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体验新版 GitCode,发现更多精彩内容 >>
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a821320d
编写于
8月 05, 2021
作者:
T
tink2123
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差异文件
update ocr_rec
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5487ab7a
变更
7
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Showing
7 changed file
with
74 addition
and
430 deletion
+74
-430
configs/rec/rec_icdar15_train.yml
configs/rec/rec_icdar15_train.yml
+5
-5
test/ocr_det_params.txt
test/ocr_det_params.txt
+0
-35
test/prepare.sh
test/prepare.sh
+0
-146
test/test.sh
test/test.sh
+0
-237
tests/ocr_det_params.txt
tests/ocr_det_params.txt
+1
-0
tests/ocr_rec_params.txt
tests/ocr_rec_params.txt
+51
-0
tests/prepare.sh
tests/prepare.sh
+17
-7
未找到文件。
configs/rec/rec_icdar15_train.yml
浏览文件 @
a821320d
...
...
@@ -10,7 +10,7 @@ Global:
cal_metric_during_train
:
True
pretrained_model
:
checkpoints
:
save_inference_dir
:
save_inference_dir
:
./
use_visualdl
:
False
infer_img
:
doc/imgs_words_en/word_10.png
# for data or label process
...
...
@@ -60,8 +60,8 @@ Metric:
Train
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data/
label_file_list
:
[
"
./train_data/
train_list
.txt"
]
data_dir
:
./train_data/
ic15_data/
label_file_list
:
[
"
./train_data/
ic15_data/rec_gt_train
.txt"
]
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
...
...
@@ -81,8 +81,8 @@ Train:
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data/
label_file_list
:
[
"
./train_data/
val_li
st.txt"
]
data_dir
:
./train_data/
ic15_data
label_file_list
:
[
"
./train_data/
ic15_data/rec_gt_te
st.txt"
]
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
...
...
test/ocr_det_params.txt
已删除
100644 → 0
浏览文件 @
5487ab7a
model_name:ocr_det
python:python3.7
gpu_list:0|0,1
Global.auto_cast:null
Global.epoch_num:10
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:
Global.use_gpu:
Global.pretrained_model:null
trainer:norm|pact
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
fpgm_train:null
distill_train:null
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py
distill_export:null
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:./test/output/
test/prepare.sh
已删除
100644 → 0
浏览文件 @
5487ab7a
#!/bin/bash
FILENAME
=
$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE
=
$2
dataline
=
$(
cat
${
FILENAME
}
)
# parser params
IFS
=
$'
\n
'
lines
=(
${
dataline
}
)
function
func_parser_key
(){
strs
=
$1
IFS
=
":"
array
=(
${
strs
}
)
tmp
=
${
array
[0]
}
echo
${
tmp
}
}
function
func_parser_value
(){
strs
=
$1
IFS
=
":"
array
=(
${
strs
}
)
tmp
=
${
array
[1]
}
echo
${
tmp
}
}
IFS
=
$'
\n
'
# The training params
model_name
=
$(
func_parser_value
"
${
lines
[0]
}
"
)
train_model_list
=
$(
func_parser_value
"
${
lines
[0]
}
"
)
trainer_list
=
$(
func_parser_value
"
${
lines
[10]
}
"
)
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE
=
$2
# prepare pretrained weights and dataset
if
[
${
train_model_list
[*]
}
=
"ocr_det"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
wget
-nc
-P
./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
cd
pretrain_models
&&
tar
xf det_mv3_db_v2.0_train.tar
&&
cd
../
fi
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
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
# todo change to bcebos
cd
./train_data/
&&
tar
xf icdar2015_lite.tar
&&
tar
xf ic15_data.tar
ln
-s
./icdar2015_lite ./icdar2015
cd
../
epoch
=
10
eval_batch_step
=
10
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
