3.SetClsBatchSize(cls_batch_size); + ppocr_v3.SetRecBatchSize(rec_batch_size); + + if (!ppocr_v3.Initialized()) { + std::cerr << "Failed to initialize PP-OCR." << std::endl; + return; + } + + auto im = cv::imread(image_file); + auto im_bak = im.clone(); + + fastdeploy::vision::OCRResult result; + if (!ppocr_v3.Predict(&im, &result)) { + std::cerr << "Failed to predict." << std::endl; + return; + } + + std::cout << result.Str() << std::endl; + + auto vis_im = fastdeploy::vision::VisOcr(im_bak, result); + cv::imwrite("vis_result.jpg", vis_im); + std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl; +} + +int main(int argc, char* argv[]) { + if (argc < 4) { + std::cout << "Usage: infer_demo path/to/model " + "path/to/rec_label_file path/to/image " + "e.g ./infer_demo ./ocr_bmodel " + "./ppocr_keys_v1.txt ./12.jpg" + << std::endl; + return -1; + } + + fastdeploy::RuntimeOption option; + option.UseSophgo(); + option.UseSophgoBackend(); + + std::string model_dir = argv[1]; + std::string rec_label_file = argv[2]; + std::string test_image = argv[3]; + InitAndInfer(model_dir, rec_label_file, test_image, option); + return 0; +} diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/python/README.md b/examples/vision/ocr/PP-OCRv3/sophgo/python/README.md new file mode 100644 index 000000000..dc9ce310b --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/python/README.md @@ -0,0 +1,38 @@ +# PPOCRv3 Python部署示例 + +在部署前，需确认以下两个步骤 + +- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/sophgo.md) + +本目录下提供`infer.py`快速完成 PPOCRv3 在SOPHGO TPU上部署的示例。执行如下脚本即可完成 + +```bash +# 下载部署示例代码 +git clone https://github.com/PaddlePaddle/FastDeploy.git +cd FastDeploy/examples/vision/ocr/PP-OCRv3/sophgo/python + +# 下载图片 +wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg + +#下载字典文件 +wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt + +# 推理 +python3 infer.py --det_model ocr_bmodel/ch_PP-OCRv3_det_1684x_f32.bmodel \ + --cls_model ocr_bmodel/ch_ppocr_mobile_v2.0_cls_1684x_f32.bmodel \ + --rec_model ocr_bmodel/ch_PP-OCRv3_rec_1684x_f32.bmodel \ + --rec_label_file ../ppocr_keys_v1.txt \ + --image ../12.jpg + +# 运行完成后返回结果如下所示 +det boxes: [[42,413],[483,391],[484,428],[43,450]]rec text: 上海斯格威铂尔大酒店 rec score:0.952958 cls label: 0 cls score: 1.000000 +det boxes: [[187,456],[399,448],[400,480],[188,488]]rec text: 打浦路15号 rec score:0.897335 cls label: 0 cls score: 1.000000 +det boxes: [[23,507],[513,488],[515,529],[24,548]]rec text: 绿洲仕格维花园公寓 rec score:0.994589 cls label: 0 cls score: 1.000000 +det boxes: [[74,553],[427,542],[428,571],[75,582]]rec text: 打浦路252935号 rec score:0.900663 cls label: 0 cls score: 1.000000 + +可视化结果保存在sophgo_result.jpg中 +``` + +## 其它文档 +- [PPOCRv3 C++部署](../cpp) +- [转换 PPOCRv3 SOPHGO模型文档](../README.md) diff --git a/examples/vision/ocr/PP-OCRv3/sophgo/python/infer.py b/examples/vision/ocr/PP-OCRv3/sophgo/python/infer.py new file mode 100644 index 000000000..356317099 --- /dev/null +++ b/examples/vision/ocr/PP-OCRv3/sophgo/python/infer.py @@ -0,0 +1,116 @@ +import fastdeploy as fd +import cv2 +import os + + +def parse_arguments(): + import argparse + import ast + parser = argparse.ArgumentParser() + parser.add_argument( + "--det_model", required=True, help="Path of Detection model of PPOCR.") + parser.add_argument( + "--cls_model", + required=True, + help="Path of Classification model of PPOCR.") + parser.add_argument( + "--rec_model", + required=True, + help="Path of Recognization model of PPOCR.") + parser.add_argument( + "--rec_label_file", + required=True, + help="Path of Recognization label of PPOCR.") + parser.add_argument( + "--image", type=str, required=True, help="Path of test image file.") + + return parser.parse_args() + + +args = parse_arguments() + +# 配置runtime，加载模型 +runtime_option = fd.RuntimeOption() +runtime_option.use_sophgo() + +# Detection模型, 检测文字框 +det_model_file = args.det_model +det_params_file = "" +# Classification模型，方向分类，可选 +cls_model_file = args.cls_model +cls_params_file = "" +# Recognition模型，文字识别模型 +rec_model_file = args.rec_model +rec_params_file = "" +rec_label_file = args.rec_label_file + +# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据 +# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置 +cls_batch_size = 1 +rec_batch_size = 1 + +# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建. +# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理. +# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数. +det_option = runtime_option +det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640], + [1, 3, 960, 960]) +# 用户可以把TRT引擎文件保存至本地 +# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt") +det_model = fd.vision.ocr.DBDetector( + det_model_file, + det_params_file, + runtime_option=det_option, + model_format=fd.ModelFormat.SOPHGO) + +cls_option = runtime_option +cls_option.set_trt_input_shape("x", [1, 3, 48, 10], + [cls_batch_size, 3, 48, 320], + [cls_batch_size, 3, 48, 1024]) +# 用户可以把TRT引擎文件保存至本地 +# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt") +cls_model = fd.vision.ocr.Classifier( + cls_model_file, + cls_params_file, + runtime_option=cls_option, + model_format=fd.ModelFormat.SOPHGO) + +rec_option = runtime_option +rec_option.set_trt_input_shape("x", [1, 3, 48, 10], + [rec_batch_size, 3, 48, 320], + [rec_batch_size, 3, 48, 2304]) +# 用户可以把TRT引擎文件保存至本地 +# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt") +rec_model = fd.vision.ocr.Recognizer( + rec_model_file, + rec_params_file, + rec_label_file, + runtime_option=rec_option, + model_format=fd.ModelFormat.SOPHGO) + +# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None +ppocr_v3 = fd.vision.ocr.PPOCRv3( + det_model=det_model, cls_model=cls_model, rec_model=rec_model) + +# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 584] +rec_model.preprocessor.static_shape_infer = True +rec_model.preprocessor.rec_image_shape = [3, 48, 584] + +# 给cls和rec模型设置推理时的batch size +# 此值能为-1, 和1到正无穷 +# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同 +ppocr_v3.cls_batch_size = cls_batch_size +ppocr_v3.rec_batch_size = rec_batch_size + +# 预测图片准备 +im = cv2.imread(args.image) + +#预测并打印结果 +result = ppocr_v3.predict(im) + +print(result) + +# 可视化结果 +vis_im = fd.vision.vis_ppocr(im, result) +cv2.imwrite("sophgo_result.jpg", vis_im) +print("Visualized result save in ./sophgo_result.jpg") diff --git a/fastdeploy/fastdeploy_model.cc b/fastdeploy/fastdeploy_model.cc index 15b924a47..9eff985fb 100644 --- a/fastdeploy/fastdeploy_model.cc +++ b/fastdeploy/fastdeploy_model.cc @@ -71,8 +71,8 @@ bool FastDeployModel::InitRuntimeWithSpecifiedBackend() { } } else if (use_sophgotpu) { if (!IsSupported(valid_sophgonpu_backends, runtime_option.backend)) { - FDERROR << "The valid rknpu backends of model " << ModelName() << " are " - << Str(valid_rknpu_backends) << ", " << runtime_option.backend + FDERROR << "The valid sophgo backends of model " << ModelName() << " are " + << Str(valid_sophgonpu_backends) << ", " << runtime_option.backend << " is not supported." << std::endl; return false; } diff --git a/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc b/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc index 8bd8770aa..8ae6e39f4 100644 --- a/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc +++ b/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc @@ -180,7 +180,7 @@ bool SophgoBackend::Infer(std::vector& inputs, assert(BM_SUCCESS == status); input_tensors[i].dtype = input_dtypes[i]; input_tensors[i].st_mode = BM_STORE_1N; - input_tensors[i].shape = *(net_info_->stages[i].input_shapes); + input_tensors[i].shape = net_info_->stages[0].input_shapes[i]; unsigned int input_byte = bmrt_tensor_bytesize(&input_tensors[i]); bm_memcpy_s2d_partial(handle_, input_tensors[i].device_mem, (void*)inputs[i].Data(), diff --git a/fastdeploy/vision/detection/ppdet/model.h b/fastdeploy/vision/detection/ppdet/model.h index da69774fa..812286d75 100755 --- a/fastdeploy/vision/detection/ppdet/model.h +++ b/fastdeploy/vision/detection/ppdet/model.h @@ -71,6 +71,7 @@ class FASTDEPLOY_DECL PPYOLOE : public PPDetBase { valid_kunlunxin_backends = {Backend::LITE}; valid_rknpu_backends = {Backend::RKNPU2}; valid_ascend_backends = {Backend::LITE}; + valid_sophgonpu_backends = {Backend::SOPHGOTPU}; initialized = Initialize(); } @@ -274,7 +275,8 @@ class FASTDEPLOY_DECL CascadeRCNN : public PPDetBase { initialized = Initialize(); } - virtual std::string ModelName() const { return "PaddleDetection/CascadeRCNN"; } + virtual std::string ModelName() const { + return "PaddleDetection/CascadeRCNN"; } }; class FASTDEPLOY_DECL PSSDet : public PPDetBase { diff --git a/fastdeploy/vision/detection/ppdet/postprocessor.cc b/fastdeploy/vision/detection/ppdet/postprocessor.cc index f09a21556..a453c4d74 100644 --- a/fastdeploy/vision/detection/ppdet/postprocessor.cc +++ b/fastdeploy/vision/detection/ppdet/postprocessor.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "fastdeploy/vision/detection/ppdet/postprocessor.h" + #include "fastdeploy/vision/detection/ppdet/multiclass_nms.h" #include "fastdeploy/vision/utils/utils.h" @@ -202,21 +203,18 @@ bool PaddleDetPostprocessor::ProcessUnDecodeResults( static_cast(round(ptr[j * 6]))); (*results)[i].scores.push_back(ptr[j * 6 + 1]); (*results)[i].boxes.emplace_back(std::array( - {ptr[j * 6 + 2] / GetScaleFactor()[1], - ptr[j * 6 + 3] / GetScaleFactor()[0], - ptr[j * 6 + 4] / GetScaleFactor()[1], - ptr[j * 6 + 5] / GetScaleFactor()[0]})); + {ptr[j * 6 + 2], ptr[j * 6 + 3], ptr[j * 6 + 4], ptr[j * 6 + 5]})); } offset += (num_boxes[i] * 6); } return true; } -std::vector PaddleDetPostprocessor::GetScaleFactor(){ +std::vector PaddleDetPostprocessor::GetScaleFactor() { return scale_factor_; } -void PaddleDetPostprocessor::SetScaleFactor(float* scale_factor_value){ +void PaddleDetPostprocessor::SetScaleFactor(float* scale_factor_value) { for (int i = 0; i < scale_factor_.size(); ++i) { scale_factor_[i] = scale_factor_value[i]; } @@ -225,6 +223,6 @@ void PaddleDetPostprocessor::SetScaleFactor(float* scale_factor_value){ bool PaddleDetPostprocessor::DecodeAndNMSApplied() { return apply_decode_and_nms_; } -} // namespace detection -} // namespace vision -} // namespace fastdeploy +} // namespace detection +} // namespace vision +} // namespace fastdeploy diff --git a/fastdeploy/vision/ocr/ppocr/classifier.cc b/fastdeploy/vision/ocr/ppocr/classifier.cc index c76750646..7f2cae13e 100755 --- a/fastdeploy/vision/ocr/ppocr/classifier.cc +++ b/fastdeploy/vision/ocr/ppocr/classifier.cc @@ -34,6 +34,7 @@ Classifier::Classifier(const std::string& model_file, valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT}; valid_kunlunxin_backends = {Backend::LITE}; valid_ascend_backends = {Backend::LITE}; + valid_sophgonpu_backends = {Backend::SOPHGOTPU}; } runtime_option = custom_option; runtime_option.model_format = model_format; diff --git a/fastdeploy/vision/ocr/ppocr/dbdetector.cc b/fastdeploy/vision/ocr/ppocr/dbdetector.cc index 306869d19..96387cde1 100755 --- a/fastdeploy/vision/ocr/ppocr/dbdetector.cc +++ b/fastdeploy/vision/ocr/ppocr/dbdetector.cc @@ -34,6 +34,7 @@ DBDetector::DBDetector(const std::string& model_file, valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT}; valid_kunlunxin_backends = {Backend::LITE}; valid_ascend_backends = {Backend::LITE}; + valid_sophgonpu_backends = {Backend::SOPHGOTPU}; } runtime_option = custom_option; diff --git a/fastdeploy/vision/ocr/ppocr/recognizer.cc b/fastdeploy/vision/ocr/ppocr/recognizer.cc index d967df0e2..47c5ef4c1 100755 --- a/fastdeploy/vision/ocr/ppocr/recognizer.cc +++ b/fastdeploy/vision/ocr/ppocr/recognizer.cc @@ -36,6 +36,7 @@ Recognizer::Recognizer(const std::string& model_file, valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT}; valid_kunlunxin_backends = {Backend::LITE}; valid_ascend_backends = {Backend::LITE}; + valid_sophgonpu_backends = {Backend::SOPHGOTPU}; } runtime_option = custom_option;