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78a27a2b
编写于
9月 24, 2020
作者:
L
LielinJiang
提交者:
GitHub
9月 24, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Reproduce summary api (#27367)
* reproduce summary api
上级
29f1560d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
173 addition
and
66 deletion
+173
-66
python/paddle/hapi/model.py
python/paddle/hapi/model.py
+2
-3
python/paddle/hapi/model_summary.py
python/paddle/hapi/model_summary.py
+161
-58
python/paddle/tests/test_model.py
python/paddle/tests/test_model.py
+10
-5
未找到文件。
python/paddle/hapi/model.py
浏览文件 @
78a27a2b
...
@@ -1813,7 +1813,7 @@ class Model(object):
...
@@ -1813,7 +1813,7 @@ class Model(object):
return
logs
,
outputs
return
logs
,
outputs
return
logs
return
logs
def
summary
(
self
,
input_size
=
None
,
batch_size
=
None
,
dtype
=
None
):
def
summary
(
self
,
input_size
=
None
,
dtype
=
None
):
"""Prints a string summary of the network.
"""Prints a string summary of the network.
Args:
Args:
...
@@ -1822,7 +1822,6 @@ class Model(object):
...
@@ -1822,7 +1822,6 @@ class Model(object):
one input, input_size can be tuple or InputSpec. if model have multiple
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.
input, input_size must be a list which contain every input's shape.
Default: None.
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.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
Returns:
Returns:
...
@@ -1859,7 +1858,7 @@ class Model(object):
...
@@ -1859,7 +1858,7 @@ class Model(object):
_input_size
=
input_size
_input_size
=
input_size
else
:
else
:
_input_size
=
self
.
_inputs
_input_size
=
self
.
_inputs
return
summary
(
self
.
network
,
_input_size
,
batch_size
,
dtype
)
return
summary
(
self
.
network
,
_input_size
,
dtype
)
def
_verify_spec
(
self
,
specs
,
is_input
=
False
):
def
_verify_spec
(
self
,
specs
,
is_input
=
False
):
out_specs
=
[]
out_specs
=
[]
...
...
python/paddle/hapi/model_summary.py
浏览文件 @
78a27a2b
...
@@ -25,7 +25,7 @@ from collections import OrderedDict
...
@@ -25,7 +25,7 @@ from collections import OrderedDict
__all__
=
[
'summary'
]
__all__
=
[
'summary'
]
def
summary
(
net
,
input_size
,
batch_size
=
None
,
dtypes
=
None
):
def
summary
(
net
,
input_size
,
dtypes
=
None
):
"""Prints a string summary of the network.
"""Prints a string summary of the network.
Args:
Args:
...
@@ -33,8 +33,8 @@ def summary(net, input_size, batch_size=None, dtypes=None):
...
@@ -33,8 +33,8 @@ def summary(net, input_size, batch_size=None, dtypes=None):
input_size (tuple|InputSpec|list[tuple|InputSpec]): size of input tensor. if model only
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 one input, input_size can be tuple or InputSpec. if model
have multiple input, input_size must be a list which contain
have multiple input, input_size must be a list which contain
every input's shape.
every input's shape.
Note that input_size only dim of
batch_size (int, optional): batch size of input tensor, Default: None
.
batch_size can be None or -1
.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
Returns:
Returns:
...
@@ -77,14 +77,12 @@ def summary(net, input_size, batch_size=None, dtypes=None):
...
@@ -77,14 +77,12 @@ def summary(net, input_size, batch_size=None, dtypes=None):
lenet = LeNet()
lenet = LeNet()
params_info = paddle.summary(lenet, (1, 28, 28))
params_info = paddle.summary(lenet, (1,
1,
28, 28))
print(params_info)
print(params_info)
"""
"""
if
isinstance
(
input_size
,
InputSpec
):
if
isinstance
(
input_size
,
InputSpec
):
_input_size
=
tuple
(
input_size
.
shape
[
1
:])
_input_size
=
tuple
(
input_size
.
shape
)
if
batch_size
is
None
:
batch_size
=
input_size
.
shape
[
0
]
elif
isinstance
(
input_size
,
list
):
elif
isinstance
(
input_size
,
list
):
_input_size
=
[]
_input_size
=
[]
for
item
in
input_size
:
for
item
in
input_size
:
...
@@ -96,9 +94,7 @@ def summary(net, input_size, batch_size=None, dtypes=None):
...
