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体验新版 GitCode,发现更多精彩内容 >>
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39adb22a
编写于
12月 06, 2022
作者:
R
Ryan
提交者:
GitHub
12月 06, 2022
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差异文件
Add IntermediateLayerGetter (#47908)
上级
3125733a
变更
4
隐藏空白更改
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Showing
4 changed file
with
202 addition
and
2 deletion
+202
-2
python/paddle/fluid/tests/unittests/test_IntermediateLayerGetter.py
...dle/fluid/tests/unittests/test_IntermediateLayerGetter.py
+92
-0
python/paddle/vision/models/_utils.py
python/paddle/vision/models/_utils.py
+108
-0
python/paddle/vision/models/mobilenetv2.py
python/paddle/vision/models/mobilenetv2.py
+1
-1
python/paddle/vision/models/mobilenetv3.py
python/paddle/vision/models/mobilenetv3.py
+1
-1
未找到文件。
python/paddle/fluid/tests/unittests/test_IntermediateLayerGetter.py
0 → 100644
浏览文件 @
39adb22a
# 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.
import
random
import
unittest
import
paddle
from
paddle.vision.models._utils
import
IntermediateLayerGetter
class
TestBase
:
def
setUp
(
self
):
self
.
init_model
()
self
.
model
.
eval
()
self
.
layer_names
=
[
(
order
,
name
)
for
order
,
(
name
,
_
)
in
enumerate
(
self
.
model
.
named_children
())
]
# choose two layer children of model randomly
self
.
start
,
self
.
end
=
sorted
(
random
.
sample
(
self
.
layer_names
,
2
),
key
=
lambda
x
:
x
[
0
]
)
self
.
return_layers_dic
=
{
self
.
start
[
1
]:
"feat1"
,
self
.
end
[
1
]:
"feat2"
}
self
.
new_model
=
IntermediateLayerGetter
(
self
.
model
,
self
.
return_layers_dic
)
def
init_model
(
self
):
self
.
model
=
None
@
paddle
.
no_grad
()
def
test_inter_result
(
self
):
inp
=
paddle
.
randn
([
1
,
3
,
80
,
80
])
inter_oup
=
self
.
new_model
(
inp
)
for
layer_name
,
layer
in
self
.
model
.
named_children
():
if
(
isinstance
(
layer
,
paddle
.
nn
.
Linear
)
and
inp
.
ndim
==
4
)
or
(
len
(
layer
.
sublayers
())
>
0
and
isinstance
(
layer
.
sublayers
()[
0
],
paddle
.
nn
.
Linear
)
and
inp
.
ndim
==
4
):
inp
=
paddle
.
flatten
(
inp
,
1
)
inp
=
layer
(
inp
)
if
layer_name
in
self
.
return_layers_dic
:
feat_name
=
self
.
return_layers_dic
[
layer_name
]
self
.
assertTrue
((
inter_oup
[
feat_name
]
==
inp
).
all
())
class
TestIntermediateLayerGetterResNet18
(
TestBase
,
unittest
.
TestCase
):
def
init_model
(
self
):
self
.
model
=
paddle
.
vision
.
models
.
resnet18
(
pretrained
=
False
)
class
TestIntermediateLayerGetterDenseNet121
(
TestBase
,
unittest
.
TestCase
):
def
init_model
(
self
):
self
.
model
=
paddle
.
vision
.
models
.
densenet121
(
pretrained
=
False
)
class
TestIntermediateLayerGetterVGG11
(
TestBase
,
unittest
.
TestCase
):
def
init_model
(
self
):
self
.
model
=
paddle
.
vision
.
models
.
vgg11
(
pretrained
=
False
)
class
TestIntermediateLayerGetterMobileNetV3Small
(
TestBase
,
unittest
.
TestCase
):
def
init_model
(
self
):
self
.
model
=
paddle
.
vision
.
models
.
MobileNetV3Small
()
class
TestIntermediateLayerGetterShuffleNetV2
(
TestBase
,
unittest
.
TestCase
):
def
init_model
(
self
):
self
.
model
=
paddle
.
vision
.
models
.
shufflenet_v2_x0_25
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/vision/models/utils.py
→
python/paddle/vision/models/
_
utils.py
浏览文件 @
39adb22a
...
