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a5262935
编写于
11月 25, 2021
作者:
L
LDOUBLEV
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add sast
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5 changed file
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347 addition
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1 deletion
+347
-1
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml
...et_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml
+111
-0
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt
...nfigs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt
+51
-0
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltextyml
...t_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltextyml
+108
-0
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt
...igs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt
+51
-0
test_tipc/prepare.sh
test_tipc/prepare.sh
+26
-1
未找到文件。
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml
0 → 100644
浏览文件 @
a5262935
Global
:
use_gpu
:
true
epoch_num
:
5000
log_smooth_window
:
20
print_batch_step
:
2
save_model_dir
:
./output/sast_r50_vd_ic15/
save_epoch_step
:
1000
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step
:
[
4000
,
5000
]
cal_metric_during_train
:
False
pretrained_model
:
./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints
:
save_inference_dir
:
use_visualdl
:
False
infer_img
:
save_res_path
:
./output/sast_r50_vd_ic15/predicts_sast.txt
Architecture
:
model_type
:
det
algorithm
:
SAST
Transform
:
Backbone
:
name
:
ResNet_SAST
layers
:
50
Neck
:
name
:
SASTFPN
with_cab
:
True
Head
:
name
:
SASTHead
Loss
:
name
:
SASTLoss
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
# name: Cosine
learning_rate
:
0.001
# warmup_epoch: 0
regularizer
:
name
:
'
L2'
factor
:
0
PostProcess
:
name
:
SASTPostProcess
score_thresh
:
0.5
sample_pts_num
:
2
nms_thresh
:
0.2
expand_scale
:
1.0
shrink_ratio_of_width
:
0.3
Metric
:
name
:
DetMetric
main_indicator
:
hmean
Train
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data/icdar2015/text_localization/
label_file_list
:
-
./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list
:
[
0.1
,
0.45
,
0.3
,
0.15
]
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
channel_first
:
False
-
DetLabelEncode
:
# Class handling label
-
SASTProcessTrain
:
image_shape
:
[
512
,
512
]
min_crop_side_ratio
:
0.3
min_crop_size
:
24
min_text_size
:
4
max_text_size
:
512
-
KeepKeys
:
keep_keys
:
[
'
image'
,
'
score_map'
,
'
border_map'
,
'
training_mask'
,
'
tvo_map'
,
'
tco_map'
]
# dataloader will return list in this order
loader
:
shuffle
:
True
drop_last
:
False
batch_size_per_card
:
4
num_workers
:
4
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data/icdar2015/text_localization/
label_file_list
:
-
./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
channel_first
:
False
-
DetLabelEncode
:
# Class handling label
-
DetResizeForTest
:
resize_long
:
1536
-
NormalizeImage
:
scale
:
1./255.
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
hwc'
-
ToCHWImage
:
-
KeepKeys
:
keep_keys
:
[
'
image'
,
'
shape'
,
'
polys'
,
'
ignore_tags'
]
loader
:
shuffle
:
False
drop_last
:
False
batch_size_per_card
:
1
# must be 1
num_workers
:
2
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt
0 → 100644
浏览文件 @
a5262935
===========================train_params===========================
model_name:sast_icdar15
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
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/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/det_r50_vd_sast_icdar15_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltextyml
0 → 100644
浏览文件 @
a5262935
Global:
use_gpu: true
epoch_num: 5000
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/sast_r50_vd_tt/
save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
save_res_path: ./output/sast_r50_vd_tt/predicts_sast.txt
Architecture:
model_type: det
algorithm: SAST
Transform:
Backbone:
name: ResNet_SAST
layers: 50
Neck:
name: SASTFPN
with_cab: True
Head:
name: SASTHead
Loss:
name: SASTLoss
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
# name: Cosine
learning_rate: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: SASTPostProcess
score_thresh: 0.5
sample_pts_num: 6
nms_thresh: 0.2
expand_scale: 1.2
shrink_ratio_of_width: 0.2
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/total_text/train
label_file_list: [./train_data/total_text/train/train.txt]
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- SASTProcessTrain:
image_shape: [512, 512]
min_crop_side_ratio: 0.3
min_crop_size: 24
min_text_size: 4
max_text_size: 512
- KeepKeys:
keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size_per_card: 4
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list:
- ./train_data/total_text/test/test.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
resize_long: 768
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
\ No newline at end of file
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt
0 → 100644
浏览文件 @
a5262935
===========================train_params===========================
model_name:sast_icdar15
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
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/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/det_r50_vd_sast_icdar15_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
test_tipc/prepare.sh
浏览文件 @
a5262935
...
