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#!/usr/bin/env bash
source test_tipc/common_func.sh
FILENAME=$1
# $MODE be one of {'lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer'}
MODE=$2
dataline=$(awk 'NR>=1{print}' $FILENAME)
# Parse params
IFS=$'\n'
lines=(${dataline})
# Training params
task_name=$(parse_first_value "${lines[1]}")
model_name=$(parse_second_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
gpu_list=$(func_parser_value "${lines[3]}")
train_use_gpu_key=$(func_parser_key "${lines[4]}")
train_use_gpu_value=$(func_parser_value "${lines[4]}")
autocast_list=$(func_parser_value "${lines[5]}")
autocast_key=$(func_parser_key "${lines[5]}")
epoch_key=$(func_parser_key "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}")
save_model_key=$(func_parser_key "${lines[7]}")
train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_params "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_value "${lines[10]}")
train_infer_img_dir=$(parse_first_value "${lines[11]}")
train_infer_img_file_list=$(parse_second_value "${lines[11]}")
train_param_key1=$(func_parser_key "${lines[12]}")
train_param_value1=$(func_parser_value "${lines[12]}")
trainer_list=$(func_parser_value "${lines[14]}")
trainer_norm=$(func_parser_key "${lines[15]}")
norm_trainer=$(func_parser_value "${lines[15]}")
pact_key=$(func_parser_key "${lines[16]}")
pact_trainer=$(func_parser_value "${lines[16]}")
fpgm_key=$(func_parser_key "${lines[17]}")
fpgm_trainer=$(func_parser_value "${lines[17]}")
distill_key=$(func_parser_key "${lines[18]}")
distill_trainer=$(func_parser_value "${lines[18]}")
trainer_key1=$(func_parser_key "${lines[19]}")
trainer_value1=$(func_parser_value "${lines[19]}")
trainer_key2=$(func_parser_key "${lines[20]}")
trainer_value2=$(func_parser_value "${lines[20]}")
eval_py=$(func_parser_value "${lines[23]}")
eval_key1=$(func_parser_key "${lines[24]}")
eval_value1=$(func_parser_value "${lines[24]}")
save_infer_key=$(func_parser_key "${lines[27]}")
export_weight=$(func_parser_key "${lines[28]}")
export_shape_key=$(func_parser_key "${lines[29]}")
export_shape_value=$(func_parser_value "${lines[29]}")
norm_export=$(func_parser_value "${lines[30]}")
pact_export=$(func_parser_value "${lines[31]}")
fpgm_export=$(func_parser_value "${lines[32]}")
distill_export=$(func_parser_value "${lines[33]}")
export_key1=$(func_parser_key "${lines[34]}")
export_value1=$(func_parser_value "${lines[34]}")
export_key2=$(func_parser_key "${lines[35]}")
export_value2=$(func_parser_value "${lines[35]}")
inference_dir=$(func_parser_value "${lines[36]}")
# Params of inference model
infer_model_dir_list=$(func_parser_value "${lines[37]}")
infer_export_list=$(func_parser_value "${lines[38]}")
infer_is_quant=$(func_parser_value "${lines[39]}")
# Inference params
inference_py=$(func_parser_value "${lines[40]}")
use_gpu_key=$(func_parser_key "${lines[41]}")
use_gpu_list=$(func_parser_value "${lines[41]}")
use_mkldnn_key=$(func_parser_key "${lines[42]}")
use_mkldnn_list=$(func_parser_value "${lines[42]}")
cpu_threads_key=$(func_parser_key "${lines[43]}")
cpu_threads_list=$(func_parser_value "${lines[43]}")
batch_size_key=$(func_parser_key "${lines[44]}")
batch_size_list=$(func_parser_value "${lines[44]}")
use_trt_key=$(func_parser_key "${lines[45]}")
use_trt_list=$(func_parser_value "${lines[45]}")
precision_key=$(func_parser_key "${lines[46]}")
precision_list=$(func_parser_value "${lines[46]}")
infer_model_key=$(func_parser_key "${lines[47]}")
file_list_key=$(func_parser_key "${lines[48]}")
infer_img_dir=$(parse_first_value "${lines[48]}")
infer_img_file_list=$(parse_second_value "${lines[48]}")
save_log_key=$(func_parser_key "${lines[49]}")
benchmark_key=$(func_parser_key "${lines[50]}")
benchmark_value=$(func_parser_value "${lines[50]}")
infer_key1=$(func_parser_key "${lines[51]}")
