yoni
4 years ago
parent
201c5ff4ee
commit
1f66e59648
@ -1,24 +0,0 @@
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CONVERTER="../../../../../mindspore/lite/build/tools/converter/converter_lite"
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if [ ! -f "$CONVERTER" ]; then
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if ! command -v converter_lite &> /dev/null
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then
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echo "converter_lite could not be found in MindSpore build directory nor in system path"
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exit
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else
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CONVERTER=converter_lite
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fi
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fi
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echo "============Exporting=========="
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if [ -n "$1" ]; then
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DOCKER_IMG=$1
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docker run -w $PWD --runtime=nvidia -v /home/$USER:/home/$USER --privileged=true ${DOCKER_IMG} /bin/bash -c "python lenet_export.py; chmod 444 lenet_tod.mindir; rm -rf __pycache__"
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else
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echo "MindSpore docker was not provided, attempting to run locally"
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python lenet_export.py
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fi
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echo "============Converting========="
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$CONVERTER --fmk=MINDIR --trainModel=true --modelFile=lenet_tod.mindir --outputFile=lenet_tod
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@ -1,82 +0,0 @@
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#!/bin/bash
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display_usage() {
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echo -e "\nUsage: prepare_and_run.sh dataset_path [mindspore_docker] [release.tar.gz]\n"
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}
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if [ -n "$1" ]; then
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MNIST_DATA_PATH=$1
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else
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echo "MNIST Dataset directory path was not provided"
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display_usage
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exit 0
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fi
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if [ -n "$2" ]; then
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DOCKER=$2
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else
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DOCKER=""
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#echo "MindSpore docker was not provided"
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#display_usage
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#exit 0
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fi
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if [ -n "$3" ]; then
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TARBALL=$3
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else
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if [ -f ../../../../output/mindspore-lite-*-runtime-arm64-cpu-train.tar.gz ]; then
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TARBALL="../../../../output/mindspore-lite-*-runtime-arm64-cpu-train.tar.gz"
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else
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echo "release.tar.gz was not found"
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display_usage
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exit 0
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fi
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fi
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# Prepare the model
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cd model/
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rm -f *.ms
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./prepare_model.sh $DOCKER
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cd -
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# Copy the .ms model to the package folder
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rm -rf package
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mkdir -p package/model
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cp model/*.ms package/model
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# Copy the running script to the package
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cp scripts/train.sh package/
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cp scripts/eval.sh package/
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# Copy the shared MindSpore ToD library
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tar -xzvf ${TARBALL} --wildcards --no-anchored libmindspore-lite.so
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tar -xzvf ${TARBALL} --wildcards --no-anchored include
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mv mindspore-*/lib package/
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mkdir msl
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mv mindspore-*/* msl/
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rm -rf mindspore-*
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# Copy the dataset to the package
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cp -r ${MNIST_DATA_PATH} package/dataset
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# Compile program
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make TARGET=arm64
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# Copy the executable to the package
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mv bin package/
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# Push the folder to the device
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adb push package /data/local/tmp/
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echo "Training on Device"
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adb shell < scripts/run_train.sh
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echo
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echo "Load trained model and evaluate accuracy"
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adb shell < scripts/run_eval.sh
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echo
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#rm -rf src/*.o package model/__pycache__ model/*.ms
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#./prepare_and_run.sh /opt/share/dataset/mnist mindspore_dev:5
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@ -1,2 +0,0 @@
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cd /data/local/tmp/package
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/system/bin/sh eval.sh
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@ -1,2 +0,0 @@
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cd /data/local/tmp/package
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/system/bin/sh train.sh
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@ -0,0 +1,41 @@
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BASE_DIR=$(realpath ../../../../)
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APP:=bin/net_runner
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MSLIB:=mindspore-lite
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MSDIR:=$(realpath package-$(TARGET)/lib)
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SRC:=src/net_runner.cc src/dataset.cc
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OBJ:=$(SRC:.cc=.o)
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CFLAGS := -Ofast -std=c++17 \
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-I . \
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-I ./msl \
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-I ./msl/third_party/flatbuffers/include
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ifeq ($(TARGET),arm64)
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CXX := ${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/bin/clang++
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CFLAGS += --target=aarch64-none-linux-android21 --gcc-toolchain=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64 --sysroot=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/sysroot -fdata-sections -ffunction-sections
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LDFLAGS := --target=aarch64-none-linux-android21 --gcc-toolchain=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64 --sysroot=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/sysroot -Wl,--gc-sections
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LDFLAGS += -L$(MSDIR) -l$(MSLIB) -pthread -llog -latomic -lm
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else
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CFLAGS += -g
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LDFLAGS := -L$(MSDIR) -l$(MSLIB) -lpthread -Wl,-rpath,$(MSDIR)
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endif
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LD := ${CXX}
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all:$(APP)
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$(APP): $(OBJ) $(MSDIR)/lib$(MSLIB).so
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@mkdir -p bin
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$(LD) $(OBJ) $(LDFLAGS) -o $@
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clean:
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rm -rf src/*.o bin/
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mrproper:
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rm -rf package* msl src/*.o bin/ model/*.mindir model/*.ms model/*.so model/converter_lite
