264 lines
11 KiB
Bash
264 lines
11 KiB
Bash
#!/bin/bash
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source test_tipc/common_func.sh
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FILENAME=$1
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GPUID=$2
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if [[ ! $GPUID ]];then
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GPUID=0
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fi
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dataline=$(awk 'NR==1, NR==19{print}' $FILENAME)
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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# parser cpp inference model
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model_name=$(func_parser_value "${lines[1]}")
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cpp_infer_type=$(func_parser_value "${lines[2]}")
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cpp_infer_model_dir=$(func_parser_value "${lines[3]}")
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cpp_det_infer_model_dir=$(func_parser_value "${lines[4]}")
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cpp_infer_is_quant=$(func_parser_value "${lines[7]}")
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# parser cpp inference
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inference_cmd=$(func_parser_value "${lines[8]}")
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cpp_use_gpu_list=$(func_parser_value "${lines[9]}")
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cpp_use_mkldnn_list=$(func_parser_value "${lines[10]}")
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cpp_cpu_threads_list=$(func_parser_value "${lines[11]}")
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cpp_batch_size_list=$(func_parser_value "${lines[12]}")
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cpp_use_trt_list=$(func_parser_value "${lines[13]}")
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cpp_precision_list=$(func_parser_value "${lines[14]}")
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cpp_image_dir_value=$(func_parser_value "${lines[15]}")
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cpp_benchmark_value=$(func_parser_value "${lines[16]}")
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generate_yaml_cmd=$(func_parser_value "${lines[17]}")
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transform_index_cmd=$(func_parser_value "${lines[18]}")
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LOG_PATH="./test_tipc/output/${model_name}"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results_cpp.log"
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# generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py"
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function func_shitu_cpp_inference(){
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IFS='|'
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_script=$1
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_model_dir=$2
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_log_path=$3
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_img_dir=$4
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_flag_quant=$5
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# inference
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for use_gpu in ${cpp_use_gpu_list[*]}; do
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if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
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for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
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if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for threads in ${cpp_cpu_threads_list[*]}; do
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for batch_size in ${cpp_batch_size_list[*]}; do
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precision="fp32"
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if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
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precison="int8"
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fi
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_save_log_path="${_log_path}/shitu_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
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eval $transform_index_cmd
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command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
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eval $command
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command="${_script} > ${_save_log_path} 2>&1"
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eval $command
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${command}" "${status_log}" "${model_name}"
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done
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done
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done
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elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
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for use_trt in ${cpp_use_trt_list[*]}; do
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for precision in ${cpp_precision_list[*]}; do
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if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
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continue
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fi
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if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
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continue
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fi
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if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for batch_size in ${cpp_batch_size_list[*]}; do
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_save_log_path="${_log_path}/shitu_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
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eval $transform_index_cmd
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command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
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eval $command
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command="${_script} > ${_save_log_path} 2>&1"
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eval $command
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${command}" "${status_log}" "${model_name}"
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done
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done
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done
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else
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echo "Does not support hardware other than CPU and GPU Currently!"
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fi
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done
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}
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function func_cls_cpp_inference(){
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IFS='|'
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_script=$1
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_model_dir=$2
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_log_path=$3
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_img_dir=$4
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_flag_quant=$5
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# inference
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for use_gpu in ${cpp_use_gpu_list[*]}; do
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if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
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for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
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if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for threads in ${cpp_cpu_threads_list[*]}; do
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for batch_size in ${cpp_batch_size_list[*]}; do
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precision="fp32"
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if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
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precison="int8"
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fi
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_save_log_path="${_log_path}/cls_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
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command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
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eval $command
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command1="${_script} > ${_save_log_path} 2>&1"
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eval ${command1}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${command1}" "${status_log}" "${model_name}"
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done
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done
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done
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elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
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for use_trt in ${cpp_use_trt_list[*]}; do
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for precision in ${cpp_precision_list[*]}; do
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if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
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continue
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fi
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if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
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continue
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fi
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if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
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continue
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fi
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for batch_size in ${cpp_batch_size_list[*]}; do
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_save_log_path="${_log_path}/cls_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
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command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
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eval $command
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command="${_script} > ${_save_log_path} 2>&1"
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eval $command
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${command}" "${status_log}" "${model_name}"
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done
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done
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done
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else
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echo "Does not support hardware other than CPU and GPU Currently!"
