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体验新版 GitCode,发现更多精彩内容 >>
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c93b4a17
编写于
11月 06, 2020
作者:
D
dyning
提交者:
GitHub
11月 06, 2020
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差异文件
Merge pull request #1123 from WenmuZhou/dygraph_rc
fix some error and make some change
上级
96c91907
a414dd86
变更
15
隐藏空白更改
内联
并排
Showing
15 changed file
with
65 addition
and
73 deletion
+65
-73
configs/det/det_mv3_db.yml
configs/det/det_mv3_db.yml
+2
-2
configs/rec/rec_mv3_none_bilstm_ctc.yml
configs/rec/rec_mv3_none_bilstm_ctc.yml
+8
-11
ppocr/data/lmdb_dataset.py
ppocr/data/lmdb_dataset.py
+0
-4
ppocr/data/simple_dataset.py
ppocr/data/simple_dataset.py
+0
-3
ppocr/metrics/__init__.py
ppocr/metrics/__init__.py
+2
-2
ppocr/metrics/det_metric.py
ppocr/metrics/det_metric.py
+0
-0
ppocr/metrics/rec_metric.py
ppocr/metrics/rec_metric.py
+0
-0
ppocr/modeling/heads/det_db_head.py
ppocr/modeling/heads/det_db_head.py
+2
-2
ppocr/modeling/necks/db_fpn.py
ppocr/modeling/necks/db_fpn.py
+10
-7
ppocr/optimizer/__init__.py
ppocr/optimizer/__init__.py
+2
-3
ppocr/optimizer/learning_rate.py
ppocr/optimizer/learning_rate.py
+23
-21
ppocr/utils/logging.py
ppocr/utils/logging.py
+1
-1
tools/program.py
tools/program.py
+10
-11
tools/train.py
tools/train.py
+4
-5
train.sh
train.sh
+1
-1
未找到文件。
configs/det/det_mv3_db.yml
浏览文件 @
c93b4a17
...
...
@@ -44,9 +44,9 @@ Optimizer:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
l
earning_rate
:
l
r
:
# name: Cosine
l
r
:
0.001
l
earning_rate
:
0.001
# warmup_epoch: 0
regularizer
:
name
:
'
L2'
...
...
configs/rec/rec_mv3_none_bilstm_ctc.yml
浏览文件 @
c93b4a17
...
...
@@ -6,7 +6,7 @@ Global:
save_model_dir
:
./output/rec/mv3_none_bilstm_ctc/
save_epoch_step
:
3
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step
:
[
0
,
1
000
]
eval_batch_step
:
[
0
,
2
000
]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train
:
True
pretrained_model
:
...
...
@@ -18,22 +18,19 @@ Global:
character_dict_path
:
character_type
:
en
max_text_length
:
25
loss_type
:
ctc
infer_mode
:
False
# use_space_char: True
# use_tps: False
use_space_char
:
False
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
l
earning_rate
:
l
r
:
0.0005
l
r
:
l
earning_rate
:
0.0005
regularizer
:
name
:
'
L2'
factor
:
0
.00001
factor
:
0
Architecture
:
model_type
:
rec
...
...
@@ -49,7 +46,7 @@ Architecture:
hidden_size
:
96
Head
:
name
:
CTCHead
fc_decay
:
0
.0004
fc_decay
:
0
Loss
:
name
:
CTCLoss
...
...
@@ -75,8 +72,8 @@ Train:
-
KeepKeys
:
keep_keys
:
[
'
image'
,
'
label'
,
'
length'
]
# dataloader will return list in this order
loader
:
shuffle
:
True
batch_size_per_card
:
256
shuffle
:
False
drop_last
:
True
num_workers
:
8
...
...
@@ -97,4 +94,4 @@ Eval:
shuffle
:
False
drop_last
:
False
batch_size_per_card
:
256
num_workers
:
2
num_workers
:
4
ppocr/data/lmdb_dataset.py
浏览文件 @
c93b4a17
...
...
@@ -11,13 +11,9 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
copy
import
numpy
as
np
import
os
import
random
import
paddle
from
paddle.io
import
Dataset
import
time
import
lmdb
import
cv2
...
...
ppocr/data/simple_dataset.py
浏览文件 @
c93b4a17
...
...
@@ -11,13 +11,10 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
copy
import
numpy
as
np
import
os
import
random
import
paddle
from
paddle.io
import
Dataset
import
time
from
.imaug
import
transform
,
create_operators
...
...
ppocr/metrics/__init__.py
浏览文件 @
c93b4a17
...