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd
./train_data/
&&
tar
xf icdar2015.tar
&&
tar
xf ic15_data.tar
&&
cd
../
epoch
=
500
eval_batch_step
=
200
elif
[
${
MODE
}
=
"whole_infer"
]
;
then
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd
./train_data/
&&
tar
xf icdar2015_infer.tar
&&
tar
xf ic15_data.tar
ln
-s
./icdar2015_infer ./icdar2015
cd
../
epoch
=
10
eval_batch_step
=
10
else
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
if
[
${
model_name
}
=
"ocr_det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_infer"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
else
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
fi
fi
IFS
=
'|'
for
train_model
in
${
train_model_list
[*]
}
;
do
if
[
${
train_model
}
=
"ocr_det"
]
;
then
model_name
=
"ocr_det"
yml_file
=
"configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
cd
./inference
&&
tar
xf ch_det_data_50.tar
&&
cd
../
img_dir
=
"./inference/ch_det_data_50/all-sum-510"
data_dir
=
./inference/ch_det_data_50/
data_label_file
=[
./inference/ch_det_data_50/test_gt_50.txt]
elif
[
${
train_model
}
=
"ocr_rec"
]
;
then
model_name
=
"ocr_rec"
yml_file
=
"configs/rec/rec_mv3_none_bilstm_ctc.yml"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
cd
./inference
&&
tar
xf rec_inference.tar
&&
cd
../
img_dir
=
"./inference/rec_inference/"
data_dir
=
./inference/rec_inference
data_label_file
=[
./inference/rec_inference/rec_gt_test.txt]
fi
# eval
for
slim_trainer
in
${
trainer_list
[*]
}
;
do
if
[
${
slim_trainer
}
=
"norm"
]
;
then
if
[
${
model_name
}
=
"det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
else
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
fi
elif
[
${
slim_trainer
}
=
"pact"
]
;
then
if
[
${
model_name
}
=
"det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_quant_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
else
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_quant_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_quant_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
fi
elif
[
${
slim_trainer
}
=
"distill"
]
;
then
if
[
${
model_name
}
=
"det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_distill_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
else
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_distill_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_distill_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
fi
elif
[
${
slim_trainer
}
=
"fpgm"
]
;
then
if
[
${
model_name
}
=
"det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_prune_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
else
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_prune_train"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_prune_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
fi
fi
done
done
test/test.sh
已删除
100644 → 0
浏览文件 @
5487ab7a
#!/bin/bash
FILENAME
=
$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
MODE
=
$2
dataline
=
$(
cat
${
FILENAME
}
)
# parser params
IFS
=
$'
\n
'
lines
=(
${
dataline
}
)
function
func_parser_key
(){
strs
=
$1
IFS
=
":"
array
=(
${
strs
}
)
tmp
=
${
array
[0]
}
echo
${
tmp
}
}
function
func_parser_value
(){
strs
=
$1
IFS
=
":"
array
=(
${
strs
}
)
tmp
=
${
array
[1]
}
echo
${
tmp
}
}
function
status_check
(){
last_status
=
$1
# the exit code
run_command
=
$2
run_log
=
$3
if
[
$last_status
-eq
0
]
;
then
echo
-e
"
\0
33[33m Run successfully with command -
${
run_command
}
!
\0
33[0m"
|
tee
-a
${
run_log
}
else
echo
-e
"
\0
33[33m Run failed with command -
${
run_command
}
!
