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体验新版 GitCode,发现更多精彩内容 >>
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d2dadea1
编写于
11月 14, 2021
作者:
J
Jiawei Wang
提交者:
GitHub
11月 14, 2021
浏览文件
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差异文件
Merge pull request #1496 from bjjwwang/save
fix save model
上级
d37a7279
09d67a20
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
527 addition
and
3 deletion
+527
-3
python/paddle_serving_client/io/__init__.py
python/paddle_serving_client/io/__init__.py
+5
-3
python/paddle_serving_client/io/paddle_io.py
python/paddle_serving_client/io/paddle_io.py
+522
-0
未找到文件。
python/paddle_serving_client/io/__init__.py
浏览文件 @
d2dadea1
...
...
@@ -19,7 +19,7 @@ from paddle.fluid.framework import core
from
paddle.fluid.framework
import
default_main_program
from
paddle.fluid.framework
import
Program
from
paddle.fluid
import
CPUPlace
from
paddle.fluid.io
import
save_inference_model
from
.paddle_io
import
save_inference_model
,
normalize_program
import
paddle.fluid
as
fluid
from
paddle.fluid.core
import
CipherUtils
from
paddle.fluid.core
import
CipherFactory
...
...
@@ -191,12 +191,14 @@ def save_model(server_model_folder,
executor
=
Executor
(
place
=
CPUPlace
())
feed_var_names
=
[
feed_var_dict
[
x
].
name
for
x
in
feed_var_dict
]
feed_vars
=
[
feed_var_dict
[
x
]
for
x
in
feed_var_dict
]
target_vars
=
[]
target_var_names
=
[]
for
key
in
sorted
(
fetch_var_dict
.
keys
()):
target_vars
.
append
(
fetch_var_dict
[
key
])
target_var_names
.
append
(
key
)
main_program
=
normalize_program
(
main_program
,
feed_vars
,
target_vars
)
if
not
encryption
and
not
show_proto
:
if
not
os
.
path
.
exists
(
server_model_folder
):
os
.
makedirs
(
server_model_folder
)
...
...
@@ -209,7 +211,7 @@ def save_model(server_model_folder,
new_params_path
=
os
.
path
.
join
(
server_model_folder
,
params_filename
)
with
open
(
new_model_path
,
"wb"
)
as
new_model_file
:
new_model_file
.
write
(
main_program
.
desc
.
serialize_to_string
())
new_model_file
.
write
(
main_program
.
_remove_training_info
(
False
).
desc
.
serialize_to_string
())
paddle
.
static
.
save_vars
(
executor
=
executor
,
...
...
@@ -229,7 +231,7 @@ def save_model(server_model_folder,
key
=
CipherUtils
.
gen_key_to_file
(
128
,
"key"
)
params
=
fluid
.
io
.
save_persistables
(
executor
=
executor
,
dirname
=
None
,
main_program
=
main_program
)
model
=
main_program
.
desc
.
serialize_to_string
()
model
=
main_program
.
_remove_training_info
(
False
).
desc
.
serialize_to_string
()
if
not
os
.
path
.
exists
(
server_model_folder
):
os
.
makedirs
(
server_model_folder
)
os
.
chdir
(
server_model_folder
)
...
...
python/paddle_serving_client/io/paddle_io.py
0 → 100644
浏览文件 @
d2dadea1
# Copyright (c) 2018 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.
from
__future__
import
print_function
import
errno
import
inspect
import
logging
import
os
import
warnings
import
six
import
numpy
as
np
import
paddle
from
paddle.fluid
import
(
core
,
Variable
,
CompiledProgram
,
default_main_program
,
Program
,
layers
,
unique_name
,
program_guard
,
)
from
paddle.fluid.io
import
prepend_feed_ops
,
append_fetch_ops
from
paddle.fluid.framework
import
static_only
,
Parameter
from
paddle.fluid.executor
import
Executor
,
global_scope
from
paddle.fluid.log_helper
import
get_logger
__all__
=
[]
_logger
=
get_logger
(
__name__
,
logging
.
INFO
,
fmt
=
'%(asctime)s-%(levelname)s: %(message)s'
)
def
_check_args
(
caller
,
args
,
supported_args
=
None
,
deprecated_args
=
None
):
supported_args
=
[]
if
supported_args
is
None
else
supported_args
deprecated_args
=
[]
if
deprecated_args
is
None
else
deprecated_args
for
arg
in
args
:
if
arg
in
deprecated_args
:
raise
ValueError
(
"argument '{}' in function '{}' is deprecated, only {} are supported."
