Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
csdn_franckjun
Paddle
提交
559d9f2b
P
Paddle
项目概览
csdn_franckjun
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
559d9f2b
编写于
9月 03, 2020
作者:
L
LielinJiang
提交者:
GitHub
9月 03, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add summary for hapi (#26386)
* add summary for hapi
上级
d5f4c39f
变更
6
显示空白变更内容
内联
并排
Showing
6 changed file
with
294 addition
and
3 deletion
+294
-3
python/paddle/__init__.py
python/paddle/__init__.py
+1
-0
python/paddle/hapi/__init__.py
python/paddle/hapi/__init__.py
+3
-2
python/paddle/hapi/model.py
python/paddle/hapi/model.py
+44
-1
python/paddle/hapi/model_summary.py
python/paddle/hapi/model_summary.py
+225
-0
python/paddle/tests/test_model.py
python/paddle/tests/test_model.py
+20
-0
python/paddle/utils/__init__.py
python/paddle/utils/__init__.py
+1
-0
未找到文件。
python/paddle/__init__.py
浏览文件 @
559d9f2b
...
...
@@ -267,5 +267,6 @@ from . import static
# high-level api
from
.hapi
import
Model
from
.hapi
import
callbacks
from
.hapi
import
summary
import
paddle.text
import
paddle.vision
python/paddle/hapi/__init__.py
浏览文件 @
559d9f2b
...
...
@@ -14,14 +14,15 @@
from
.
import
logger
from
.
import
callbacks
from
.
import
model_summary
from
.
import
model
from
.model
import
*
from
.model_summary
import
summary
from
.dygraph_layer_patch
import
monkey_patch_layer
logger
.
setup_logger
()
__all__
=
[
'callbacks'
]
+
model
.
__all__
__all__
=
[
'callbacks'
]
+
model
.
__all__
+
[
'summary'
]
monkey_patch_layer
()
python/paddle/hapi/model.py
浏览文件 @
559d9f2b
...
...
@@ -47,10 +47,10 @@ from paddle.io import DataLoader, Dataset, DistributedBatchSampler
from
paddle.fluid.executor
import
scope_guard
,
Executor
from
paddle.fluid.dygraph.layers
import
Layer
from
paddle.metric
import
Metric
from
paddle.static
import
InputSpec
as
Input
from
.callbacks
import
config_callbacks
from
.model_summary
import
summary
__all__
=
[
'Model'
,
]
...
...
@@ -1828,6 +1828,49 @@ class Model(object):
return
logs
,
outputs
return
logs
def
summary
(
self
,
input_size
=
None
,
batch_size
=
None
,
dtype
=
None
):
"""Prints a string summary of the network.
Args:
input_size (tuple|InputSpec|list[tuple|InputSpec], optional): size of input tensor.
if not set, input_size will get from ``self._inputs`` if network only have
one input, input_size can be tuple or InputSpec. if model have multiple
input, input_size must be a list which contain every input's shape.
Default: None.
batch_size (int, optional): batch size of input tensor, Default: None.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
Returns:
Dict: a summary of the network including total params and total trainable params.
Examples:
.. code-block:: python
import paddle
from paddle.static import InputSpec
dynamic = True
device = paddle.set_device('cpu')
paddle.disable_static(device) if dynamic else None
input = InputSpec([None, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
model = paddle.Model(paddle.vision.LeNet(classifier_activation=None),
input, label)
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
paddle.nn.CrossEntropyLoss())
params_info = model.summary()
print(params_info)
"""
return
summary
(
self
.
network
,
self
.
_inputs
,
batch_size
,
dtype
)
def
_verify_spec
(
self
,
specs
,
is_input
=
False
):
out_specs
=
[]
...
...
python/paddle/hapi/model_summary.py
0 → 100644
浏览文件 @
559d9f2b
# 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.
import
numpy
as
np
import
paddle
import
paddle.nn
as
nn
from
paddle.static
import
InputSpec
from
collections
import
OrderedDict
__all__
=
[
'summary'
]
def
summary
(
net
,
input_size
,
batch_size
=
None
,
dtypes
=
None
):
"""Prints a string summary of the network.