\0
33[0m"
|
tee
-a
${
run_log
}
fi
}
IFS
=
$'
\n
'
# The training params
model_name
=
$(
func_parser_value
"
${
lines
[0]
}
"
)
python
=
$(
func_parser_value
"
${
lines
[1]
}
"
)
gpu_list
=
$(
func_parser_value
"
${
lines
[2]
}
"
)
autocast_list
=
$(
func_parser_value
"
${
lines
[3]
}
"
)
autocast_key
=
$(
func_parser_key
"
${
lines
[3]
}
"
)
epoch_key
=
$(
func_parser_key
"
${
lines
[4]
}
"
)
epoch_num
=
$(
func_parser_value
"
${
lines
[4]
}
"
)
save_model_key
=
$(
func_parser_key
"
${
lines
[5]
}
"
)
train_batch_key
=
$(
func_parser_key
"
${
lines
[6]
}
"
)
train_use_gpu_key
=
$(
func_parser_key
"
${
lines
[7]
}
"
)
pretrain_model_key
=
$(
func_parser_key
"
${
lines
[8]
}
"
)
pretrain_model_value
=
$(
func_parser_value
"
${
lines
[8]
}
"
)
trainer_list
=
$(
func_parser_value
"
${
lines
[9]
}
"
)
norm_trainer
=
$(
func_parser_value
"
${
lines
[10]
}
"
)
pact_trainer
=
$(
func_parser_value
"
${
lines
[11]
}
"
)
fpgm_trainer
=
$(
func_parser_value
"
${
lines
[12]
}
"
)
distill_trainer
=
$(
func_parser_value
"
${
lines
[13]
}
"
)
eval_py
=
$(
func_parser_value
"
${
lines
[14]
}
"
)
save_infer_key
=
$(
func_parser_key
"
${
lines
[15]
}
"
)
export_weight
=
$(
func_parser_key
"
${
lines
[16]
}
"
)
norm_export
=
$(
func_parser_value
"
${
lines
[17]
}
"
)
pact_export
=
$(
func_parser_value
"
${
lines
[18]
}
"
)
fpgm_export
=
$(
func_parser_value
"
${
lines
[19]
}
"
)
distill_export
=
$(
func_parser_value
"
${
lines
[20]
}
"
)
inference_py
=
$(
func_parser_value
"
${
lines
[21]
}
"
)
use_gpu_key
=
$(
func_parser_key
"
${
lines
[22]
}
"
)
use_gpu_list
=
$(
func_parser_value
"
${
lines
[22]
}
"
)
use_mkldnn_key
=
$(
func_parser_key
"
${
lines
[23]
}
"
)
use_mkldnn_list
=
$(
func_parser_value
"
${
lines
[23]
}
"
)
cpu_threads_key
=
$(
func_parser_key
"
${
lines
[24]
}
"
)
cpu_threads_list
=
$(
func_parser_value
"
${
lines
[24]
}
"
)
batch_size_key
=
$(
func_parser_key
"
${
lines
[25]
}
"
)
batch_size_list
=
$(
func_parser_value
"
${
lines
[25]
}
"
)
use_trt_key
=
$(
func_parser_key
"
${
lines
[26]
}
"
)
use_trt_list
=
$(
func_parser_value
"
${
lines
[26]
}
"
)
precision_key
=
$(
func_parser_key
"
${
lines
[27]
}
"
)
precision_list
=
$(
func_parser_value
"
${
lines
[27]
}
"
)
infer_model_key
=
$(
func_parser_key
"
${
lines
[28]
}
"
)
infer_model
=
$(
func_parser_value
"
${
lines
[28]
}
"
)
image_dir_key
=
$(
func_parser_key
"
${
lines
[29]
}
"
)
infer_img_dir
=
$(
func_parser_value
"
${
lines
[29]
}
"
)
save_log_key
=
$(
func_parser_key
"
${
lines
[30]
}
"
)
LOG_PATH
=
"./test/output"
mkdir
-p
${
LOG_PATH
}
status_log
=
"
${
LOG_PATH
}
/results.log"
function
func_inference
(){
IFS
=
'|'
_python
=
$1
_script
=
$2
_model_dir
=
$3
_log_path
=
$4
_img_dir
=
$5
# inference
for
use_gpu
in
${
use_gpu_list
[*]
}
;
do
if
[
${
use_gpu
}
=
"False"
]
;
then
for
use_mkldnn
in
${
use_mkldnn_list
[*]
}
;
do
for
threads
in
${
cpu_threads_list
[*]
}
;
do
for
batch_size
in
${
batch_size_list
[*]
}
;
do
_save_log_path
=
"
${
_log_path
}
/infer_cpu_usemkldnn_
${
use_mkldnn
}
_threads_
${
threads
}
_batchsize_
${
batch_size
}
.log"
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"
eval
$command
status_check
$?
"
${
command
}
"
"
${
status_log
}
"
done
done
done
else
for
use_trt
in
${
use_trt_list
[*]
}
;
do
for
precision
in
${
precision_list
[*]
}
;
do
if
[
${
use_trt
}
=
"False"
]
&&
[
${
precision
}
!=
"fp32"
]
;
then
continue
fi
for
batch_size
in
${
batch_size_list
[*]
}
;
do
_save_log_path
=
"
${
_log_path
}
/infer_gpu_usetrt_
${
use_trt
}
_precision_
${
precision
}
_batchsize_
${
batch_size
}
.log"
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"
eval
$command
status_check
$?