.
format
(
arg
,
caller
,
supported_args
))
elif
arg
not
in
supported_args
:
raise
ValueError
(
"function '{}' doesn't support argument '{}',
\n
only {} are supported."
.
format
(
caller
,
arg
,
supported_args
))
def
_check_vars
(
name
,
var_list
):
if
not
isinstance
(
var_list
,
list
):
var_list
=
[
var_list
]
if
not
var_list
or
not
all
([
isinstance
(
var
,
Variable
)
for
var
in
var_list
]):
raise
ValueError
(
"'{}' should be a Variable or a list of Variable."
.
format
(
name
))
def
_normalize_path_prefix
(
path_prefix
):
"""
convert path_prefix to absolute path.
"""
if
not
isinstance
(
path_prefix
,
six
.
string_types
):
raise
ValueError
(
"'path_prefix' should be a string."
)
if
path_prefix
.
endswith
(
"/"
):
raise
ValueError
(
"'path_prefix' should not be a directory"
)
path_prefix
=
os
.
path
.
normpath
(
path_prefix
)
path_prefix
=
os
.
path
.
abspath
(
path_prefix
)
return
path_prefix
def
_get_valid_program
(
program
=
None
):
"""
return default main program if program is None.
"""
if
program
is
None
:
program
=
default_main_program
()
elif
isinstance
(
program
,
CompiledProgram
):
program
=
program
.
_program
if
program
is
None
:
raise
TypeError
(
"The type of input program is invalid, expected tyep is Program, but received None"
)
warnings
.
warn
(
"The input is a CompiledProgram, this is not recommended."
)
if
not
isinstance
(
program
,
Program
):
raise
TypeError
(
"The type of input program is invalid, expected type is fluid.Program, but received %s"
%
type
(
program
))
return
program
def
_clone_var_in_block
(
block
,
var
):
assert
isinstance
(
var
,
Variable
)
if
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
LOD_TENSOR
:
return
block
.
create_var
(
name
=
var
.
name
,
shape
=
var
.
shape
,
dtype
=
var
.
dtype
,
type
=
var
.
type
,
lod_level
=
var
.
lod_level
,
persistable
=
True
)
else
:
return
block
.
create_var
(
name
=
var
.
name
,
shape
=
var
.
shape
,
dtype
=
var
.
dtype
,
type
=
var
.
type
,
persistable
=
True
)
def
normalize_program
(
program
,
feed_vars
,
fetch_vars
):
"""
:api_attr: Static Graph
Normalize/Optimize a program according to feed_vars and fetch_vars.
Args:
program(Program): Specify a program you want to optimize.
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
Returns:
Program: Normalized/Optimized program.
Raises:
TypeError: If `program` is not a Program, an exception is thrown.
TypeError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
TypeError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
predict = paddle.static.nn.fc(image, 10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(predict, label)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
# normalize main program.
program = paddle.static.default_main_program()
normalized_program = paddle.static.normalize_program(program, [image], [predict])
"""
if
not
isinstance
(
program
,
Program
):
raise
TypeError
(
"program type must be `fluid.Program`, but received `%s`"
%
type
(
program
))
if
not
isinstance
(
feed_vars
,
list
):
feed_vars
=
[
feed_vars
]
if
not
all
(
isinstance
(
v
,
Variable
)
for
v
in
feed_vars
):
raise
TypeError
(
"feed_vars type must be a Variable or a list of Variable."
)
if
not
isinstance
(
fetch_vars
,
list
):
fetch_vars
=
[
fetch_vars
]
if
not
all
(
isinstance
(
v
,
Variable
)
for
v
in
fetch_vars
):
raise
TypeError
(
"fetch_vars type must be a Variable or a list of Variable."
)
# remind users to set auc_states to 0 if auc op were found.
for
op
in
program
.
global_block
().
ops
:
# clear device of Op
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
op
.
_set_attr
(
device_attr_name
,
""
)
if
op
.
type
==
'auc'
:
warnings
.
warn
(
"Be sure that you have set auc states to 0 "
"before saving inference model."
)
break
# fix the bug that the activation op's output as target will be pruned.
# will affect the inference performance.
# TODO(Superjomn) add an IR pass to remove 1-scale op.
#with program_guard(program):
# uniq_fetch_vars = []
# for i, var in enumerate(fetch_vars):
# if var.dtype != paddle.bool:
# var = layers.scale(
# var, 1., name="save_infer_model/scale_{}".format(i))
# uniq_fetch_vars.append(var)
# fetch_vars = uniq_fetch_vars
# serialize program
copy_program
=
program
.
clone
()
global_block
=
copy_program
.
global_block
()
remove_op_idx
=
[]
for
i
,
op
in
enumerate
(
global_block
.
ops
):
op
.
desc
.
set_is_target
(
False
)
if
op
.
type
==
"feed"
or
op
.
type
==
"fetch"
:
remove_op_idx
.
append
(
i
)
for
idx
in
remove_op_idx
[::
-
1
]:
global_block
.
