train_infer_python.txt

===========================train_params===========================
model_name:layoutxlm_ser
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=8|whole_train_whole_infer=8
Architecture.Backbone.checkpoints:null
train_model_name:latest
train_infer_img_dir:ppstructure/docs/vqa/input/zh_val_42.jpg
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/vqa/ser/layoutxlm_xfund_zh.yml -o Global.print_batch_step=1 Global.eval_batch_step=[1000,1000] Train.loader.shuffle=false
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params=========================== 
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Architecture.Backbone.checkpoints:
norm_export:tools/export_model.py -c configs/vqa/ser/layoutxlm_xfund_zh.yml -o 
quant_export:
fpgm_export: 
distill_export:null
export1:null
export2:null
##
infer_model:null
infer_export:null
infer_quant:False
inference:ppstructure/vqa/predict_vqa_token_ser.py  --vqa_algorithm=LayoutXLM  --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--ser_model_dir:
--image_dir:./ppstructure/docs/vqa/input/zh_val_42.jpg
null:null
--benchmark:False
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
===========================train_benchmark_params==========================
batch_size:4
fp_items:fp32|fp16
epoch:3
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98