"
${
command
}
"
"
${
status_log
}
"
done
done
done
fi
done
}
if
[
${
MODE
}
!=
"infer"
]
;
then
IFS
=
"|"
for
gpu
in
${
gpu_list
[*]
}
;
do
use_gpu
=
True
if
[
${
gpu
}
=
"-1"
]
;
then
use_gpu
=
False
env
=
""
elif
[
${#
gpu
}
-le
1
]
;
then
env
=
"export CUDA_VISIBLE_DEVICES=
${
gpu
}
"
eval
${
env
}
elif
[
${#
gpu
}
-le
15
]
;
then
IFS
=
","
array
=(
${
gpu
}
)
env
=
"export CUDA_VISIBLE_DEVICES=
${
array
[0]
}
"
IFS
=
"|"
else
IFS
=
";"
array
=(
${
gpu
}
)
ips
=
${
array
[0]
}
gpu
=
${
array
[1]
}
IFS
=
"|"
env
=
" "
fi
for
autocast
in
${
autocast_list
[*]
}
;
do
for
trainer
in
${
trainer_list
[*]
}
;
do
if
[
${
trainer
}
=
"pact"
]
;
then
run_train
=
${
pact_trainer
}
run_export
=
${
pact_export
}
elif
[
${
trainer
}
=
"fpgm"
]
;
then
run_train
=
${
fpgm_trainer
}
run_export
=
${
fpgm_export
}
elif
[
${
trainer
}
=
"distill"
]
;
then
run_train
=
${
distill_trainer
}
run_export
=
${
distill_export
}
else
run_train
=
${
norm_trainer
}
run_export
=
${
norm_export
}
fi
if
[
${
run_train
}
=
"null"
]
;
then
continue
fi
if
[
${
run_export
}
=
"null"
]
;
then
continue
fi
# not set autocast when autocast is null
if
[
${
autocast
}
=
"null"
]
;
then
set_autocast
=
" "
else
set_autocast
=
"
${
autocast_key
}
=
${
autocast
}
"
fi
# not set epoch when whole_train_infer
if
[
${
MODE
}
!=
"whole_train_infer"
]
;
then
set_epoch
=
"
${
epoch_key
}
=
${
epoch_num
}
"
else
set_epoch
=
" "
fi
# set pretrain
if
[
${
pretrain_model_value
}
!=
"null"
]
;
then
set_pretrain
=
"
${
pretrain_model_key
}
=
${
pretrain_model_value
}
"
else
set_pretrain
=
" "
fi
save_log
=
"
${
LOG_PATH
}
/
${
trainer
}
_gpus_
${
gpu
}
_autocast_
${
autocast
}
"
if
[
${#
gpu
}
-le
2
]
;
then
# train with cpu or single gpu
cmd
=
"
${
python
}
${
run_train
}
${
train_use_gpu_key
}
=
${
use_gpu
}
${
save_model_key
}
=
${
save_log
}
${
set_epoch
}
${
set_pretrain
}
${
set_autocast
}
"
elif
[
${#
gpu
}
-le
15
]
;
then
# train with multi-gpu
cmd
=
"
${
python
}
-m paddle.distributed.launch --gpus=
${
gpu
}
${
run_train
}
${
save_model_key
}
=
${
save_log
}
${
set_epoch
}
${
set_pretrain
}
${
set_autocast
}
"
else
# train with multi-machine
cmd
=
"
${
python
}
-m paddle.distributed.launch --ips=
${
ips
}
--gpus=
${
gpu
}
${
run_train
}
${
save_model_key
}
=
${
save_log
}
${
set_pretrain
}
${
set_epoch
}
${
set_autocast
}
"
fi
# run train
eval
$cmd
status_check
$?
"
${
cmd
}
"
"
${
status_log
}
"
# run eval
eval_cmd
=
"
${
python
}
${
eval_py
}
${
save_model_key
}
=
${
save_log
}
${
pretrain_model_key
}
=
${
save_log
}
/latest"
eval
$eval_cmd
status_check
$?
