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体验新版 GitCode,发现更多精彩内容 >>
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5f2f08a0
编写于
12月 09, 2020
作者:
L
LDOUBLEV
浏览文件
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浏览文件
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电子邮件补丁
差异文件
add ppocr_v2 ch_db
上级
a948584c
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
313 addition
and
14 deletion
+313
-14
configs/det/ch_det_mv3_db.yml
configs/det/ch_det_mv3_db.yml
+134
-0
configs/det/ch_det_res18_db.yml
configs/det/ch_det_res18_db.yml
+133
-0
ppocr/data/imaug/operators.py
ppocr/data/imaug/operators.py
+2
-0
ppocr/data/simple_dataset.py
ppocr/data/simple_dataset.py
+23
-5
ppocr/modeling/backbones/det_mobilenet_v3.py
ppocr/modeling/backbones/det_mobilenet_v3.py
+12
-4
ppocr/postprocess/db_postprocess.py
ppocr/postprocess/db_postprocess.py
+7
-3
ppocr/utils/save_load.py
ppocr/utils/save_load.py
+2
-2
未找到文件。
configs/det/ch_det_mv3_db.yml
0 → 100644
浏览文件 @
5f2f08a0
Global
:
use_gpu
:
true
epoch_num
:
1200
log_smooth_window
:
20
print_batch_step
:
2
save_model_dir
:
./output/ch_db_mv3/
save_epoch_step
:
1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step
:
[
3000
,
2000
]
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights
:
True
cal_metric_during_train
:
False
pretrained_model
:
./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints
:
#./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
save_inference_dir
:
use_visualdl
:
False
infer_img
:
doc/imgs_en/img_10.jpg
save_res_path
:
./output/det_db/predicts_db.txt
Architecture
:
model_type
:
det
algorithm
:
DB
Transform
:
Backbone
:
name
:
MobileNetV3
scale
:
0.5
model_name
:
large
disable_se
:
True
Neck
:
name
:
DBFPN
out_channels
:
96
Head
:
name
:
DBHead
k
:
50
Loss
:
name
:
DBLoss
balance_loss
:
true
main_loss_type
:
DiceLoss
alpha
:
5
beta
:
10
ohem_ratio
:
3
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
2
regularizer
:
name
:
'
L2'
factor
:
0
PostProcess
:
name
:
DBPostProcess
thresh
:
0.3
box_thresh
:
0.6
max_candidates
:
1000
unclip_ratio
:
1.5
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
:
[
1.0
]
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
channel_first
:
False
-
DetLabelEncode
:
# Class handling label
-
IaaAugment
:
augmenter_args
:
-
{
'
type'
:
Fliplr
,
'
args'
:
{
'
p'
:
0.5
}
}
-
{
'
type'
:
Affine
,
'
args'
:
{
'
rotate'
:
[
-10
,
10
]
}
}
-
{
'
type'
:
Resize
,
'
args'
:
{
'
size'
:
[
0.5
,
3
]
}
}
-
EastRandomCropData
:
size
:
[
960
,
960
]
max_tries
:
50
keep_ratio
:
true
-
MakeBorderMap
:
shrink_ratio
:
0.4
thresh_min
:
0.3
thresh_max
:
0.7
-
MakeShrinkMap
:
shrink_ratio
:
0.4
min_text_size
:
8
-
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'
,
'
threshold_map'
,
'
threshold_mask'
,
'
shrink_map'
,
'
shrink_mask'
]
# the order of the dataloader list
loader
:
shuffle
:
True
drop_last
:
False
batch_size_per_card
:
8
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
:
# image_shape: [736, 1280]
-
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
configs/det/ch_det_res18_db.yml
0 → 100644
浏览文件 @
5f2f08a0
Global
:
use_gpu
:
true
epoch_num
:
1200
log_smooth_window
:
20
print_batch_step
:
2
save_model_dir
:
./output/ch_db_res18/
save_epoch_step
:
1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step
:
[
3000
,
2000
]
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights
:
True
cal_metric_during_train
:
False
pretrained_model
:
./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints
:
#./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
save_inference_dir
:
use_visualdl
:
False
infer_img
:
doc/imgs_en/img_10.jpg
save_res_path
:
./output/det_db/predicts_db.txt
Architecture
:
model_type
:
det
algorithm
:
DB
Transform
:
Backbone
:
name
:
ResNet
layers
:
18
disable_se
:
True
Neck
:
name
:
DBFPN
out_channels
:
256
Head
:
name
:
DBHead
k
:
50
Loss
:
name
:
DBLoss
balance_loss
:
true
main_loss_type
:
DiceLoss
alpha
:
5
beta
:
10
ohem_ratio
:
3
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
2
regularizer
:
name
:
'
L2'
factor
:
0
PostProcess
:
name
:
DBPostProcess
thresh
:
0.3
box_thresh
:
0.6
max_candidates
:
1000
unclip_ratio
:
1.5
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
:
[
1.0
]
transforms
:
-
DecodeImage
:
# load image
img_mode
:
BGR
channel_first
:
False
-
DetLabelEncode
:
# Class handling label
-
IaaAugment
:
augmenter_args
:
-
{
'
type'
:
Fliplr
,
'
args'
:
{
'
p'
:
0.5
}
}
-
{
'
type'
:
Affine
,
'
args'
:
{
'
rotate'
:
[
-10
,
10
]
}
}
-
{
'
type'
:
Resize
,
'
args'
:
{
'
size'
:
[
0.5
,
3
]
}
}
-
EastRandomCropData
:
size
:
[
960
,
960
]
max_tries
:
50
keep_ratio
:
true
-
MakeBorderMap
:
shrink_ratio
:
0.4
thresh_min
:
0.3
thresh_max
:
0.7
-
MakeShrinkMap
:
shrink_ratio
:
0.4
min_text_size
:
8
-
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'
,
'
threshold_map'
,
'
threshold_mask'
,
'
shrink_map'
,
'
shrink_mask'
]
# the order of the dataloader list
loader
:
shuffle
:
True
drop_last
:
False
batch_size_per_card
:
8
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
:
# image_shape: [736, 1280]
-
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
ppocr/data/imaug/operators.py
浏览文件 @
5f2f08a0
...