...
@@ -23,8 +23,8 @@ __all__ = ['build_metric']
def
build_metric
(
config
):
from
.
DetM
etric
import
DetMetric
from
.
RecM
etric
import
RecMetric
from
.
det_m
etric
import
DetMetric
from
.
rec_m
etric
import
RecMetric
support_dict
=
[
'DetMetric'
,
'RecMetric'
]
...
...
ppocr/metrics/
DetM
etric.py
→
ppocr/metrics/
det_m
etric.py
浏览文件 @
c93b4a17
文件已移动
ppocr/metrics/
RecM
etric.py
→
ppocr/metrics/
rec_m
etric.py
浏览文件 @
c93b4a17
文件已移动
ppocr/modeling/heads/det_db_head.py
浏览文件 @
c93b4a17
...
...
@@ -58,7 +58,7 @@ class Head(nn.Layer):
stride
=
2
,
weight_attr
=
ParamAttr
(
name
=
name_list
[
2
]
+
'.w_0'
,
initializer
=
paddle
.
nn
.
initializer
.
Kaiming
Normal
()),
initializer
=
paddle
.
nn
.
initializer
.
Kaiming
Uniform
()),
bias_attr
=
get_bias_attr
(
in_channels
//
4
,
name_list
[
-
1
]
+
"conv2"
))
self
.
conv_bn2
=
nn
.
BatchNorm
(
num_channels
=
in_channels
//
4
,
...
...
@@ -78,7 +78,7 @@ class Head(nn.Layer):
stride
=
2
,
weight_attr
=
ParamAttr
(
name
=
name_list
[
4
]
+
'.w_0'
,
initializer
=
paddle
.
nn
.
initializer
.
Kaiming
Normal
()),
initializer
=
paddle
.
nn
.
initializer
.
Kaiming
Uniform
()),
bias_attr
=
get_bias_attr
(
in_channels
//
4
,
name_list
[
-
1
]
+
"conv3"
),
)
...
...
ppocr/modeling/necks/db_fpn.py
浏览文件 @
c93b4a17
...
...
@@ -26,7 +26,7 @@ class DBFPN(nn.Layer):
def
__init__
(
self
,
in_channels
,
out_channels
,
**
kwargs
):
super
(
DBFPN
,
self
).
__init__
()
self
.
out_channels
=
out_channels
weight_attr
=
paddle
.
nn
.
initializer
.
Kaiming
Normal
()
weight_attr
=
paddle
.
nn
.
initializer
.
Kaiming
Uniform
()
self
.
in2_conv
=
nn
.
Conv2D
(
in_channels
=
in_channels
[
0
],
...
...
@@ -97,17 +97,20 @@ class DBFPN(nn.Layer):
in3
=
self
.
in3_conv
(
c3
)
in2
=
self
.
in2_conv
(
c2
)
out4
=
in4
+
F
.
upsample
(
in5
,
scale_factor
=
2
,
mode
=
"nearest"
)
# 1/16
out3
=
in3
+
F
.
upsample
(
out4
,
scale_factor
=
2
,
mode
=
"nearest"
)
# 1/8
out2
=
in2
+
F
.
upsample
(
out3
,
scale_factor
=
2
,
mode
=
"nearest"
)
# 1/4
out4
=
in4
+
F
.
upsample
(
in5
,
scale_factor
=
2
,
mode
=
"nearest"
,
align_mode
=
1
)
# 1/16
out3
=
in3
+
F
.
upsample
(
out4
,
scale_factor
=
2
,
mode
=
"nearest"
,
align_mode
=
1
)
# 1/8
out2
=
in2
+
F
.
upsample
(
out3
,
scale_factor
=
2
,
mode
=
"nearest"
,
align_mode
=
1
)
# 1/4
p5
=
self
.
p5_conv
(
in5
)
p4
=
self
.
p4_conv
(
out4
)
p3
=
self
.
p3_conv
(
out3
)
p2
=
self
.
p2_conv
(
out2
)
p5
=
F
.
upsample
(
p5
,
scale_factor
=
8
,
mode
=
"nearest"
)
p4
=
F
.
upsample
(
p4
,
scale_factor
=
4
,
mode
=
"nearest"
)
p3
=
F
.
upsample
(
p3
,
scale_factor
=
2
,
mode
=
"nearest"
)
p5
=
F
.
upsample
(
p5
,
scale_factor
=
8
,
mode
=
"nearest"
,
align_mode
=
1
)
p4
=
F
.
upsample
(
p4
,
scale_factor
=
4
,
mode
=
"nearest"
,
align_mode
=
1
)
p3
=
F
.
upsample
(
p3
,
scale_factor
=
2
,
mode
=
"nearest"
,
align_mode
=
1
)
fuse
=
paddle
.
concat
([
p5
,
p4
,
p3
,
p2
],
axis
=
1
)
return
fuse
ppocr/optimizer/__init__.py
浏览文件 @
c93b4a17
...
