Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
Calex_1990
PaddleOCR
提交
b8972b36
P
PaddleOCR
项目概览
Calex_1990
/
PaddleOCR
与 Fork 源项目一致
Fork自
PaddlePaddle / PaddleOCR
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleOCR
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
b8972b36
编写于
5月 26, 2021
作者:
L
LDOUBLEV
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add python benchmark for ocr
上级
5d24736a
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
595 addition
and
84 deletion
+595
-84
tools/infer/benchmark_utils.py
tools/infer/benchmark_utils.py
+232
-0
tools/infer/predict_cls.py
tools/infer/predict_cls.py
+18
-3
tools/infer/predict_det.py
tools/infer/predict_det.py
+71
-10
tools/infer/predict_rec.py
tools/infer/predict_rec.py
+76
-40
tools/infer/predict_system.py
tools/infer/predict_system.py
+81
-28
tools/infer/utility.py
tools/infer/utility.py
+117
-3
未找到文件。
tools/infer/benchmark_utils.py
0 → 100644
浏览文件 @
b8972b36
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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
argparse
import
os
import
time
import
logging
import
paddle
import
paddle.inference
as
paddle_infer
from
pathlib
import
Path
CUR_DIR
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
class
PaddleInferBenchmark
(
object
):
def
__init__
(
self
,
config
,
model_info
:
dict
=
{},
data_info
:
dict
=
{},
perf_info
:
dict
=
{},
resource_info
:
dict
=
{},
save_log_path
:
str
=
""
,
**
kwargs
):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
"""
# PaddleInferBenchmark Log Version
self
.
log_version
=
1.0
# Paddle Version
self
.
paddle_version
=
paddle
.
__version__
self
.
paddle_commit
=
paddle
.
__git_commit__
paddle_infer_info
=
paddle_infer
.
get_version
()
self
.
paddle_branch
=
paddle_infer_info
.
strip
().
split
(
': '
)[
-
1
]
# model info
self
.
model_info
=
model_info
# data info
self
.
data_info
=
data_info
# perf info
self
.
perf_info
=
perf_info
try
:
self
.
model_name
=
model_info
[
'model_name'
]
self
.
precision
=
model_info
[
'precision'
]
self
.
batch_size
=
data_info
[
'batch_size'
]
self
.
shape
=
data_info
[
'shape'
]
self
.
data_num
=
data_info
[
'data_num'
]
self
.
preprocess_time_s
=
round
(
perf_info
[
'preprocess_time_s'
],
4
)
self
.
inference_time_s
=
round
(
perf_info
[
'inference_time_s'
],
4
)
self
.
postprocess_time_s
=
round
(
perf_info
[
'postprocess_time_s'
],
4
)
self
.
total_time_s
=
round
(
perf_info
[
'total_time_s'
],
4
)
except
:
self
.
print_help
()
raise
ValueError
(
"Set argument wrong, please check input argument and its type"
)
# conf info
self
.
config_status
=
self
.
parse_config
(
config
)
self
.
save_log_path
=
save_log_path
# mem info
if
isinstance
(
resource_info
,
dict
):
self
.
cpu_rss_mb
=
int
(
resource_info
.
get
(
'cpu_rss_mb'
,
0
))
self
.
gpu_rss_mb
=
int
(
resource_info
.
get
(
'gpu_rss_mb'
,
0
))
self
.
gpu_util
=
round
(
resource_info
.
get
(
'gpu_util'
,
0
),
2
)
else
:
self
.
cpu_rss_mb
=
0
self
.
gpu_rss_mb
=
0
self
.
gpu_util
=
0
# init benchmark logger
self
.
benchmark_logger
()
def
benchmark_logger
(
self
):
"""
benchmark logger
"""
# Init logger
FORMAT
=
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
log_output
=
f
"
{
self
.
save_log_path
}
/
{
self
.
model_name
}
.log"
Path
(
f
"
{
self
.
save_log_path
}
"
).
mkdir
(
parents
=
True
,
exist_ok
=
True
)
logging
.
basicConfig
(
level
=
logging
.
INFO
,
format
=
FORMAT
,
handlers
=
[
logging
.
FileHandler
(
filename
=
log_output
,
mode
=
'w'
),
logging
.
StreamHandler
(),
])
self
.
logger
=
logging
.
getLogger
(
__name__
)
self
.
logger
.
info
(
f
"Paddle Inference benchmark log will be saved to
{
log_output
}
"
)
def
parse_config
(
self
,
config
)
->
dict
:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
config_status
=
{}
config_status
[
'runtime_device'
]
=
"gpu"
if
config
.
use_gpu
()
else
"cpu"
config_status
[
'ir_optim'
]
=
config
.
ir_optim
()
config_status
[
'enable_tensorrt'
]
=
config
.
tensorrt_engine_enabled
()
config_status
[
'precision'
]
=
self
.
precision
config_status
[
'enable_mkldnn'
]
=
config
.
mkldnn_enabled
()
config_status
[
'cpu_math_library_num_threads'
]
=
config
.
cpu_math_library_num_threads
(
)
return
config_status
def
report
(
self
,
identifier
=
None
):
"""
print log report
args:
identifier(string): identify log
"""
if
identifier
:
identifier
=
f
"[
{
identifier
}
]"
else
:
identifier
=
""
self
.
logger
.
info
(
"
\n
"
)
self
.
logger
.
info
(
"---------------------- Paddle info ----------------------"
)
self
.
logger
.
info
(
f
"
{
identifier
}
paddle_version:
{
self
.
paddle_version
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
paddle_commit:
{
self
.
paddle_commit
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
paddle_branch:
{
self
.
