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[Hackathon 181] Add TVM support for FastDeploy on macOS (PaddlePaddle…
…#1969) * update for tvm backend * update third_party * update third_party * update * update * update * update * update * update * update * update --------- Co-authored-by: DefTruth <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| # set path | ||
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| set(TVM_URL_BASE "https://bj.bcebos.com/fastdeploy/third_libs/") | ||
| set(TVM_VERSION "0.12.0") | ||
| set(TVM_SYSTEM "") | ||
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| if (${CMAKE_SYSTEM} MATCHES "Darwin") | ||
| if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "arm64") | ||
| set(TVM_SYSTEM "macos-arm64") | ||
| endif () | ||
| elseif (${CMAKE_SYSTEM} MATCHES "Linux") | ||
| if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86") | ||
| set(TVM_SYSTEM "linux-x86") | ||
| endif () | ||
| else () | ||
| error("TVM only support MacOS in Arm64 or linux in x86") | ||
| endif () | ||
| set(TVM_FILE "tvm-${TVM_SYSTEM}-${TVM_VERSION}.tgz") | ||
| set(TVM_URL "${TVM_URL_BASE}${TVM_FILE}") | ||
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| set(TVM_RUNTIME_PATH "${THIRD_PARTY_PATH}/install/tvm") | ||
| execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${TVM_RUNTIME_PATH}") | ||
| download_and_decompress(${TVM_URL} | ||
| "${CMAKE_CURRENT_BINARY_DIR}/${TVM_FILE}" | ||
| "${THIRD_PARTY_PATH}/install/") | ||
| include_directories(${TVM_RUNTIME_PATH}/include) | ||
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| # copy dlpack to third_party | ||
| set(DLPACK_PATH "${THIRD_PARTY_PATH}/install/dlpack") | ||
| execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${DLPACK_PATH}") | ||
| execute_process(COMMAND ${CMAKE_COMMAND} -E copy_directory | ||
| "${PROJECT_SOURCE_DIR}/third_party/dlpack" | ||
| "${THIRD_PARTY_PATH}/install/") | ||
| include_directories(${DLPACK_PATH}/include) | ||
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| set(DMLC_CORE_PATH "${THIRD_PARTY_PATH}/install/dmlc-core") | ||
| execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${DMLC_CORE_PATH}") | ||
| set(DMLC_CORE_URL https://bj.bcebos.com/fastdeploy/third_libs/dmlc-core.tgz) | ||
| download_and_decompress(${DMLC_CORE_URL} | ||
| "${CMAKE_CURRENT_BINARY_DIR}/dmlc-core.tgz" | ||
| "${THIRD_PARTY_PATH}/install/") | ||
| include_directories(${DMLC_CORE_PATH}/include) | ||
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| # include lib | ||
| if (EXISTS ${TVM_RUNTIME_PATH}) | ||
| if (${CMAKE_SYSTEM} MATCHES "Darwin") | ||
| set(TVM_RUNTIME_LIB ${TVM_RUNTIME_PATH}/lib/libtvm_runtime.dylib) | ||
| elseif (${CMAKE_SYSTEM} MATCHES "Linux") | ||
| set(TVM_RUNTIME_LIB ${TVM_RUNTIME_PATH}/lib/libtvm_runtime.so) | ||
| endif () | ||
| include(${TVM_RUNTIME_PATH}/lib/cmake/tvm/tvmConfig.cmake) | ||
| add_definitions(-DDMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>) | ||
| else () | ||
| error(FATAL_ERROR "[tvm.cmake] TVM_RUNTIME_PATH does not exist.") | ||
| endif () |
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| @@ -0,0 +1,35 @@ | ||
| [English](README.md) | 简体中文 | ||
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| # PaddleDetection TVM部署示例 | ||
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| 在TVM上已经通过测试的PaddleDetection模型如下: | ||
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| * picodet | ||
| * PPYOLOE | ||
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| ### Paddle模型转换为TVM模型 | ||
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| 由于TVM不支持NMS算子，因此在转换模型前我们需要对PaddleDetection模型进行裁剪，将模型的输出节点改为NMS节点的输入节点。 | ||
| 输入以下命令，你将得到一个裁剪后的PPYOLOE模型。 | ||
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| ```bash | ||
| git clone https://github.com/PaddlePaddle/Paddle2ONNX.git | ||
| cd Paddle2ONNX/tools/paddle | ||
| wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_plus_crn_m_80e_coco.tgz | ||
| tar xvf ppyoloe_plus_crn_m_80e_coco.tgz | ||
| python prune_paddle_model.py --model_dir ppyoloe_plus_crn_m_80e_coco \ | ||
| --model_filename model.pdmodel \ | ||
| --params_filename model.pdiparams \ | ||
| --output_names tmp_17 concat_14.tmp_0 \ | ||
| --save_dir ppyoloe_plus_crn_m_80e_coco | ||
| ``` | ||
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| 裁剪完模型后我们就可以通过tvm python库实现编译模型，这里为了方便大家使用，提供了转换脚本。 | ||
| 输入以下命令，你将得到转换过后的TVM模型。 | ||
| 注意，FastDeploy在推理PPYOLOE时不关依赖模型，还依赖yml文件，因此你还需要将对应的yml文件拷贝到模型目录下。 | ||
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| ```bash | ||
| python path/to/FastDeploy/tools/tvm/paddle2tvm.py --model_path=./ppyoloe_plus_crn_m_80e_coco/model \ | ||
| --shape_dict="{'image': [1, 3, 640, 640], 'scale_factor': [1, 2]}" | ||
| cp ppyoloe_plus_crn_m_80e_coco/infer_cfg.yml tvm_save | ||
| ``` |
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examples/vision/detection/paddledetection/tvm/cpp/CMakeLists.txt
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| PROJECT(infer_demo C CXX) | ||
| CMAKE_MINIMUM_REQUIRED (VERSION 3.10) | ||
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| # 指定下载解压后的fastdeploy库路径 | ||
| option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") | ||
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| include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) | ||
