train_infer_python.txt

===========================train_params===========================
model_name:Pix2pix
python:python3.7
gpu_list:0
##
auto_cast:null
epochs:lite_train_lite_infer=10|lite_train_whole_infer=10|whole_train_whole_infer=200
output_dir:./output/
dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
pretrained_model:null
train_model_name:pix2pix_facades*/*checkpoint.pdparams
train_infer_img_dir:./data/facades/test
null:null
##
trainer:norm_train
norm_train:tools/main.py -c configs/pix2pix_facades.yaml --seed 123 -o dataset.train.num_workers=0 log_config.interval=1
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params=========================== 
eval:null
null:null
##
===========================infer_params===========================
--output_dir:./output/
load:null
norm_export:tools/export_model.py -c configs/pix2pix_facades.yaml --inputs_size="-1,3,-1,-1"  --model_name inference --load 
quant_export:null 
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:inference
train_model:./inference/pix2pix_facade/pix2pixmodel_netG
infer_export:null
infer_quant:False
inference:tools/inference.py --model_type pix2pix --seed 123 -c configs/pix2pix_facades.yaml --output_path test_tipc/output/
--device:cpu
null:null
null:null
null:null
null:null
null:null
--model_path:
null:null
null:null
--benchmark:True
null:null
===========================train_benchmark_params==========================
batch_size:1
fp_items:fp32
epoch:10
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,256,256]}]