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体验新版 GitCode,发现更多精彩内容 >>
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3423022e
编写于
1月 12, 2018
作者:
Y
Yan Chunwei
提交者:
GitHub
1月 12, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
feature/add print op (#6799)
上级
b4d1811c
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
284 addition
and
1 deletion
+284
-1
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-0
paddle/operators/print_op.cc
paddle/operators/print_op.cc
+206
-0
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+56
-1
python/paddle/v2/fluid/tests/test_print_op.py
python/paddle/v2/fluid/tests/test_print_op.py
+21
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
3423022e
...
...
@@ -135,6 +135,7 @@ op_library(detection_output_op DEPS softmax)
op_library
(
sequence_softmax_op DEPS softmax
)
op_library
(
sum_op DEPS selected_rows_functor
)
op_library
(
sgd_op DEPS selected_rows_functor
)
op_library
(
print_op DEPS lod_tensor
)
op_library
(
adagrad_op DEPS selected_rows_functor
)
op_library
(
conv_op DEPS vol2col
)
op_library
(
pool_op DEPS pooling
)
...
...
paddle/operators/print_op.cc
0 → 100644
浏览文件 @
3423022e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include <algorithm>
#include <ctime>
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
#define CLOG std::cout
struct
Formater
{
std
::
string
message
;
std
::
string
name
;
std
::
vector
<
int
>
dims
;
std
::
type_index
dtype
{
typeid
(
char
)};
framework
::
LoD
lod
;
int
summarize
;
void
*
data
{
nullptr
};
void
operator
()(
size_t
size
)
{
PrintMessage
();
PrintName
();
PrintDims
();
PrintDtype
();
PrintLod
();
PrintData
(
size
);
}
private:
void
PrintMessage
()
{
CLOG
<<
std
::
time
(
nullptr
)
<<
"
\t
"
<<
message
;
}
void
PrintName
()
{
if
(
!
name
.
empty
())
{
CLOG
<<
"Tensor["
<<
name
<<
"]"
<<
std
::
endl
;
}
}
void
PrintDims
()
{
if
(
!
dims
.
empty
())
{
CLOG
<<
"
\t
shape: ["
;
for
(
auto
i
:
dims
)
{
CLOG
<<
i
<<
","
;
}
CLOG
<<
"]"
<<
std
::
endl
;
}
}
void
PrintDtype
()
{
if
(
dtype
.
hash_code
()
!=
typeid
(
char
).
hash_code
())
{
CLOG
<<
"
\t
dtype: "
<<
dtype
.
name
()
<<
std
::
endl
;
}
}
void
PrintLod
()
{
if
(
!
lod
.
empty
())
{
CLOG
<<
"
\t
LoD: ["
;
for
(
auto
level
:
lod
)
{
CLOG
<<
"[ "
;
for
(
auto
i
:
level
)
{
CLOG
<<
i
<<
","
;
}
CLOG
<<
" ]"
;
}
CLOG
<<
"]"
<<
std
::
endl
;
}
}
void
PrintData
(
size_t
size
)
{
PADDLE_ENFORCE_NOT_NULL
(
data
);
// print float
if
(
dtype
.
hash_code
()
==
typeid
(
float
).
hash_code
())
{
Display
<
float
>
(
size
);
}
if
(
dtype
.
hash_code
()
==
typeid
(
double
).
hash_code
())
{
Display
<
double
>
(
size
);
}
if
(
dtype
.
hash_code
()
==
typeid
(
int
).
hash_code
())
{
Display
<
int
>
(
size
);
}
if
(
dtype
.
hash_code
()
==
typeid
(
int64_t
).
hash_code
())
{
Display
<
int64_t
>
(
size
);
}
}
template
<
typename
T
>
void
Display
(
size_t
size
)
{
auto
*
d
=
(
T
*
)
data
;
CLOG
<<
"
\t
data: "
;
if
(
summarize
!=
-
1
)
{
summarize
=
std
::
min
(
size
,
(
size_t
)
summarize
);
for
(
int
i
=
0
;
i
<
summarize
;
i
++
)
{
CLOG
<<
d
[
i
]
<<
","
;
}
}
else
{
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
CLOG
<<
d
[
i
]
<<
","
;
}
}
CLOG
<<
std
::
endl
;
}
};
// TODO(ChunweiYan) there should be some other printers for TensorArray
class
TensorPrintOp
:
public
framework
::
OperatorBase
{
public:
TensorPrintOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
TensorPrintOp
(
const
TensorPrintOp
&
o
)
:
framework
::
OperatorBase
(
static_cast
<
const
framework
::
OperatorBase
&>
(
o
))
{
PADDLE_THROW
(
"Not implemented"
);
}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
// Only run the `first_n` times.
int
first_n
=
Attr
<
int
>
(
"first_n"
);
if
(
first_n
>
0
&&
++
times_
>
first_n
)
return
;
PADDLE_ENFORCE
(
!
Inputs
(
"input"
).
empty
(),
"input should be set"
);
auto
*
input_var
=
scope
.