@@ -96,9 +94,7 @@ def summary(net, input_size, batch_size=None, dtypes=None):
type
(
item
))
type
(
item
))
if
isinstance
(
item
,
InputSpec
):
if
isinstance
(
item
,
InputSpec
):
_input_size
.
append
(
tuple
(
item
.
shape
[
1
:]))
_input_size
.
append
(
tuple
(
item
.
shape
))
if
batch_size
is
None
:
batch_size
=
item
.
shape
[
0
]
else
:
else
:
_input_size
.
append
(
item
)
_input_size
.
append
(
item
)
elif
isinstance
(
input_size
,
int
):
elif
isinstance
(
input_size
,
int
):
...
@@ -106,28 +102,88 @@ def summary(net, input_size, batch_size=None, dtypes=None):
...
@@ -106,28 +102,88 @@ def summary(net, input_size, batch_size=None, dtypes=None):
else
:
else
:
_input_size
=
input_size
_input_size
=
input_size
if
batch_size
is
None
:
batch_size
=
-
1
if
not
paddle
.
in_dynamic_mode
():
if
not
paddle
.
in_dynamic_mode
():
warnings
.
warn
(
warnings
.
warn
(
"Your model was created in static mode, this may not get correct summary information!"
"Your model was created in static mode, this may not get correct summary information!"
)
)
result
,
params_info
=
summary_string
(
net
,
_input_size
,
batch_size
,
dtypes
)
def
_is_shape
(
shape
):
for
item
in
shape
:
if
isinstance
(
item
,
(
list
,
tuple
)):
return
False
return
True
def
_check_shape
(
shape
):
num_unknown
=
0
new_shape
=
[]
for
i
in
range
(
len
(
shape
)):
item
=
shape
[
i
]
if
item
is
None
or
item
==
-
1
:
num_unknown
+=
1
if
num_unknown
>
1
:
raise
ValueError
(
'Option input_size only the dim of batch_size can be None or -1.'
)
item
=
1
elif
isinstance
(
item
,
numbers
.
Number
):
if
item
<=
0
:
raise
ValueError
(
"Expected element in input size greater than zero, but got {}"
.
format
(
item
))
new_shape
.
append
(
item
)
return
tuple
(
new_shape
)
def
_check_input
(
input_size
):
if
isinstance
(
input_size
,
(
list
,
tuple
))
and
_is_shape
(
input_size
):
return
_check_shape
(
input_size
)
else
:
return
[
_check_input
(
i
)
for
i
in
input_size
]
_input_size
=
_check_input
(
_input_size
)
result
,
params_info
=
summary_string
(
net
,
_input_size
,
dtypes
)
print
(
result
)
print
(
result
)
return
params_info
return
params_info
def
summary_string
(
model
,
input_size
,
batch_size
=-
1
,
dtypes
=
None
):
def
summary_string
(
model
,
input_size
,
dtypes
=
None
):
if
dtypes
==
None
:
def
_all_is_numper
(
items
):
dtypes
=
[
'float32'
]
*
len
(
input_size
)
for
item
in
items
:
if
not
isinstance
(
item
,
numbers
.
Number
):
return
False
return
True
def
_build_dtypes
(
input_size
,
dtype
):
if
dtype
is
None
:
dtype
=
'float32'
if
isinstance
(
input_size
,
(
list
,
tuple
))
and
_all_is_numper
(
input_size
):
return
[
dtype
]
else
:
return
[
_build_dtypes
(
i
,
dtype
)
for
i
in
input_size
]
if
not
isinstance
(
dtypes
,
(
list
,
tuple
)):
dtypes
=
_build_dtypes
(
input_size
,
dtypes
)
batch_size
=
1
summary_str
=
''
summary_str
=
''
depth
=
len
(
list
(
model
.
sublayers
()))
depth
=
len
(
list
(
model
.
sublayers
()))
def
_get_shape_from_tensor
(
x
):
if
isinstance
(
x
,
(
paddle
.
fluid
.
Variable
,
paddle
.
fluid
.
core
.
VarBase
)):
return
list
(
x
.
shape
)
elif
isinstance
(
x
,
(
list
,
tuple
)):
return
[
_get_shape_from_tensor
(
xx
)
for
xx
in
x
]
def
_get_output_shape
(
output
):
if
isinstance
(
output
,
(
list
,
tuple
)):
output_shape
=
[
_get_output_shape
(
o
)
for
o
in
output
]
else
:
output_shape
=
list
(
output
.
shape
)
return
output_shape
def
register_hook
(
layer
):
def
register_hook
(
layer
):
def
hook
(
layer
,
input
,
output
):
def
hook
(
layer
,
input
,
output
):
class_name
=
str
(
layer
.