paddle_branch
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
log_api_version:
{
self
.
log_version
}
"
)
self
.
logger
.
info
(
"----------------------- Conf info -----------------------"
)
self
.
logger
.
info
(
f
"
{
identifier
}
runtime_device:
{
self
.
config_status
[
'runtime_device'
]
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
ir_optim:
{
self
.
config_status
[
'ir_optim'
]
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
enable_memory_optim:
{
True
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
enable_tensorrt:
{
self
.
config_status
[
'enable_tensorrt'
]
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
enable_mkldnn:
{
self
.
config_status
[
'enable_mkldnn'
]
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
cpu_math_library_num_threads:
{
self
.
config_status
[
'cpu_math_library_num_threads'
]
}
"
)
self
.
logger
.
info
(
"----------------------- Model info ----------------------"
)
self
.
logger
.
info
(
f
"
{
identifier
}
model_name:
{
self
.
model_name
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
precision:
{
self
.
precision
}
"
)
self
.
logger
.
info
(
"----------------------- Data info -----------------------"
)
self
.
logger
.
info
(
f
"
{
identifier
}
batch_size:
{
self
.
batch_size
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
input_shape:
{
self
.
shape
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
data_num:
{
self
.
data_num
}
"
)
self
.
logger
.
info
(
"----------------------- Perf info -----------------------"
)
self
.
logger
.
info
(
f
"
{
identifier
}
cpu_rss(MB):
{
self
.
cpu_rss_mb
}
, gpu_rss(MB):
{
self
.
gpu_rss_mb
}
, gpu_util:
{
self
.
gpu_util
}
%"
)
self
.
logger
.
info
(
f
"
{
identifier
}
total time spent(s):
{
self
.
total_time_s
}
"
)
self
.
logger
.
info
(
f
"
{
identifier
}
preprocess_time(ms):
{
round
(
self
.
preprocess_time_s
*
1000
,
1
)
}
, inference_time(ms):
{
round
(
self
.
inference_time_s
*
1000
,
1
)
}
, postprocess_time(ms):
{
round
(
self
.
postprocess_time_s
*
1000
,
1
)
}
"
)
def
print_help
(
self
):
"""
print function help
"""
print
(
"""Usage:
==== Print inference benchmark logs. ====
config = paddle.inference.Config()
model_info = {'model_name': 'resnet50'
'precision': 'fp32'}
data_info = {'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info = {'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info = {'cpu_rss_mb': 100
'gpu_rss_mb': 100
'gpu_util': 60}
log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
log('Test')
"""
)
def
__call__
(
self
,
identifier
=
None
):
"""
__call__
args:
identifier(string): identify log
"""
self
.
report
(
identifier
)
tools/infer/predict_cls.py
浏览文件 @
b8972b36
...
...
@@ -45,9 +45,11 @@ class TextClassifier(object):
"label_list"
:
args
.
label_list
,
}
self
.
postprocess_op
=
build_post_process
(
postprocess_params
)
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
=
\
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
,
_
=
\
utility
.
create_predictor
(
args
,
'cls'
,
logger
)
self
.
cls_times
=
utility
.
Timer
()
def
resize_norm_img
(
self
,
img
):
imgC
,
imgH
,
imgW
=
self
.
cls_image_shape
h
=
img
.
shape
[
0
]
...
...
@@ -83,7 +85,9 @@ class TextClassifier(object):
cls_res
=
[[
''
,
0.0
]]
*
img_num
batch_num
=
self
.
cls_batch_num
elapse
=
0
self
.
cls_times
.
total_time
.
start
()
for
beg_img_no
in
range
(
0
,
img_num
,
batch_num
):
end_img_no
=
min
(
img_num
,
beg_img_no
+
batch_num
)
norm_img_batch
=
[]
max_wh_ratio
=
0
...
...
@@ -91,6 +95,7 @@ class TextClassifier(object):
h
,
w
=
img_list
[
indices
[
ino
]].
shape
[
0
:
2
]
wh_ratio
=
w
*
1.0
/
h
max_wh_ratio
=
max
(
max_wh_ratio
,
wh_ratio
)
self
.
cls_times
.
preprocess_time
.
start
()
for
ino
in
range
(
beg_img_no
,
end_img_no
):
norm_img
=
self
.
resize_norm_img
(
img_list
[
indices
[
ino
]])
norm_img
=
norm_img
[
np
.
newaxis
,
:]
...
...
@@ -98,11 +103,17 @@ class TextClassifier(object):
norm_img_batch
=
np
.
concatenate
(
norm_img_batch
)
norm_img_batch
=
norm_img_batch
.
copy
()
starttime
=
time
.
time
()
self
.
cls_times
.
preprocess_time
.
end
()
self
.
cls_times
.
inference_time
.
start
()
self
.
input_tensor
.
copy_from_cpu
(
norm_img_batch
)
self
.
predictor
.
run
()
prob_out
=
self
.
output_tensors
[
0
].
copy_to_cpu
()
self
.
cls_times
.
inference_time
.
end
()
self
.
cls_times
.
postprocess_time
.
start
()
self
.
predictor
.
try_shrink_memory
()
cls_result
=
self
.
postprocess_op
(
prob_out
)
self
.
cls_times
.
postprocess_time
.
end
()
elapse
+=
time
.
time
()
-
starttime
for
rno
in
range
(
len
(
cls_result
)):
label
,
score
=
cls_result
[
rno
]
...
...