"
${
eval_cmd
}
"
"
${
status_log
}
"
# run export model
save_infer_path
=
"
${
save_log
}
"
export_cmd
=
"
${
python
}
${
run_export
}
${
save_model_key
}
=
${
save_log
}
${
export_weight
}
=
${
save_log
}
/latest
${
save_infer_key
}
=
${
save_infer_path
}
"
eval
$export_cmd
status_check
$?
"
${
export_cmd
}
"
"
${
status_log
}
"
#run inference
eval
$env
save_infer_path
=
"
${
save_log
}
"
func_inference
"
${
python
}
"
"
${
inference_py
}
"
"
${
save_infer_path
}
"
"
${
LOG_PATH
}
"
"
${
infer_img_dir
}
"
eval
"unset CUDA_VISIBLE_DEVICES"
done
done
done
else
GPUID
=
$3
if
[
${#
GPUID
}
-le
0
]
;
then
env
=
" "
else
env
=
"export CUDA_VISIBLE_DEVICES=
${
GPUID
}
"
fi
echo
$env
#run inference
func_inference
"
${
python
}
"
"
${
inference_py
}
"
"
${
infer_model
}
"
"
${
LOG_PATH
}
"
"
${
infer_img_dir
}
"
fi
tests/ocr_det_params.txt
浏览文件 @
a821320d
...
...
@@ -49,3 +49,4 @@ inference:tools/infer/predict_det.py
--save_log_path:null
--benchmark:True
null:null
test/ocr_rec_params.txt
→
test
s
/ocr_rec_params.txt
浏览文件 @
a821320d
===========================train_params===========================
model_name:ocr_rec
python:python
gpu_list:0|0,1
python:python3.7
gpu_list:0|2,3
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:
1
0
Global.epoch_num:
lite_train_infer=2|whole_train_infer=30
0
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:
Global.use_gpu:
Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
Global.pretrained_model:null
trainer:norm|pact
norm_train:tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
quant_train:deploy/slim/quantization/quant.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
train_model_name:latest
train_infer_img_dir:./train_data/ic15_data/train
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
fpgm_train:null
distill_train:null
eval:tools/eval.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/rec/rec_
mv3_none_bilstm_ctc
.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_
mv3_none_bilstm_ctc
.yml -o
norm_export:tools/export_model.py -c configs/rec/rec_
icdar15_train
.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_
icdar15_train
.yml -o
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
...
...
@@ -30,6 +44,8 @@ inference:tools/infer/predict_rec.py
--rec_batch_num:1
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--rec_model_dir:
./inference/ch_ppocr_mobile_v2.0_rec_infer/
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
tests/prepare.sh
浏览文件 @
a821320d
...
...
@@ -35,32 +35,42 @@ if [ ${MODE} = "lite_train_infer" ];then
# pretrain lite train data
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm
-rf
./train_data/icdar2015
rm
-rf
./train_data/ic15_data
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
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
# todo change to bcebos
cd
./train_data/
&&
tar
xf icdar2015_lite.tar
&&
tar
xf ic15_data.tar
ln
-s
./icdar2015_lite ./icdar2015
cd
../
elif
[
${
MODE
}
=
"whole_train_infer"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm
-rf
./train_data/icdar2015
rm
-rf
./train_data/ic15_data
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
cd
./train_data/
&&
tar
xf icdar2015.tar
&&
cd
../
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd
./train_data/
&&
tar
xf icdar2015.tar
&&
tar
xf ic15_data.tar
&&
cd
../
elif
[
${
MODE
}
=
"whole_infer"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm
-rf
./train_data/icdar2015
rm
-rf
./train_data/ic15_data
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
cd
./train_data/
&&
tar
xf icdar2015_infer.tar
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd
./train_data/
&&
tar
xf icdar2015_infer.tar
&&
tar
xf ic15_data.tar
ln
-s
./icdar2015_infer ./icdar2015
cd
../
else
rm
-rf
./train_data/icdar2015
if
[[
${
model_name
}
=
"ocr_det"
]]
;
then
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
if
[
${
model_name
}
=
"ocr_det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_infer"
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
tar
xf ch_det_data_50.tar
&&
cd
../
else
rm
-rf
./train_data/ic15_data
eval_model_name
=
"ch_ppocr_mobile_v2.0_rec_train"
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
cd
../
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
tar
xf ic15_data.tar
&&
cd
../
fi
fi
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