...
@@ -29,7 +29,7 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
lr_name
=
lr_config
.
pop
(
'name'
)
lr
=
getattr
(
learning_rate
,
lr_name
)(
**
lr_config
)()
else
:
lr
=
lr_config
[
'l
r
'
]
lr
=
lr_config
[
'l
earning_rate
'
]
return
lr
...
...
@@ -37,8 +37,7 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
from
.
import
regularizer
,
optimizer
config
=
copy
.
deepcopy
(
config
)
# step1 build lr
lr
=
build_lr_scheduler
(
config
.
pop
(
'learning_rate'
),
epochs
,
step_each_epoch
)
lr
=
build_lr_scheduler
(
config
.
pop
(
'lr'
),
epochs
,
step_each_epoch
)
# step2 build regularization
if
'regularizer'
in
config
and
config
[
'regularizer'
]
is
not
None
:
...
...
ppocr/optimizer/learning_rate.py
浏览文件 @
c93b4a17
...
...
@@ -17,7 +17,7 @@ from __future__ import division
from
__future__
import
print_function
from
__future__
import
unicode_literals
from
paddle.optimizer
import
lr
as
lr_scheduler
from
paddle.optimizer
import
lr
class
Linear
(
object
):
...
...
@@ -32,7 +32,7 @@ class Linear(object):
"""
def
__init__
(
self
,
l
r
,
l
earning_rate
,
epochs
,
step_each_epoch
,
end_lr
=
0.0
,
...
...
@@ -41,7 +41,7 @@ class Linear(object):
last_epoch
=-
1
,
**
kwargs
):
super
(
Linear
,
self
).
__init__
()
self
.
l
r
=
lr
self
.
l
earning_rate
=
learning_rate
self
.
epochs
=
epochs
*
step_each_epoch
self
.
end_lr
=
end_lr
self
.
power
=
power
...
...
@@ -49,18 +49,18 @@ class Linear(object):
self
.
warmup_epoch
=
warmup_epoch
*
step_each_epoch
def
__call__
(
self
):
learning_rate
=
lr
_scheduler
.
PolynomialLR
(
learning_rate
=
self
.
l
r
,
learning_rate
=
lr
.
PolynomialDecay
(
learning_rate
=
self
.
l
earning_rate
,
decay_steps
=
self
.
epochs
,
end_lr
=
self
.
end_lr
,
power
=
self
.
power
,
last_epoch
=
self
.
last_epoch
)
if
self
.
warmup_epoch
>
0
:
learning_rate
=
lr
_scheduler
.
LinearL
rWarmup
(
learning_rate
=
lr
.
Linea
rWarmup
(
learning_rate
=
learning_rate
,
warmup_steps
=
self
.
warmup_epoch
,
start_lr
=
0.0
,
end_lr
=
self
.
l
r
,
end_lr
=
self
.
l
earning_rate
,
last_epoch
=
self
.
last_epoch
)
return
learning_rate
...
...
@@ -77,27 +77,29 @@ class Cosine(object):
"""
def
__init__
(
self
,
l
r
,
l
earning_rate
,
step_each_epoch
,
epochs
,
warmup_epoch
=
0
,
last_epoch
=-
1
,
**
kwargs
):
super
(
Cosine
,
self
).
__init__
()
self
.
l
r
=
lr
self
.
l
earning_rate
=
learning_rate
self
.
T_max
=
step_each_epoch
*
epochs
self
.
last_epoch
=
last_epoch
self
.
warmup_epoch
=
warmup_epoch
*
step_each_epoch
def
__call__
(
self
):
learning_rate
=
lr_scheduler
.
CosineAnnealingLR
(
learning_rate
=
self
.
lr
,
T_max
=
self
.
T_max
,
last_epoch
=
self
.
last_epoch
)
learning_rate
=
lr
.
CosineAnnealingDecay
(
learning_rate
=
self
.
learning_rate
,
T_max
=
self
.
T_max
,
last_epoch
=
self
.
last_epoch
)
if
self
.
warmup_epoch
>
0
:
learning_rate
=
lr
_scheduler
.
LinearL
rWarmup
(
learning_rate
=
lr
.
Linea
rWarmup
(
learning_rate
=
learning_rate
,
warmup_steps
=
self
.
warmup_epoch
,
start_lr
=
0.0
,
end_lr
=
self
.
l
r
,
end_lr
=
self
.
l
earning_rate
,
last_epoch
=
self
.
last_epoch
)
return
learning_rate
...
...
@@ -115,7 +117,7 @@ class Step(object):
"""
def
__init__
(
self
,
l
r
,
l
earning_rate
,
step_size
,
step_each_epoch
,
gamma
,
...
...
@@ -124,23 +126,23 @@ class Step(object):
**
kwargs
):
super
(
Step
,
self
).
__init__
()
self
.
step_size
=
step_each_epoch
*
step_size
self
.
l
r
=
lr
self
.
l
earning_rate
=
learning_rate
self
.
gamma
=
gamma
self
.
last_epoch
=
last_epoch
self
.
warmup_epoch
=
warmup_epoch
*
step_each_epoch
def
__call__
(
self
):
learning_rate
=
lr
_scheduler
.
StepLR
(
learning_rate
=
self
.
l
r
,
learning_rate
=
lr
.
StepDecay
(
learning_rate
=
self
.
l
earning_rate
,
step_size
=
self
.
step_size
,
gamma
=
self
.
gamma
,
last_epoch
=
self
.
last_epoch
)
if
self
.
warmup_epoch
>
0
:
learning_rate
=
lr
_scheduler
.
LinearL
rWarmup
(
learning_rate
=
lr
.
Linea
rWarmup
(
learning_rate
=
learning_rate
,
warmup_steps
=
self
.
warmup_epoch
,
start_lr
=
0.0
,
end_lr
=
self
.
l
r
,
end_lr
=
self
.
l
earning_rate
,
last_epoch
=
self
.
last_epoch
)
return
learning_rate
...
...
@@ -169,12 +171,12 @@ class Piecewise(object):
self
.
warmup_epoch
=
warmup_epoch
*
step_each_epoch
def
__call__
(
self
):
learning_rate
=
lr
_scheduler
.
PiecewiseLR
(
learning_rate
=
lr
.
PiecewiseDecay
(
boundaries
=
self
.
boundaries
,
values
=
self
.
values
,
last_epoch
=
self
.
last_epoch
)
if
self
.
warmup_epoch
>
0
:
learning_rate
=
lr
_scheduler
.
LinearL
rWarmup
(
learning_rate
=
lr
.
Linea
rWarmup
(
learning_rate
=
learning_rate
,
warmup_steps
=
self
.
warmup_epoch
,
start_lr
=
0.0
,
...
...
ppocr/utils/logging.py
浏览文件 @
c93b4a17
...
...
@@ -22,7 +22,7 @@ logger_initialized = {}
@
functools
.
lru_cache
()
def
get_logger
(
name
=
'
ppocr
'
,
log_file
=
None
,
log_level
=
logging
.
INFO
):
def
get_logger
(
name
=
'
root
'
,
log_file
=
None
,
log_level
=
logging
.
INFO
):
"""Initialize and get a logger by name.
If the logger has not been initialized, this method will initialize the
logger by adding one or two handlers, otherwise the initialized logger will
...
...
tools/program.py
浏览文件 @
c93b4a17
...
...
@@ -152,7 +152,6 @@ def train(config,
pre_best_model_dict
,
logger
,
vdl_writer
=
None
):
cal_metric_during_train
=
config
[
'Global'
].
get
(
'cal_metric_during_train'
,
False
)
log_smooth_window
=
config
[
'Global'
][
'log_smooth_window'
]
...
...
@@ -185,14 +184,13 @@ def train(config,
for
epoch
in
range
(
start_epoch
,
epoch_num
):
if
epoch
>
0
:
train_
loader
=
build_dataloader
(
config
,
'Train'
,
device
)
train_
dataloader
=
build_dataloader
(
config
,
'Train'
,
device
,
logger
)
for
idx
,
batch
in
enumerate
(
train_dataloader
):
if
idx
>=
len
(
train_dataloader
):
break
lr
=
optimizer
.
get_lr
()
t1
=
time
.
time
()
batch
=
[
paddle
.
to_tensor
(
x
)
for
x
in
batch
]
images
=
batch
[
0
]
preds
=
model
(
images
)
loss
=
loss_class
(
preds
,
batch
)
...