__class__
).
split
(
"."
)[
-
1
].
split
(
"'"
)[
0
]
class_name
=
str
(
layer
.
__class__
).
split
(
"."
)[
-
1
].
split
(
"'"
)[
0
]
...
@@ -139,14 +195,18 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
...
@@ -139,14 +195,18 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
m_key
=
"%s-%i"
%
(
class_name
,
layer_idx
+
1
)
m_key
=
"%s-%i"
%
(
class_name
,
layer_idx
+
1
)
summary
[
m_key
]
=
OrderedDict
()
summary
[
m_key
]
=
OrderedDict
()
summary
[
m_key
][
"input_shape"
]
=
list
(
input
[
0
].
shape
)
summary
[
m_key
][
"input_shape"
][
0
]
=
batch_size
try
:
if
isinstance
(
output
,
(
list
,
tuple
)):
summary
[
m_key
][
"input_shape"
]
=
_get_shape_from_tensor
(
input
)
summary
[
m_key
][
"output_shape"
]
=
[[
-
1
]
+
list
(
o
.
shape
)[
1
:]
except
:
for
o
in
output
]
warnings
.
warn
(
'Get layer {} input shape failed!'
)
else
:
summary
[
m_key
][
"input_shape"
]
=
[]
summary
[
m_key
][
"output_shape"
]
=
list
(
output
.
shape
)
summary
[
m_key
][
"output_shape"
][
0
]
=
batch_size
try
:
summary
[
m_key
][
"output_shape"
]
=
_get_output_shape
(
output
)
except
:
warnings
.
warn
(
'Get layer {} output shape failed!'
)
summary
[
m_key
][
"output_shape"
]
params
=
0
params
=
0
...
@@ -175,29 +235,22 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
...
@@ -175,29 +235,22 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
hooks
.
append
(
layer
.
register_forward_post_hook
(
hook
))
hooks
.
append
(
layer
.
register_forward_post_hook
(
hook
))
def
_check_input_size
(
input_sizes
):
for
input_size
in
input_sizes
:
for
item
in
input_size
:
if
not
isinstance
(
item
,
numbers
.
Number
):
raise
TypeError
(
"Expected item in input size be a number, but got {}"
.
format
(
type
(
item
)))
if
item
<=
0
:
raise
ValueError
(
"Expected item in input size greater than zero, but got {}"
.
format
(
item
))
if
isinstance
(
input_size
,
tuple
):
if
isinstance
(
input_size
,
tuple
):
input_size
=
[
input_size
]
input_size
=
[
input_size
]
_check_input_size
(
input_size
)
def
build_input
(
input_size
,
dtypes
):
if
isinstance
(
input_size
,
(
list
,
tuple
))
and
_all_is_numper
(
input_size
):
if
isinstance
(
dtypes
,
(
list
,
tuple
)):
dtype
=
dtypes
[
0
]
else
:
dtype
=
dtypes
return
paddle
.
rand
(
list
(
input_size
),
dtype
)
else
:
return
[
build_input
(
i
,
dtype
)
for
i
,
dtype
in
zip
(
input_size
,
dtypes
)
]
x
=
[
x
=
build_input
(
input_size
,
dtypes
)
paddle
.
rand
(
[
2
]
+
list
(
in_size
),
dtype
=
dtype
)
for
in_size
,
dtype
in
zip
(
input_size
,
dtypes
)
]
# create properties
# create properties
summary
=
OrderedDict
()
summary
=
OrderedDict
()
...
@@ -213,22 +266,65 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
...
@@ -213,22 +266,65 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
for
h
in
hooks
:
for
h
in
hooks
:
h
.
remove
()
h
.
remove
()
table_width
=
80
def
_get_str_length
(
summary
):
summary_str
+=
"-"
*
table_width
+
"
\n
"
head_length
=
{
line_new
=
"{:>15} {:>20} {:>20} {:>15}"
.