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%.o:%.cc
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$(CXX) $(CFLAGS) -c $< -o $@
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@ -0,0 +1,32 @@
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#!/bin/bash
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echo "============Exporting=========="
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if [ -n "$1" ]; then
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DOCKER_IMG=$1
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docker run -w $PWD --runtime=nvidia -v /home/$USER:/home/$USER --privileged=true ${DOCKER_IMG} /bin/bash -c "PYTHONPATH=../../../../../model_zoo/official/cv/lenet/src python lenet_export.py; chmod 444 lenet_tod.mindir; rm -rf __pycache__"
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else
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echo "MindSpore docker was not provided, attempting to run locally"
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PYTHONPATH=../../../../../model_zoo/official/cv/lenet/src python lenet_export.py
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fi
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CONVERTER="../../../build/tools/converter/converter_lite"
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if [ ! -f "$CONVERTER" ]; then
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if ! command -v converter_lite &> /dev/null
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then
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tar -xzf ../../../../../output/mindspore-lite-*-converter-ubuntu-train.tar.gz --strip-components 2 --wildcards --no-anchored converter_lite libmindspore_gvar.so
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if [ -f ./converter_lite ]; then
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CONVERTER=./converter_lite
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else
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echo "converter_lite could not be found in MindSpore build directory nor in system path"
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exit 1
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fi
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else
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CONVERTER=converter_lite
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fi
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fi
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echo "============Converting========="
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LD_LIBRARY_PATH=./ $CONVERTER --fmk=MINDIR --trainModel=true --modelFile=lenet_tod.mindir --outputFile=lenet_tod
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@ -0,0 +1,116 @@
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#!/bin/bash
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display_usage()
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{
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echo -e "\nUsage: prepare_and_run.sh -D dataset_path [-d mindspore_docker] [-r release.tar.gz] [-t arm64|x86]\n"
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}
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checkopts()
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{
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TARGET="arm64"
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DOCKER=""
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MNIST_DATA_PATH=""
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while getopts 'D:d:r:t:' opt
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do
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OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
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case "${opt}" in
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D)
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MNIST_DATA_PATH=$OPTARG
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;;
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d)
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DOCKER=$OPTARG
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;;
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t)
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if [ "$OPTARG" == "arm64" ] || [ "$OPTARG" == "x86" ]; then
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TARGET=$OPTARG
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else
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echo "No such target " $OPTARG
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display_usage
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exit 1
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fi
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;;
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r)
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TARBALL=$OPTARG
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;;
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*)
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echo "Unknown option ${opt}!"
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display_usage
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exit 1
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esac
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done
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}
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checkopts "$@"
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if [ "$MNIST_DATA_PATH" == "" ]; then
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echo "MNIST Dataset directory path was not provided"
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display_usage
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exit 1
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fi
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if [ "$TARBALL" == "" ]; then
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file=$(ls ../../../../output/mindspore-lite-*-runtime-${TARGET}-cpu-train.tar.gz)
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if [ -f ${file} ]; then
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TARBALL=${file}
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else
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echo "release.tar.gz was not found"
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display_usage
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exit 1
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fi
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fi
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# Prepare the model
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cd model/ || exit 1
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rm -f *.ms
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./prepare_model.sh $DOCKER || exit 1
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cd ../
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# Copy the .ms model to the package folder
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PACKAGE=package-${TARGET}
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rm -rf ${PACKAGE}
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mkdir -p ${PACKAGE}/model
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cp model/*.ms ${PACKAGE}/model
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# Copy the running script to the package
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cp scripts/*.sh ${PACKAGE}/
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# Copy the shared MindSpore ToD library
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tar -xzf ${TARBALL} --wildcards --no-anchored libmindspore-lite.so
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tar -xzf ${TARBALL} --wildcards --no-anchored include
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mv mindspore-*/lib ${PACKAGE}/
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rm -rf msl
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mkdir msl
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mv mindspore-*/* msl/
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rm -rf mindspore-*
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# Copy the dataset to the package
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cp -r ${MNIST_DATA_PATH} ${PACKAGE}/dataset
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echo "==========Compiling============"
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make TARGET=${TARGET}
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# Copy the executable to the package
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mv bin ${PACKAGE}/ || exit 1
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if [ "${TARGET}" == "arm64" ]; then
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echo "=======Pushing to device======="
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adb push ${PACKAGE} /data/local/tmp/
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echo "========Training on Device====="
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adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh train.sh"
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echo
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echo "===Evaluating trained Model====="
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adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh eval.sh"
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echo
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else
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cd ${PACKAGE} || exit 1
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echo "======Training Locally========="
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./train.sh
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echo "===Evaluating trained Model====="
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./eval.sh
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cd ..