...
@@ -12,6 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
collections
import
OrderedDict
from
typing
import
Dict
import
paddle
import
paddle.nn
as
nn
def
_make_divisible
(
v
,
divisor
=
8
,
min_value
=
None
):
"""
...
...
@@ -30,3 +36,73 @@ def _make_divisible(v, divisor=8, min_value=None):
if
new_v
<
0.9
*
v
:
new_v
+=
divisor
return
new_v
class
IntermediateLayerGetter
(
nn
.
LayerDict
):
"""
Layer wrapper that returns intermediate layers from a model.
It has a strong assumption that the layers have been registered into the model in the
same order as they are used. This means that one should **not** reuse the same nn.Layer
twice in the forward if you want this to work.
Additionally, it is only able to query sublayer that are directly assigned to the model.
So if `model` is passed, `model.feature1` can be returned, but not `model.feature1.layer2`.
Args:
model (nn.Layer): model on which we will extract the features
return_layers (Dict[name, new_name]): a dict containing the names of the layers for
which the activations will be returned as the key of the dict, and the value of the
dict is the name of the returned activation (which the user can specify).
Examples:
.. code-block:: python
import paddle
m = paddle.vision.models.resnet18(pretrained=False)
# extract layer1 and layer3, giving as names `feat1` and feat2`
new_m = paddle.vision.models._utils.IntermediateLayerGetter(m,
{'layer1': 'feat1', 'layer3': 'feat2'})
out = new_m(paddle.rand([1, 3, 224, 224]))
print([(k, v.shape) for k, v in out.items()])
# [('feat1', [1, 64, 56, 56]), ('feat2', [1, 256, 14, 14])]
"""
__annotations__
=
{
"return_layers"
:
Dict
[
str
,
str
],
}
def
__init__
(
self
,
model
:
nn
.
Layer
,
return_layers
:
Dict
[
str
,
str
])
->
None
:
if
not
set
(
return_layers
).
issubset
(
[
name
for
name
,
_
in
model
.
named_children
()]
):
raise
ValueError
(
"return_layers are not present in model"
)
orig_return_layers
=
return_layers
return_layers
=
{
str
(
k
):
str
(
v
)
for
k
,
v
in
return_layers
.
items
()}
layers
=
OrderedDict
()
for
name
,
module
in
model
.
named_children
():
layers
[
name
]
=
module
if
name
in
return_layers
:
del
return_layers
[
name
]
if
not
return_layers
:
break
super
(
IntermediateLayerGetter
,
self
).
__init__
(
layers
)
self
.
return_layers
=
orig_return_layers
def
forward
(
self
,
x
):
out
=
OrderedDict
()
for
name
,
module
in
self
.
items
():
if
(
isinstance
(
module
,
nn
.
Linear
)
and
x
.
ndim
==
4
)
or
(
len
(
module
.
sublayers
())
>
0
and
isinstance
(
module
.
sublayers
()[
0
],
nn
.
Linear
)
and
x
.
ndim
==
4
):
x
=
paddle
.
flatten
(
x
,
1
)
x
=
module
(
x
)
if
name
in
self
.
return_layers
:
out_name
=
self
.
return_layers
[
name
]
out
[
out_name
]
=
x
return
out
python/paddle/vision/models/mobilenetv2.py
浏览文件 @
39adb22a
...
...
@@ -17,7 +17,7 @@ import paddle.nn as nn
from
paddle.utils.download
import
get_weights_path_from_url
from
..ops
import
ConvNormActivation
from
.utils
import
_make_divisible
from
.
_
utils
import
_make_divisible
__all__
=
[]
...
...
python/paddle/vision/models/mobilenetv3.py
浏览文件 @
39adb22a
...
...
@@ -19,7 +19,7 @@ import paddle.nn as nn
from
paddle.utils.download
import
get_weights_path_from_url
from
..ops
import
ConvNormActivation
from
.utils
import
_make_divisible
from
.
_
utils
import
_make_divisible
__all__
=
[]
...
...
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