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eb827bb3
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体验新版 GitCode,发现更多精彩内容 >>
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eb827bb3
编写于
6月 08, 2021
作者:
L
LDOUBLEV
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add test shell
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test/params.txt
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test/test.sh
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test/params.txt
0 → 100644
浏览文件 @
eb827bb3
train_model_list: det;benchmark/benchmark_det.yml
gpu_list: -1|0|0,1
auto_cast_list: False|True
trainer_list: norm|quant|prune
python: python3.7
inference: python|C++
devices: cpu|gpu
use_mkldnn_list: True|False
cpu_threads_list: 1|6
rec_batch_size_list: 1|6
gpu_trt_list: True|False
gpu_precision_list: fp32|fp16|int8
img_dir: /paddle/OCR/test_set/benchmark_eval
epoch: 10
checkpoints: None
test/test.sh
0 → 100644
浏览文件 @
eb827bb3
#!/bin/bash
FILENAME
=
$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE
=
$2
# prepare pretrained weights and dataset
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
if
[
${
MODE
}
=
"lite_train_infer"
]
;
then
# pretrain lite train data
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
cd
./train_data/
&&
tar
xf icdar2015_lite.tar
&&
ln
-s
./icdar2015_lite ./icdar2015
cd
../
elif
[
${
MODE
}
=
"whole_train_infer"
]
;
then
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
cd
./train_data/
&&
tar
xf icdar2015.tar
&&
cd
../
else
echo
"Do Nothing"
fi
dataline
=
$(
cat
${
FILENAME
}
)
# parser params
IFS
=
$'
\n
'
lines
=(
${
dataline
}
)
function
func_parser
(){
strs
=
$1
IFS
=
":"
array
=(
${
strs
}
)
tmp
=
${
array
[1]
}
echo
${
tmp
}
}
IFS
=
$'
\n
'
# The training params
train_model_list
=
$(
func_parser
"
${
lines
[0]
}
"
)
gpu_list
=
$(
func_parser
"
${
lines
[1]
}
"
)
auto_cast_list
=
$(
func_parser
"
${
lines
[2]
}
"
)
slim_trainer_list
=
$(
func_parser
"
${
lines
[3]
}
"
)
python
=
$(
func_parser
"
${
lines
[4]
}
"
)
# inference params
inference
=
$(
func_parser
"
${
lines
[5]
}
"
)
devices
=
$(
func_parser
"
${
lines
[6]
}
"
)
use_mkldnn_list
=
$(
func_parser
"
${
lines
[7]
}
"
)
cpu_threads_list
=
$(
func_parser
"
${
lines
[8]
}
"
)
rec_batch_size_list
=
$(
func_parser
"
${
lines
[9]
}
"
)
gpu_trt_list
=
$(
func_parser
"
${
lines
[10]
}
"
)
gpu_precision_list
=
$(
func_parser
"
${
lines
[11]
}
"
)
img_dir
=
$(
func_parser
"
${
lines
[12]
}
"
)
# train superparameters
epoch
=
$(
func_parser
"
${
lines
[13]
}
"
)
checkpoints
=
$(
func_parser
"
${
lines
[14]
}
"
)
for
train_model
in
${
train_model_list
[*]
}
;
do
if
[
${
train_model
}
=
"det"
]
;
then
model_name
=
"det"
yml_file
=
"configs/det/det_mv3_db.yml"
elif
[
${
train_model
}
=
"rec"
]
;
then
model_name
=
"rec"
yml_file
=
"configs/rec/rec_mv3_none_bilstm_ctc.yml"
else
model_name
=
"det"
yml_file
=
"configs/det/det_mv3_db.yml"
fi
# array=(${train_model})
# for j in "${!array[@]}"; do
# model_name=${array[0]}
# yml_file=${array[1]}
# done
IFS
=
"|"
for
gpu
in
${
gpu_list
[*]
}
;
do
use_gpu
=
True
if
[
${
gpu
}
=
"-1"
]
;
then
lanuch
=
""
use_gpu
=
False
elif
[
${#
gpu
}
-le
1
]
;
then
launch
=
""
else
launch
=
"-m paddle.distributed.launch --log_dir=./debug/ --gpus
${
gpu
}
"
fi
# echo "model_name: ${model_name} yml_file: ${yml_file} launch: ${launch} gpu: ${gpu}"
for
auto_cast