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fi
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@ -0,0 +1,41 @@
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BASE_DIR=$(realpath ../../../../)
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APP:=bin/net_runner
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MSLIB:=mindspore-lite
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MSDIR:=$(realpath package-$(TARGET)/lib)
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SRC:=src/net_runner.cc src/dataset.cc
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OBJ:=$(SRC:.cc=.o)
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CFLAGS := -Ofast -std=c++17 \
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-I . \
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-I ./msl \
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-I ./msl/third_party/flatbuffers/include
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ifeq ($(TARGET),arm64)
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CXX := ${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/bin/clang++
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CFLAGS += --target=aarch64-none-linux-android21 --gcc-toolchain=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64 --sysroot=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/sysroot -fdata-sections -ffunction-sections
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LDFLAGS := --target=aarch64-none-linux-android21 --gcc-toolchain=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64 --sysroot=${ANDROID_NDK}/toolchains/llvm/prebuilt/linux-x86_64/sysroot -Wl,--gc-sections
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LDFLAGS += -L$(MSDIR) -l$(MSLIB) -pthread -llog -latomic -lm
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else
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CFLAGS += -g
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LDFLAGS := -L$(MSDIR) -l$(MSLIB) -lpthread -Wl,-rpath,$(MSDIR)
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endif
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LD := ${CXX}
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all:$(APP)
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$(APP): $(OBJ) $(MSDIR)/lib$(MSLIB).so
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@mkdir -p bin
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$(LD) $(OBJ) $(LDFLAGS) -o $@
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clean:
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rm -rf src/*.o bin/
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mrproper:
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rm -rf dataset package* msl src/*.o bin/ model/*.mindir model/*.ms
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%.o:%.cc
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$(CXX) $(CFLAGS) -c $< -o $@
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,34 @@
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#!/bin/bash
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if [ ! -f "efficient_net_b0.ckpt" ]; then
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echo "Pretrained model weights are missing, downloading efficient_net_b0.ckpt"
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wget https://download.mindspore.cn/model_zoo/official/lite/efficient_net/efficient_net_b0.ckpt
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fi
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echo "============Exporting=========="
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if [ -n "$1" ]; then
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DOCKER_IMG=$1
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docker run -w $PWD --runtime=nvidia -v /home/$USER:/home/$USER --privileged=true ${DOCKER_IMG} /bin/bash -c "python transfer_learning_export.py; chmod 444 transfer_learning_tod.mindir; rm -rf __pycache__"
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else
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echo "MindSpore docker was not provided, attempting to run locally"
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python transfer_learning_export.py
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fi
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CONVERTER="../../../build/tools/converter/converter_lite"
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if [ ! -f "$CONVERTER" ]; then
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if ! command -v converter_lite &> /dev/null
|
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then
|
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tar -xzf ../../../../../output/mindspore-lite-*-converter-ubuntu-train.tar.gz --strip-components 2 --wildcards --no-anchored converter_lite libmindspore_gvar.so
|
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if [ -f ./converter_lite ]; then
|
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CONVERTER=./converter_lite
|
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else
|
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echo "converter_lite could not be found in MindSpore build directory nor in system path"
|
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exit 1
|
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fi
|
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else
|
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CONVERTER=converter_lite
|
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fi
|
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fi
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|
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echo "============Converting========="
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LD_LIBRARY_PATH=./ $CONVERTER --fmk=MINDIR --trainModel=true --modelFile=transfer_learning_tod.mindir --outputFile=transfer_learning_tod
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@ -0,0 +1,34 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
|
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#
|
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# 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.
