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822f2834
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822f2834
编写于
11月 21, 2017
作者:
S
sweetsky0901
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into my_unpool_max_2d
上级
200f07c2
a5e73f9e
变更
15
隐藏空白更改
内联
并排
Showing
15 changed file
with
408 addition
and
101 deletion
+408
-101
paddle/operators/activation_op.cc
paddle/operators/activation_op.cc
+37
-53
paddle/operators/adadelta_op.cc
paddle/operators/adadelta_op.cc
+2
-1
paddle/operators/adadelta_op.cu
paddle/operators/adadelta_op.cu
+2
-1
paddle/operators/adadelta_op.h
paddle/operators/adadelta_op.h
+2
-2
paddle/operators/adagrad_op.cu
paddle/operators/adagrad_op.cu
+3
-3
paddle/operators/adam_op.cc
paddle/operators/adam_op.cc
+2
-1
paddle/operators/adam_op.cu
paddle/operators/adam_op.cu
+2
-1
paddle/operators/adam_op.h
paddle/operators/adam_op.h
+3
-3
paddle/operators/adamax_op.cc
paddle/operators/adamax_op.cc
+2
-1
paddle/operators/adamax_op.cu
paddle/operators/adamax_op.cu
+2
-1
paddle/operators/adamax_op.h
paddle/operators/adamax_op.h
+3
-3
paddle/operators/sequence_conv_op.cc
paddle/operators/sequence_conv_op.cc
+4
-2
paddle/operators/sequence_conv_op.cu.cc
paddle/operators/sequence_conv_op.cu.cc
+4
-2
python/paddle/v2/fluid/layers.py
python/paddle/v2/fluid/layers.py
+186
-27
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
+154
-0
未找到文件。
paddle/operators/activation_op.cc
浏览文件 @
822f2834
...
...
@@ -98,7 +98,6 @@ $y = \max(x, 0)$
}
};
template
<
typename
AttrType
>
class
LeakyReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
LeakyReluOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -106,8 +105,7 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of LeakyRelu operator"
);
AddOutput
(
"Y"
,
"Output of LeakyRelu operator"
);
AddAttr
<
AttrType
>
(
"alpha"
,
"The small negative slope"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0.02
f
));
AddAttr
<
float
>
(
"alpha"
,
"The small negative slope"
).
SetDefault
(
0.02
f
);
AddComment
(
R"DOC(
LeakyRelu Activation Operator.
...
...
@@ -117,7 +115,6 @@ $y = \max(x, \alpha * x)$
}
};
template
<
typename
AttrType
>
class
SoftShrinkOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SoftShrinkOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -125,8 +122,7 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Softshrink operator"
);
AddOutput
(
"Y"
,
"Output of Softshrink operator"
);
AddAttr
<
AttrType
>
(
"lambda"
,
"non-negative offset"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0.5
f
));
AddAttr
<
float
>
(
"lambda"
,
"non-negative offset"
).
SetDefault
(
0.5
f
);
AddComment
(
R"DOC(
Softshrink Activation Operator.
...
...
@@ -173,7 +169,6 @@ $$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
}
};
template
<
typename
AttrType
>
class
HardShrinkOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
HardShrinkOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -181,8 +176,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of HardShrink operator"
);
AddOutput
(
"Y"
,
"Output of HardShrink operator"
);
AddAttr
<
AttrType
>
(
"threshold"
,
"The value of threshold for HardShrink"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0.5
)
);
AddAttr
<
float
>
(
"threshold"
,
"The value of threshold for HardShrink"
)
.
SetDefault
(
0.5
f
);
AddComment
(
R"DOC(
HardShrink Activation Operator.
...
...
@@ -308,17 +303,16 @@ $$y = \frac{x}{1 + |x|}$$
}
};
template
<
typename
AttrType
>
class
BReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
BReluOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of BRelu operator"
);
AddOutput
(
"Y"
,
"Output of BRelu operator"
);
AddAttr
<
AttrType
>
(
"t_min"
,
"The min marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0
));
AddAttr
<
AttrType
>
(
"t_max"
,
"The max marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
24
));
AddAttr
<
float
>
(
"t_min"
,
"The min marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
float
>
(
0
));
AddAttr
<
float
>
(
"t_max"
,
"The max marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
float
>
(
24
));
AddComment
(
R"DOC(
BRelu Activation Operator.
...
...
@@ -328,7 +322,6 @@ $y = \max(\min(x, t_{min}), t_{max})$
}
};
template
<
typename
AttrType
>
class
SoftReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SoftReluOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -336,8 +329,8 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SoftRelu operator"
);
AddOutput
(
"Y"
,
"Output of SoftRelu operator"
);
AddAttr
<
AttrType
>
(
"threshold"
,
"The threshold value of SoftRelu"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
40
)
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold value of SoftRelu"
)
.
