delete launch
@@ -102,14 +102,11 @@ for train_model in ${train_model_list[*]}; do
for gpu in ${gpu_list[*]}; do
use_gpu=True
if [ ${gpu} = "-1" ];then
- lanuch=""
use_gpu=False
env=""
elif [ ${#gpu} -le 1 ];then
- launch=""
env="CUDA_VISIBLE_DEVICES=${gpu}"
else
- launch="-m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu}"
IFS=","
array=(${gpu})
env="CUDA_VISIBLE_DEVICES=${array[0]}"
@@ -139,8 +136,13 @@ for train_model in ${train_model_list[*]}; do
pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
fi
save_log="${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}"
- command="${python} ${launch} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
- ${python} ${launch} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
+ if [ ${#gpu} -le 2 ];then
+ command="${python} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
+ ${python} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
+ else
+ command="${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
+ ${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
+ fi
status_check $? "${trainer}" "${command}" "${status_log}"
command="${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest Global.save_inference_dir=${save_log}_infer/ Global.save_model_dir=${save_log}"
