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体验新版 GitCode,发现更多精彩内容 >>
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60eba8b7
编写于
1月 14, 2018
作者:
Y
Yang yaming
提交者:
GitHub
1月 14, 2018
浏览文件
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差异文件
Merge pull request #7517 from pkuyym/fix-7478
Enhance print_op.
上级
9deb1756
a091d1a3
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
169 addition
and
52 deletion
+169
-52
paddle/operators/print_op.cc
paddle/operators/print_op.cc
+105
-28
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+20
-14
python/paddle/v2/fluid/tests/test_print_op.py
python/paddle/v2/fluid/tests/test_print_op.py
+44
-10
未找到文件。
paddle/operators/print_op.cc
浏览文件 @
60eba8b7
...
...
@@ -16,12 +16,17 @@
#include <ctime>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/variable.h"
namespace
paddle
{
namespace
operators
{
#define CLOG std::cout
const
std
::
string
kForward
=
"FORWARD"
;
const
std
::
string
kBackward
=
"BACKWARD"
;
const
std
::
string
kBoth
=
"BOTH"
;
struct
Formater
{
std
::
string
message
;
std
::
string
name
;
...
...
@@ -122,40 +127,77 @@ class TensorPrintOp : public framework::OperatorBase {
TensorPrintOp
(
const
TensorPrintOp
&
o
)
:
framework
::
OperatorBase
(
static_cast
<
const
framework
::
OperatorBase
&>
(
o
))
{
PADDLE_THROW
(
"Not implemented"
);
PADDLE_THROW
(
"Not implemented
.
"
);
}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
// Only run the `first_n` times.
const
framework
::
Variable
*
in_var_ptr
=
nullptr
;
std
::
string
phase
=
kForward
;
std
::
string
printed_var_name
=
""
;
auto
&
inputs
=
Inputs
();
if
(
inputs
.
find
(
"In"
)
!=
inputs
.
end
()
&&
!
Inputs
(
"In"
).
empty
())
{
in_var_ptr
=
scope
.
FindVar
(
Input
(
"In"
));
printed_var_name
=
Inputs
(
"In"
).
front
();
}
else
if
(
inputs
.
find
(
"In@GRAD"
)
!=
inputs
.
end
()
&&
!
Inputs
(
"In@GRAD"
).
empty
())
{
in_var_ptr
=
scope
.
FindVar
(
Input
(
"In@GRAD"
));
printed_var_name
=
Inputs
(
"In@GRAD"
).
front
();
phase
=
kBackward
;
}
else
{
PADDLE_THROW
(
"Unknown phase, should be forward or backward."
);
}
PADDLE_ENFORCE_NOT_NULL
(
in_var_ptr
);
auto
&
in_tensor
=
in_var_ptr
->
Get
<
framework
::
LoDTensor
>
();
auto
*
out_var_ptr
=
scope
.
FindVar
(
Output
(
"Out"
));
auto
&
out_tensor
=
*
out_var_ptr
->
GetMutable
<
framework
::
LoDTensor
>
();
// Just copy data from input tensor to output tensor
// output tensor share same memory with input tensor
out_tensor
.
ShareDataWith
(
in_tensor
);
out_tensor
.
set_lod
(
in_tensor
.
lod
());
std
::
string
print_phase
=
Attr
<
std
::
string
>
(
"print_phase"
);
if
(
print_phase
!=
phase
&&
print_phase
!=
kBoth
)
{
return
;
}
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
>
();
framework
::
LoDTensor
printed_tensor
;
printed_tensor
.
set_lod
(
in_tensor
.
lod
());
printed_tensor
.
Resize
(
in_tensor
.
dims
());
// TODO(ChunweiYan) support GPU
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
tensor
.
place
()));
if
(
platform
::
is_cpu_place
(
in_tensor
.
place
()))
{
printed_tensor
.