in
${
auto_cast_list
[*]
}
;
do
for
slim_trainer
in
${
slim_trainer_list
[*]
}
;
do
if
[
${
slim_trainer
}
=
"norm"
]
;
then
trainer
=
"tools/train.py"
export_model
=
"tools/export_model.py"
elif
[
${
slim_trainer
}
=
"quant"
]
;
then
trainer
=
"deploy/slim/quantization/quant.py"
export_model
=
"deploy/slim/quantization/export_model.py"
elif
[
${
slim_trainer
}
=
"prune"
]
;
then
trainer
=
"deploy/slim/prune/sensitivity_anal.py"
export_model
=
"deploy/slim/prune/export_prune_model.py"
elif
[
${
slim_trainer
}
=
"distill"
]
;
then
trainer
=
"deploy/slim/distill/train_dml.py"
export_model
=
"deploy/slim/distill/export_distill_model.py"
else
trainer
=
"tools/train.py"
export_model
=
"tools/export_model.py"
fi
# dataset="Train.dataset.data_dir=${train_dir} Train.dataset.label_file_list=${train_label_file} Eval.dataset.data_dir=${eval_dir} Eval.dataset.label_file_list=${eval_label_file}"
save_log
=
${
log_path
}
/
${
model_name
}
_
${
slim_trainer
}
_autocast_
${
auto_cast
}
_gpuid_
${
gpu
}
echo
${
python
}
${
launch
}
${
trainer
}
-c
${
yml_file
}
-o
Global.auto_cast
=
${
auto_cast
}
Global.save_model_dir
=
${
save_log
}
Global.use_gpu
=
${
use_gpu
}
Global.epoch
=
${
epoch
}
echo
${
python
}
${
export_model
}
-c
${
yml_file
}
-o
Global.pretrained_model
=
${
save_log
}
/best_accuracy Global.save_inference_dir
=
${
save_log
}
/export_inference/
if
[
"
${
model_name
}
"
=
"det"
]
;
then
export
rec_batch_size_list
=(
"1"
)
inference
=
"tools/infer/predict_det.py"
elif
[
"
${
model_name
}
"
=
"rec"
]
;
then
inference
=
"tools/infer/predict_rec.py"
fi
# inference
for
device
in
${
devices
[*]
}
;
do
if
[
${
device
}
=
"cpu"
]
;
then
for
use_mkldnn
in
${
use_mkldnn_list
[*]
}
;
do
for
threads
in
${
cpu_threads_list
[*]
}
;
do
for
rec_batch_size
in
${
rec_batch_size_list
[*]
}
;
do
echo
${
python
}
${
inference
}
--enable_mkldnn
=
${
use_mkldnn
}
--use_gpu
=
False
--cpu_threads
=
${
threads
}
--benchmark
=
True
--det_model_dir
=
${
save_log
}
/export_inference/
--rec_batch_num
=
${
rec_batch_size
}
--rec_model_dir
=
${
rec_model_dir
}
--image_dir
=
${
img_dir
}
--save_log_path
=
${
log_path
}
/
${
model_name
}
_
${
slim_trainer
}
_cpu_usemkldnn_
${
use_mkldnn
}
_cputhreads_
${
threads
}
_recbatchnum_
${
rec_batch_size
}
_infer.log
# ${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${save_log}/export_inference/ --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} 2>&1 | tee ${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
done
done
done
else
for
use_trt
in
${
gpu_trt_list
[*]
}
;
do
for
precision
in
${
gpu_precision_list
[*]
}
;
do
if
[
${
use_trt
}
=
"False"
]
&&
[
${
precision
}
!=
"fp32"
]
;
then
continue
fi
for
rec_batch_size
in
${
rec_batch_size_list
[*]
}
;
do
# echo "${model_name} ${det_model_dir} ${rec_model_dir}, use_trt: ${use_trt} use_fp16: ${use_fp16}"
echo
${
python
}
${
inference
}
--use_gpu
=
True
--use_tensorrt
=
${
use_trt
}
--precision
=
${
precision
}
--benchmark
=
True
--det_model_dir
=
${
save_log
}
/export_inference/
--rec_batch_num
=
${
rec_batch_size
}
--rec_model_dir
=
${
rec_model_dir
}
--image_dir
=
${
img_dir
}
--save_log_path
=
${
log_path
}
/
${
model_name
}
_
${
slim_trainer
}
_gpu_usetensorrt_
${
use_trt
}
_usefp16_
${
precision
}
_recbatchnum_
${
rec_batch_size
}
_infer.log
done
done
done
fi
done
done
done
done
done
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