@@ -110,6 +121,9 @@ class TextClassifier(object):
if
'180'
in
label
and
score
>
self
.
cls_thresh
:
img_list
[
indices
[
beg_img_no
+
rno
]]
=
cv2
.
rotate
(
img_list
[
indices
[
beg_img_no
+
rno
]],
1
)
self
.
cls_times
.
total_time
.
end
()
self
.
cls_times
.
img_num
+=
img_num
elapse
=
self
.
cls_times
.
total_time
.
value
()
return
img_list
,
cls_res
,
elapse
...
...
@@ -141,8 +155,9 @@ def main(args):
for
ino
in
range
(
len
(
img_list
)):
logger
.
info
(
"Predicts of {}:{}"
.
format
(
valid_image_file_list
[
ino
],
cls_res
[
ino
]))
logger
.
info
(
"Total predict time for {} images, cost: {:.3f}"
.
format
(
len
(
img_list
),
predict_time
))
logger
.
info
(
"The predict time about text angle classify module is as follows: "
)
text_classifier
.
cls_times
.
info
(
average
=
False
)
if
__name__
==
"__main__"
:
...
...
tools/infer/predict_det.py
浏览文件 @
b8972b36
...
...
@@ -31,6 +31,8 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from
ppocr.data
import
create_operators
,
transform
from
ppocr.postprocess
import
build_post_process
import
tools.infer.benchmark_utils
as
benchmark_utils
logger
=
get_logger
()
...
...
@@ -95,9 +97,10 @@ class TextDetector(object):
self
.
preprocess_op
=
create_operators
(
pre_process_list
)
self
.
postprocess_op
=
build_post_process
(
postprocess_params
)
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
=
utility
.
create_predictor
(
args
,
'det'
,
logger
)
# paddle.jit.load(args.det_model_dir)
# self.predictor.eval()
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
,
self
.
config
=
utility
.
create_predictor
(
args
,
'det'
,
logger
)
self
.
det_times
=
utility
.
Timer
()
def
order_points_clockwise
(
self
,
pts
):
"""
...
...
@@ -155,6 +158,8 @@ class TextDetector(object):
def
__call__
(
self
,
img
):
ori_im
=
img
.
copy
()
data
=
{
'image'
:
img
}
self
.
det_times
.
total_time
.
start
()
self
.
det_times
.
preprocess_time
.
start
()
data
=
transform
(
data
,
self
.
preprocess_op
)
img
,
shape_list
=
data
if
img
is
None
:
...
...
@@ -162,7 +167,9 @@ class TextDetector(object):
img
=
np
.
expand_dims
(
img
,
axis
=
0
)
shape_list
=
np
.
expand_dims
(
shape_list
,
axis
=
0
)
img
=
img
.
copy
()
starttime
=
time
.
time
()
self
.
det_times
.
preprocess_time
.
end
()
self
.
det_times
.
inference_time
.
start
()
self
.
input_tensor
.
copy_from_cpu
(
img
)
self
.
predictor
.
run
()
...
...
@@ -170,6 +177,7 @@ class TextDetector(object):
for
output_tensor
in
self
.
output_tensors
:
output
=
output_tensor
.
copy_to_cpu
()
outputs
.
append
(
output
)
self
.
det_times
.
inference_time
.
end
()
preds
=
{}
if
self
.
det_algorithm
==
"EAST"
:
...
...
@@ -184,6 +192,9 @@ class TextDetector(object):
preds
[
'maps'
]
=
outputs
[
0
]
else
:
raise
NotImplementedError
self
.
det_times
.
postprocess_time
.
start
()
self
.
predictor
.
try_shrink_memory
()
post_result
=
self
.
postprocess_op
(
preds
,
shape_list
)
dt_boxes
=
post_result
[
0
][
'points'
]
...
...
@@ -191,8 +202,11 @@ class TextDetector(object):
dt_boxes
=
self
.
filter_tag_det_res_only_clip
(
dt_boxes
,
ori_im
.
shape
)
else
:
dt_boxes
=
self
.
filter_tag_det_res
(
dt_boxes
,
ori_im
.
shape
)
elapse
=
time
.
time
()
-
starttime
return
dt_boxes
,
elapse
self
.
det_times
.
postprocess_time
.
end
()
self
.
det_times
.
total_time
.
end
()
self
.
det_times
.
img_num
+=
1
return
dt_boxes
,
self
.
det_times
.
total_time
.
value
()
if
__name__
==
"__main__"
:
...
...
@@ -202,6 +216,13 @@ if __name__ == "__main__":
count
=
0
total_time
=
0
draw_img_save
=
"./inference_results"
cpu_mem
,
gpu_mem
,
gpu_util
=
0
,
0
,
0
# warmup 10 times
fake_img
=
np
.
random
.
uniform
(
-
1
,
1
,
[
640
,
640
,
3
]).
astype
(
np
.
float32
)
for
i
in
range
(
10
):
dt_boxes
,
_
=
text_detector
(
fake_img
)
if
not
os
.
path
.
exists
(
draw_img_save
):
os
.
makedirs
(
draw_img_save
)
for
image_file
in
image_file_list
:
...
...