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| # 添加FastDeploy依赖头文件 | ||
| include_directories(${FASTDEPLOY_INCS}) | ||
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| add_executable(infer_ppyoloe_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe_demo.cc) | ||
| target_link_libraries(infer_ppyoloe_demo ${FASTDEPLOY_LIBS}) |
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examples/vision/detection/paddledetection/tvm/cpp/README.md
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| Original file line number | Diff line number | Diff line change |
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| [English](README.md) | 简体中文 | ||
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| # PaddleDetection C++部署示例 | ||
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| 本目录下提供`infer_ppyoloe_demo.cc`快速完成PPDetection模型使用TVM加速部署的示例。 | ||
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| ## 转换模型并运行 | ||
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| ```bash | ||
| # build example | ||
| mkdir build | ||
| cd build | ||
| cmake .. -DFASTDEPLOY_INSTALL_DIR=/path/to/fastdeploy-sdk | ||
| make -j | ||
| wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg | ||
| ./infer_ppyoloe_demo ../tvm_save 000000014439.jpg | ||
| ``` | ||
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| ## PaddleDetection C++接口 | ||
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| ### 模型类 | ||
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| PaddleDetection目前支持6种模型系列，类名分别为`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，`SSD`,`PaddleYOLOv5`,`PaddleYOLOv6`,`PaddleYOLOv7`,`RTMDet`,`CascadeRCNN`,`PSSDet`,`RetinaNet`,`PPYOLOESOD`,`FCOS`,`TTFNet`,`TOOD`,`GFL`所有类名的构造函数和预测函数在参数上完全一致，本文档以PPYOLOE为例讲解API | ||
| ```c++ | ||
| fastdeploy::vision::detection::PPYOLOE( | ||
| const string& model_file, | ||
| const string& params_file, | ||
| const string& config_file | ||
| const RuntimeOption& runtime_option = RuntimeOption(), | ||
| const ModelFormat& model_format = ModelFormat::PADDLE) | ||
| ``` | ||
| PaddleDetection PPYOLOE模型加载和初始化，其中model_file为导出的ONNX模型格式。 | ||
| **参数** | ||
| > * **model_file**(str): 模型文件路径 | ||
| > * **params_file**(str): 参数文件路径 | ||
| > * **config_file**(str): 配置文件路径，即PaddleDetection导出的部署yaml文件 | ||
| > * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置 | ||
| > * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式 | ||
| #### Predict函数 | ||
| > ```c++ | ||
| > PPYOLOE::Predict(cv::Mat* im, DetectionResult* result) | ||
| > ``` | ||
| > | ||
| > 模型预测接口，输入图像直接输出检测结果。 | ||
| > | ||
| > **参数** | ||
| > | ||
| > > * **im**: 输入图像，注意需为HWC，BGR格式 | ||
| > > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/) | ||
| - [模型介绍](../../) | ||
| - [Python部署](../python) | ||
| - [视觉模型预测结果](../../../../../docs/api/vision_results/) | ||
| - [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) |
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examples/vision/detection/paddledetection/tvm/cpp/infer_ppyoloe_demo.cc
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| // 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. | ||
|
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| #include "fastdeploy/vision.h" | ||
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| void TVMInfer(const std::string& model_dir, const std::string& image_file) { | ||
| auto model_file = model_dir + "/tvm_model"; | ||
| auto params_file = ""; | ||
| auto config_file = model_dir + "/infer_cfg.yml"; | ||
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| auto option = fastdeploy::RuntimeOption(); | ||
| option.UseCpu(); | ||
| option.UseTVMBackend(); | ||
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| auto format = fastdeploy::ModelFormat::TVMFormat; | ||
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| auto model = fastdeploy::vision::detection::PPYOLOE( | ||
| model_file, params_file, config_file, option, format); | ||
| model.GetPostprocessor().ApplyNMS(); | ||
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| auto im = cv::imread(image_file); | ||
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| fastdeploy::vision::DetectionResult res; | ||
| if (!model.Predict(&im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return; | ||
| } | ||
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| std::cout << res.Str() << std::endl; | ||
| auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); | ||
| cv::imwrite("infer.jpg", vis_im); | ||
| std::cout << "Visualized result saved in ./infer.jpg" << std::endl; | ||
| } | ||
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| 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; | ||
| } | ||
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| TVMInfer(argv[1], argv[2]); | ||
| return 0; | ||
| } |
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| @@ -0,0 +1,21 @@ | ||
| // 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. | ||
| #pragma once | ||
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| namespace fastdeploy { | ||
| struct TVMBackendOption { | ||
| TVMBackendOption() {} | ||
| }; | ||
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| } // namespace fastdeploy |
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