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Merge pull request #32 from MegEngine/update-internal
Update internal
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‎README.md

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@@ -33,6 +33,7 @@ MegCC supports Arm64/ArmV7/X86/BareMatal backend. You may want to check [support
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* Download release compiler suit from [release page](https://github.com/MegEngine/MegCC/releases)
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* Compiler from source, please fellow the [compiler doc](compiler/README.md)
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* Build the release tar, please fellow the [release doc](doc/how-to-release.md)
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* Get benchmark of different model please reference [benchmark](benchmark/README.md)
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#### How to use MegCC
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‎benchmark/.gitignore

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model/benchmark_*
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model/generated_models
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config
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output

‎benchmark/CMakeLists.txt

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cmake_minimum_required(VERSION 3.15.2)
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set(CMAKE_EXPORT_COMPILE_COMMANDS
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ON
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CACHE INTERNAL "")
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project(Benchmarker)
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option(ENABLE_MEGENGINE_FRAMEWORK "build benchmark for megengine" OFF)
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configure_file(src/build_config.h.in
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${CMAKE_CURRENT_BINARY_DIR}/genfiles/build_config.h)
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# set megcc lib
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if(NOT DEFINED RUNTIME_KERNEL_DIR)
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message(FATAL_ERROR "build MegCC runtime kernel dir RUNTIME_KERNEL_DIR is empty, use -DRUNTIME_KERNEL_DIR=your_model_kernel_dir to set")
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else()
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message(STATUS "build MegCC runtime with kernel dir ${RUNTIME_KERNEL_DIR}")
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endif()
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add_library(TinyNN STATIC IMPORTED)
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set_target_properties(
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TinyNN PROPERTIES IMPORTED_LOCATION
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"${RUNTIME_KERNEL_DIR}/runtime/install/lib/libTinyNN.a")
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if(ENABLE_MEGENGINE_FRAMEWORK)
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message(STATUS "build benchmark with megengine ${ENABLE_MEGENGINE_FRAMEWORK}")
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option(X86_BACKEND "Build bechmarker with X86 megengine lib" ON)
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# set megengine lib
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if(NOT DEFINED MEGENGINE_INSTALL_DIR)
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message(FATAL_ERROR "MEGENGINE_INSTALL_DIR is empty use -DMEGENGINE_INSTALL_DIR=your_megengine_install_dir to set")
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else()
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message(STATUS "MEGENGINE_INSTALL_DIR is ${MEGENGINE_INSTALL_DIR}")
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endif()
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add_library(mgb_imported INTERFACE IMPORTED)
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if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lite/lib/aarch64/liblite_static_all_in_one.a)
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS})
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elseif(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a")
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lite/lib/armv7/liblite_static_all_in_one.a)
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS})
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elseif(CMAKE_SYSTEM_PROCESSOR STREQUAL "riscv64")
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lite/lib/riscv64/liblite_static_all_in_one.a)
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS})
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else()
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if(X86_BACKEND)
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if(${CMAKE_SYSTEM_PROCESSOR} STREQUAL "x86_64" OR ${CMAKE_SYSTEM_PROCESSOR} STREQUAL "AMD64")
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set(MKL_LIBS
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/x86_64/lib/libmkl_core.a
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/x86_64/lib/libmkl_sequential.a
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/x86_64/lib/libmkl_intel_ilp64.a
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)
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lite/lib/x86_64/liblite_static_all_in_one.a)
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target_compile_definitions(mgb_imported INTERFACE -DMKL_ILP64)
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# WARNING: i386 is not test locally
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elseif(${CMAKE_SYSTEM_PROCESSOR} STREQUAL "i386" OR ${CMAKE_SYSTEM_PROCESSOR} STREQUAL "i686")
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set(MKL_LIBS
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/i386/lib/libmkl_core.a
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/x86_64/lib/libmkl_sequential.a
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${PROJECT_SOURCE_DIR}/../third_party/MegEngine/third_party/mkl/x86_64/lib/libmkl_intel_32.a
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)
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lite/lib/i386/liblite_static_all_in_one.a)
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endif()
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set(MKL_DNN_LIBS
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${MEGENGINE_INSTALL_DIR}/lib/libdnnl.a
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${MEGENGINE_INSTALL_DIR}/lib/libmkldnn.a
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)
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if(UNIX AND NOT APPLE)
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS} ${MKL_DNN_LIBS} -Wl,--start-group -ldl ${MKL_LIBS} -Wl,--end-group)
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else()
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS} ${MKL_DNN_LIBS} ${MKL_LIBS})
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endif()
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else()
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set(MGE_INSTALL_LIBS ${MEGENGINE_INSTALL_DIR}/lib/libmegengine.a ${MEGENGINE_INSTALL_DIR}/lib/libflatbuffers.a)
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target_link_libraries(mgb_imported INTERFACE ${MGE_INSTALL_LIBS})
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endif()
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endif()
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target_include_directories(mgb_imported INTERFACE ${MEGENGINE_INSTALL_DIR}/include)
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endif()
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# benchmarker config
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file(GLOB_RECURSE SOURCES main.cpp src/*.cpp src/*.h)
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add_executable(benchmarker ${SOURCES})
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target_include_directories(
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benchmarker PUBLIC $<BUILD_INTERFACE:${RUNTIME_KERNEL_DIR}/runtime/install/include> $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/genfiles>)
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if(ENABLE_MEGENGINE_FRAMEWORK)
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target_link_libraries(benchmarker -pthread TinyNN mgb_imported)
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else()
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target_link_libraries(benchmarker -pthread TinyNN)
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endif()
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message(STATUS "${CMAKE_TOOLCHAIN_FILE}")
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if(CMAKE_TOOLCHAIN_FILE)
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if(ANDROID)
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target_link_libraries(benchmarker log)
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endif()
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endif()
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install(TARGETS benchmarker LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX})

