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体验新版 GitCode,发现更多精彩内容 >>
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ffcb6537
编写于
8月 05, 2020
作者:
L
LielinJiang
提交者:
GitHub
8月 05, 2020
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差异文件
Add uncombined_weight_to_state_dict api (#25649)
* add uncombined_weight_to_state_dict API
上级
a43b0d15
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
286 addition
and
0 deletion
+286
-0
python/paddle/incubate/hapi/__init__.py
python/paddle/incubate/hapi/__init__.py
+2
-0
python/paddle/incubate/hapi/tests/test_uncombined_weight2state_dict.py
.../incubate/hapi/tests/test_uncombined_weight2state_dict.py
+126
-0
python/paddle/incubate/hapi/utils.py
python/paddle/incubate/hapi/utils.py
+158
-0
未找到文件。
python/paddle/incubate/hapi/__init__.py
浏览文件 @
ffcb6537
...
...
@@ -25,6 +25,7 @@ from . import datasets
from
.
import
distributed
from
.
import
vision
from
.
import
text
from
.
import
utils
from
.
import
device
from
.device
import
*
...
...
@@ -41,6 +42,7 @@ __all__ = [
'metrics'
,
'vision'
,
'text'
,
'utils'
,
]
+
model
.
__all__
+
device
.
__all__
monkey_patch_layer
()
python/paddle/incubate/hapi/tests/test_uncombined_weight2state_dict.py
0 → 100644
浏览文件 @
ffcb6537
# copyright (c) 2020 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
division
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
shutil
import
tempfile
from
paddle
import
fluid
from
paddle.nn
import
Conv2D
,
Pool2D
,
Linear
,
ReLU
,
Sequential
from
paddle.incubate.hapi.utils
import
uncombined_weight_to_state_dict
class
LeNetDygraph
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
num_classes
=
10
,
classifier_activation
=
'softmax'
):
super
(
LeNetDygraph
,
self
).
__init__
()
self
.
num_classes
=
num_classes
self
.
features
=
Sequential
(
Conv2D
(
1
,
6
,
3
,
stride
=
1
,
padding
=
1
),
ReLU
(),
Pool2D
(
2
,
'max'
,
2
),
Conv2D
(
6
,
16
,
5
,
stride
=
1
,
padding
=
0
),
ReLU
(),
Pool2D
(
2
,
'max'
,
2
))
if
num_classes
>
0
:
self
.
fc
=
Sequential
(
Linear
(
400
,
120
),
Linear
(
120
,
84
),
Linear
(
84
,
10
,
act
=
classifier_activation
))
def
forward
(
self
,
inputs
):
x
=
self
.
features
(
inputs
)
if
self
.
num_classes
>
0
:
x
=
fluid
.
layers
.
flatten
(
x
,
1
)
x
=
self
.
fc
(
x
)
return
x
class
TestUncombinedWeight2StateDict
(
unittest
.
TestCase
):
@
classmethod
def
setUpClass
(
cls
):
cls
.
save_dir
=
tempfile
.
mkdtemp
()
@
classmethod
def
tearDownClass
(
cls
):
shutil
.
rmtree
(
cls
.
save_dir
)
def
test_infer
(
self
):
start_prog
=
fluid
.
Program
()
train_prog
=
fluid
.
Program
()
x
=
fluid
.
data
(
name
=
'x'
,
shape
=
[
None
,
1
,
28
,
28
],
dtype
=
'float32'
)
with
fluid
.
program_guard
(
train_prog
,
start_prog
):
with
fluid
.
unique_name
.
guard
():
x
=
fluid
.
data
(
name
=
'x'
,
shape
=
[
None
,
1
,
28
,
28
],
dtype
=
'float32'
)
model
=
LeNetDygraph
()
output
=
model
.
forward
(
x
)
excutor
=
fluid
.
Executor
()
excutor
.
run
(
start_prog
)
test_prog
=
train_prog
.
clone
(
for_test
=
True
)
fluid
.
io
.
save_params
(
excutor
,
self
.
save_dir
,
test_prog
)
rand_x
=
np
.
random
.
rand
(
1
,
1
,
28
,
28
).
astype
(
'float32'
)
out
=
excutor
.
run
(
program
=
test_prog
,
feed
=
{
'x'
:
rand_x
},
fetch_list
=
[
output
.
name
],
return_numpy
=
True
)
state_dict
=
uncombined_weight_to_state_dict
(
self
.
save_dir
)
key2key_dict
=
{
'features.0.weight'
:
'conv2d_0.w_0'
,
'features.0.bias'
:
'conv2d_0.b_0'
,
'features.3.weight'
:
'conv2d_1.w_0'
,
'features.3.bias'
:
'conv2d_1.b_0'
,
'fc.0.weight'
:
'linear_0.w_0'
,
'fc.0.bias'
:
'linear_0.b_0'
,
'fc.1.weight'
:
'linear_1.w_0'
,
'fc.1.bias'
:
'linear_1.b_0'
,
'fc.2.weight'
:
'linear_2.w_0'
,
'fc.2.bias'
:
'linear_2.b_0'
}
fluid
.
enable_imperative
()
dygraph_model
=
LeNetDygraph
()
converted_state_dict
=
dygraph_model
.
state_dict
()
for
k1
,
k2
in
key2key_dict
.
items
():
converted_state_dict
[
k1
]
=
state_dict
[
k2
]
dygraph_model
.
set_dict
(
converted_state_dict
)
dygraph_model
.
eval
()
dy_out
=
dygraph_model
(
fluid
.
dygraph
.
to_variable
(
rand_x
))
np
.
testing
.
assert_allclose
(
dy_out
.
numpy
(),
out
[
0
],
atol
=
1e-5
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/incubate/hapi/utils.py
浏览文件 @
ffcb6537
...
