Double_V
GitHub
commit
66a76f68cf
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@ -113,7 +113,7 @@ def main():
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use_srn = config['Architecture']['algorithm'] == "SRN"
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model_type = config['Architecture']['model_type']
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# start eval
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metirc = program.eval(model, valid_dataloader, post_process_class,
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metric = program.eval(model, valid_dataloader, post_process_class,
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eval_class, model_type, use_srn)
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logger.info('metric eval ***************')
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@ -1,13 +1,12 @@
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model_name:ocr_det
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python:python3.7
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gpu_list:0|0,1
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Global.auto_cast:False
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Global.auto_cast:null
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Global.epoch_num:10
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Global.save_model_dir:./output/
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Global.save_inference_dir:./output/
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Train.loader.batch_size_per_card:
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Global.use_gpu
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Global.pretrained_model
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Global.use_gpu:
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Global.pretrained_model:null
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trainer:norm|pact
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norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
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@ -17,6 +16,8 @@ distill_train:null
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eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
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Global.save_inference_dir:./output/
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Global.checkpoints:
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norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
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quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
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fpgm_export:deploy/slim/prune/export_prune_model.py
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@ -29,7 +30,6 @@ inference:tools/infer/predict_det.py
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--rec_batch_num:1
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--use_tensorrt:True|False
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--precision:fp32|fp16|int8
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--det_model_dir
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--image_dir
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--save_log_path
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--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
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--image_dir:./inference/ch_det_data_50/all-sum-510/
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--save_log_path:./test/output/
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@ -26,8 +26,10 @@ IFS=$'\n'
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# The training params
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model_name=$(func_parser_value "${lines[0]}")
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train_model_list=$(func_parser_value "${lines[0]}")
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trainer_list=$(func_parser_value "${lines[10]}")
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
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MODE=$2
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# prepare pretrained weights and dataset
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@ -62,8 +64,8 @@ else
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
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@ -94,7 +96,7 @@ for train_model in ${train_model_list[*]}; do
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# eval
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for slim_trainer in ${trainer_list[*]}; do
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if [ ${slim_trainer} = "norm" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -104,7 +106,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "pact" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -114,7 +116,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "distill" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -124,7 +126,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "fpgm" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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138
test/test.sh
138
test/test.sh
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@ -41,59 +41,51 @@ gpu_list=$(func_parser_value "${lines[2]}")
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autocast_list=$(func_parser_value "${lines[3]}")
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autocast_key=$(func_parser_key "${lines[3]}")
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epoch_key=$(func_parser_key "${lines[4]}")
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epoch_num=$(func_parser_value "${lines[4]}")
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save_model_key=$(func_parser_key "${lines[5]}")
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save_infer_key=$(func_parser_key "${lines[6]}")
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train_batch_key=$(func_parser_key "${lines[7]}")
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train_use_gpu_key=$(func_parser_key "${lines[8]}")
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pretrain_model_key=$(func_parser_key "${lines[9]}")
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train_batch_key=$(func_parser_key "${lines[6]}")
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train_use_gpu_key=$(func_parser_key "${lines[7]}")
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pretrain_model_key=$(func_parser_key "${lines[8]}")
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pretrain_model_value=$(func_parser_value "${lines[8]}")
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trainer_list=$(func_parser_value "${lines[10]}")
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norm_trainer=$(func_parser_value "${lines[11]}")
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pact_trainer=$(func_parser_value "${lines[12]}")
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fpgm_trainer=$(func_parser_value "${lines[13]}")
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distill_trainer=$(func_parser_value "${lines[14]}")
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trainer_list=$(func_parser_value "${lines[9]}")
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norm_trainer=$(func_parser_value "${lines[10]}")
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pact_trainer=$(func_parser_value "${lines[11]}")
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fpgm_trainer=$(func_parser_value "${lines[12]}")
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distill_trainer=$(func_parser_value "${lines[13]}")
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eval_py=$(func_parser_value "${lines[15]}")
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norm_export=$(func_parser_value "${lines[16]}")
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pact_export=$(func_parser_value "${lines[17]}")
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fpgm_export=$(func_parser_value "${lines[18]}")
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distill_export=$(func_parser_value "${lines[19]}")
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eval_py=$(func_parser_value "${lines[14]}")
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inference_py=$(func_parser_value "${lines[20]}")
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use_gpu_key=$(func_parser_key "${lines[21]}")
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use_gpu_list=$(func_parser_value "${lines[21]}")
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use_mkldnn_key=$(func_parser_key "${lines[22]}")
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use_mkldnn_list=$(func_parser_value "${lines[22]}")
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cpu_threads_key=$(func_parser_key "${lines[23]}")
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cpu_threads_list=$(func_parser_value "${lines[23]}")
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batch_size_key=$(func_parser_key "${lines[24]}")
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batch_size_list=$(func_parser_value "${lines[24]}")
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use_trt_key=$(func_parser_key "${lines[25]}")
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use_trt_list=$(func_parser_value "${lines[25]}")
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precision_key=$(func_parser_key "${lines[26]}")
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precision_list=$(func_parser_value "${lines[26]}")
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model_dir_key=$(func_parser_key "${lines[27]}")
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image_dir_key=$(func_parser_key "${lines[28]}")
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save_log_key=$(func_parser_key "${lines[29]}")
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save_infer_key=$(func_parser_key "${lines[15]}")
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export_weight=$(func_parser_key "${lines[16]}")
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norm_export=$(func_parser_value "${lines[17]}")
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pact_export=$(func_parser_value "${lines[18]}")
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fpgm_export=$(func_parser_value "${lines[19]}")
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distill_export=$(func_parser_value "${lines[20]}")
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inference_py=$(func_parser_value "${lines[21]}")
