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0677a1c1
编写于
12月 17, 2019
作者:
H
Huihuang Zheng
提交者:
GitHub
12月 17, 2019
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差异文件
Fix That conditional_block_op Doesn't Have InferShape (#21733)
上级
ac666b8a
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
64 addition
and
26 deletion
+64
-26
paddle/fluid/operators/controlflow/conditional_block_op.cc
paddle/fluid/operators/controlflow/conditional_block_op.cc
+10
-0
python/paddle/fluid/tests/unittests/test_conditional_block.py
...on/paddle/fluid/tests/unittests/test_conditional_block.py
+54
-26
未找到文件。
paddle/fluid/operators/controlflow/conditional_block_op.cc
浏览文件 @
0677a1c1
...
...
@@ -74,6 +74,15 @@ class ConditionalBlockOp : public ConditionalOp {
}
};
class
ConditionalBlockInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE_EQ
(
context
->
HasInputs
(
ConditionalOp
::
kCondition
),
true
,
platform
::
errors
::
InvalidArgument
(
"conditional_block_op must have condition input"
));
}
};
class
ConditionalBlockGradOp
:
public
ConditionalOp
{
public:
ConditionalBlockGradOp
(
const
std
::
string
&
type
,
...
...
@@ -278,6 +287,7 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpMaker<T> {
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
conditional_block
,
ops
::
ConditionalBlockOp
,
ops
::
ConditionalBlockInferShape
,
ops
::
ConditionalBlockOpProtoMaker
,
ops
::
ConditionalBlockGradMaker
<
paddle
::
framework
::
OpDesc
>
);
REGISTER_OPERATOR
(
conditional_block_grad
,
ops
::
ConditionalBlockGradOp
,
...
...
python/paddle/fluid/tests/unittests/test_conditional_block.py
浏览文件 @
0677a1c1
...
...
@@ -14,42 +14,70 @@
from
__future__
import
print_function
import
numpy
as
np
import
unittest
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
import
paddle.fluid.core
as
core
from
paddle.fluid.framework
import
default_startup_program
,
default_main_program
from
paddle.fluid.executor
import
Executor
from
paddle.fluid.backward
import
append_backward
from
paddle.fluid.layers.control_flow
import
ConditionalBlock
import
numpy
class
ConditionalBlockTest
(
unittest
.
TestCase
):
def
test_forward
(
self
):
data
=
layers
.
data
(
name
=
'X'
,
shape
=
[
1
],
dtype
=
'float32'
)
data
.
stop_gradient
=
False
cond
=
ConditionalBlock
(
inputs
=
[
data
])
out
=
layers
.
create_tensor
(
dtype
=
'float32'
)
with
cond
.
block
():
hidden
=
layers
.
fc
(
input
=
data
,
size
=
10
)
layers
.
assign
(
hidden
,
out
)
cpu
=
core
.
CPUPlace
()
exe
=
Executor
(
cpu
)
exe
.
run
(
default_startup_program
())
x
=
numpy
.
random
.
random
(
size
=
(
10
,
1
)).
astype
(
'float32'
)
outs
=
exe
.
run
(
feed
=
{
'X'
:
x
},
fetch_list
=
[
out
])[
0
]
print
(
outs
)
loss
=
layers
.
mean
(
out
)
append_backward
(
loss
=
loss
)
outs
=
exe
.
run
(
feed
=
{
'X'
:
x
},
fetch_list
=
[
default_main_program
().
block
(
0
).
var
(
data
.
name
+
"@GRAD"
)
])[
0
]
print
(
outs
)
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
data
=
layers
.
data
(
name
=
'X'
,
shape
=
[
1
],
dtype
=
'float32'
)
data
.
stop_gradient
=
False
cond
=
ConditionalBlock
(
inputs
=
[
data
])
out
=
layers
.
create_tensor
(
dtype
=
'float32'
)
with
cond
.
block
():
hidden
=
layers
.
fc
(
input
=
data
,
size
=
10
)
layers
.
assign
(
hidden
,
out
)
cpu
=
core
.
CPUPlace
()
exe
=
Executor
(
cpu
)
exe
.
run
(
startup_program
)
x
=
np
.
random
.
random
(
size
=
(
10
,
1
)).
astype
(
'float32'
)
outs
=
exe
.
run
(
main_program
,
feed
=
{
'X'
:
x
},
fetch_list
=
[
out
])[
0
]
print
(
outs
)
loss
=
layers
.
mean
(
out
)
append_backward
(
loss
=
loss
)
outs
=
exe
.
run
(
main_program
,
feed
=
{
'X'
:
x
},
fetch_list
=
[
main_program
.
block
(
0
).
var
(
data
.
name
+
"@GRAD"
)])[
0
]
print
(
outs
)
class
TestConditionalBlockOpInferShape
(
unittest
.
TestCase
):
def
test_infer_shape
(
self
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
global_block
=
main_program
.
global_block
()
sub_block
=
main_program
.
_create_block
()
main_program
.
_rollback
()
step_scope
=
global_block
.
create_var
(
type
=
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
cond_var
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'bool'
,
value
=
False
)
op
=
global_block
.
append_op
(
type
=
'conditional_block'
,
inputs
=
{
'Cond'
:
[
cond_var
],
'Input'
:
[],
},
outputs
=
{
'Out'
:
[],
'Scope'
:
[
step_scope
]},
attrs
=
{
'sub_block'
:
sub_block
,
'is_scalar_condition'
:
True
})
op
.
desc
.
infer_shape
(
global_block
.
desc
)
if
__name__
==
'__main__'
:
...
...
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