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fi
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done
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}
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if [[ $cpp_infer_type == "cls" ]]; then
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cd deploy/cpp
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elif [[ $cpp_infer_type == "shitu" ]]; then
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cd deploy/cpp_shitu
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else
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echo "Only support cls and shitu"
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exit 0
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fi
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if [[ $cpp_infer_type == "shitu" ]]; then
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echo "################### update cmake ###################"
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wget -nc https://github.com/Kitware/CMake/releases/download/v3.22.0/cmake-3.22.0.tar.gz
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tar xf cmake-3.22.0.tar.gz
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cd ./cmake-3.22.0
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export root_path=$PWD
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export install_path=${root_path}/cmake
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eval "./bootstrap --prefix=${install_path}"
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make -j
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make install
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export PATH=${install_path}/bin:$PATH
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cd ..
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echo "################### update cmake done ###################"
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echo "################### build faiss ###################"
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apt-get install -y libopenblas-dev
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git clone https://github.com/facebookresearch/faiss.git
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cd faiss
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export faiss_install_path=$PWD/faiss_install
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eval "cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}"
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make -C build -j faiss
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make -C build install
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cd ..
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fi
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echo "################### build PaddleClas demo ####################"
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# pwd = /workspace/hesensen/PaddleClas/deploy/cpp_shitu
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OPENCV_DIR=$(dirname $PWD)/cpp/opencv-3.4.7/opencv3/
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LIB_DIR=$(dirname $PWD)/cpp/paddle_inference/
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CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
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CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
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BUILD_DIR=build
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rm -rf ${BUILD_DIR}
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mkdir ${BUILD_DIR}
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cd ${BUILD_DIR}
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if [[ $cpp_infer_type == cls ]]; then
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cmake .. \
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-DPADDLE_LIB=${LIB_DIR} \
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-DWITH_MKL=ON \
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-DWITH_GPU=ON \
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-DWITH_STATIC_LIB=OFF \
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-DWITH_TENSORRT=OFF \
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-DOPENCV_DIR=${OPENCV_DIR} \
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-DCUDNN_LIB=${CUDNN_LIB_DIR} \
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-DCUDA_LIB=${CUDA_LIB_DIR} \
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-DTENSORRT_DIR=${TENSORRT_DIR}
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else
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cmake ..\
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-DPADDLE_LIB=${LIB_DIR} \
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-DWITH_MKL=ON \
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-DWITH_GPU=ON \
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-DWITH_STATIC_LIB=OFF \
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-DWITH_TENSORRT=OFF \
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-DOPENCV_DIR=${OPENCV_DIR} \
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-DCUDNN_LIB=${CUDNN_LIB_DIR} \
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-DCUDA_LIB=${CUDA_LIB_DIR} \
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-DTENSORRT_DIR=${TENSORRT_DIR} \
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-DFAISS_DIR=${faiss_install_path} \
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-DFAISS_WITH_MKL=OFF
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fi
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make -j
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cd ../../../
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# cd ../../
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echo "################### build PaddleClas demo finished ###################"
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# set cuda device
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# GPUID=$2
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# if [ ${#GPUID} -le 0 ];then
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# env="export CUDA_VISIBLE_DEVICES=0"
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# else
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# env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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# fi
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# set CUDA_VISIBLE_DEVICES
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# eval $env
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echo "################### run test ###################"
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export Count=0
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IFS="|"
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infer_quant_flag=(${cpp_infer_is_quant})
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for infer_model in ${cpp_infer_model_dir[*]}; do
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#run inference
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is_quant=${infer_quant_flag[Count]}
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if [[ $cpp_infer_type == "cls" ]]; then
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func_cls_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
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else
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func_shitu_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
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fi
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Count=$(($Count + 1))
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done
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