‎benchmark/README.md

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# How to use megcc benchmark
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## introduction
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megcc benchmark is a easy tool to get the benchmark result of different model in megcc
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the file struction is shown bellow:
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```
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├── clean.sh
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├── CMakeLists.txt
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├── main.cpp
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├── model
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│ ├── model_arm.json
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│ ├── model_riscv.json
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│ ├── model_x86.json
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│ └── request.txt
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├── python
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│ ├── example.py
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│ ├── format.sh
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│ └── src
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│ ├── benchmark.py
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│ └── models.py
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├── README.md
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├── src
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│ ├── benchmark.h
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│ ├── build_config.h.in
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│ ├── CCbenchmark.cpp
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│ ├── CCbenchmark.h
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│ ├── MGEbenchmark.cpp
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│ └── MGEbenchmark.h
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└── tools
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├── cc_analysis.py
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└── inference_visual.py
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```
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in src, it is a c++ application to run benchmark result on different platform.
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in python, the model convertion, other related preparing work and the benchmarker example is given
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the tools contains some usable scripts to analysis benchmark results
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## supported model
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mobilenetv2, resnet18, efficientnetb0 shufflenetv2 vgg16
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## request
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```bash
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mgeconvert > v.1.0.2
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onnx==1.11.0
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torch==1.10.0
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# or
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git clone https://github.com/MegEngine/mgeconvert.git
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cd mgeconvert
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git checkout master
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python3 -m pip install . --user --install-option="--targets=onnx"
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```
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the mgeconvert can be install by following command:
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```bash
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git clone https://github.com/MegEngine/mgeconvert.git
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cd mgeconvert
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git checkout master
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python3 -m pip install . --user --install-option="--targets=onnx"
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```
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## get model and run benchmark example
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``` bash
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cd megcc/benchmark
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export MEGCC_MGB_TO_TINYNN_PATH=<your_mgb_to_tinynn_path>
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python3 python/example.py
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```
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if you want to run in other platform, please reference the example add your new run_platform_xxx function in BenchmarkRunner,
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the example given a ssh remote device test template
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## analysis megcc log
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the `output` directory is generated by `example.py`
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### visualize the inference result of different model
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```bash
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python3 benchmark/tools/inference_visual.py benchmark/output -o figure_dir
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```
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### visualize the profile result of different kernel in different model
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```bash
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python3 benchmark/tools/cc_analysis.py benchmark/output -o figure_dir
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```

‎benchmark/clean.sh

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# /bin/bash -e
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set -x
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rm -rf ./build* ./output ./config ./model/benchmark* ./model/generate*

