forked from huawei/mindspore2022
In code examples, reduced training in CI to single epoch
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7a537f4cfc
commit
a2a7ded639
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@ -2,7 +2,7 @@
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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] [-q] [-o] [-b virtual_batch] [-m mindir]\n"
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echo -e "\nUsage: prepare_and_run.sh -D dataset_path [-d mindspore_docker] [-r release.tar.gz] [-t arm64|x86] [-q] [-o] [-b virtual_batch] [-m mindir] [-e epochs_to_train]\n"
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}
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checkopts()
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@ -14,7 +14,8 @@ checkopts()
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QUANTIZE=""
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FP16_FLAG=""
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VIRTUAL_BATCH=-1
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while getopts 'D:b:d:m:oqr:t:' opt
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EPOCHS="-e 5"
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while getopts 'D:b:d:e:m:oqr:t:' opt
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do
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case "${opt}" in
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b)
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@ -26,6 +27,9 @@ checkopts()
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d)
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DOCKER=$OPTARG
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;;
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e)
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EPOCHS="-e $OPTARG"
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;;
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m)
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MINDIR_FILE=$OPTARG
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;;
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@ -138,7 +142,7 @@ if [ "${TARGET}" == "arm64" ]; then
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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 ${FP16_FLAG} -b ${VIRTUAL_BATCH}"
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adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh train.sh ${EPOCHS} ${FP16_FLAG} -b ${VIRTUAL_BATCH}"
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echo
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echo "===Evaluating trained Model====="
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@ -147,7 +151,7 @@ if [ "${TARGET}" == "arm64" ]; then
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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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./train.sh ${EPOCHS}
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echo "===Evaluating trained Model====="
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./eval.sh
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@ -15,4 +15,4 @@
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# ============================================================================
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# an simple tutorial as follows, more parameters can be setting
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LD_LIBRARY_PATH=./lib/ bin/net_runner -f model/lenet_tod.ms -e 5 -d dataset $1 $2 $3
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LD_LIBRARY_PATH=./lib/ bin/net_runner -f model/lenet_tod.ms -d dataset "$@"
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@ -2,7 +2,7 @@
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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] [-o] [-b virtual_batch] [-m mindir]\n"
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echo -e "\nUsage: prepare_and_run.sh -D dataset_path [-d mindspore_docker] [-r release.tar.gz] [-t arm64|x86] [-o] [-b virtual_batch] [-m mindir] [-e epochs_to_train]\n"
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}
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checkopts()
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@ -13,7 +13,8 @@ checkopts()
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ENABLEFP16=""
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VIRTUAL_BATCH=-1
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MINDIR_FILE=""
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while getopts 'D:d:m:r:t:ob:' opt
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EPOCHS="-e 5"
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while getopts 'D:d:e:m:r:t:ob:' opt
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do
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case "${opt}" in
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D)
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@ -22,6 +23,9 @@ checkopts()
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d)
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DOCKER=$OPTARG
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;;
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e)
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EPOCHS="-e $OPTARG"
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;;
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m)
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MINDIR_FILE=$OPTARG
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;;
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@ -135,7 +139,7 @@ if [ "${TARGET}" == "arm64" ]; then
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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 ${ENABLEFP16} -b ${VIRTUAL_BATCH}"
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adb shell "cd /data/local/tmp/package-arm64 && /system/bin/sh train.sh ${EPOCHS} ${ENABLEFP16} -b ${VIRTUAL_BATCH}"
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echo
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echo "===Evaluating trained Model====="
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@ -148,7 +152,7 @@ if [ "${TARGET}" == "arm64" ]; then
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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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./train.sh ${EPOCHS}
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echo "===Evaluating trained Model====="
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./eval.sh
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@ -15,4 +15,4 @@
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# ============================================================================
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# an simple tutorial as follows, more parameters can be setting
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LD_LIBRARY_PATH=./lib/ bin/net_runner -f model/lenet_tod.ms -e 5 -d dataset $1 $2 $3
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LD_LIBRARY_PATH=./lib/ bin/net_runner -f model/lenet_tod.ms -d dataset "$@"
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@ -375,8 +375,8 @@ function Run_CodeExamples() {
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cd ${basepath}/../../examples/unified_api || exit 1
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chmod 777 ./prepare_and_run.sh
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chmod 777 ./*/*.sh
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./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir >> ${run_code_examples_log_file}
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accurate=$(tail -20 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.9) }')
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./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir -e 1 >> ${run_code_examples_log_file}
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accurate=$(tail -20 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.6) }')
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if [ $accurate -eq 1 ]; then
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echo "Unified API Trained and reached accuracy" >> ${run_code_examples_log_file}
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echo 'code_examples: unified_api pass' >> ${run_benchmark_train_result_file}
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@ -391,8 +391,8 @@ function Run_CodeExamples() {
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cd ${basepath}/../../examples/train_lenet || exit 1
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chmod 777 ./prepare_and_run.sh
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chmod 777 ./*/*.sh
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./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir >> ${run_code_examples_log_file}
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accurate=$(tail -10 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.9) }')
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./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir -e 1 >> ${run_code_examples_log_file}
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accurate=$(tail -10 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.6) }')
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if [ $accurate -eq 1 ]; then
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echo "Lenet Trained and reached accuracy" >> ${run_code_examples_log_file}
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echo 'code_examples: train_lenet pass' >> ${run_benchmark_train_result_file}
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