@@ -211,16 +232,56 @@ if __name__ == "__main__":
if
img
is
None
:
logger
.
info
(
"error in loading image:{}"
.
format
(
image_file
))
continue
dt_boxes
,
elapse
=
text_detector
(
img
)
st
=
time
.
time
()
dt_boxes
,
_
=
text_detector
(
img
)
elapse
=
time
.
time
()
-
st
if
count
>
0
:
total_time
+=
elapse
count
+=
1
if
args
.
benchmark
:
cm
,
gm
,
gu
=
utility
.
get_current_memory_mb
(
0
)
cpu_mem
+=
cm
gpu_mem
+=
gm
gpu_util
+=
gu
logger
.
info
(
"Predict time of {}: {}"
.
format
(
image_file
,
elapse
))
src_im
=
utility
.
draw_text_det_res
(
dt_boxes
,
image_file
)
img_name_pure
=
os
.
path
.
split
(
image_file
)[
-
1
]
img_path
=
os
.
path
.
join
(
draw_img_save
,
"det_res_{}"
.
format
(
img_name_pure
))
cv2
.
imwrite
(
img_path
,
src_im
)
logger
.
info
(
"The visualized image saved in {}"
.
format
(
img_path
))
if
count
>
1
:
logger
.
info
(
"Avg Time: {}"
.
format
(
total_time
/
(
count
-
1
)))
# print the information about memory and time-spent
if
args
.
benchmark
:
mems
=
{
'cpu_rss_mb'
:
cpu_mem
/
count
,
'gpu_rss_mb'
:
gpu_mem
/
count
,
'gpu_util'
:
gpu_util
*
100
/
count
}
else
:
mems
=
None
logger
.
info
(
"The predict time about detection module is as follows: "
)
det_time_dict
=
text_detector
.
det_times
.
report
(
average
=
True
)
det_model_name
=
args
.
det_model_dir
if
args
.
benchmark
:
# construct log information
model_info
=
{
'model_name'
:
args
.
det_model_dir
.
split
(
'/'
)[
-
1
],
'precision'
:
args
.
precision
}
data_info
=
{
'batch_size'
:
1
,
'shape'
:
'dynamic_shape'
,
'data_num'
:
det_time_dict
[
'img_num'
]
}
perf_info
=
{
'preprocess_time_s'
:
det_time_dict
[
'preprocess_time'
],
'inference_time_s'
:
det_time_dict
[
'inference_time'
],
'postprocess_time_s'
:
det_time_dict
[
'postprocess_time'
],
'total_time_s'
:
det_time_dict
[
'total_time'
]
}
benchmark_log
=
benchmark_utils
.
PaddleInferBenchmark
(
text_detector
.
config
,
model_info
,
data_info
,
perf_info
,
mems
)
benchmark_log
(
"Det"
)
tools/infer/predict_rec.py
浏览文件 @
b8972b36
...
...
@@ -28,6 +28,7 @@ import traceback
import
paddle
import
tools.infer.utility
as
utility
import
tools.infer.benchmark_utils
as
benchmark_utils
from
ppocr.postprocess
import
build_post_process
from
ppocr.utils.logging
import
get_logger
from
ppocr.utils.utility
import
get_image_file_list
,
check_and_read_gif
...
...
@@ -41,7 +42,6 @@ class TextRecognizer(object):
self
.
character_type
=
args
.
rec_char_type
self
.
rec_batch_num
=
args
.
rec_batch_num
self
.
rec_algorithm
=
args
.
rec_algorithm
self
.
max_text_length
=
args
.
max_text_length
postprocess_params
=
{
'name'
:
'CTCLabelDecode'
,
"character_type"
:
args
.
rec_char_type
,
...
...
@@ -63,9 +63,11 @@ class TextRecognizer(object):
"use_space_char"
:
args
.
use_space_char
}
self
.
postprocess_op
=
build_post_process
(
postprocess_params
)
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
=
\
self
.
predictor
,
self
.
input_tensor
,
self
.
output_tensors
,
self
.
config
=
\
utility
.
create_predictor
(
args
,
'rec'
,
logger
)
self
.
rec_times
=
utility
.
Timer
()
def
resize_norm_img
(
self
,
img
,
max_wh_ratio
):
imgC
,
imgH
,
imgW
=
self
.
rec_image_shape
assert
imgC
==
img
.
shape
[
2
]
...
...
@@ -166,17 +168,15 @@ class TextRecognizer(object):
width_list
.
append
(
img
.
shape
[
1
]
/
float
(
img
.
shape
[
0
]))
# Sorting can speed up the recognition process
indices
=
np
.
argsort
(
np
.
array
(
width_list
))
# rec_res = []
self
.
rec_times
.
total_time
.
start
()
rec_res
=
[[
''
,
0.0
]]
*
img_num
batch_num
=
self
.
rec_batch_num
elapse
=
0
for
beg_img_no
in
range
(
0
,
img_num
,
batch_num
):
end_img_no
=
min
(
img_num
,
beg_img_no
+
batch_num
)
norm_img_batch
=
[]
max_wh_ratio
=
0
self
.
rec_times
.
preprocess_time
.
start
()
for
ino
in
range
(
beg_img_no
,
end_img_no
):
# h, w = img_list[ino].shape[0:2]
h
,
w
=
img_list
[
indices
[
ino
]].
shape
[
0
:
2
]
wh_ratio
=
w
*
1.0
/
h
max_wh_ratio
=
max
(
max_wh_ratio
,
wh_ratio
)
...
...
@@ -187,9 +187,8 @@ class TextRecognizer(object):
norm_img
=
norm_img
[
np
.
newaxis
,
:]
norm_img_batch
.
append
(
norm_img
)
else
:
norm_img
=
self
.
process_image_srn
(
img_list
[
indices
[
ino
]],
self
.
rec_image_shape
,
8
,
self
.
max_text_length
)
norm_img
=
self
.
process_image_srn
(
img_list
[
indices
[
ino
]],
self
.
rec_image_shape
,
8
,
25
)
encoder_word_pos_list
=
[]
gsrm_word_pos_list
=
[]
gsrm_slf_attn_bias1_list
=
[]
...
...
