train_infer_python.txt

===========================train_params===========================
model_name:det_r18_ct
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/total_text/test/rgb/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/det/det_r18_vd_ct.yml -o Global.print_batch_step=1 Train.loader.shuffle=false
quant_export:null
fpgm_export:null
distill_train:null
null:null
null:null
##
===========================eval_params=========================== 
eval:tools/eval.py -c configs/det/det_r18_vd_ct.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c configs/det/det_r18_vd_ct.yml -o 
quant_export:null 
fpgm_export:null
distill_export:null
export1:null
export2:null
##
train_model:./inference/det_r18_vd_ct/best_accuracy
infer_export:tools/export_model.py -c configs/det/det_r18_vd_ct.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]