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体验新版 GitCode,发现更多精彩内容 >>
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26cefa3d
编写于
6月 15, 2020
作者:
J
jack
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add seg batch predict
上级
cb6edab6
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
220 addition
and
26 deletion
+220
-26
deploy/cpp/demo/classifier.cpp
deploy/cpp/demo/classifier.cpp
+19
-14
deploy/cpp/demo/segmenter.cpp
deploy/cpp/demo/segmenter.cpp
+54
-12
deploy/cpp/include/paddlex/paddlex.h
deploy/cpp/include/paddlex/paddlex.h
+4
-0
deploy/cpp/src/paddlex.cpp
deploy/cpp/src/paddlex.cpp
+143
-0
未找到文件。
deploy/cpp/demo/classifier.cpp
浏览文件 @
26cefa3d
...
@@ -32,7 +32,7 @@ DEFINE_int32(gpu_id, 0, "GPU card id");
...
@@ -32,7 +32,7 @@ DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string
(
key
,
""
,
"key of encryption"
);
DEFINE_string
(
key
,
""
,
"key of encryption"
);
DEFINE_string
(
image
,
""
,
"Path of test image file"
);
DEFINE_string
(
image
,
""
,
"Path of test image file"
);
DEFINE_string
(
image_list
,
""
,
"Path of test image list file"
);
DEFINE_string
(
image_list
,
""
,
"Path of test image list file"
);
DEFINE_int32
(
batch_size
,
1
,
"Batch size
when
infering"
);
DEFINE_int32
(
batch_size
,
1
,
"Batch size
of
infering"
);
int
main
(
int
argc
,
char
**
argv
)
{
int
main
(
int
argc
,
char
**
argv
)
{
// Parsing command-line
// Parsing command-line
...
@@ -53,8 +53,8 @@ int main(int argc, char** argv) {
...
@@ -53,8 +53,8 @@ int main(int argc, char** argv) {
// 进行预测
// 进行预测
double
total_running_time_s
=
0.0
;
double
total_running_time_s
=
0.0
;
double
total_imrea
a
d_time_s
=
0.0
;
double
total_imread_time_s
=
0.0
;
int
imgs
=
1
;
if
(
FLAGS_image_list
!=
""
)
{
if
(
FLAGS_image_list
!=
""
)
{
std
::
ifstream
inf
(
FLAGS_image_list
);
std
::
ifstream
inf
(
FLAGS_image_list
);
if
(
!
inf
)
{
if
(
!
inf
)
{
...
@@ -63,31 +63,32 @@ int main(int argc, char** argv) {
...
@@ -63,31 +63,32 @@ int main(int argc, char** argv) {
}
}
// 多batch预测
// 多batch预测
std
::
string
image_path
;
std
::
string
image_path
;
std
::
vector
<
std
::
string
>
image_path
_vec
;
std
::
vector
<
std
::
string
>
image_path
s
;
while
(
getline
(
inf
,
image_path
))
{
while
(
getline
(
inf
,
image_path
))
{
image_path
_vec
.
push_back
(
image_path
);
image_path
s
.
push_back
(
image_path
);
}
}
for
(
int
i
=
0
;
i
<
image_path_vec
.
size
();
i
+=
FLAGS_batch_size
)
{
imgs
=
image_paths
.
size
();
for
(
int
i
=
0
;
i
<
image_paths
.
size
();
i
+=
FLAGS_batch_size
)
{
auto
start
=
system_clock
::
now
();
auto
start
=
system_clock
::
now
();
// 读图像
// 读图像
int
im_vec_size
=
std
::
min
((
int
)
image_path
_vec
.
size
(),
i
+
FLAGS_batch_size
);
int
im_vec_size
=
std
::
min
((
int
)
image_path
s
.
size
(),
i
+
FLAGS_batch_size
);
std
::
vector
<
cv
::
Mat
>
im_vec
(
im_vec_size
-
i
);
std
::
vector
<
cv
::
Mat
>
im_vec
(
im_vec_size
-
i
);
std
::
vector
<
PaddleX
::
ClsResult
>
results
(
im_vec_size
-
i
,
PaddleX
::
ClsResult
());
std
::
vector
<
PaddleX
::
ClsResult
>
results
(
im_vec_size
-
i
,
PaddleX
::
ClsResult
());
#pragma omp parallel for num_threads(im_vec_size - i)
#pragma omp parallel for num_threads(im_vec_size - i)
for
(
int
j
=
i
;
j
<
im_vec_size
;
++
j
){
for
(
int
j
=
i
;
j
<
im_vec_size
;
++
j
){
im_vec
[
j
-
i
]
=
std
::
move
(
cv
::
imread
(
image_path
_vec
[
j
],
1
));
im_vec
[
j
-
i
]
=
std
::
move
(
cv
::
imread
(
image_path
s
[
j
],
1
));
}
}
auto
imread_end
=
system_clock
::
now
();
auto
imread_end
=
system_clock
::
now
();
model
.
