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PaddleOCR
提交 c270500a 编写于 作者: L LDOUBLEV
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delete launch

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test/test.sh
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...@@ -102,14 +102,11 @@ for train_model in ${train_model_list[*]}; do ...@@ -102,14 +102,11 @@ for train_model in ${train_model_list[*]}; do
for gpu in ${gpu_list[*]}; do for gpu in ${gpu_list[*]}; do
use_gpu=True use_gpu=True
if [ ${gpu} = "-1" ];then if [ ${gpu} = "-1" ];then
lanuch=""
use_gpu=False use_gpu=False
env="" env=""
elif [ ${#gpu} -le 1 ];then elif [ ${#gpu} -le 1 ];then
launch=""
env="CUDA_VISIBLE_DEVICES=${gpu}" env="CUDA_VISIBLE_DEVICES=${gpu}"
else else
launch="-m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu}"
IFS="," IFS=","
array=(${gpu}) array=(${gpu})
env="CUDA_VISIBLE_DEVICES=${array[0]}" env="CUDA_VISIBLE_DEVICES=${array[0]}"
...@@ -139,8 +136,13 @@ for train_model in ${train_model_list[*]}; do ...@@ -139,8 +136,13 @@ for train_model in ${train_model_list[*]}; do
pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained" pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
fi fi
save_log="${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}" 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" if [ ${#gpu} -le 2 ];then
${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=${use_gpu} Train.loader.batch_size_per_card=2 command="${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}" 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}" 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}"
......
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