format
(
'layer_width'
:
15
,
"Layer (type)"
,
"Input Shape"
,
"Output Shape"
,
"Param #"
)
'input_shape_width'
:
20
,
'output_shape_width'
:
20
,
'params_width'
:
15
,
'table_width'
:
75
}
for
layer
in
summary
:
if
head_length
[
'output_shape_width'
]
<
len
(
str
(
summary
[
layer
][
"output_shape"
])):
head_length
[
'output_shape_width'
]
=
len
(
str
(
summary
[
layer
][
"output_shape"
]))
if
head_length
[
'input_shape_width'
]
<
len
(
str
(
summary
[
layer
][
"input_shape"
])):
head_length
[
'input_shape_width'
]
=
len
(
str
(
summary
[
layer
][
"input_shape"
]))
if
head_length
[
'layer_width'
]
<
len
(
str
(
layer
)):
head_length
[
'layer_width'
]
=
len
(
str
(
layer
))
if
head_length
[
'params_width'
]
<
len
(
str
(
summary
[
layer
][
"nb_params"
])):
head_length
[
'params_width'
]
=
len
(
str
(
summary
[
layer
][
"nb_params"
]))
_temp_width
=
0
for
k
,
v
in
head_length
.
items
():
if
k
!=
'table_width'
:
_temp_width
+=
v
if
head_length
[
'table_width'
]
<
_temp_width
+
5
:
head_length
[
'table_width'
]
=
_temp_width
+
5
return
head_length
table_width
=
_get_str_length
(
summary
)
summary_str
+=
"-"
*
table_width
[
'table_width'
]
+
"
\n
"
line_new
=
"{:^{}} {:^{}} {:^{}} {:^{}}"
.
format
(
"Layer (type)"
,
table_width
[
'layer_width'
],
"Input Shape"
,
table_width
[
'input_shape_width'
],
"Output Shape"
,
table_width
[
'output_shape_width'
],
"Param #"
,
table_width
[
'params_width'
])
summary_str
+=
line_new
+
"
\n
"
summary_str
+=
line_new
+
"
\n
"
summary_str
+=
"="
*
table_width
+
"
\n
"
summary_str
+=
"="
*
table_width
[
'table_width'
]
+
"
\n
"
total_params
=
0
total_params
=
0
total_output
=
0
total_output
=
0
trainable_params
=
0
trainable_params
=
0
max_length
=
0
for
layer
in
summary
:
for
layer
in
summary
:
# input_shape, output_shape, trainable, nb_params
# input_shape, output_shape, trainable, nb_params
line_new
=
"{:
>15} {:>20} {:>20} {:>15
}"
.
format
(
line_new
=
"{:
^{}} {:^{}} {:^{}} {:^{}
}"
.
format
(
layer
,
layer
,
table_width
[
'layer_width'
],
str
(
summary
[
layer
][
"input_shape"
]),
str
(
summary
[
layer
][
"input_shape"
]),
table_width
[
'input_shape_width'
],
str
(
summary
[
layer
][
"output_shape"
]),
str
(
summary
[
layer
][
"output_shape"
]),
"{0:,}"
.
format
(
summary
[
layer
][
"nb_params"
]),
)
table_width
[
'output_shape_width'
],
"{0:,}"
.
format
(
summary
[
layer
][
"nb_params"
]),
table_width
[
'params_width'
])
total_params
+=
summary
[
layer
][
"nb_params"
]
total_params
+=
summary
[
layer
][
"nb_params"
]
try
:
try
:
...
@@ -242,25 +338,32 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
...
@@ -242,25 +338,32 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
trainable_params
+=
summary
[
layer
][
"nb_params"
]
trainable_params
+=
summary
[
layer
][
"nb_params"
]
summary_str
+=
line_new
+
"
\n
"
summary_str
+=
line_new
+
"
\n
"
# assume 4 bytes/number (float on cuda).
def
_get_input_size
(
input_size
,
size
):
total_input_size
=
abs
(
if
isinstance
(
input_size
,
(
list
,
tuple
))
and
_all_is_numper
(
input_size
):
np
.
prod
(
sum
(
input_size
,
()))
*
batch_size
*
4.
/
(
1024
**
2.
))
size
=
abs
(
np
.
prod
(
input_size
)
*
4.
/
(
1024
**
2.
))
else
:
size
=
sum
([
_get_input_size
(
i
,
size
)
for
i
in
input_size
])
return
size
total_input_size
=
_get_input_size
(
input_size
,
0
)
total_output_size
=
abs
(
2.
*
total_output
*
4.
/
total_output_size
=
abs
(
2.
*
total_output
*
4.
/
(
1024
**
2.
))
# x2 for gradients
(
1024
**
2.
))
# x2 for gradients
total_params_size
=
abs
(
total_params
*
4.
/
(
1024
**
2.
