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57f51e5b
编写于
3月 28, 2019
作者:
L
lidanqing
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preprocess with PIL the full val dataset and save binary
test=develop
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paddle/fluid/inference/tests/api/preprocess.py
paddle/fluid/inference/tests/api/preprocess.py
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paddle/fluid/inference/tests/api/preprocess.py
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# copyright (c) 2019 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
unittest
import
os
import
numpy
as
np
import
time
import
sys
import
random
import
functools
import
contextlib
from
PIL
import
Image
,
ImageEnhance
import
math
random
.
seed
(
0
)
np
.
random
.
seed
(
0
)
DATA_DIM
=
224
SIZE_FLOAT32
=
4
SIZE_INT64
=
8
DATA_DIR
=
'/data/ILSVRC2012'
img_mean
=
np
.
array
([
0.485
,
0.456
,
0.406
]).
reshape
((
3
,
1
,
1
))
img_std
=
np
.
array
([
0.229
,
0.224
,
0.225
]).
reshape
((
3
,
1
,
1
))
def
resize_short
(
img
,
target_size
):
percent
=
float
(
target_size
)
/
min
(
img
.
size
[
0
],
img
.
size
[
1
])
resized_width
=
int
(
round
(
img
.
size
[
0
]
*
percent
))
resized_height
=
int
(
round
(
img
.
size
[
1
]
*
percent
))
img
=
img
.
resize
((
resized_width
,
resized_height
),
Image
.
LANCZOS
)
return
img
def
crop_image
(
img
,
target_size
,
center
):
width
,
height
=
img
.
size
size
=
target_size
if
center
==
True
:
w_start
=
(
width
-
size
)
/
2
h_start
=
(
height
-
size
)
/
2
else
:
w_start
=
np
.
random
.
randint
(
0
,
width
-
size
+
1
)
h_start
=
np
.
random
.
randint
(
0
,
height
-
size
+
1
)
w_end
=
w_start
+
size
h_end
=
h_start
+
size
img
=
img
.
crop
((
w_start
,
h_start
,
w_end
,
h_end
))
return
img
def
process_image
(
img_path
,
mode
,
color_jitter
,
rotate
):
img
=
Image
.
open
(
img_path
)
img
=
resize_short
(
img
,
target_size
=
256
)
img
=
crop_image
(
img
,
target_size
=
DATA_DIM
,
center
=
True
)
if
img
.
mode
!=
'RGB'
:
img
=
img
.
convert
(
'RGB'
)
img
=
np
.
array
(
img
).
astype
(
'float32'
).
transpose
((
2
,
0
,
1
))
/
255
img
-=
img_mean
img
/=
img_std
return
img
def
reader
():
data_dir
=
DATA_DIR
file_list
=
os
.
path
.
join
(
data_dir
,
'val_list.txt'
)
bin_file
=
os
.
path
.
join
(
data_dir
,
'data.bin'
)
with
open
(
file_list
)
as
flist
:
lines
=
[
line
.
strip
()
for
line
in
flist
]
num_images
=
len
(
lines
)
with
open
(
bin_file
,
"w+b"
)
as
of
:
of
.
seek
(
0
)
num
=
np
.
array
(
int
(
num_images
)).
astype
(
'int64'
)
of
.
write
(
num
.
tobytes
())
for
idx
,
line
in
enumerate
(
lines
):
img_path
,
label
=
line
.
split
()
img_path
=
os
.
path
.
join
(
data_dir
,
img_path
)
if
not
os
.
path
.
exists
(
img_path
):
continue
#save image(float32) to file
img
=
process_image
(
img_path
,
'val'
,
color_jitter
=
False
,
rotate
=
False
)
np_img
=
np
.
array
(
img
)
of
.
seek
(
SIZE_INT64
+
SIZE_FLOAT32
*
DATA_DIM
*
DATA_DIM
*
3
*
idx
)
of
.
write
(
np_img
.
astype
(
'float32'
).
tobytes
())
#save label(int64_t) to file
label_int
=
(
int
)(
label
)
np_label
=
np
.
array
(
label_int
)
of
.
seek
(
SIZE_INT64
+
SIZE_FLOAT32
*
DATA_DIM
*
DATA_DIM
*
3
*
num_images
+
idx
*
SIZE_INT64
)
of
.
write
(
np_label
.
astype
(
'int64'
).
tobytes
())
if
__name__
==
'__main__'
:
reader
()
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