@@ -203,7 +202,6 @@ class TextRecognizer(object):
norm_img_batch
=
norm_img_batch
.
copy
()
if
self
.
rec_algorithm
==
"SRN"
:
starttime
=
time
.
time
()
encoder_word_pos_list
=
np
.
concatenate
(
encoder_word_pos_list
)
gsrm_word_pos_list
=
np
.
concatenate
(
gsrm_word_pos_list
)
gsrm_slf_attn_bias1_list
=
np
.
concatenate
(
...
...
@@ -218,19 +216,23 @@ class TextRecognizer(object):
gsrm_slf_attn_bias1_list
,
gsrm_slf_attn_bias2_list
,
]
self
.
rec_times
.
preprocess_time
.
end
()
self
.
rec_times
.
inference_time
.
start
()
input_names
=
self
.
predictor
.
get_input_names
()
for
i
in
range
(
len
(
input_names
)):
input_tensor
=
self
.
predictor
.
get_input_handle
(
input_names
[
i
])
input_tensor
.
copy_from_cpu
(
inputs
[
i
])
self
.
predictor
.
run
()
self
.
rec_times
.
inference_time
.
end
()
outputs
=
[]
for
output_tensor
in
self
.
output_tensors
:
output
=
output_tensor
.
copy_to_cpu
()
outputs
.
append
(
output
)
preds
=
{
"predict"
:
outputs
[
2
]}
else
:
starttime
=
time
.
time
()
self
.
rec_times
.
preprocess_time
.
end
()
self
.
rec_times
.
inference_time
.
start
()
self
.
input_tensor
.
copy_from_cpu
(
norm_img_batch
)
self
.
predictor
.
run
()
...
...
@@ -239,22 +241,31 @@ class TextRecognizer(object):
output
=
output_tensor
.
copy_to_cpu
()
outputs
.
append
(
output
)
preds
=
outputs
[
0
]
self
.
predictor
.
try_shrink_memory
()
self
.
rec_times
.
inference_time
.
end
()
self
.
rec_times
.
postprocess_time
.
start
()
rec_result
=
self
.
postprocess_op
(
preds
)
for
rno
in
range
(
len
(
rec_result
)):
rec_res
[
indices
[
beg_img_no
+
rno
]]
=
rec_result
[
rno
]
elapse
+=
time
.
time
()
-
starttime
return
rec_res
,
elapse
self
.
rec_times
.
postprocess_time
.
end
()
self
.
rec_times
.
img_num
+=
int
(
norm_img_batch
.
shape
[
0
])
self
.
rec_times
.
total_time
.
end
()
return
rec_res
,
self
.
rec_times
.
total_time
.
value
()
def
main
(
args
):
image_file_list
=
get_image_file_list
(
args
.
image_dir
)
text_recognizer
=
TextRecognizer
(
args
)
total_run_time
=
0.0
total_images_num
=
0
valid_image_file_list
=
[]
img_list
=
[]
for
idx
,
image_file
in
enumerate
(
image_file_list
):
cpu_mem
,
gpu_mem
,
gpu_util
=
0
,
0
,
0
count
=
0
# warmup 10 times
fake_img
=
np
.
random
.
uniform
(
-
1
,
1
,
[
1
,
32
,
320
,
3
]).
astype
(
np
.
float32
)
for
i
in
range
(
10
):
dt_boxes
,
_
=
text_recognizer
(
fake_img
)
for
image_file
in
image_file_list
:
img
,
flag
=
check_and_read_gif
(
image_file
)
if
not
flag
:
img
=
cv2
.
imread
(
image_file
)
...
...
@@ -263,29 +274,54 @@ def main(args):
continue
valid_image_file_list
.
append
(
image_file
)
img_list
.
append
(
img
)
if
len
(
img_list
)
>=
args
.
rec_batch_num
or
idx
==
len
(
image_file_list
)
-
1
:
try
:
rec_res
,
predict_time
=
text_recognizer
(
img_list
)
total_run_time
+=
predict_time
except
:
logger
.
info
(
traceback
.
format_exc
())
logger
.
info
(
"ERROR!!!!
\n
"
"Please read the FAQ:https://github.com/PaddlePaddle/PaddleOCR#faq
\n
"
"If your model has tps module: "
"TPS does not support variable shape.
\n
"
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
)
exit
()
for
ino
in
range
(
len
(
img_list
)):
logger
.
info
(
"Predicts of {}:{}"
.
format
(
valid_image_file_list
[
ino
],
rec_res
[
ino
]))
total_images_num
+=
len
(
valid_image_file_list
)
valid_image_file_list
=
[]
img_list
=
[]
logger
.
info
(
"Total predict time for {} images, cost: {:.3f}"
.
format
(
total_images_num
,
total_run_time
))
try
:
rec_res
,
_
=
text_recognizer
(
img_list
)
if
args
.
benchmark
:
cm
,
gm
,
gu
=
utility
.
get_current_memory_mb
(
0
)
cpu_mem
+=
cm
gpu_mem
+=
gm
gpu_util
+=
gu
count
+=
1
except
Exception
as
E
:
logger
.
info
(
traceback
.
format_exc
())
logger
.
info
(
E
)
exit
()
for
ino
in
range
(
len
(
img_list
)):
logger
.
info
(
"Predicts of {}:{}"
.
format
(
valid_image_file_list
[
ino
],
rec_res
[
ino
]))
if
args
.
benchmark
:
mems
=
{
'cpu_rss_mb'
:
cpu_mem
/
count
,
'gpu_rss_mb'
:
gpu_mem
/
count
,
'gpu_util'
:
gpu_util
*
100
/
count
}
else
:
mems
=
None
logger
.
info
(
"The predict time about recognizer module is as follows: "
)
rec_time_dict
=
text_recognizer
.
rec_times
.
report
(
average
=
True
)
rec_model_name
=
args
.
rec_model_dir
if
args
.
benchmark
:
# construct log information
model_info
=
{
'model_name'
:
args
.
rec_model_dir
.
split
(
'/'
)[
-
1
],
'precision'
:
args
.
precision
}
data_info
=
{
'batch_size'
:
args
.
rec_batch_num
,
'shape'
:
'dynamic_shape'
,
'data_num'
:
rec_time_dict
[
'img_num'
]
}
perf_info
=
{
'preprocess_time_s'
:
rec_time_dict
[
'preprocess_time'
],
'inference_time_s'
:
rec_time_dict
[
'inference_time'
],
'postprocess_time_s'
:
rec_time_dict
[
'postprocess_time'
],
'total_time_s'
:
rec_time_dict
[
'total_time'
]
}
benchmark_log
=
benchmark_utils
.
PaddleInferBenchmark
(
text_recognizer
.
config
,
model_info
,
data_info
,
perf_info
,
mems
)
benchmark_log
(
"Rec"
)
if
__name__
==
"__main__"
:
...
...
tools/infer/predict_system.py
浏览文件 @
b8972b36
...
...
@@ -13,7 +13,6 @@
# limitations under the License.
import
os
import
sys
import
subprocess
__dir__
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
sys
.
path
.
append
(
__dir__
)
...
...
@@ -32,8 +31,8 @@ import tools.infer.predict_det as predict_det
import
tools.infer.predict_cls
as
predict_cls
from
ppocr.utils.utility
import
get_image_file_list
,
check_and_read_gif
from
ppocr.utils.logging
import
get_logger
from
tools.infer.utility
import
draw_ocr_box_txt
from
tools.infer.utility
import
draw_ocr_box_txt
,
get_current_memory_mb
import
tools.infer.benchmark_utils
as
benchmark_utils
logger
=
get_logger
()
...
...
@@ -88,8 +87,7 @@ class TextSystem(object):
def
__call__
(
self
,
img
):
ori_im
=
img
.
copy
()
dt_boxes
,
elapse
=
self
.
text_detector
(
img
)
logger
.
info
(
"dt_boxes num : {}, elapse : {}"
.
format
(
len
(
dt_boxes
),
elapse
))
if
dt_boxes
is
None
:
return
None
,
None
img_crop_list
=
[]
...
...
@@ -103,13 +101,9 @@ class TextSystem(object):
if
self
.
use_angle_cls
:
img_crop_list
,
angle_list
,
elapse
=
self
.
text_classifier
(
img_crop_list
)
logger
.
info
(
"cls num : {}, elapse : {}"
.
format
(
len
(
img_crop_list
),
elapse
))
rec_res
,
elapse
=
self
.
text_recognizer
(
img_crop_list
)
logger
.
info
(
"rec_res num : {}, elapse : {}"
.
format
(
len
(
rec_res
),
elapse
))
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
filter_boxes
,
filter_rec_res
=
[],
[]
for
box
,
rec_reuslt
in
zip
(
dt_boxes
,
rec_res
):
text
,
score
=
rec_reuslt
...
...
@@ -142,12 +136,15 @@ def sorted_boxes(dt_boxes):
def
main
(
args
):
image_file_list
=
get_image_file_list
(
args
.
image_dir
)
image_file_list
=
image_file_list
[
args
.
process_id
::
args
.
total_process_num
]
text_sys
=
TextSystem
(
args
)
is_visualize
=
True
font_path
=
args
.
vis_font_path
drop_score
=
args
.
drop_score
for
image_file
in
image_file_list
:
total_time
=
0
cpu_mem
,
gpu_mem
,
gpu_util
=
0
,
0
,
0
_st
=
time
.
time
()
count
=
0
for
idx
,
image_file
in
enumerate
(
image_file_list
):
img
,
flag
=
check_and_read_gif
(
image_file
)
if
not
flag
:
img
=
cv2
.
imread
(
image_file
)
...
...
@@ -157,8 +154,16 @@ def main(args):
starttime
=
time
.
time
()
dt_boxes
,
rec_res
=
text_sys
(
img
)
elapse
=
time
.
time
()
-
starttime
logger
.
info
(
"Predict time of %s: %.3fs"
%
(
image_file
,
elapse
))
total_time
+=
elapse
if
args
.
benchmark
and
idx
%
20
==
0
:
cm
,
gm
,
gu
=
get_current_memory_mb
(
0
)
cpu_mem
+=
cm
gpu_mem
+=
gm
gpu_util
+=
gu
count
+=
1
logger
.
info
(
str
(
idx
)
+
" Predict time of %s: %.3fs"
%
(
image_file
,
elapse
))
for
text
,
score
in
rec_res
:
logger
.
info
(
"{}, {:.3f}"
.
format
(
text
,
score
))
...
...
@@ -178,26 +183,74 @@ def main(args):
draw_img_save
=
"./inference_results/"
if
not
os
.
path
.
exists
(
draw_img_save
):
os
.
makedirs
(
draw_img_save
)
if
flag
:
image_file
=
image_file
[:
-
3
]
+
"png"
cv2
.
imwrite
(
os
.
path
.
join
(
draw_img_save
,
os
.
path
.
basename
(
image_file
)),
draw_img
[:,
:,
::
-
1
])
logger
.
info
(
"The visualized image saved in {}"
.
format
(
os
.
path
.
join
(
draw_img_save
,
os
.
path
.
basename
(
image_file
))))
logger
.
info
(
"The predict total time is {}"
.
format
(
time
.
time
()
-
_st
))
logger
.
info
(
"
\n
The predict total time is {}"
.
format
(
total_time
))
if
__name__
==
"__main__"
:
args
=
utility
.
parse_args
()
if
args
.
use_mp
:
p_list
=
[]
total_process_num
=
args
.
total_process_num
for
process_id
in
range
(
total_process_num
):
cmd
=
[
sys
.
executable
,
"-u"
]
+
sys
.
argv
+
[
"--process_id={}"
.
format
(
process_id
),
"--use_mp={}"
.
format
(
False
)
]
p
=
subprocess
.
Popen
(
cmd
,
stdout
=
sys
.
stdout
,
stderr
=
sys
.
stdout
)
p_list
.
append
(
p
)
for
p
in
p_list
:
p
.
wait
()
img_num
=
text_sys
.
text_detector
.
det_times
.
img_num
if
args
.
benchmark
:
mems
=
{
'cpu_rss_mb'
:
cpu_mem
/
count
,
'gpu_rss_mb'
:
gpu_mem
/
count
,
'gpu_util'
:
gpu_util
*
100
/
count
}
else
:
main
(
args
)
mems
=
None
det_time_dict
=
text_sys
.
text_detector
.
det_times
.
report
(
average
=
True
)
rec_time_dict
=
text_sys
.
text_recognizer
.
rec_times
.
report
(
average
=
True
)
det_model_name
=
args
.
det_model_dir
rec_model_name
=
args
.
rec_model_dir
# construct det log information
model_info
=
{
'model_name'
:
args
.
det_model_dir
.
split
(
'/'
)[
-
1
],
'precision'
:
args
.
precision
}
data_info
=
{
'batch_size'
:
1
,
'shape'
:
'dynamic_shape'
,
'data_num'
:
det_time_dict
[
'img_num'
]
}
perf_info
=
{
'preprocess_time_s'
:
det_time_dict
[
'preprocess_time'
],
'inference_time_s'
:
det_time_dict
[
'inference_time'
],
'postprocess_time_s'
:
det_time_dict
[
'postprocess_time'
],
'total_time_s'
:
det_time_dict
[
'total_time'
]
}
benchmark_log
=
benchmark_utils
.
PaddleInferBenchmark
(
text_sys
.
text_detector
.
config
,
model_info
,
data_info
,
perf_info
,
mems
,
args
.
save_log_path
)
benchmark_log
(
"Det"
)
# construct rec log information
model_info
=
{
'model_name'
:
args
.
rec_model_dir
.
split
(
'/'
)[
-
1
],
'precision'
:
args
.
precision
}
data_info
=
{
'batch_size'
:
args
.
rec_batch_num
,
'shape'
:
'dynamic_shape'
,
'data_num'
:
rec_time_dict
[
'img_num'
]
}
perf_info
=
{
'preprocess_time_s'
:
rec_time_dict
[
'preprocess_time'
],
'inference_time_s'
:
rec_time_dict
[
'inference_time'
],
'postprocess_time_s'
:
rec_time_dict
[
'postprocess_time'
],
'total_time_s'
:
rec_time_dict
[
'total_time'
]
}
benchmark_log
=
benchmark_utils
.
PaddleInferBenchmark
(
text_sys
.
text_recognizer
.
config
,
model_info
,
data_info
,
perf_info
,
mems
,
args
.
save_log_path
)
benchmark_log
(
"Rec"
)
if
__name__
==
"__main__"
:
main
(
utility
.
parse_args
())
tools/infer/utility.py
浏览文件 @
b8972b36
...
...
@@ -21,6 +21,9 @@ import json
from
PIL
import
Image
,
ImageDraw
,
ImageFont
import
math
from
paddle
import
inference
import
time
from
ppocr.utils.logging
import
get_logger
logger
=
get_logger
()
def
parse_args
():
...
...
@@ -32,7 +35,7 @@ def parse_args():
parser
.
add_argument
(
"--use_gpu"
,
type
=
str2bool
,
default
=
True
)
parser
.
add_argument
(
"--ir_optim"
,
type
=
str2bool
,
default
=
True
)
parser
.
add_argument
(
"--use_tensorrt"
,
type
=
str2bool
,
default
=
False
)
parser
.
add_argument
(
"--
use_fp16"
,
type
=
str2bool
,
default
=
False
)
parser
.
add_argument
(
"--
precision"
,
type
=
str
,
default
=
"fp32"
)
parser
.
add_argument
(
"--gpu_mem"
,
type
=
int
,
default
=
500
)
# params for text detector
...
...
@@ -98,15 +101,88 @@ def parse_args():
parser
.
add_argument
(
"--cls_thresh"
,
type
=
float
,
default
=
0.9
)
parser
.
add_argument
(
"--enable_mkldnn"
,
type
=
str2bool
,
default
=
False
)
parser
.
add_argument
(
"--cpu_threads"
,
type
=
int
,
default
=
10
)
parser
.
add_argument
(
"--use_pdserving"
,
type
=
str2bool
,
default
=
False
)
parser
.
add_argument
(
"--use_mp"
,
type
=
str2bool
,
default
=
False
)
parser
.
add_argument
(
"--total_process_num"
,
type
=
int
,
default
=
1
)
parser
.
add_argument
(
"--process_id"
,
type
=
int
,
default
=
0
)
parser
.
add_argument
(
"--benchmark"
,
type
=
bool
,
default
=
False
)
parser
.
add_argument
(
"--save_log_path"
,
type
=
str
,
default
=
"./log_output/"
)
return
parser
.
parse_args
()
class
Times
(
object
):
def
__init__
(
self
):
self
.
time
=
0.
self
.
st
=
0.
self
.
et
=
0.
def
start
(
self
):
self
.
st
=
time
.
time
()
def
end
(
self
,
accumulative
=
True
):
self
.
et
=
time
.
time
()
if
accumulative
:
self
.
time
+=
self
.
et
-
self
.
st
else
:
self
.
time
=
self
.
et
-
self
.
st
def
reset
(
self
):
self
.
time
=
0.
self
.
st
=
0.
self
.
et
=
0.
def
value
(
self
):
return
round
(
self
.
time
,
4
)
class
Timer
(
Times
):
def
__init__
(
self
):
super
(
Timer
,
self
).