predict
(
im_vec
,
results
);
model
.
predict
(
im_vec
,
results
);
auto
imread_duration
=
duration_cast
<
microseconds
>
(
imread_end
-
start
);
auto
imread_duration
=
duration_cast
<
microseconds
>
(
imread_end
-
start
);
total_imrea
a
d_time_s
+=
double
(
imread_duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
total_imread_time_s
+=
double
(
imread_duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
auto
end
=
system_clock
::
now
();
auto
end
=
system_clock
::
now
();
auto
duration
=
duration_cast
<
microseconds
>
(
end
-
start
);
auto
duration
=
duration_cast
<
microseconds
>
(
end
-
start
);
total_running_time_s
+=
double
(
duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
total_running_time_s
+=
double
(
duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
for
(
int
j
=
i
;
j
<
im_vec_size
;
++
j
)
{
for
(
int
j
=
i
;
j
<
im_vec_size
;
++
j
)
{
std
::
cout
<<
"Path:"
<<
image_path
_vec
[
j
]
std
::
cout
<<
"Path:"
<<
image_path
s
[
j
]
<<
", predict label: "
<<
results
[
j
-
i
].
category
<<
", predict label: "
<<
results
[
j
-
i
].
category
<<
", label_id:"
<<
results
[
j
-
i
].
category_id
<<
", label_id:"
<<
results
[
j
-
i
].
category_id
<<
", score: "
<<
results
[
j
-
i
].
score
<<
std
::
endl
;
<<
", score: "
<<
results
[
j
-
i
].
score
<<
std
::
endl
;
...
@@ -105,11 +106,15 @@ int main(int argc, char** argv) {
...
@@ -105,11 +106,15 @@ int main(int argc, char** argv) {
<<
", label_id:"
<<
result
.
category_id
<<
", label_id:"
<<
result
.
category_id
<<
", score: "
<<
result
.
score
<<
std
::
endl
;
<<
", score: "
<<
result
.
score
<<
std
::
endl
;
}
}
std
::
cout
<<
"Total
average
running time: "
std
::
cout
<<
"Total running time: "
<<
total_running_time_s
<<
total_running_time_s
<<
" s, total average read img time: "
<<
" s, average running time: "
<<
total_imreaad_time_s
<<
total_running_time_s
/
imgs
<<
" s, batch_size = "
<<
" s/img, total read img time: "
<<
total_imread_time_s
<<
" s, average read time: "
<<
total_imread_time_s
/
imgs
<<
" s/img, batch_size = "
<<
FLAGS_batch_size
<<
FLAGS_batch_size
<<
std
::
endl
;
<<
std
::
endl
;
return
0
;
return
0
;
...
...
deploy/cpp/demo/segmenter.cpp
浏览文件 @
26cefa3d
...
@@ -14,14 +14,18 @@
...
@@ -14,14 +14,18 @@
#include <glog/logging.h>
#include <glog/logging.h>
#include <algorithm>
#include <chrono>
#include <fstream>
#include <fstream>
#include <iostream>
#include <iostream>
#include <string>
#include <string>
#include <vector>
#include <vector>
#include <utility>
#include "include/paddlex/paddlex.h"
#include "include/paddlex/paddlex.h"
#include "include/paddlex/visualize.h"
#include "include/paddlex/visualize.h"
using
namespace
std
::
chrono
;
DEFINE_string
(
model_dir
,
""
,
"Path of inference model"
);
DEFINE_string
(
model_dir
,
""
,
"Path of inference model"
);
DEFINE_bool
(
use_gpu
,
false
,
"Infering with GPU or CPU"
);
DEFINE_bool
(
use_gpu
,
false
,
"Infering with GPU or CPU"
);
DEFINE_bool
(
use_trt
,
false
,
"Infering with TensorRT"
);
DEFINE_bool
(
use_trt
,
false
,
"Infering with TensorRT"
);
...