...
@@ -42,6 +42,8 @@ class DecodeImage(object):
img
)
>
0
,
"invalid input 'img' in DecodeImage"
img
=
np
.
frombuffer
(
img
,
dtype
=
'uint8'
)
img
=
cv2
.
imdecode
(
img
,
1
)
if
img
is
None
:
return
None
if
self
.
img_mode
==
'GRAY'
:
img
=
cv2
.
cvtColor
(
img
,
cv2
.
COLOR_GRAY2BGR
)
elif
self
.
img_mode
==
'RGB'
:
...
...
ppocr/data/simple_dataset.py
浏览文件 @
5f2f08a0
...
...
@@ -27,7 +27,10 @@ class SimpleDataSet(Dataset):
global_config
=
config
[
'Global'
]
dataset_config
=
config
[
mode
][
'dataset'
]
loader_config
=
config
[
mode
][
'loader'
]
batch_size
=
loader_config
[
'batch_size_per_card'
]
if
'data_num_per_epoch'
in
loader_config
.
keys
():
data_num_per_epoch
=
loader_config
[
'data_num_per_epoch'
]
else
:
data_num_per_epoch
=
None
self
.
delimiter
=
dataset_config
.
get
(
'delimiter'
,
'
\t
'
)
label_file_list
=
dataset_config
.
pop
(
'label_file_list'
)
...
...
@@ -43,21 +46,34 @@ class SimpleDataSet(Dataset):
self
.
do_shuffle
=
loader_config
[
'shuffle'
]
logger
.
info
(
"Initialize indexs of datasets:%s"
%
label_file_list
)
self
.
data_lines
=
self
.
get_image_info_list
(
label_file_list
,
ratio_list
)
self
.
data_lines
=
self
.
get_image_info_list
(
label_file_list
,
ratio_list
,
data_num_per_epoch
)
self
.
data_idx_order_list
=
list
(
range
(
len
(
self
.
data_lines
)))
if
mode
.
lower
()
==
"train"
:
self
.
shuffle_data_random
()
self
.
ops
=
create_operators
(
dataset_config
[
'transforms'
],
global_config
)
def
get_image_info_list
(
self
,
file_list
,
ratio_list
):
def
_sample_dataset
(
self
,
datas
,
sample_ratio
,
data_num_per_epoch
=
None
):
sample_num
=
round
(
len
(
datas
)
*
sample_ratio
)
if
data_num_per_epoch
is
not
None
:
sample_num
=
data_num_per_epoch
*
sample_ratio
nums
,
rem
=
sample_num
//
len
(
datas
),
sample_num
%
len
(
datas
)
return
list
(
datas
)
*
nums
+
random
.
sample
(
datas
,
rem
)
def
get_image_info_list
(
self
,
file_list
,
ratio_list
,
data_num_per_epoch
=
None
):
if
isinstance
(
file_list
,
str
):
file_list
=
[
file_list
]
data_lines
=
[]
for
idx
,
file
in
enumerate
(
file_list
):
with
open
(
file
,
"rb"
)
as
f
:
lines
=
f
.
readlines
()
lines
=
random
.
sample
(
lines
,
round
(
len
(
lines
)
*
ratio_list
[
idx
])
)
lines
=
self
.
_sample_dataset
(
lines
,
ratio_list
[
idx
]
,
data_num_per_epoch
)
data_lines
.
extend
(
lines
)
return
data_lines
...