|
||||
# ============================================================================
|
||||
"""train_utils."""
|
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|
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import mindspore.nn as nn
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from mindspore.common.parameter import ParameterTuple
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|
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def TrainWrap(net, loss_fn=None, optimizer=None, weights=None):
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"""
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TrainWrap
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"""
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if loss_fn is None:
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loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
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loss_net = nn.WithLossCell(net, loss_fn)
|
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loss_net.set_train()
|
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if weights is None:
|
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weights = ParameterTuple(net.trainable_params())
|
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if optimizer is None:
|
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optimizer = nn.Adam(weights, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8,
|
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use_locking=False, use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
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train_net = nn.TrainOneStepCell(loss_net, optimizer)
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return train_net
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@ -0,0 +1,63 @@
|
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# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""transfer_learning_export."""
|
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|
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import mindspore as M
|
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from mindspore.nn import Cell
|
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from mindspore.train.serialization import load_checkpoint
|
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from mindspore.common.parameter import ParameterTuple
|
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from mindspore.train.serialization import export
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from effnet import effnet
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import numpy as np
|
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from train_utils import TrainWrap
|
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|
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|
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class TransferNet(Cell):
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def __init__(self, backbone, head):
|
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super().__init__(TransferNet)
|
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self.backbone = backbone
|
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self.head = head
|
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|
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def construct(self, x):
|
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x = self.backbone(x)
|
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x = self.head(x)
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return x
|
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|
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|
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BACKBONE = effnet(num_classes=1000)
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load_checkpoint("efficient_net_b0.ckpt", BACKBONE)
|
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HEAD = M.nn.Dense(1000, 10)
|
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HEAD.weight.set_data(M.Tensor(np.random.normal(
|
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0, 0.1, HEAD.weight.data.shape).astype("float32")))
|
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HEAD.bias.set_data(M.Tensor(np.zeros(HEAD.bias.data.shape, dtype="float32")))
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|
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n = TransferNet(BACKBONE, HEAD)
|
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|
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trainable_weights_list = []
|
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trainable_weights_list.extend(n.head.trainable_params())
|
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trainable_weights = ParameterTuple(trainable_weights_list)
|
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|
||||
M.context.set_context(mode=M.context.PYNATIVE_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
BATCH_SIZE = 32
|
||||
X = M.Tensor(np.ones((BATCH_SIZE, 3, 224, 224)), M.float32)
|
||||
label = M.Tensor(np.zeros([BATCH_SIZE, 10]).astype(np.float32))
|
||||
|
||||
sgd = M.nn.SGD(trainable_weights, learning_rate=0.01, momentum=0.9,
|
||||
dampening=0.01, weight_decay=0.0, nesterov=False, loss_scale=1.0)
|
||||
net = TrainWrap(n, optimizer=sgd, weights=trainable_weights)
|
||||
export(net, X, label, file_name="transfer_learning_tod.mindir", file_format='MINDIR')
|
||||
|
||||
print("Exported")
|
||||
@ -0,0 +1,122 @@
|
||||
#!/bin/bash
|
||||
|
||||
display_usage()
|
||||
{
|
||||
echo -e "\nUsage: prepare_and_run.sh -D dataset_path [-d mindspore_docker] [-r release.tar.gz] [-t arm64|x86]\n"
|
||||
}
|
||||
|
||||
checkopts()
|
||||
{
|
||||
TARGET="arm64"
|
||||
DOCKER=""
|
||||
PLACES_DATA_PATH=""
|
||||
while getopts 'D:d:r:t:' opt
|
||||
do
|
||||
OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
|
||||
case "${opt}" in
|
||||
D)
|
||||
PLACES_DATA_PATH=$OPTARG
|
||||
;;
|
||||
d)
|
||||
DOCKER=$OPTARG
|
||||
;;
|
||||
t)
|
||||
if [ "$OPTARG" == "arm64" ] || [ "$OPTARG" == "x86" ]; then
|
||||
TARGET=$OPTARG
|
||||
else
|
||||
echo "No such target " $OPTARG
|
||||
display_usage
|
||||
exit 1
|
||||
fi
|
||||
;;
|
||||
r)
|
||||
TARBALL=$OPTARG
|
||||
;;
|
||||
*)
|
||||
echo "Unknown option ${opt}!"