))
total_params_size
=
abs
(
total_params
*
4.
/
(
1024
**
2.
))
total_size
=
total_params_size
+
total_output_size
+
total_input_size
total_size
=
total_params_size
+
total_output_size
+
total_input_size
summary_str
+=
"="
*
table_width
+
"
\n
"
summary_str
+=
"="
*
table_width
[
'table_width'
]
+
"
\n
"
summary_str
+=
"Total params: {0:,}"
.
format
(
total_params
)
+
"
\n
"
summary_str
+=
"Total params: {0:,}"
.
format
(
total_params
)
+
"
\n
"
summary_str
+=
"Trainable params: {0:,}"
.
format
(
trainable_params
)
+
"
\n
"
summary_str
+=
"Trainable params: {0:,}"
.
format
(
trainable_params
)
+
"
\n
"
summary_str
+=
"Non-trainable params: {0:,}"
.
format
(
total_params
-
summary_str
+=
"Non-trainable params: {0:,}"
.
format
(
total_params
-
trainable_params
)
+
"
\n
"
trainable_params
)
+
"
\n
"
summary_str
+=
"-"
*
table_width
+
"
\n
"
summary_str
+=
"-"
*
table_width
[
'table_width'
]
+
"
\n
"
summary_str
+=
"Input size (MB): %0.2f"
%
total_input_size
+
"
\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
+=
"Forward/backward pass size (MB): %0.2f"
%
total_output_size
+
"
\n
"
summary_str
+=
"Params size (MB): %0.2f"
%
total_params_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
+=
"Estimated Total Size (MB): %0.2f"
%
total_size
+
"
\n
"
summary_str
+=
"-"
*
table_width
+
"
\n
"
summary_str
+=
"-"
*
table_width
[
'table_width'
]
+
"
\n
"
# return summary
# return summary
return
summary_str
,
{
return
summary_str
,
{
'total_params'
:
total_params
,
'total_params'
:
total_params
,
...
...
python/paddle/tests/test_model.py
浏览文件 @
78a27a2b
...
@@ -494,17 +494,22 @@ class TestModelFunction(unittest.TestCase):
...
@@ -494,17 +494,22 @@ class TestModelFunction(unittest.TestCase):
model
.
summary
(
input_size
=
(
20
))
model
.
summary
(
input_size
=
(
20
))
model
.
summary
(
input_size
=
[(
20
)])
model
.
summary
(
input_size
=
[(
20
)])
model
.
summary
(
input_size
=
(
20
),
batch_size
=
2
)
model
.
summary
(
input_size
=
(
20
),
dtype
=
'float32'
)
def
test_summary_nlp
(
self
):
def
test_summary_nlp
(
self
):
paddle
.
enable_static
()
paddle
.
enable_static
()
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
paddle
.
summary
(
nlp_net
,
(
1
,
2
))
hidden_size
=
3
,
num_layers
=
3
,
direction
=
"bidirectional"
)
paddle
.
summary
(
nlp_net
,
(
1
,
1
,
2
))
rnn
=
paddle
.
nn
.
LSTM
(
16
,
32
,
2
)
paddle
.
summary
(
rnn
,
[(
-
1
,
23
,
16
),
((
2
,
None
,
32
),
(
2
,
-
1
,
32
))])
def
test_summary_error
(
self
):
def
test_summary_error
(
self
):
with
self
.
assertRaises
(
TypeError
):
with
self
.
assertRaises
(
TypeError
):
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
paddle
.
summary
(
nlp_net
,
(
1
,
'2'
))
paddle
.
summary
(
nlp_net
,
(
1
,
1
,
'2'
))
with
self
.
assertRaises
(
ValueError
):
with
self
.
assertRaises
(
ValueError
):
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
...
@@ -512,7 +517,7 @@ class TestModelFunction(unittest.TestCase):
...
@@ -512,7 +517,7 @@ class TestModelFunction(unittest.TestCase):
paddle
.
disable_static
()
paddle
.
disable_static
()
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
nlp_net
=
paddle
.
nn
.
GRU
(
input_size
=
2
,
hidden_size
=
3
,
num_layers
=
3
)
paddle
.
summary
(
nlp_net
,
(
1
,
2
))
paddle
.
summary
(
nlp_net
,
(
1
,
1
,
2
))
def
test_export_deploy_model
(
self
):
def
test_export_deploy_model
(
self
):
for
dynamic
in
[
True
,
False
]:
for
dynamic
in
[
True
,
False
]:
...
...
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