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use_gpu_key=$(func_parser_key "${lines[22]}")
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use_gpu_list=$(func_parser_value "${lines[22]}")
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use_mkldnn_key=$(func_parser_key "${lines[23]}")
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use_mkldnn_list=$(func_parser_value "${lines[23]}")
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cpu_threads_key=$(func_parser_key "${lines[24]}")
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cpu_threads_list=$(func_parser_value "${lines[24]}")
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batch_size_key=$(func_parser_key "${lines[25]}")
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batch_size_list=$(func_parser_value "${lines[25]}")
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use_trt_key=$(func_parser_key "${lines[26]}")
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use_trt_list=$(func_parser_value "${lines[26]}")
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precision_key=$(func_parser_key "${lines[27]}")
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precision_list=$(func_parser_value "${lines[27]}")
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infer_model_key=$(func_parser_key "${lines[28]}")
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infer_model=$(func_parser_value "${lines[28]}")
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image_dir_key=$(func_parser_key "${lines[29]}")
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infer_img_dir=$(func_parser_value "${lines[29]}")
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save_log_key=$(func_parser_key "${lines[30]}")
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LOG_PATH="./test/output"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results.log"
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if [ ${MODE} = "lite_train_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=10
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elif [ ${MODE} = "whole_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=10
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elif [ ${MODE} = "whole_train_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=300
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else
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export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
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export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
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fi
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function func_inference(){
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IFS='|'
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@ -110,7 +102,7 @@ function func_inference(){
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for threads in ${cpu_threads_list[*]}; do
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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@ -124,7 +116,7 @@ function func_inference(){
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fi
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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@ -138,9 +130,9 @@ if [ ${MODE} != "infer" ]; then
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IFS="|"
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for gpu in ${gpu_list[*]}; do
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train_use_gpu=True
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use_gpu=True
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if [ ${gpu} = "-1" ];then
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train_use_gpu=False
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use_gpu=False
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env=""
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elif [ ${#gpu} -le 1 ];then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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@ -155,6 +147,7 @@ for gpu in ${gpu_list[*]}; do
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ips=${array[0]}
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gpu=${array[1]}
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IFS="|"
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env=" "
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fi
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for autocast in ${autocast_list[*]}; do
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for trainer in ${trainer_list[*]}; do
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@ -179,13 +172,32 @@ for gpu in ${gpu_list[*]}; do
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continue
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fi
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save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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if [ ${#gpu} -le 2 ];then # epoch_num #TODO
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cmd="${python} ${run_train} ${train_use_gpu_key}=${train_use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
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elif [ ${#gpu} -le 15 ];then
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cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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# not set autocast when autocast is null
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if [ ${autocast} = "null" ]; then
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set_autocast=" "
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else
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cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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set_autocast="${autocast_key}=${autocast}"
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fi
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# not set epoch when whole_train_infer
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if [ ${MODE} != "whole_train_infer" ]; then
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set_epoch="${epoch_key}=${epoch_num}"
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else
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set_epoch=" "
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fi
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# set pretrain
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if [ ${pretrain_model_value} != "null" ]; then
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set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
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else
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set_pretrain=" "
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fi
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save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
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cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
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elif [ ${#gpu} -le 15 ];then # train with multi-gpu
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cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
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else # train with multi-machine
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cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
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fi
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# run train
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eval $cmd
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@ -198,11 +210,12 @@ for gpu in ${gpu_list[*]}; do
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# run export model
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save_infer_path="${save_log}"
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export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
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export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
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eval $export_cmd
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status_check $? "${export_cmd}" "${status_log}"
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#run inference
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echo $env
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save_infer_path="${save_log}"
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func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
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done
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@ -210,12 +223,13 @@ for gpu in ${gpu_list[*]}; do
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done
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else
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save_infer_path="${LOG_PATH}/${MODE}"
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run_export=${norm_export}
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export_cmd="${python} ${run_export} ${save_model_key}=${save_infer_path} ${pretrain_model_key}=${infer_model_dir} ${save_infer_key}=${save_infer_path}"
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eval $export_cmd
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status_check $? "${export_cmd}" "${status_log}"
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GPUID=$3
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if [ ${#GPUID} -le 0 ];then
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env=" "
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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echo $env
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#run inference
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func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
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func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
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fi
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@ -37,7 +37,7 @@ def init_args():
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--min_subgraph_size", type=int, default=3)
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parser.add_argument("--min_subgraph_size", type=int, default=10)
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parser.add_argument("--precision", type=str, default="fp32")
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parser.add_argument("--gpu_mem", type=int, default=500)
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