ShareDataWith
(
in_tensor
);
}
else
{
// copy data to cpu to print
platform
::
CPUPlace
place
;
framework
::
Copy
(
in_tensor
,
place
,
&
printed_tensor
);
}
Formater
formater
;
if
(
Attr
<
bool
>
(
"print_tensor_name"
))
{
formater
.
name
=
Inputs
(
"input"
).
front
()
;
formater
.
name
=
printed_var_name
;
}
if
(
Attr
<
bool
>
(
"print_tensor_type"
))
{
formater
.
dtype
=
tensor
.
type
();
formater
.
dtype
=
printed_
tensor
.
type
();
}
if
(
Attr
<
bool
>
(
"print_tensor_shape"
))
{
formater
.
dims
.
assign
(
tensor
.
dims
()[
0
],
tensor
.
dims
()[
tensor
.
dims
().
size
()
-
1
]);
auto
&
dims
=
printed_tensor
.
dims
();
formater
.
dims
.
resize
(
dims
.
size
());
for
(
int
i
=
0
;
i
<
dims
.
size
();
++
i
)
formater
.
dims
[
i
]
=
dims
[
i
];
}
if
(
Attr
<
bool
>
(
"print_tensor_lod"
))
{
formater
.
lod
=
tensor
.
lod
();
formater
.
lod
=
printed_
tensor
.
lod
();
}
formater
.
summarize
=
Attr
<
int
>
(
"summarize"
);
formater
.
data
=
(
void
*
)
tensor
.
data
<
void
>
();
formater
(
tensor
.
numel
());
formater
.
data
=
(
void
*
)
printed_
tensor
.
data
<
void
>
();
formater
(
printed_
tensor
.
numel
());
}
private:
...
...
@@ -166,27 +208,46 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
public:
PrintOpProtoAndCheckMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"
input"
,
"the tensor that will
be displayed."
);
AddInput
(
"
In"
,
"Input tensor to
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
<
int
>
(
"summarize"
,
"
Number of elements printed
."
);
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."
);
AddAttr
<
std
::
string
>
(
"print_phase"
,
"(string, default 'BOTH') Which phase to display including 'FORWARD' "
"'BACKWARD' and 'BOTH'."
)
.
SetDefault
(
kBoth
)
.
InEnum
({
kForward
,
kBackward
,
kBoth
});
AddOutput
(
"Out"
,
"Output tensor with same data as input tensor."
);
AddComment
(
R"DOC(
Creates a print op that will print when a tensor is accessed.
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"
);
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
{
class
InferShape
Forward
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"input"
),
"input should be set"
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"In"
),
"Input(In) should not be null."
);
context
->
ShareLoD
(
"In"
,
/*->*/
"Out"
);
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"In"
));
}
};
class
InferShapeBackward
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"In@GRAD"
),
"Input(In@GRAD) should not be null."
);
context
->
ShareLoD
(
"In@GRAD"
,
/*->*/
"Out"
);
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"In@GRAD"
));
}
};
...
...
@@ -196,11 +257,27 @@ class InferVarType : public framework::VarTypeInference {
framework
::
BlockDesc
*
block
)
const
override
{}
};
class
PrintOpProtoAndCheckGradOpMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
op_desc_ptr
=
new
framework
::
OpDesc
();
op_desc_ptr
->
SetType
(
"print_grad"
);
op_desc_ptr
->
SetInput
(
"In@GRAD"
,
OutputGrad
(
"Out"
));
op_desc_ptr
->
SetOutput
(
"Out"
,
InputGrad
(
"In"
));
op_desc_ptr
->
SetAttrMap
(
Attrs
());
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
op_desc_ptr
);
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
print
,
paddle
::
operators
::
TensorPrintOp
,
paddle
::
operators
::
PrintOpProtoAndCheckMaker
,
paddle
::
operators
::
InferShape
,
paddle
::
operators
::
InferVarType
,
paddle
::
framework
::
EmptyGradOpMaker
);
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
print
,
ops
::
TensorPrintOp
,
ops
::
PrintOpProtoAndCheckMaker
,
ops
::
PrintOpProtoAndCheckGradOpMaker
,
ops
::
InferShapeForward
,
ops
::
InferVarType
);
REGISTER_OPERATOR
(
print_grad
,
ops
::
TensorPrintOp
,
ops
::
InferShapeBackward
);
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
60eba8b7
...
...
@@ -117,7 +117,8 @@ def Print(input,
print_tensor_name
=
True
,
print_tensor_type
=
True
,
print_tensor_shape
=
True
,
print_tensor_lod
=
True
):
print_tensor_lod
=
True
,
print_phase
=
'both'
):
'''
**Print operator**
...