|
||||
display_usage
|
||||
exit 1
|
||||
esac
|
||||
done
|
||||
}
|
||||
checkopts "$@"
|
||||
if [ "$PLACES_DATA_PATH" == "" ]; then
|
||||
echo "Places Dataset directory path was not provided"
|
||||
display_usage
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ "$TARBALL" == "" ]; then
|
||||
file=$(ls ../../../../output/mindspore-lite-*-runtime-${TARGET}-cpu-train.tar.gz)
|
||||
if [ -f ${file} ]; then
|
||||
TARBALL=${file}
|
||||
else
|
||||
echo "release.tar.gz was not found"
|
||||
display_usage
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
# Prepare the model
|
||||
cd model/ || exit 1
|
||||
rm -f *.ms
|
||||
./prepare_model.sh $DOCKER || exit 1
|
||||
cd ../
|
||||
|
||||
# Copy the .ms model to the package folder
|
||||
|
||||
PACKAGE=package-${TARGET}
|
||||
|
||||
rm -rf ${PACKAGE}
|
||||
mkdir -p ${PACKAGE}/model
|
||||
cp model/*.ms ${PACKAGE}/model
|
||||
|
||||
# Copy the running script to the package
|
||||
cp scripts/*.sh ${PACKAGE}/
|
||||
|
||||
# Copy the shared MindSpore ToD library
|
||||
tar -xzf ${TARBALL} --wildcards --no-anchored libmindspore-lite.so
|
||||
tar -xzf ${TARBALL} --wildcards --no-anchored include
|
||||
mv mindspore-*/lib ${PACKAGE}/
|
||||
rm -rf msl
|
||||
mkdir msl
|
||||
mv mindspore-*/* msl/
|
||||
rm -rf mindspore-*
|
||||
|
||||
# Convert the dataset into the package
|
||||
./prepare_dataset.sh ${PLACES_DATA_PATH}
|
||||
cp -r dataset ${PACKAGE}
|
||||
|
||||
echo "==========Compiling============"
|
||||
make TARGET=${TARGET}
|
||||
|
||||
# Copy the executable to the package
|
||||
mv bin ${PACKAGE}/ || exit 1
|
||||
|
||||
if [ "${TARGET}" == "arm64" ]; then
|
||||
echo "=======Pushing to device======="
|
||||
adb push ${PACKAGE} /data/local/tmp/
|
||||
|
||||
echo "==Evaluating Untrained Model==="
|
||||
adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh eval_untrained.sh"
|
||||
|
||||
echo "========Training on Device====="
|
||||
adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh train.sh"
|
||||
|
||||
echo
|
||||
echo "===Evaluating trained Model====="
|
||||
adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh eval.sh"
|
||||
echo
|
||||
else
|
||||
cd ${PACKAGE} || exit 1
|
||||
echo "==Evaluating Untrained Model==="
|
||||
./eval_untrained.sh
|
||||
|
||||
echo "======Training Locally========="
|
||||
./train.sh
|
||||
|
||||
echo "===Evaluating trained Model====="
|
||||
./eval.sh
|
||||
cd ..
|
||||
fi
|
||||
|
||||
@ -0,0 +1,19 @@
|
||||
#!/bin/bash
|
||||
|
||||
echo "=======Preparing Dataset======="
|
||||
[ -d "dataset" ] && echo "dataset was already created" && exit
|
||||
echo "Preparing dataset"
|
||||
PLACES_DATA_PATH=$1
|
||||
class_id=0
|
||||
classes=("4" "98" "6" "7" "10" "15" "17" "70" "26" "30")
|
||||
for class in "${classes[@]}"; do
|
||||
mkdir -p dataset/$class_id
|
||||
i=0
|
||||
cat scripts/places365_val.txt | grep -w ${class} | awk '{print $1}' | while read line
|
||||
do
|
||||
echo converting ${PLACES_DATA_PATH}/val_256/$line to bmp
|
||||
convert -colorspace RGB -gravity center -crop '224x224+0+0' ${PLACES_DATA_PATH}/val_256/$line dataset/$class_id/$i.bmp;
|
||||
i=$(($i+1));
|
||||
done
|
||||
class_id=$(($class_id+1))
|
||||
done
|
||||
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
LD_LIBRARY_PATH=./lib/ bin/net_runner -f model/transfer_learning_tod_trained.ms -e 0 -d dataset
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue