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53c5850d
编写于
9月 25, 2020
作者:
L
littletomatodonkey
提交者:
GitHub
9月 25, 2020
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Merge pull request #291 from TingquanGao/ppcls-dev
fix export_model to support dygraph
上级
0971c22d
5ac48b77
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1
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Showing
1 changed file
with
31 addition
and
37 deletion
+31
-37
tools/export_model.py
tools/export_model.py
+31
-37
未找到文件。
tools/export_model.py
浏览文件 @
53c5850d
...
...
@@ -15,63 +15,57 @@
import
argparse
from
ppcls.modeling
import
architectures
import
paddle.fluid
as
fluid
from
ppcls.utils.save_load
import
load_dygraph_pretrain
import
paddle
import
paddle.nn.functional
as
F
from
paddle.jit
import
to_static
def
parse_args
():
def
str2bool
(
v
):
return
v
.
lower
()
in
(
"true"
,
"t"
,
"1"
)
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"-m"
,
"--model"
,
type
=
str
)
parser
.
add_argument
(
"-p"
,
"--pretrained_model"
,
type
=
str
)
parser
.
add_argument
(
"-o"
,
"--output_path"
,
type
=
str
)
parser
.
add_argument
(
"--class_dim"
,
type
=
int
,
default
=
1000
)
parser
.
add_argument
(
"--img_size"
,
type
=
int
,
default
=
224
)
parser
.
add_argument
(
"--load_static_weights"
,
type
=
str2bool
,
default
=
True
)
# parser.add_argument("--img_size", type=int, default=224)
return
parser
.
parse_args
()
def
create_input
(
img_size
=
224
):
image
=
fluid
.
data
(
name
=
'image'
,
shape
=
[
None
,
3
,
img_size
,
img_size
],
dtype
=
'float32'
)
return
image
class
Net
(
paddle
.
nn
.
Layer
):
def
__init__
(
self
,
net
,
to_static
,
class_dim
):
super
(
Net
,
self
).
__init__
(
)
self
.
pre_net
=
net
(
class_dim
=
class_dim
)
self
.
to_static
=
to_static
def
create_model
(
args
,
model
,
input
,
class_dim
=
1000
):
if
args
.
model
==
"GoogLeNet"
:
out
,
_
,
_
=
model
.
net
(
input
=
input
,
class_dim
=
class_dim
)
else
:
out
=
model
.
net
(
input
=
input
,
class_dim
=
class_dim
)
out
=
fluid
.
layers
.
softmax
(
out
)
return
out
# Please modify the 'shape' according to actual needs
@
to_static
(
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
,
3
,
224
,
224
],
dtype
=
'float32'
)
])
def
forward
(
self
,
inputs
):
x
=
self
.
pre_net
(
inputs
)
x
=
F
.
softmax
(
x
)
return
x
def
main
():
args
=
parse_args
()
model
=
architectures
.
__dict__
[
args
.
model
]()
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
startup_prog
=
fluid
.
Program
()
infer_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
infer_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
image
=
create_input
(
args
.
img_size
)
out
=
create_model
(
args
,
model
,
image
,
class_dim
=
args
.
class_dim
)
paddle
.
disable_static
()
net
=
architectures
.
__dict__
[
args
.
model
]
infer_prog
=
infer_prog
.
clone
(
for_test
=
True
)
fluid
.
load
(
program
=
infer_prog
,
model_path
=
args
.
pretrained_model
,
executor
=
exe
)
model
=
Net
(
net
,
to_static
,
args
.
class_dim
)
fluid
.
io
.
save_inference_model
(
dirname
=
args
.
output_path
,
feeded_var_names
=
[
image
.
name
],
main_program
=
infer_prog
,
target_vars
=
out
,
executor
=
exe
,
model_filename
=
'model'
,
params_filename
=
'params'
)
load_dygraph_pretrain
(
model
.
pre_net
,
path
=
args
.
pretrained_model
,
load_static_weights
=
args
.
load_static_weights
)
paddle
.
jit
.
save
(
model
,
args
.
output_path
)
if
__name__
==
"__main__"
:
...
...
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