...
@@ -47,6 +47,12 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
cd
./pretrain_models/
&&
tar
xf en_server_pgnetA.tar
&&
cd
../
cd
./train_data
&&
tar
xf total_text_lite.tar
&&
ln
-s
total_text
&&
cd
../
fi
if
[
${
model_name
}
==
"sast_icdar15"
]
||
[
${
model_name
}
==
"sast_totaltext"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
--no-check-certificate
wget
-nc
-P
./train_data/ wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar
--no-check-certificate
cd
./train_data
&&
tar
xf total_text_lite.tar
&&
ln
-s
total_text
&&
cd
../
fi
elif
[
${
MODE
}
=
"whole_train_whole_infer"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
--no-check-certificate
rm
-rf
./train_data/icdar2015
...
...
@@ -58,6 +64,17 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
wget
-nc
-P
./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar
--no-check-certificate
cd
./pretrain_models/
&&
tar
xf ch_PP-OCRv2_det_distill_train.tar
&&
cd
../
fi
if
[
${
model_name
}
==
"en_pgnetA"
]
;
then
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar
--no-check-certificate
wget
-nc
-P
./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar
--no-check-certificate
cd
./pretrain_models/
&&
tar
xf en_server_pgnetA.tar
&&
cd
../
cd
./train_data
&&
tar
xf total_text.tar
&&
ln
-s
total_text
&&
cd
../
fi
if
[
${
model_name
}
==
"sast_totaltext"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
--no-check-certificate
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar
--no-check-certificate
cd
./train_data
&&
tar
xf total_text.tar
&&
ln
-s
total_text
&&
cd
../
fi
elif
[
${
MODE
}
=
"lite_train_whole_infer"
]
;
then
wget
-nc
-P
./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
--no-check-certificate
rm
-rf
./train_data/icdar2015
...
...
@@ -72,6 +89,7 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
cd
./pretrain_models/
&&
tar
xf ch_PP-OCRv2_det_distill_train.tar
&&
cd
../
fi
elif
[
${
MODE
}
=
"whole_infer"
]
;
then
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
--no-check-certificate
if
[
${
model_name
}
=
"ocr_det"
]
;
then
eval_model_name
=
"ch_ppocr_mobile_v2.0_det_train"
rm
-rf
./train_data/icdar2015
...
...
@@ -106,7 +124,6 @@ elif [ ${MODE} = "whole_infer" ];then
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar
--no-check-certificate
cd
./inference
&&
tar
xf
${
eval_model_name
}
.tar
&&
tar
xf rec_inference.tar
&&
cd
../
fi
elif
[
${
model_name
}
=
"PPOCRv2_ocr_det"
]
;
then
eval_model_name
=
"ch_PP-OCRv2_det_infer"
wget
-nc
-P
./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
--no-check-certificate
...
...
@@ -118,6 +135,14 @@ elif [ ${MODE} = "whole_infer" ];then
wget
-nc
-P
./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar
--no-check-certificate
cd
./inference
&&
tar
xf e2e_server_pgnetA_infer.tar
&&
tar
xf ch_det_data_50.tar
&&
cd
../
fi
if
[
${
model_name
}
==
"en_pgnetA"
]
;
then
wget
-nc
-P
./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar
--no-check-certificate
cd
./inference
&&
tar
xf en_server_pgnetA.tar
&&
cd
../
fi
if
[
${
model_name
}
==
"sast_icdar15"
]
;
then
wget
-nc
-P
./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar
--no-check-certificate
cd
./inference/
&&
tar
det_r50_vd_sast_icdar15_v2.0_train.tar
&&
cd
../
fi
if
[
${
MODE
}
=
"klquant_whole_infer"
]
;
then
if
[
${
model_name
}
=
"ocr_det"
]
;
then
...
...
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