@@ -30,6 +34,7 @@ DEFINE_string(key, "", "key of encryption");
...
@@ -30,6 +34,7 @@ DEFINE_string(key, "", "key of encryption");
DEFINE_string
(
image
,
""
,
"Path of test image file"
);
DEFINE_string
(
image
,
""
,
"Path of test image file"
);
DEFINE_string
(
image_list
,
""
,
"Path of test image list file"
);
DEFINE_string
(
image_list
,
""
,
"Path of test image list file"
);
DEFINE_string
(
save_dir
,
"output"
,
"Path to save visualized image"
);
DEFINE_string
(
save_dir
,
"output"
,
"Path to save visualized image"
);
DEFINE_int32
(
batch_size
,
1
,
"Batch size of infering"
);
int
main
(
int
argc
,
char
**
argv
)
{
int
main
(
int
argc
,
char
**
argv
)
{
// 解析命令行参数
// 解析命令行参数
...
@@ -46,8 +51,11 @@ int main(int argc, char** argv) {
...
@@ -46,8 +51,11 @@ int main(int argc, char** argv) {
// 加载模型
// 加载模型
PaddleX
::
Model
model
;
PaddleX
::
Model
model
;
model
.
Init
(
FLAGS_model_dir
,
FLAGS_use_gpu
,
FLAGS_use_trt
,
FLAGS_gpu_id
,
FLAGS_key
);
model
.
Init
(
FLAGS_model_dir
,
FLAGS_use_gpu
,
FLAGS_use_trt
,
FLAGS_gpu_id
,
FLAGS_key
,
FLAGS_batch_size
);
double
total_running_time_s
=
0.0
;
double
total_imread_time_s
=
0.0
;
int
imgs
=
1
;
auto
colormap
=
PaddleX
::
GenerateColorMap
(
model
.
labels
.
size
());
auto
colormap
=
PaddleX
::
GenerateColorMap
(
model
.
labels
.
size
());
// 进行预测
// 进行预测
if
(
FLAGS_image_list
!=
""
)
{
if
(
FLAGS_image_list
!=
""
)
{
...
@@ -57,23 +65,46 @@ int main(int argc, char** argv) {
...
@@ -57,23 +65,46 @@ int main(int argc, char** argv) {
return
-
1
;
return
-
1
;
}
}
std
::
string
image_path
;
std
::
string
image_path
;
std
::
vector
<
std
::
string
>
image_paths
;
while
(
getline
(
inf
,
image_path
))
{
while
(
getline
(
inf
,
image_path
))
{
PaddleX
::
SegResult
result
;
image_paths
.
push_back
(
image_path
);
cv
::
Mat
im
=
cv
::
imread
(
image_path
,
1
);
}
model
.
predict
(
im
,
&
result
);
imgs
=
image_paths
.
size
();
for
(
int
i
=
0
;
i
<
image_paths
.
size
();
i
+=
FLAGS_batch_size
){
auto
start
=
system_clock
::
now
();
int
im_vec_size
=
std
::
min
((
int
)
image_paths
.
size
(),
i
+
FLAGS_batch_size
);
std
::
vector
<
cv
::
Mat
>
im_vec
(
im_vec_size
-
i
);
std
::
vector
<
PaddleX
::
SegResult
>
results
(
im_vec_size
-
i
,
PaddleX
::
SegResult
());
#pragma omp parallel for num_threads(im_vec_size - i)
for
(
int
j
=
i
;
j
<
im_vec_size
;
++
j
){
im_vec
[
j
-
i
]
=
std
::
move
(
cv
::
imread
(
image_paths
[
j
],
1
));
}
auto
imread_end
=
system_clock
::
now
();
model
.
predict
(
im_vec
,
results
);
auto
imread_duration
=
duration_cast
<
microseconds
>
(
imread_end
-
start
);
total_imread_time_s
+=
double
(
imread_duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
auto
end
=
system_clock
::
now
();
auto
duration
=
duration_cast
<
microseconds
>
(
end
-
start
);
total_running_time_s
+=
double
(
duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
// 可视化
// 可视化
cv
::
Mat
vis_img
=
for
(
int
j
=
0
;
j
<
im_vec_size
-
i
;
++
j
)
{
PaddleX
::
Visualize
(
im
,
result
,
model
.
labels
,
colormap
);
cv
::
Mat
vis_img
=
std
::
string
save_path
=
PaddleX
::
Visualize
(
im_vec
[
j
],
results
[
j
],
model
.