...
@@ -76,6 +92,8 @@ class SimpleDataSet(Dataset):
label
=
substr
[
1
]
img_path
=
os
.
path
.
join
(
self
.
data_dir
,
file_name
)
data
=
{
'img_path'
:
img_path
,
'label'
:
label
}
if
not
os
.
path
.
exists
(
img_path
):
raise
Exception
(
"{} does not exist!"
.
format
(
img_path
))
with
open
(
data
[
'img_path'
],
'rb'
)
as
f
:
img
=
f
.
read
()
data
[
'image'
]
=
img
...
...
ppocr/modeling/backbones/det_mobilenet_v3.py
浏览文件 @
5f2f08a0
...
...
@@ -34,13 +34,21 @@ def make_divisible(v, divisor=8, min_value=None):
class
MobileNetV3
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
=
3
,
model_name
=
'large'
,
scale
=
0.5
,
**
kwargs
):
def
__init__
(
self
,
in_channels
=
3
,
model_name
=
'large'
,
scale
=
0.5
,
disable_se
=
False
,
**
kwargs
):
"""
the MobilenetV3 backbone network for detection module.
Args:
params(dict): the super parameters for build network
"""
super
(
MobileNetV3
,
self
).
__init__
()
self
.
disable_se
=
disable_se
if
model_name
==
"large"
:
cfg
=
[
# k, exp, c, se, nl, s,
...
...
@@ -223,7 +231,7 @@ class ResidualUnit(nn.Layer):
if_act
=
True
,
act
=
act
,
name
=
name
+
"_depthwise"
)
if
self
.
if_se
:
if
self
.
if_se
and
not
self
.
disable_se
:
self
.
mid_se
=
SEModule
(
mid_channels
,
name
=
name
+
"_se"
)
self
.
linear_conv
=
ConvBNLayer
(
in_channels
=
mid_channels
,
...
...
@@ -238,7 +246,7 @@ class ResidualUnit(nn.Layer):
def
forward
(
self
,
inputs
):
x
=
self
.
expand_conv
(
inputs
)
x
=
self
.
bottleneck_conv
(
x
)
if
self
.
if_se
:
if
self
.
if_se
and
not
self
.
disable_se
:
x
=
self
.
mid_se
(
x
)
x
=
self
.
linear_conv
(
x
)
if
self
.
if_shortcut
:
...
...
@@ -273,4 +281,4 @@ class SEModule(nn.Layer):
outputs
=
F
.
relu
(
outputs
)
outputs
=
self
.
conv2
(
outputs
)
outputs
=
F
.
activation
.
hard_sigmoid
(
outputs
)
return
inputs
*
outputs
\ No newline at end of file
return
inputs
*
outputs
ppocr/postprocess/db_postprocess.py
浏览文件 @
5f2f08a0
...
...
@@ -39,6 +39,7 @@ class DBPostProcess(object):
self
.
max_candidates
=
max_candidates
self
.
unclip_ratio
=
unclip_ratio
self
.
min_size
=
3
self
.
dilation_kernel
=
np
.
array
([[
1
,
1
],
[
1
,
1
]])
def
boxes_from_bitmap
(
self
,
pred
,
_bitmap
,
dest_width
,
dest_height
):
'''
...
...
@@ -139,8 +140,11 @@ class DBPostProcess(object):
boxes_batch
=
[]
for
batch_index
in
range
(
pred
.
shape
[
0
]):
height
,
width
=
shape_list
[
batch_index
]
boxes
,
scores
=
self
.
boxes_from_bitmap
(
pred
[
batch_index
],
segmentation
[
batch_index
],
width
,
height
)
mask
=
cv2
.
dilate
(
np
.
array
(
segmentation
[
batch_index
]).
astype
(
np
.
uint8
),
self
.
dilation_kernel
)
boxes
,
scores
=
self
.
boxes_from_bitmap
(
pred
[
batch_index
],
mask
,
width
,
height
)
boxes_batch
.
append
({
'points'
:
boxes
})
return
boxes_batch
\ No newline at end of file
return
boxes_batch
ppocr/utils/save_load.py
浏览文件 @
5f2f08a0
...
...
@@ -55,8 +55,8 @@ def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False):
weight_name
=
weight_name
.
replace
(
'binarize'
,
''
).
replace
(
'thresh'
,
''
)
# for DB
if
weight_name
in
pre_state_dict
.
keys
():
logger
.
info
(
'Load weight: {}, shape: {}'
.
format
(
weight_name
,
pre_state_dict
[
weight_name
].
shape
))
#
logger.info('Load weight: {}, shape: {}'.format(
#
weight_name, pre_state_dict[weight_name].shape))
if
'encoder_rnn'
in
key
:
# delete axis which is 1
pre_state_dict
[
weight_name
]
=
pre_state_dict
[
...
...
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