Args:
net (Layer): the network which must be a subinstance of Layer.
input_size (tuple|InputSpec|list[tuple|InputSpec]): size of input tensor. if model only
have one input, input_size can be tuple or InputSpec. if model
have multiple input, input_size must be a list which contain
every input's shape.
batch_size (int, optional): batch size of input tensor, Default: None.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
Returns:
Dict: a summary of the network including total params and total trainable params.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
class LeNet(nn.Layer):
def __init__(self, num_classes=10):
super(LeNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2d(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(
6, 16, 5, stride=1, padding=0),
nn.ReLU(),
nn.MaxPool2d(2, 2))
if num_classes > 0:
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84),
nn.Linear(
84, 10))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
lenet = LeNet()
params_info = paddle.summary(lenet, (1, 28, 28))
print(params_info)
"""
if
isinstance
(
input_size
,
InputSpec
):
_input_size
=
tuple
(
input_size
.
shape
[
1
:])
if
batch_size
is
None
:
batch_size
=
input_size
.
shape
[
0
]
elif
isinstance
(
input_size
,
list
):
_input_size
=
[]
for
item
in
input_size
:
assert
isinstance
(
item
,
(
list
,
InputSpec
)),
'When input_size is list,
\
expect item in input_size is a tuple or InputSpec, but got {}'
.
format
(
type
(
item
))
if
isinstance
(
item
,
InputSpec
):
_input_size
.
append
(
tuple
(
item
.
shape
[
1
:]))
if
batch_size
is
None
:
batch_size
=
item
.
shape
[
0
]
else
:
_input_size
.
append
(
item
)
else
:
_input_size
=
input_size
if
batch_size
is
None
:
batch_size
=
-
1
result
,
params_info
=
summary_string
(
net
,
_input_size
,
batch_size
,
dtypes
)
print
(
result
)
return
params_info
def
summary_string
(
model
,
input_size
,
batch_size
=-
1
,
dtypes
=
None
):
if
dtypes
==
None
:
dtypes
=
[
'float32'
]
*
len
(
input_size
)
summary_str
=
''
depth
=
len
(
list
(
model
.
sublayers
()))
def
register_hook
(
module
):
def
hook
(
module
,
input
,
output
):
class_name
=
str
(
module
.
__class__
).
split
(
"."
)[
-
1
].
split
(
"'"
)[
0
]
try
:
module_idx
=
int
(
module
.
_full_name
.
split
(
'_'
)[
-
1
])
except
:
module_idx
=
len
(
summary
)
m_key
=
"%s-%i"
%
(
class_name
,
module_idx
+
1
)
summary
[
m_key
]
=
OrderedDict
()
summary
[
m_key
][
"input_shape"
]
=
list
(
input
[
0
].
shape
)
summary
[
m_key
][
"input_shape"
][
0
]
=
batch_size
if
isinstance
(
output
,
(
list
,
tuple
)):
summary
[
m_key
][
"output_shape"
]
=
[[
-
1
]
+
list
(
o
.
shape
)[
1
:]
for
o
in
output
]
else
:
summary
[
m_key
][
"output_shape"
]
=
list
(
output
.
shape
)
summary
[
m_key
][
"output_shape"
][
0
]
=
batch_size
params
=
0
if
hasattr
(
module
,
"weight"
):
params
+=
np
.
prod
(
module
.
weight
.
shape
)
summary
[
m_key
][
"trainable"
]
=
module
.
weight
.
trainable
or
(
not
module
.
weight
.
stop_gradient
)
if
hasattr
(
module
,
"bias"
):
params
+=
np
.
prod
(
module
.
bias
.
shape
)
summary
[
m_key
][
"nb_params"
]
=
params
if
(
not
isinstance
(
module
,
nn
.
Sequential
)
and
not
isinstance
(
module
,
nn
.
LayerList
)
and
(
not
(
module
==
model
)
or
depth
<
1
)):
hooks
.
append
(
module
.
register_forward_post_hook
(
hook
))
if
isinstance
(
input_size
,
tuple
):
input_size
=
[
input_size
]
x
=
[
paddle
.
rand
(
[
2
]
+
list
(
in_size
),
dtype
=
dtype
)
for
in_size
,
dtype
in
zip
(
input_size
,
dtypes
)
]
# create properties
summary
=
OrderedDict
()
hooks
=
[]
# register hook
model
.
apply
(
register_hook
)
# make a forward pass
model
(
*
x
)
# remove these hooks
for
h
in
hooks
:
h
.
remove
()
table_width
=
80
summary_str
+=
"-"
*
table_width
+
"
\n
"
line_new
=
"{:>15} {:>20} {:>20} {:>15}"
.