labels
,
colormap
);
PaddleX
::
generate_save_path
(
FLAGS_save_dir
,
image_path
);
std
::
string
save_path
=
cv
::
imwrite
(
save_path
,
vis_img
);
PaddleX
::
generate_save_path
(
FLAGS_save_dir
,
image_paths
[
i
+
j
]);
result
.
clear
();
cv
::
imwrite
(
save_path
,
vis_img
);
std
::
cout
<<
"Visualized output saved as "
<<
save_path
<<
std
::
endl
;
std
::
cout
<<
"Visualized output saved as "
<<
save_path
<<
std
::
endl
;
}
}
}
}
else
{
}
else
{
auto
start
=
system_clock
::
now
();
PaddleX
::
SegResult
result
;
PaddleX
::
SegResult
result
;
cv
::
Mat
im
=
cv
::
imread
(
FLAGS_image
,
1
);
cv
::
Mat
im
=
cv
::
imread
(
FLAGS_image
,
1
);
model
.
predict
(
im
,
&
result
);
model
.
predict
(
im
,
&
result
);
auto
end
=
system_clock
::
now
();
auto
duration
=
duration_cast
<
microseconds
>
(
end
-
start
);
total_running_time_s
+=
double
(
duration
.
count
())
*
microseconds
::
period
::
num
/
microseconds
::
period
::
den
;
// 可视化
// 可视化
cv
::
Mat
vis_img
=
PaddleX
::
Visualize
(
im
,
result
,
model
.
labels
,
colormap
);
cv
::
Mat
vis_img
=
PaddleX
::
Visualize
(
im
,
result
,
model
.
labels
,
colormap
);
std
::
string
save_path
=
std
::
string
save_path
=
...
@@ -82,6 +113,17 @@ int main(int argc, char** argv) {
...
@@ -82,6 +113,17 @@ int main(int argc, char** argv) {
result
.
clear
();
result
.
clear
();
std
::
cout
<<
"Visualized output saved as "
<<
save_path
<<
std
::
endl
;
std
::
cout
<<
"Visualized output saved as "
<<
save_path
<<
std
::
endl
;
}
}
std
::
cout
<<
"Total running time: "
<<
total_running_time_s
<<
" s, average running time: "
<<
total_running_time_s
/
imgs
<<
" s/img, total read img time: "
<<
total_imread_time_s
<<
" s, average read img time: "
<<
total_imread_time_s
/
imgs
<<
" s, batch_size = "
<<
FLAGS_batch_size
<<
std
::
endl
;
return
0
;
return
0
;
}
}
deploy/cpp/include/paddlex/paddlex.h
浏览文件 @
26cefa3d
...
@@ -69,8 +69,12 @@ class Model {
...
@@ -69,8 +69,12 @@ class Model {
bool
predict
(
const
cv
::
Mat
&
im
,
DetResult
*
result
);
bool
predict
(
const
cv
::
Mat
&
im
,
DetResult
*
result
);
bool
predict
(
const
std
::
vector
<
cv
::
Mat
>
&
im_batch
,
std
::
vector
<
DetResult
>
&
result
);
bool
predict
(
const
cv
::
Mat
&
im
,
SegResult
*
result
);
bool
predict
(
const
cv
::
Mat
&
im
,
SegResult
*
result
);
bool
predict
(
const
std
::
vector
<
cv
::
Mat
>
&
im_batch
,
std
::
vector
<
SegResult
>
&
result
);
bool
postprocess
(
SegResult
*
result
);
bool
postprocess
(
SegResult
*
result
);
bool
postprocess
(
DetResult
*
result
);
bool
postprocess
(
DetResult
*
result
);
...
...
deploy/cpp/src/paddlex.cpp
浏览文件 @
26cefa3d
...
@@ -161,6 +161,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
...
@@ -161,6 +161,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
result
->
category_id
=
std
::
distance
(
std
::
begin
(
outputs_
),
ptr
);
result
->
category_id
=
std
::
distance
(
std
::
begin
(
outputs_
),
ptr
);
result
->
score
=
*
ptr
;
result
->
score
=
*
ptr
;
result
->
category
=
labels
[
result
->
category_id
];
result
->
category
=
labels
[
result
->
category_id
];
return
true
;
}
}
bool
Model
::
predict
(
const
std
::
vector
<
cv
::
Mat
>
&
im_batch
,
std
::
vector
<
ClsResult
>
&
results
)
{
bool
Model
::
predict
(
const
std
::
vector
<
cv
::
Mat
>
&
im_batch
,
std
::
vector
<
ClsResult
>
&
results
)
{
...