infer_value1=$(func_parser_value "${lines[51]}")
infer_key2=$(func_parser_key "${lines[52]}")
infer_value2=$(func_parser_value "${lines[52]}")
OUT_PATH="./test_tipc/output/${task_name}/${model_name}/${MODE}"
mkdir -p ${OUT_PATH}
status_log="${OUT_PATH}/results_python.log"
echo "------------------------ ${MODE} ------------------------" >> "${status_log}"
# Parse extra args
parse_extra_args "${lines[@]}"
for params in ${extra_args[*]}; do
IFS=':'
arr=(${params})
key=${arr[0]}
value=${arr[1]}
:
done
function func_inference() {
local IFS='|'
local _python=$1
local _script="$2"
local _model_dir="$3"
local _log_path="$4"
local _img_dir="$5"
local _file_list="$6"
# Do inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = 'False' ] || [ ${use_gpu} = 'cpu' ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = 'False' ]; then
continue
fi
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
for precision in ${precision_list[*]}; do
if [ ${use_mkldnn} = 'False' ] && [ ${precision} = 'fp16' ]; then
continue
fi # Skip when enable fp16 but disable mkldnn
set_precision=$(func_set_params "${precision_key}" "${precision}")
_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
infer_value1="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}_results"
set_device=$(func_set_params "${use_gpu_key}" "${use_gpu}")
set_mkldnn=$(func_set_params "${use_mkldnn_key}" "${use_mkldnn}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
set_infer_params2=$(func_set_params "${infer_key2}" "${infer_value2}")
cmd="${_python} ${_script} ${file_list_key} ${_img_dir} ${_file_list} ${set_device} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_benchmark} ${set_precision} ${set_infer_params1} ${set_infer_params2}"
echo ${cmd}
run_command "${cmd}" "${_save_log_path}"
last_status=${PIPESTATUS[0]}
status_check ${last_status} "${cmd}" "${status_log}" "${model_name}"
done
done
done
done
elif [ ${use_gpu} = 'True' ] || [ ${use_gpu} = 'gpu' ]; then
for use_trt in ${use_trt_list[*]}; do
for precision in ${precision_list[*]}; do
if [ ${precision} = 'fp16' ] && [ ${use_trt} = 'False' ]; then
continue
fi # Skip when enable fp16 but disable trt
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
infer_value1="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}_results"
set_device=$(func_set_params "${use_gpu_key}" "${use_gpu}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${precision_key}" "${precision}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
set_infer_params2=$(func_set_params "${infer_key2}" "${infer_value2}")
cmd="${_python} ${_script} ${file_list_key} ${_img_dir} ${_file_list} ${set_device} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_benchmark} ${set_infer_params2}"
echo ${cmd}
run_command "${cmd}" "${_save_log_path}"
last_status=${PIPESTATUS[0]}
status_check $last_status "${cmd}" "${status_log}" "${model_name}"
done
done
done
else
echo "Currently, hardwares other than CPU and GPU are not supported!"
fi
done
}
if [ ${MODE} = 'whole_infer' ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ]; then
env=""
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
if [ ${infer_model_dir_list} == 'null' ]; then
echo -e "\033[33m No inference model is specified! \033[0m"
exit 1
fi
# Set CUDA_VISIBLE_DEVICES
eval ${env}
export count=0
IFS='|'
infer_run_exports=(${infer_export_list})
for infer_model in ${infer_model_dir_list[*]}; do
# Run export
if [ ${infer_run_exports[count]} != 'null' ]; then
save_infer_dir="${infer_model}/static"
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_export_shape=$(func_set_params "${export_shape_key}" "${export_shape_value}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
export_cmd="${python} ${infer_run_exports[count]} ${set_export_weight} ${set_save_infer_key} ${set_export_shape}"
echo ${infer_run_exports[count]}
eval ${export_cmd}
status_export=$?