SetDefault
(
40.0
f
);
AddComment
(
R"DOC(
SoftRelu Activation Operator.
...
...
@@ -347,15 +340,13 @@ $y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
}
};
template
<
typename
AttrType
>
class
ELUOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ELUOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of ELU operator"
);
AddOutput
(
"Y"
,
"Output of ELU operator"
);
AddAttr
<
AttrType
>
(
"alpha"
,
"The alpha value of ELU"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1.0
f
));
AddAttr
<
float
>
(
"alpha"
,
"The alpha value of ELU"
).
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
ELU Activation Operator.
...
...
@@ -368,15 +359,14 @@ $y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
}
};
template
<
typename
AttrType
>
class
Relu6OpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Relu6OpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Relu6 operator"
);
AddOutput
(
"Y"
,
"Output of Relu6 operator"
);
AddAttr
<
AttrType
>
(
"threshold"
,
"The threshold value of Relu6"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
6
)
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold value of Relu6"
)
.
SetDefault
(
6.0
f
);
AddComment
(
R"DOC(
Relu6 Activation Operator.
...
...
@@ -386,15 +376,13 @@ $y = \min(\max(0, x), 6)$
}
};
template
<
typename
AttrType
>
class
PowOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
PowOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Pow operator"
);
AddOutput
(
"Y"
,
"Output of Pow operator"
);
AddAttr
<
AttrType
>
(
"factor"
,
"The exponential factor of Pow"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1
));
AddAttr
<
float
>
(
"factor"
,
"The exponential factor of Pow"
).
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
Pow Activation Operator.
...
...
@@ -404,17 +392,16 @@ $y = x^{factor}$
}
};
template
<
typename
AttrType
>
class
STanhOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
STanhOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of STanh operator"
);
AddOutput
(
"Y"
,
"Output of STanh operator"
);
AddAttr
<
AttrType
>
(
"scale_a"
,
"The scale parameter of a for the input"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
2
/
3
)
);
AddAttr
<
AttrType
>
(
"scale_b"
,
"The scale parameter of b for the input"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1.7159
)
);
AddAttr
<
float
>
(
"scale_a"
,
"The scale parameter of a for the input"
)
.
SetDefault
(
2.0
f
/
3.0
f
);
AddAttr
<
float
>
(
"scale_b"
,
"The scale parameter of b for the input"
)
.
SetDefault
(
1.7159
f
);
AddComment
(
R"DOC(
STanh Activation Operator.
...
...
@@ -424,7 +411,6 @@ $$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
}
};
template
<
typename
AttrType
>
class
ThresholdedReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ThresholdedReluOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -432,8 +418,8 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of ThresholdedRelu operator"
);
AddOutput
(
"Y"
,
"Output of ThresholdedRelu operator"
);
AddAttr
<
AttrType
>
(
"threshold"
,
"The threshold location of activation"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
1.0
)
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold location of activation"
)
.
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
ThresholdedRelu Activation Operator.
...
...
@@ -448,7 +434,6 @@ $$
}
};
template
<
typename
AttrType
>
class
HardSigmoidOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
HardSigmoidOpMaker
(
framework
::
OpProto
*
proto
,
...
...
@@ -456,10 +441,10 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of HardSigmoid operator"
);
AddOutput
(
"Y"
,
"Output of HardSigmoid operator"
);
AddAttr
<
AttrType
>
(
"slope"
,
"Slope for linear approximation of sigmoid"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0.2
)
);
AddAttr
<
AttrType
>
(
"offset"
,
"Offset for linear approximation of sigmoid"
)
.
SetDefault
(
static_cast
<
AttrType
>
(
0.5
)
);
AddAttr
<
float
>
(
"slope"
,
"Slope for linear approximation of sigmoid"
)
.
SetDefault
(
0.2
f
);
AddAttr
<
float
>
(
"offset"
,
"Offset for linear approximation of sigmoid"
)
.
SetDefault
(
0.5
f
);
AddComment
(
R"DOC(
HardSigmoid Activation Operator.
...
...
@@ -499,7 +484,7 @@ REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
REGISTER_OP
(
tanh_shrink
,
ops
::
ActivationOp
,
ops
::
TanhShrinkOpMaker
,
tanh_shrink_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
softshrink
,
ops
::
ActivationOp
,
ops
::
SoftShrinkOpMaker
<
float
>
,
REGISTER_OP
(
softshrink
,
ops
::
ActivationOp
,
ops
::
SoftShrinkOpMaker
,
softshrink_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
sqrt
,
ops
::
ActivationOp
,
ops
::
SqrtOpMaker
,
sqrt_grad
,
...
...