FindVar
(
Input
(
"input"
));
PADDLE_ENFORCE_NOT_NULL
(
input_var
);
auto
&
tensor
=
input_var
->
Get
<
framework
::
LoDTensor
>
();
// TODO(ChunweiYan) support GPU
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
tensor
.
place
()));
Formater
formater
;
if
(
Attr
<
bool
>
(
"print_tensor_name"
))
{
formater
.
name
=
Inputs
(
"input"
).
front
();
}
if
(
Attr
<
bool
>
(
"print_tensor_type"
))
{
formater
.
dtype
=
tensor
.
type
();
}
if
(
Attr
<
bool
>
(
"print_tensor_shape"
))
{
formater
.
dims
.
assign
(
tensor
.
dims
()[
0
],
tensor
.
dims
()[
tensor
.
dims
().
size
()
-
1
]);
}
if
(
Attr
<
bool
>
(
"print_tensor_lod"
))
{
formater
.
lod
=
tensor
.
lod
();
}
formater
.
summarize
=
Attr
<
int
>
(
"summarize"
);
formater
.
data
=
(
void
*
)
tensor
.
data
<
void
>
();
formater
(
tensor
.
numel
());
}
private:
mutable
int
times_
{
0
};
};
class
PrintOpProtoAndCheckMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
PrintOpProtoAndCheckMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"input"
,
"the tensor that will be displayed."
);
AddAttr
<
int
>
(
"first_n"
,
"Only log `first_n` number of times."
);
AddAttr
<
std
::
string
>
(
"message"
,
"A string message to print as a prefix."
);
AddAttr
<
int
>
(
"summarize"
,
"Print this number of elements in the tensor."
);
AddAttr
<
bool
>
(
"print_tensor_name"
,
"Whether to print the tensor name."
);
AddAttr
<
bool
>
(
"print_tensor_type"
,
"Whether to print the tensor's dtype."
);
AddAttr
<
bool
>
(
"print_tensor_shape"
,
"Whether to print the tensor's shape."
);
AddAttr
<
bool
>
(
"print_tensor_lod"
,
"Whether to print the tensor's lod."
);
AddComment
(
R"DOC(
Creates a print op that will print when a tensor is accessed.
Wraps the tensor passed in so that whenever that a tensor is accessed,
the message `message` is printed, along with the current value of the
tensor `t`.)DOC"
);
}
};
class
InferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"input"
),
"input should be set"
);
}
};
class
InferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
print
,
paddle
::
operators
::
TensorPrintOp
,
paddle
::
operators
::
PrintOpProtoAndCheckMaker
,
paddle
::
operators
::
InferShape
,
paddle
::
operators
::
InferVarType
,
paddle
::
framework
::
EmptyGradOpMaker
);
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
3423022e
...
...
@@ -12,7 +12,7 @@ __all__ = [
'array_to_lod_tensor'
,
'increment'
,
'array_write'
,
'create_array'
,
'less_than'
,
'array_read'
,
'shrink_memory'
,
'array_length'
,
'IfElse'
,
'DynamicRNN'
,
'ConditionalBlock'
,
'StaticRNN'
,
'reorder_lod_tensor_by_rank'
,
'ParallelDo'
'ParallelDo'
,
'Print'
]
...
...
@@ -110,6 +110,61 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
return
out
def
Print
(
input
,
first_n
=-
1
,
message
=
None
,
summarize
=-
1
,
print_tensor_name
=
True
,
print_tensor_type
=
True
,
print_tensor_shape
=
True
,
print_tensor_lod
=
True
):
'''
**Print operator**
This creates a print op that will print when a tensor is accessed.
Wraps the tensor passed in so that whenever that a tensor is accessed,
the message `message` is printed, along with the current value of the
tensor `t`.
Args:
input(Variable): A Tensor to print.
summarize(int): Print this number of elements in the tensor, will print all
if left negative.
message(str): A string message to print as a prefix.
first_n(int): Only log `first_n` number of times.
print_tensor_name(bool): Print the tensor name.
print_tensor_type(bool): Print the tensor type.
print_tensor_shape(bool): Print the tensor shape.
print_tensor_lod(bool): Print the tensor lod.
Returns:
None
Examples:
.. code-block:: python
value = some_layer(...)
Print(value, summarize=10,
message="The content of some_layer: ")
'''
helper
=
LayerHelper
(
'print'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
'print'
,
inputs
=
{
'input'
:
input
},
attrs
=
{
'first_n'
:
first_n
,
'summarize'
:
summarize
,
'message'
:
message
or
""
,
'print_tensor_name'
:
print_tensor_name
,
'print_tensor_type'
:
print_tensor_type
,
'print_tensor_shape'
:
print_tensor_shape
,
'print_tensor_lod'
:
print_tensor_lod
,
})
return
out
class
BlockGuard
(
object
):
"""
BlockGuard class.
...
...
python/paddle/v2/fluid/tests/test_print_op.py
0 → 100644
浏览文件 @
3423022e
import
unittest
import
numpy
as
np
from
paddle.v2.fluid.executor
import
Executor
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid.layers
as
pd
class
TestSumOp
(
unittest
.
TestCase
):
def
test_tensor
(
self
):
i
=
pd
.
zeros
(
shape
=
[
2
,
10
],
dtype
=
'float32'
)
pd
.
Print
(
i
,
message
=
"I am a message"
,
summarize
=
10
)
cpu
=
core
.
CPUPlace
()
exe
=
Executor
(
cpu
)
exe
.
run
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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