...
@@ -12,13 +12,171 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
os
import
inspect
import
numpy
as
np
from
collections
import
OrderedDict
from
paddle
import
fluid
from
paddle.fluid.framework
import
Variable
from
paddle.fluid.executor
import
global_scope
__all__
=
[
'uncombined_weight_to_state_dict'
]
def
uncombined_weight_to_state_dict
(
weight_dir
):
"""
Convert uncombined weight which getted by using `fluid.io.save_params` or `fluid.io.save_persistables` to state_dict
Args:
weight_dir (str): weight direcotory path.
Returns:
OrderDict: weight dict.
Examples:
.. code-block:: python
import os
from paddle import fluid
from paddle.nn import Conv2D, Pool2D, Linear, ReLU, Sequential
from paddle.incubate.hapi.utils import uncombined_weight_to_state_dict
class LeNetDygraph(fluid.dygraph.Layer):
def __init__(self, num_classes=10, classifier_activation='softmax'):
super(LeNetDygraph, self).__init__()
self.num_classes = num_classes
self.features = Sequential(
Conv2D(
1, 6, 3, stride=1, padding=1),
ReLU(),
Pool2D(2, 'max', 2),
Conv2D(
6, 16, 5, stride=1, padding=0),
ReLU(),
Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = Sequential(
Linear(400, 120),
Linear(120, 84),
Linear(
84, 10, act=classifier_activation))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = fluid.layers.flatten(x, 1)
x = self.fc(x)
return x
# save weight use fluid.io.save_params
save_dir = 'temp'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
start_prog = fluid.Program()
train_prog = fluid.Program()
x = fluid.data(name='x', shape=[None, 1, 28, 28], dtype='float32')
with fluid.program_guard(train_prog, start_prog):
with fluid.unique_name.guard():
x = fluid.data(
name='x', shape=[None, 1, 28, 28], dtype='float32')
model = LeNetDygraph()
output = model.forward(x)
excutor = fluid.Executor()
excutor.run(start_prog)
test_prog = train_prog.clone(for_test=True)
fluid.io.save_params(excutor, save_dir, test_prog)
# convert uncombined weight to state dict
state_dict = uncombined_weight_to_state_dict(save_dir)
key2key_dict = {
'features.0.weight': 'conv2d_0.w_0',
'features.0.bias': 'conv2d_0.b_0',
'features.3.weight': 'conv2d_1.w_0',
'features.3.bias': 'conv2d_1.b_0',
'fc.0.weight': 'linear_0.w_0',
'fc.0.bias': 'linear_0.b_0',
'fc.1.weight': 'linear_1.w_0',
'fc.1.bias': 'linear_1.b_0',
'fc.2.weight': 'linear_2.w_0',
'fc.2.bias': 'linear_2.b_0'
}
fluid.enable_imperative()
dygraph_model = LeNetDygraph()
converted_state_dict = dygraph_model.state_dict()
for k1, k2 in key2key_dict.items():
converted_state_dict[k1] = state_dict[k2]
# dygraph model load state dict which converted from uncombined weight
dygraph_model.set_dict(converted_state_dict)
"""
def
_get_all_params_name
(
dir
):
params_name
=
[]
dir
=
os
.
path
.
expanduser
(
dir
)
dir_len
=
len
(
dir
)
for
root
,
_
,
fnames
in
sorted
(
os
.
walk
(
dir
,
followlinks
=
True
)):
for
fname
in
sorted
(
fnames
):
path
=
os
.
path
.
join
(
root
[
dir_len
:],
fname
)
params_name
.
append
(
path
)
return
params_name
class
Load
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
):
super
(
Load
,
self
).
__init__
()
def
forward
(
self
,
filename
):
weight
=
self
.
create_parameter
(
shape
=
[
1
],
dtype
=
'float32'
,
default_initializer
=
fluid
.
initializer
.
ConstantInitializer
(
0.0
))
self
.
_helper
.
append_op
(
type
=
'load'
,
inputs
=
{},
outputs
=
{
'Out'
:
[
weight
]},
attrs
=
{
'file_path'
:
filename
})
return
weight
params_name_list
=
_get_all_params_name
(
weight_dir
)
if
not
fluid
.
in_dygraph_mode
():
dygraph_enabled
=
False
fluid
.
enable_imperative
()
else
:
dygraph_enabled
=
True
load
=
Load
()
state_dict
=
OrderedDict
()
for
param_name
in
params_name_list
:
param_path
=
os
.
path
.
join
(
weight_dir
,
param_name
)
weight
=
load
(
param_path
)
try
:
weight
=
weight
.
numpy
()
except
Exception
as
e
:
print
(
e
)
state_dict
[
param_name
]
=
weight
if
not
dygraph_enabled
:
fluid
.
disable_imperative
()
return
state_dict
def
to_list
(
value
):
if
value
is
None
:
...
...
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