__init__
()
self
.
total_time
=
Times
()
self
.
preprocess_time
=
Times
()
self
.
inference_time
=
Times
()
self
.
postprocess_time
=
Times
()
self
.
img_num
=
0
def
info
(
self
,
average
=
False
):
logger
.
info
(
"----------------------- Perf info -----------------------"
)
logger
.
info
(
"total_time: {}, img_num: {}"
.
format
(
self
.
total_time
.
value
(
),
self
.
img_num
))
preprocess_time
=
round
(
self
.
preprocess_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
preprocess_time
.
value
()
postprocess_time
=
round
(
self
.
postprocess_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
postprocess_time
.
value
()
inference_time
=
round
(
self
.
inference_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
inference_time
.
value
()
average_latency
=
self
.
total_time
.
value
()
/
self
.
img_num
logger
.
info
(
"average_latency(ms): {:.2f}, QPS: {:2f}"
.
format
(
average_latency
*
1000
,
1
/
average_latency
))
logger
.
info
(
"preprocess_latency(ms): {:.2f}, inference_latency(ms): {:.2f}, postprocess_latency(ms): {:.2f}"
.
format
(
preprocess_time
*
1000
,
inference_time
*
1000
,
postprocess_time
*
1000
))
def
report
(
self
,
average
=
False
):
dic
=
{}
dic
[
'preprocess_time'
]
=
round
(
self
.
preprocess_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
preprocess_time
.
value
()
dic
[
'postprocess_time'
]
=
round
(
self
.
postprocess_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
postprocess_time
.
value
()
dic
[
'inference_time'
]
=
round
(
self
.
inference_time
.
value
()
/
self
.
img_num
,
4
)
if
average
else
self
.
inference_time
.
value
()
dic
[
'img_num'
]
=
self
.
img_num
dic
[
'total_time'
]
=
round
(
self
.
total_time
.
value
(),
4
)
return
dic
def
create_predictor
(
args
,
mode
,
logger
):
if
mode
==
"det"
:
model_dir
=
args
.
det_model_dir
...
...
@@ -131,6 +207,16 @@ def create_predictor(args, mode, logger):
config
=
inference
.
Config
(
model_file_path
,
params_file_path
)
if
hasattr
(
args
,
'precision'
):
if
args
.
precision
==
"fp16"
and
args
.
use_tensorrt
:
precision
=
inference
.
PrecisionType
.
Half
elif
args
.
precision
==
"int8"
:
precision
=
inference
.
PrecisionType
.
Int8
else
:
precision
=
inference
.
PrecisionType
.
Float32
else
:
precision
=
inference
.
PrecisionType
.
Float32
if
args
.
use_gpu
:
config
.
enable_use_gpu
(
args
.
gpu_mem
,
0
)
if
args
.
use_tensorrt
:
...
...
@@ -140,7 +226,10 @@ def create_predictor(args, mode, logger):
max_batch_size
=
args
.
max_batch_size
)
else
:
config
.
disable_gpu
()
config
.
set_cpu_math_library_num_threads
(
6
)
if
hasattr
(
args
,
"cpu_threads"
):
config
.
set_cpu_math_library_num_threads
(
args
.
cpu_threads
)
else
:
config
.
set_cpu_math_library_num_threads
(
10
)
if
args
.
enable_mkldnn
:
# cache 10 different shapes for mkldnn to avoid memory leak
config
.
set_mkldnn_cache_capacity
(
10
)
...
...
@@ -166,7 +255,7 @@ def create_predictor(args, mode, logger):
for
output_name
in
output_names
:
output_tensor
=
predictor
.
get_output_handle
(
output_name
)
output_tensors
.
append
(
output_tensor
)
return
predictor
,
input_tensor
,
output_tensors
return
predictor
,
input_tensor
,
output_tensors
,
config
def
draw_e2e_res
(
dt_boxes
,
strs
,
img_path
):
...
...
@@ -417,6 +506,31 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5):
return
image
def
get_current_memory_mb
(
gpu_id
=
None
):
"""
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
"""
import
pynvml
import
psutil
import
GPUtil
pid
=
os
.
getpid
()
p
=
psutil
.
Process
(
pid
)
info
=
p
.
memory_full_info
()
cpu_mem
=
info
.
uss
/
1024.
/
1024.
gpu_mem
=
0
gpu_percent
=
0
if
gpu_id
is
not
None
:
GPUs
=
GPUtil
.
getGPUs
()
gpu_load
=
GPUs
[
gpu_id
].
load
gpu_percent
=
gpu_load
pynvml
.
nvmlInit
()
handle
=
pynvml
.
nvmlDeviceGetHandleByIndex
(
0
)
meminfo
=
pynvml
.
nvmlDeviceGetMemoryInfo
(
handle
)
gpu_mem
=
meminfo
.
used
/
1024.
/
1024.
return
round
(
cpu_mem
,
4
),
round
(
gpu_mem
,
4
),
round
(
gpu_percent
,
4
)
if
__name__
==
'__main__'
:
test_img
=
"./doc/test_v2"
predict_txt
=
"./doc/predict.txt"
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录