_remove_op
(
idx
)
copy_program
.
desc
.
flush
()
feed_var_names
=
[
var
.
name
for
var
in
feed_vars
]
copy_program
=
copy_program
.
_prune_with_input
(
feeded_var_names
=
feed_var_names
,
targets
=
fetch_vars
)
copy_program
=
copy_program
.
_inference_optimize
(
prune_read_op
=
True
)
fetch_var_names
=
[
var
.
name
for
var
in
fetch_vars
]
prepend_feed_ops
(
copy_program
,
feed_var_names
)
append_fetch_ops
(
copy_program
,
fetch_var_names
)
copy_program
.
desc
.
_set_version
()
return
copy_program
def
is_persistable
(
var
):
"""
Check whether the given variable is persistable.
Args:
var(Variable): The variable to be checked.
Returns:
bool: True if the given `var` is persistable
False if not.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
param = fluid.default_main_program().global_block().var('fc.b')
res = fluid.io.is_persistable(param)
"""
if
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
FEED_MINIBATCH
or
\
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
FETCH_LIST
or
\
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
READER
:
return
False
return
var
.
persistable
@
static_only
def
serialize_program
(
feed_vars
,
fetch_vars
,
**
kwargs
):
"""
:api_attr: Static Graph
Serialize default main program according to feed_vars and fetch_vars.
Args:
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly.
- program(Program): specify a program if you don't want to use default main program.
Returns:
bytes: serialized program.
Raises:
ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
predict = paddle.static.nn.fc(image, 10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(predict, label)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
# serialize the default main program to bytes.
serialized_program = paddle.static.serialize_program([image], [predict])
# deserialize bytes to program
deserialized_program = paddle.static.deserialize_program(serialized_program)
"""
# verify feed_vars
_check_vars
(
'feed_vars'
,
feed_vars
)
# verify fetch_vars
_check_vars
(
'fetch_vars'
,
fetch_vars
)
program
=
_get_valid_program
(
kwargs
.
get
(
'program'
,
None
))
program
=
normalize_program
(
program
,
feed_vars
,
fetch_vars
)
return
_serialize_program
(
program
)
def
_serialize_program
(
program
):
"""
serialize given program to bytes.
"""
return
program
.
desc
.
serialize_to_string
()
@
static_only
def
serialize_persistables
(
feed_vars
,
fetch_vars
,
executor
,
**
kwargs
):
"""
:api_attr: Static Graph
Serialize parameters using given executor and default main program according to feed_vars and fetch_vars.
Args:
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly.
- program(Program): specify a program if you don't want to use default main program.
Returns:
bytes: serialized program.
Raises:
ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
predict = paddle.static.nn.fc(image, 10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(predict, label)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
# serialize parameters to bytes.
serialized_params = paddle.static.serialize_persistables([image], [predict], exe)
# deserialize bytes to parameters.
main_program = paddle.static.default_main_program()
deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe)
"""
# verify feed_vars
_check_vars
(
'feed_vars'
,
feed_vars
)
# verify fetch_vars
_check_vars
(
'fetch_vars'
,
fetch_vars
)
program
=
_get_valid_program
(
kwargs
.
get
(
'program'
,
None
))
program
=
normalize_program
(
program
,
feed_vars
,
fetch_vars
)
return
_serialize_persistables
(
program
,
executor
)
def
_serialize_persistables
(
program
,
executor
):
"""
Serialize parameters using given program and executor.
"""
vars_
=
list
(
filter
(
is_persistable
,
program
.
list_vars
()))
# warn if no variable found in model
if
len
(
vars_
)
==
0
:
warnings
.
warn
(
"no variable in your model, please ensure there are any "
"variables in your model to save"
)
return
None
# create a new program and clone persitable vars to it
save_program
=
Program
()
save_block
=
save_program
.
global_block
()
save_var_map
=
{}
for
var
in
vars_
:
if
var
.
type
!=
core
.
VarDesc
.
VarType
.