@@ -322,6 +323,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
...
@@ -322,6 +323,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
static_cast
<
int
>
(
box
->
coordinate
[
3
])};
static_cast
<
int
>
(
box
->
coordinate
[
3
])};
}
}
}
}
return
true
;
}
}
bool
Model
::
predict
(
const
cv
::
Mat
&
im
,
SegResult
*
result
)
{
bool
Model
::
predict
(
const
cv
::
Mat
&
im
,
SegResult
*
result
)
{
...
@@ -430,6 +432,147 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
...
@@ -430,6 +432,147 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
result
->
score_map
.
data
.
assign
(
mask_score
.
begin
<
float
>
(),
result
->
score_map
.
data
.
assign
(
mask_score
.
begin
<
float
>
(),
mask_score
.
end
<
float
>
());
mask_score
.
end
<
float
>
());
result
->
score_map
.
shape
=
{
mask_score
.
rows
,
mask_score
.
cols
};
result
->
score_map
.
shape
=
{
mask_score
.
rows
,
mask_score
.
cols
};
return
true
;
}
bool
Model
::
predict
(
const
std
::
vector
<
cv
::
Mat
>
&
im_batch
,
std
::
vector
<
SegResult
>
&
result
)
{
for
(
auto
&
inputs
:
inputs_batch_
)
{
inputs
.
clear
();
}
if
(
type
==
"classifier"
)
{
std
::
cerr
<<
"Loading model is a 'classifier', ClsResult should be passed "
"to function predict()!"
<<
std
::
endl
;
return
false
;
}
else
if
(
type
==
"detector"
)
{
std
::
cerr
<<
"Loading model is a 'detector', DetResult should be passed to "
"function predict()!"
<<
std
::
endl
;
return
false
;
}
// 处理输入图像
if
(
!
preprocess
(
im_batch
,
inputs_batch_
))
{
std
::
cerr
<<
"Preprocess failed!"
<<
std
::
endl
;
return
false
;
}
int
batch_size
=
im_batch
.
size
();
result
.
clear
();
result
.
resize
(
batch_size
);
int
h
=
inputs_batch_
[
0
].
new_im_size_
[
0
];
int
w
=
inputs_batch_
[
0
].
new_im_size_
[
1
];
auto
im_tensor
=
predictor_
->
GetInputTensor
(
"image"
);
im_tensor
->
Reshape
({
batch_size
,
3
,
h
,
w
});
std
::
vector
<
float
>
inputs_data
(
batch_size
*
3
*
h
*
w
);
for
(
int
i
=
0
;
i
<
inputs_batch_
.
size
();
++
i
)
{
std
::
copy
(
inputs_batch_
[
i
].
im_data_
.
begin
(),
inputs_batch_
[
i
].
im_data_
.
end
(),
inputs_data
.
begin
()
+
i
*
3
*
h
*
w
);
}
im_tensor
->
copy_from_cpu
(
inputs_data
.
data
());
//im_tensor->copy_from_cpu(inputs_.im_data_.data());
// 使用加载的模型进行预测
predictor_
->
ZeroCopyRun
();
// 获取预测置信度,经过argmax后的labelmap
auto
output_names
=
predictor_
->
GetOutputNames
();
auto
output_label_tensor
=
predictor_
->
GetOutputTensor
(
output_names
[
0
]);
std
::
vector
<
int
>
output_label_shape
=
output_label_tensor
->
shape
();
int
size
=
1
;
for
(
const
auto
&
i
:
output_label_shape
)
{
size
*=
i
;
}
std
::
vector
<
int64_t
>
output_labels
(
size
,
0
);
output_label_tensor
->
copy_to_cpu
(
output_labels
.
data
());
auto
output_labels_iter
=
output_labels
.
begin
();
int
single_batch_size
=
size
/
batch_size
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
result
[
i
].
label_map
.
data
.
resize
(
single_batch_size
);
result
[
i
].
label_map
.
shape
.
push_back
(
1
);
for
(
int
j
=
1
;
j
<
output_label_shape
.
size
();
++
j
)
{
result
[
i
].
label_map
.
shape
.
push_back
(
output_label_shape
[
j
]);
}
std
::
copy
(
output_labels_iter
+
i
*
single_batch_size
,
output_labels_iter
+
(
i
+
1
)
*
single_batch_size
,
result
[
i
].