status_check ${status_export} "${export_cmd}" "${status_log}" "${model_name}"
else
save_infer_dir=${infer_model}
fi
# Run inference
func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${OUT_PATH}" "${infer_img_dir}" "${infer_img_file_list}"
count=$((${count} + 1))
done
else
IFS='|'
export count=0
USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do
train_use_gpu=${USE_GPU_KEY[count]}
count=$((${count} + 1))
ips=""
if [ ${gpu} = '-1' ]; then
env=""
elif [ ${#gpu} -le 1 ]; then
env="export CUDA_VISIBLE_DEVICES=${gpu}"
eval ${env}
elif [ ${#gpu} -le 15 ]; then
IFS=','
array=(${gpu})
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
IFS='|'
else
IFS=';'
array=(${gpu})
ips=${array[0]}
gpu=${array[1]}
IFS='|'
env=""
fi
for autocast in ${autocast_list[*]}; do
if [ ${autocast} = 'amp' ]; then
set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
else
set_amp_config=""
fi
for trainer in ${trainer_list[*]}; do
if [ ${trainer} = ${pact_key} ]; then
run_train=${pact_trainer}
run_export=${pact_export}
elif [ ${trainer} = "${fpgm_key}" ]; then
run_train=${fpgm_trainer}
run_export=${fpgm_export}
elif [ ${trainer} = "${distill_key}" ]; then
run_train=${distill_trainer}
run_export=${distill_export}
elif [ ${trainer} = ${trainer_key1} ]; then
run_train=${trainer_value1}
run_export=${export_value1}
elif [[ ${trainer} = ${trainer_key2} ]]; then
run_train=${trainer_value2}
run_export=${export_value2}
else
run_train=${norm_trainer}
run_export=${norm_export}
fi
if [ ${run_train} = 'null' ]; then
continue
fi
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
# If length of ips >= 15, then it is seen as multi-machine.
# 15 is the min length of ips info for multi-machine: 0.0.0.0,0.0.0.0
if [ ${#ips} -le 15 ]; then
save_dir="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
else
IFS=','
ips_array=(${ips})
IFS='|'
nodes=${#ips_array[@]}
save_dir="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
fi
log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"
# Load pretrained model from norm training if current trainer is pact or fpgm trainer.
if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
set_pretrain="${load_norm_train_model}"
fi
set_save_model=$(func_set_params "${save_model_key}" "${save_dir}")
if [ ${#gpu} -le 2 ]; then # Train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
elif [ ${#ips} -le 15 ]; then # Train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
else # Train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
fi
echo ${cmd}
# Run train
run_command "${cmd}" "${log_path}"
status_check $? "${cmd}" "${status_log}" "${model_name}"
if [[ "${cmd}" == *'paddle.distributed.launch'* ]]; then
cat log/workerlog.0 >> ${log_path}
fi
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_dir}/${train_model_name}/model.pdparams")
# Save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1 ]; then
load_norm_train_model=${set_eval_pretrain}
fi
# Run evaluation
if [ ${eval_py} != 'null' ]; then
log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_eval.log"
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
run_command "${eval_cmd}" "${log_path}"
status_check $? "${eval_cmd}" "${status_log}" "${model_name}"
fi
# Run export model
if [ ${run_export} != 'null' ]; then
log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_export.log"
save_infer_path="${save_dir}/static"
set_export_weight=$(func_set_params "${export_weight}" "${save_dir}/${train_model_name}")
set_export_shape=$(func_set_params "${export_shape_key}" "${export_shape_value}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key} ${set_export_shape}"
run_command "${export_cmd}" "${log_path}"
status_check $? "${export_cmd}" "${status_log}" "${model_name}"
# Run inference
eval ${env}
if [[ ${inference_dir} != 'null' ]] && [[ ${inference_dir} != '##' ]]; then
infer_model_dir="${save_infer_path}/${inference_dir}"
else
infer_model_dir=${save_infer_path}
fi
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${OUT_PATH}" "${train_infer_img_dir}" "${train_infer_img_file_list}"
eval "unset CUDA_VISIBLE_DEVICES"
fi
done # Done with: for trainer in ${trainer_list[*]}; do
done # Done with: for autocast in ${autocast_list[*]}; do
done # Done with: for gpu in ${gpu_list[*]}; do
fi # End if [ ${MODE} = 'infer' ]; then