RAW
:
var_copy
=
_clone_var_in_block
(
save_block
,
var
)
save_var_map
[
var_copy
.
name
]
=
var
# create in_vars and out_var, then append a save_combine op to save_program
in_vars
=
[]
for
name
in
sorted
(
save_var_map
.
keys
()):
in_vars
.
append
(
save_var_map
[
name
])
out_var_name
=
unique_name
.
generate
(
"out_var"
)
out_var
=
save_block
.
create_var
(
type
=
core
.
VarDesc
.
VarType
.
RAW
,
name
=
out_var_name
)
out_var
.
desc
.
set_persistable
(
True
)
save_block
.
append_op
(
type
=
'save_combine'
,
inputs
=
{
'X'
:
in_vars
},
outputs
=
{
'Y'
:
out_var
},
attrs
=
{
'file_path'
:
''
,
'save_to_memory'
:
True
})
# run save_program to save vars
# NOTE(zhiqiu): save op will add variable kLookupTablePath to save_program.desc,
# which leads to diff between save_program and its desc. Call _sync_with_cpp
# to keep consistency.
save_program
.
_sync_with_cpp
()
executor
.
run
(
save_program
)
# return serialized bytes in out_var
return
global_scope
().
find_var
(
out_var_name
).
get_bytes
()
def
save_to_file
(
path
,
content
):
"""
Save content to given path.
Args:
path(str): Path to write content to.
content(bytes): Content to write.
Returns:
None
"""
if
not
isinstance
(
content
,
bytes
):
raise
ValueError
(
"'content' type should be bytes."
)
with
open
(
path
,
"wb"
)
as
f
:
f
.
write
(
content
)
@
static_only
def
save_inference_model
(
path_prefix
,
feed_vars
,
fetch_vars
,
executor
,
**
kwargs
):
"""
:api_attr: Static Graph
Save current model and its parameters to given path. i.e.
Given path_prefix = "/path/to/modelname", after invoking
save_inference_model(path_prefix, feed_vars, fetch_vars, executor),
you will find two files named modelname.pdmodel and modelname.pdiparams
under "/path/to", which represent your model and parameters respectively.
Args:
path_prefix(str): Directory path to save model + model name without suffix.
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
executor(Executor): The executor that saves the inference model. You can refer
to :ref:`api_guide_executor_en` for more details.
kwargs: Supported keys including 'program' and "clip_extra". Attention please, kwargs is used for backward compatibility mainly.
- program(Program): specify a program if you don't want to use default main program.
- clip_extra(bool): set to True if you want to clip extra information for every operator.
Returns:
None
Raises:
ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
predict = paddle.static.nn.fc(image, 10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(predict, label)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
# Feed data and train process
# Save inference model. Note we don't save label and loss in this example
paddle.static.save_inference_model(path_prefix, [image], [predict], exe)
# In this example, the save_inference_mode inference will prune the default
# main program according to the network's input node (img) and output node(predict).
# The pruned inference program is going to be saved in file "./infer_model.pdmodel"
# and parameters are going to be saved in file "./infer_model.pdiparams".
"""
# check path_prefix, set model_path and params_path
path_prefix
=
_normalize_path_prefix
(
path_prefix
)
try
:
# mkdir may conflict if pserver and trainer are running on the same machine
dirname
=
os
.
path
.
dirname
(
path_prefix
)
os
.
makedirs
(
dirname
)
except
OSError
as
e
:
if
e
.
errno
!=
errno
.
EEXIST
:
raise
model_path
=
path_prefix
+
".pdmodel"
params_path
=
path_prefix
+
".pdiparams"
if
os
.
path
.
isdir
(
model_path
):
raise
ValueError
(
"'{}' is an existing directory."
.
format
(
model_path
))
if
os
.
path
.
isdir
(
params_path
):
raise
ValueError
(
"'{}' is an existing directory."
.
format
(
params_path
))
# verify feed_vars
_check_vars
(
'feed_vars'
,
feed_vars
)
# verify fetch_vars
_check_vars
(
'fetch_vars'
,
fetch_vars
)
program
=
_get_valid_program
(
kwargs
.
get
(
'program'
,
None
))
clip_extra
=
kwargs
.
get
(
'clip_extra'
,
False
)
program
=
normalize_program
(
program
,
feed_vars
,
fetch_vars
)
# serialize and save program
program_bytes
=
_serialize_program
(
program
.
_remove_training_info
(
clip_extra
=
clip_extra
))
save_to_file
(
model_path
,
program_bytes
)
# serialize and save params
params_bytes
=
_serialize_persistables
(
program
,
executor
)
save_to_file
(
params_path
,
params_bytes
)
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