label_map
.
data
.
data
());
}
// 获取预测置信度scoremap
auto
output_score_tensor
=
predictor_
->
GetOutputTensor
(
output_names
[
1
]);
std
::
vector
<
int
>
output_score_shape
=
output_score_tensor
->
shape
();
size
=
1
;
for
(
const
auto
&
i
:
output_score_shape
)
{
size
*=
i
;
}
std
::
vector
<
float
>
output_scores
(
size
,
0
);
output_score_tensor
->
copy_to_cpu
(
output_scores
.
data
());
auto
output_scores_iter
=
output_scores
.
begin
();
int
single_batch_score_size
=
size
/
batch_size
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
result
[
i
].
score_map
.
data
.
resize
(
single_batch_score_size
);
result
[
i
].
score_map
.
shape
.
push_back
(
1
);
for
(
int
j
=
1
;
j
<
output_score_shape
.
size
();
++
j
)
{
result
[
i
].
score_map
.
shape
.
push_back
(
output_score_shape
[
j
]);
}
std
::
copy
(
output_scores_iter
+
i
*
single_batch_score_size
,
output_scores_iter
+
(
i
+
1
)
*
single_batch_score_size
,
result
[
i
].
score_map
.
data
.
data
());
}
// 解析输出结果到原图大小
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
std
::
vector
<
uint8_t
>
label_map
(
result
[
i
].
label_map
.
data
.
begin
(),
result
[
i
].
label_map
.
data
.
end
());
cv
::
Mat
mask_label
(
result
[
i
].
label_map
.
shape
[
1
],
result
[
i
].
label_map
.
shape
[
2
],
CV_8UC1
,
label_map
.
data
());
cv
::
Mat
mask_score
(
result
[
i
].
score_map
.
shape
[
2
],
result
[
i
].
score_map
.
shape
[
3
],
CV_32FC1
,
result
[
i
].
score_map
.
data
.
data
());
int
idx
=
1
;
int
len_postprocess
=
inputs_batch_
[
i
].
im_size_before_resize_
.
size
();
for
(
std
::
vector
<
std
::
string
>::
reverse_iterator
iter
=
inputs_batch_
[
i
].
reshape_order_
.
rbegin
();
iter
!=
inputs_batch_
[
i
].
reshape_order_
.
rend
();
++
iter
)
{
if
(
*
iter
==
"padding"
)
{
auto
before_shape
=
inputs_batch_
[
i
].
im_size_before_resize_
[
len_postprocess
-
idx
];
inputs_batch_
[
i
].
im_size_before_resize_
.
pop_back
();
auto
padding_w
=
before_shape
[
0
];
auto
padding_h
=
before_shape
[
1
];
mask_label
=
mask_label
(
cv
::
Rect
(
0
,
0
,
padding_h
,
padding_w
));
mask_score
=
mask_score
(
cv
::
Rect
(
0
,
0
,
padding_h
,
padding_w
));
}
else
if
(
*
iter
==
"resize"
)
{
auto
before_shape
=
inputs_batch_
[
i
].
im_size_before_resize_
[
len_postprocess
-
idx
];
inputs_batch_
[
i
].
im_size_before_resize_
.
pop_back
();
auto
resize_w
=
before_shape
[
0
];
auto
resize_h
=
before_shape
[
1
];
cv
::
resize
(
mask_label
,
mask_label
,
cv
::
Size
(
resize_h
,
resize_w
),
0
,
0
,
cv
::
INTER_NEAREST
);
cv
::
resize
(
mask_score
,
mask_score
,
cv
::
Size
(
resize_h
,
resize_w
),
0
,
0
,
cv
::
INTER_LINEAR
);
}
++
idx
;
}
result
[
i
].
label_map
.
data
.
assign
(
mask_label
.
begin
<
uint8_t
>
(),
mask_label
.
end
<
uint8_t
>
());
result
[
i
].
label_map
.
shape
=
{
mask_label
.
rows
,
mask_label
.
cols
};
result
[
i
].
score_map
.
data
.
assign
(
mask_score
.
begin
<
float
>
(),
mask_score
.
end
<
float
>
());
result
[
i
].
score_map
.
shape
=
{
mask_score
.
rows
,
mask_score
.
cols
};
}
return
true
;
}
}
}
// namespce of PaddleX
}
// namespce of PaddleX
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