...
@@ -301,11 +299,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
with
paddle
.
no_grad
():
total_frame
=
0.0
total_time
=
0.0
#
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
pbar
=
tqdm
(
total
=
len
(
valid_dataloader
),
desc
=
'eval model:'
)
for
idx
,
batch
in
enumerate
(
valid_dataloader
):
if
idx
>=
len
(
valid_dataloader
):
break
images
=
paddle
.
to_tensor
(
batch
[
0
])
images
=
batch
[
0
]
start
=
time
.
time
()
preds
=
model
(
images
)
...
...
@@ -315,15 +313,15 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
total_time
+=
time
.
time
()
-
start
# Evaluate the results of the current batch
eval_class
(
post_result
,
batch
)
#
pbar.update(1)
pbar
.
update
(
1
)
total_frame
+=
len
(
images
)
if
idx
%
print_batch_step
==
0
and
dist
.
get_rank
()
==
0
:
logger
.
info
(
'tackling images for eval: {}/{}'
.
format
(
idx
,
len
(
valid_dataloader
)))
#
if idx % print_batch_step == 0 and dist.get_rank() == 0:
#
logger.info('tackling images for eval: {}/{}'.format(
#
idx, len(valid_dataloader)))
# Get final metirc,eg. acc or hmean
metirc
=
eval_class
.
get_metric
()
#
pbar.close()
pbar
.
close
()
model
.
train
()
metirc
[
'fps'
]
=
total_frame
/
total_time
return
metirc
...
...
@@ -354,7 +352,8 @@ def preprocess():
with
open
(
os
.
path
.
join
(
save_model_dir
,
'config.yml'
),
'w'
)
as
f
:
yaml
.
dump
(
dict
(
config
),
f
,
default_flow_style
=
False
,
sort_keys
=
False
)
logger
=
get_logger
(
log_file
=
'{}/train.log'
.
format
(
save_model_dir
))
logger
=
get_logger
(
name
=
'root'
,
log_file
=
'{}/train.log'
.
format
(
save_model_dir
))
if
config
[
'Global'
][
'use_visualdl'
]:
from
visualdl
import
LogWriter
vdl_writer_path
=
'{}/vdl/'
.
format
(
save_model_dir
)
...
...
tools/train.py
浏览文件 @
c93b4a17
...
...
@@ -36,7 +36,6 @@ from ppocr.optimizer import build_optimizer
from
ppocr.postprocess
import
build_post_process
from
ppocr.metrics
import
build_metric
from
ppocr.utils.save_load
import
init_model
from
ppocr.utils.utility
import
print_dict
import
tools.program
as
program
dist
.
get_world_size
()
...
...
@@ -61,7 +60,7 @@ def main(config, device, logger, vdl_writer):
global_config
)
# build model
#for rec algorithm
#
for rec algorithm
if
hasattr
(
post_process_class
,
'character'
):
char_num
=
len
(
getattr
(
post_process_class
,
'character'
))
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
...
...
@@ -81,10 +80,11 @@ def main(config, device, logger, vdl_writer):
# build metric
eval_class
=
build_metric
(
config
[
'Metric'
])
# load pretrain model
pre_best_model_dict
=
init_model
(
config
,
model
,
logger
,
optimizer
)
logger
.
info
(
'train dataloader has {} iters, valid dataloader has {} iters'
.
format
(
len
(
train_dataloader
),
len
(
valid_dataloader
)))
# start train
program
.
train
(
config
,
train_dataloader
,
valid_dataloader
,
device
,
model
,
loss_class
,
optimizer
,
lr_scheduler
,
post_process_class
,
...
...
@@ -92,8 +92,7 @@ def main(config, device, logger, vdl_writer):
def
test_reader
(
config
,
device
,
logger
):
loader
=
build_dataloader
(
config
,
'Train'
,
device
)
# loader = build_dataloader(config, 'Eval', device)
loader
=
build_dataloader
(
config
,
'Train'
,
device
,
logger
)
import
time
starttime
=
time
.
time
()
count
=
0
...
...
train.sh
浏览文件 @
c93b4a17
python
-m
paddle.distributed.launch
--selected_gpus
'0,1,2,3,4,5,6,7'
tools/train.py
-c
configs/det/det_mv3_db.yml
\ No newline at end of file
python3
-m
paddle.distributed.launch
--selected_gpus
'0,1,2,3,4,5,6,7'
tools/train.py
-c
configs/rec/rec_mv3_none_bilstm_ctc.yml
\ No newline at end of file
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