...
@@ -128,18 +129,21 @@ def Print(input,
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.
input (Variable): A Tensor to print.
summarize (int): Print this number of elements in the tensor, will print
all if left is 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.
print_phase (bool): Which phase to displace, including 'forward',
'backward' and 'both'. If set to 'backward' or 'both', will
print the gradients of input tensor.
Returns:
None
Variable: Output tensor, same data with input tensor.
Examples:
.. code-block:: python
...
...
@@ -149,10 +153,10 @@ def Print(input,
message="The content of some_layer: ")
'''
helper
=
LayerHelper
(
'print'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
'int32'
)
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
()
)
helper
.
append_op
(
type
=
'print'
,
inputs
=
{
'
input
'
:
input
},
inputs
=
{
'
In
'
:
input
},
attrs
=
{
'first_n'
:
first_n
,
'summarize'
:
summarize
,
...
...
@@ -161,7 +165,9 @@ def Print(input,
'print_tensor_type'
:
print_tensor_type
,
'print_tensor_shape'
:
print_tensor_shape
,
'print_tensor_lod'
:
print_tensor_lod
,
})
'print_phase'
:
print_phase
.
upper
()
},
outputs
=
{
'Out'
:
out
})
return
out
...
...
python/paddle/v2/fluid/tests/test_print_op.py
浏览文件 @
60eba8b7
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
from
paddle.v2.fluid.executor
import
Executor
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.backward
import
append_backward
from
paddle.v2.fluid.framework
import
switch_main_program
from
paddle.v2.fluid.framework
import
Program
import
numpy
as
np
class
TestPrintOpCPU
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
place
=
core
.
CPUPlace
()
self
.
x_tensor
=
core
.
LoDTensor
()
tensor_np
=
np
.
random
.
random
(
size
=
(
2
,
3
)).
astype
(
'float32'
)
self
.
x_tensor
.
set
(
tensor_np
,
self
.
place
)
self
.
x_tensor
.
set_lod
([[
0
,
1
,
1
]])
def
build_network
(
self
,
only_forward
,
**
kargs
):
x
=
layers
.
data
(
'x'
,
shape
=
[
3
],
dtype
=
'float32'
,
lod_level
=
1
)
x
.
stop_gradient
=
False
printed
=
layers
.
Print
(
input
=
x
,
**
kargs
)
if
only_forward
:
return
printed
loss
=
layers
.
mean
(
x
=
printed
)
append_backward
(
loss
=
loss
)
return
loss
class
TestSumOp
(
unittest
.
TestCase
):
def
test_tensor
(
self
):
i
=
pd
.
zeros
(
shape
=
[
2
,
10
],
dtype
=
'float32'
)
def
test_forward
(
self
):
switch_main_program
(
Program
())
printed
=
self
.
build_network
(
True
,
print_phase
=
'forward'
)
exe
=
Executor
(
self
.
place
)
outs
=
exe
.
run
(
feed
=
{
'x'
:
self
.
x_tensor
},
fetch_list
=
[
printed
],
return_numpy
=
False
)
pd
.
Print
(
i
,
message
=
"I am a message"
,
summarize
=
10
)
def
test_backward
(
self
):
switch_main_program
(
Program
())
loss
=
self
.
build_network
(
False
,
print_phase
=
'backward'
)
exe
=
Executor
(
self
.
place
)
outs
=
exe
.
run
(
feed
=
{
'x'
:
self
.
x_tensor
},
fetch_list
=
[
loss
],
return_numpy
=
False
)
cpu
=
core
.
CPUPlace
()
exe
=
Executor
(
cpu
)
exe
.
run
()
class
TestPrintOpGPU
(
TestPrintOpCPU
):
def
setUp
(
self
):
self
.
place
=
core
.
CUDAPlace
(
0
)
self
.
x_tensor
=
core
.
LoDTensor
()
tensor_np
=
np
.
random
.
random
(
size
=
(
2
,
3
)).
astype
(
'float32'
)
self
.
x_tensor
.
set
(
tensor_np
,
self
.
place
)
self
.
x_tensor
.
set_lod
([[
0
,
1
,
1
]])
if
__name__
==
'__main__'
:
...
...
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