format
(
"Layer (type)"
,
"Input Shape"
,
"Output Shape"
,
"Param #"
)
summary_str
+=
line_new
+
"
\n
"
summary_str
+=
"="
*
table_width
+
"
\n
"
total_params
=
0
total_output
=
0
trainable_params
=
0
for
layer
in
summary
:
# input_shape, output_shape, trainable, nb_params
line_new
=
"{:>15} {:>20} {:>20} {:>15}"
.
format
(
layer
,
str
(
summary
[
layer
][
"input_shape"
]),
str
(
summary
[
layer
][
"output_shape"
]),
"{0:,}"
.
format
(
summary
[
layer
][
"nb_params"
]),
)
total_params
+=
summary
[
layer
][
"nb_params"
]
total_output
+=
np
.
prod
(
summary
[
layer
][
"output_shape"
])
if
"trainable"
in
summary
[
layer
]:
if
summary
[
layer
][
"trainable"
]
==
True
:
trainable_params
+=
summary
[
layer
][
"nb_params"
]
summary_str
+=
line_new
+
"
\n
"
# assume 4 bytes/number (float on cuda).
total_input_size
=
abs
(
np
.
prod
(
sum
(
input_size
,
()))
*
batch_size
*
4.
/
(
1024
**
2.
))
total_output_size
=
abs
(
2.
*
total_output
*
4.
/
(
1024
**
2.
))
# x2 for gradients
total_params_size
=
abs
(
total_params
*
4.
/
(
1024
**
2.
))
total_size
=
total_params_size
+
total_output_size
+
total_input_size
summary_str
+=
"="
*
table_width
+
"
\n
"
summary_str
+=
"Total params: {0:,}"
.
format
(
total_params
)
+
"
\n
"
summary_str
+=
"Trainable params: {0:,}"
.
format
(
trainable_params
)
+
"
\n
"
summary_str
+=
"Non-trainable params: {0:,}"
.
format
(
total_params
-
trainable_params
)
+
"
\n
"
summary_str
+=
"-"
*
table_width
+
"
\n
"
summary_str
+=
"Input size (MB): %0.2f"
%
total_input_size
+
"
\n
"
summary_str
+=
"Forward/backward pass size (MB): %0.2f"
%
total_output_size
+
"
\n
"
summary_str
+=
"Params size (MB): %0.2f"
%
total_params_size
+
"
\n
"
summary_str
+=
"Estimated Total Size (MB): %0.2f"
%
total_size
+
"
\n
"
summary_str
+=
"-"
*
table_width
+
"
\n
"
# return summary
return
summary_str
,
{
'total_params'
:
total_params
,
'trainable_params'
:
trainable_params
}
python/paddle/tests/test_model.py
浏览文件 @
559d9f2b
...
...
@@ -499,6 +499,26 @@ class TestModelFunction(unittest.TestCase):
self
.
assertTrue
(
params
[
0
].
shape
[
1
]
==
10
)
fluid
.
disable_dygraph
()
if
dynamic
else
None
def
test_summary
(
self
):
def
_get_param_from_state_dict
(
state_dict
):
params
=
0
for
k
,
v
in
state_dict
.
items
():
params
+=
np
.
prod
(
v
.
numpy
().
shape
)
return
params
for
dynamic
in
[
True
,
False
]:
device
=
paddle
.
set_device
(
'cpu'
)
fluid
.
enable_dygraph
(
device
)
if
dynamic
else
None
net
=
MyModel
()
inputs
=
[
InputSpec
([
None
,
20
],
'float32'
,
'x'
)]
model
=
Model
(
net
,
inputs
)
model
.
prepare
()
params_info
=
model
.
summary
()
gt_params
=
_get_param_from_state_dict
(
net
.
state_dict
())
np
.
testing
.
assert_allclose
(
params_info
[
'total_params'
],
gt_params
)
print
(
params_info
)
def
test_export_deploy_model
(
self
):
for
dynamic
in
[
True
,
False
]:
fluid
.
enable_dygraph
()
if
dynamic
else
None
...
...
python/paddle/utils/__init__.py
浏览文件 @
559d9f2b
...
...
@@ -16,6 +16,7 @@ from .profiler import ProfilerOptions
from
.profiler
import
Profiler
from
.profiler
import
get_profiler
from
.deprecated
import
deprecated
from
.
import
download
__all__
=
[
'dump_config'
,
'deprecated'
,
'download'
]
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录