‎benchmark/main.cpp

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/**
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* \file benchmark/main.cpp
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*
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* This file is part of MegCC, a deep learning compiler developed by Megvii.
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*
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* \copyright Copyright (c) 2021-2022 Megvii Inc. All rights reserved.
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*/
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#include <cmath>
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#include <cstdio>
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#include <memory>
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#include "src/CCbenchmark.h"
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#include "src/MGEbenchmark.h"
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using namespace megcc;
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using namespace Benchmark;
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int main(int argc, char** argv) {
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if (argc < 2 && argc > 4) {
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fprintf(stderr, "cmdline error, please run with:\n");
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fprintf(stderr, "benchmarker <input_model> [options] ... \n");
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fprintf(stderr,
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"tips:\n\t you can use --profile and --mge to profile model "
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"and enable megengine framework (\"megcc\" is default)\n");
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return -1;
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}
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int log_level = 3;
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std::string framework = "megcc";
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std::string model_path = argv[1];
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int idx = 2;
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while (idx < argc) {
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std::string args = argv[idx];
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if (args == "--profile") {
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log_level = 0;
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} else if (args == "--mge") {
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framework = "mge";
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} else {
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fprintf(stderr, "invalid option: %s\n", argv[idx]);
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}
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++idx;
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}
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std::vector<std::shared_ptr<Benchmarker>> benchmarkers;
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if (framework == "megcc") {
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benchmarkers.push_back(
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std::make_shared<CCBenchmarker>(model_path, log_level));
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}
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#if ENABLE_MEGENGINE_FRAMEWORK
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else if (framework == "mge") {
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benchmarkers.push_back(
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std::make_shared<MGEBenchmarker>(model_path, log_level));
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}
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#endif
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else {
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fprintf(stderr,
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"unsupport framework: %s, megcc, mge(export "
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"ENABLE_MEGENGINE_FRAMEWORK=ON) is supported\n",
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framework.c_str());
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}
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for (size_t i = 0; i < benchmarkers.size(); ++i) {
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benchmarkers[i]->load_model();
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benchmarkers[i]->profile();
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}
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return 0;
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}

‎benchmark/model/model_arm.json

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{
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"dump_dir": "./benchmark_kernel_arm/",
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"models": [
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{
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"model_name": "mobilenetv2",
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"model_path": "./generated_models/mobilenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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},
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{
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"model_name": "resnet18",
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"model_path": "./generated_models/resnet18.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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},
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{
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"model_name": "resnet50",
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"model_path": "./generated_models/resnet50.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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},
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{
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"model_name": "efficientnetb0",
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"model_path": "./generated_models/efficientnetb0.mge",
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"input_shape_str": "data=(1,3,256,256)",
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"enable_nchw44": true
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},
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{
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"model_name": "shufflenetv2",
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"model_path": "./generated_models/shufflenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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},
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{
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"model_name": "vgg11",
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"model_path": "./generated_models/vgg11.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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},
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{
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"model_name": "vgg16",
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"model_path": "./generated_models/vgg16.mge",
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"input_shape_str": "data=(1,3,224,224)",
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"enable_nchw44": true
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}
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]
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}

‎benchmark/model/model_riscv.json

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{
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"dump_dir": "./benchmark_kernel_riscv/",
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"models": [
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{
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"model_name": "mobilenetv2",
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"model_path": "./generated_models/mobilenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "resnet18",
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"model_path": "./generated_models/resnet18.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "resnet50",
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"model_path": "./generated_models/resnet50.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "efficientnetb0",
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"model_path": "./generated_models/efficientnetb0.mge",
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"input_shape_str": "data=(1,3,256,256)"
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},
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{
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"model_name": "shufflenetv2",
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"model_path": "./generated_models/shufflenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "vgg11",
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"model_path": "./generated_models/vgg11.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "vgg16",
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"model_path": "./generated_models/vgg16.mge",
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"input_shape_str": "data=(1,3,224,224)"
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}
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]
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}

‎benchmark/model/model_x86.json

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{
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"dump_dir": "./benchmark_kernel_x86/",
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"models": [
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{
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"model_name": "mobilenetv2",
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"model_path": "./generated_models/mobilenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "resnet18",
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"model_path": "./generated_models/resnet18.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "resnet50",
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"model_path": "./generated_models/resnet50.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "efficientnetb0",
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"model_path": "./generated_models/efficientnetb0.mge",
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"input_shape_str": "data=(1,3,256,256)"
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},
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{
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"model_name": "shufflenetv2",
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"model_path": "./generated_models/shufflenetv2.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "vgg11",
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"model_path": "./generated_models/vgg11.mge",
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"input_shape_str": "data=(1,3,224,224)"
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},
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{
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"model_name": "vgg16",
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"model_path": "./generated_models/vgg16.mge",
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"input_shape_str": "data=(1,3,224,224)"
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}
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]
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}

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