@@ -523,35 +508,34 @@ REGISTER_OP(softplus, ops::ActivationOp, ops::SoftplusOpMaker, softplus_grad,
REGISTER_OP
(
softsign
,
ops
::
ActivationOp
,
ops
::
SoftsignOpMaker
,
softsign_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
brelu
,
ops
::
ActivationOp
,
ops
::
BReluOpMaker
<
float
>
,
brelu_grad
,
REGISTER_OP
(
brelu
,
ops
::
ActivationOp
,
ops
::
BReluOpMaker
,
brelu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
leaky_relu
,
ops
::
ActivationOp
,
ops
::
LeakyReluOpMaker
<
float
>
,
REGISTER_OP
(
leaky_relu
,
ops
::
ActivationOp
,
ops
::
LeakyReluOpMaker
,
leaky_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
soft_relu
,
ops
::
ActivationOp
,
ops
::
SoftReluOpMaker
<
float
>
,
soft_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
soft_relu
,
ops
::
ActivationOp
,
ops
::
SoftReluOpMaker
,
soft_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
elu
,
ops
::
ActivationOp
,
ops
::
ELUOpMaker
<
float
>
,
elu_grad
,
REGISTER_OP
(
elu
,
ops
::
ActivationOp
,
ops
::
ELUOpMaker
,
elu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
relu6
,
ops
::
ActivationOp
,
ops
::
Relu6OpMaker
<
float
>
,
relu6_grad
,
REGISTER_OP
(
relu6
,
ops
::
ActivationOp
,
ops
::
Relu6OpMaker
,
relu6_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
pow
,
ops
::
ActivationOp
,
ops
::
PowOpMaker
<
float
>
,
pow_grad
,
REGISTER_OP
(
pow
,
ops
::
ActivationOp
,
ops
::
PowOpMaker
,
pow_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
stanh
,
ops
::
ActivationOp
,
ops
::
STanhOpMaker
<
float
>
,
stanh_grad
,
REGISTER_OP
(
stanh
,
ops
::
ActivationOp
,
ops
::
STanhOpMaker
,
stanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
hard_shrink
,
ops
::
ActivationOp
,
ops
::
HardShrinkOpMaker
<
float
>
,
REGISTER_OP
(
hard_shrink
,
ops
::
ActivationOp
,
ops
::
HardShrinkOpMaker
,
hard_shrink_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
thresholded_relu
,
ops
::
ActivationOp
,
ops
::
ThresholdedReluOpMaker
<
float
>
,
thresholded_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
thresholded_relu
,
ops
::
ActivationOp
,
ops
::
ThresholdedReluOpMaker
,
thresholded_relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
hard_sigmoid
,
ops
::
ActivationOp
,
ops
::
HardSigmoidOpMaker
<
float
>
,
REGISTER_OP
(
hard_sigmoid
,
ops
::
ActivationOp
,
ops
::
HardSigmoidOpMaker
,
hard_sigmoid_grad
,
ops
::
ActivationOpGrad
);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
...
...
paddle/operators/adadelta_op.cc
浏览文件 @
822f2834
...
...
@@ -109,4 +109,5 @@ paramOut = param + paramUpdate$$
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
adadelta
,
ops
::
AdadeltaOp
,
ops
::
AdadeltaOpMaker
);
REGISTER_OP_CPU_KERNEL
(
adadelta
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
adadelta
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/adadelta_op.cu
浏览文件 @
822f2834
...
...
@@ -17,4 +17,5 @@
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
adadelta
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
adadelta
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
AdadeltaOpKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
paddle/operators/adadelta_op.h
浏览文件 @
822f2834
...
...
@@ -33,8 +33,8 @@ class AdadeltaOpKernel : public framework::OpKernel<T> {
avg_squared_grad_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
avg_squared_update_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
float
rho
=
ctx
.
Attr
<
float
>
(
"rho"
);
float
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
T
rho
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"rho"
)
);
T
epsilon
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
)
);
auto
param
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
));
...
...
paddle/operators/adagrad_op.cu
浏览文件 @
822f2834
...
...
@@ -14,8 +14,8 @@
#define EIGEN_USE_GPU
#include "paddle/operators/adagrad_op.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/cuda_helper.h"
namespace
paddle
{
...
...
@@ -134,8 +134,8 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
T
,
256
><<<
grid2
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
grad_merge_data
,
grad_merge
->
rows
().
data
(),
lr
,
param_data
,
moment_data
,
grad_width
,
epsilon
);
lr
,
param_data
,
moment_data
,
grad_width
,
epsilon
);
}
};
...
...
paddle/operators/adam_op.cc
浏览文件 @
822f2834
...
...
@@ -127,4 +127,5 @@ paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
adam
,
ops
::
AdamOp
,
ops
::
AdamOpMaker
);
REGISTER_OP_CPU_KERNEL
(
adam
,
ops
::
AdamOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
AdamOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
AdamOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/adam_op.cu
浏览文件 @
822f2834
...
...
@@ -17,4 +17,5 @@
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
adam
,
ops
::
AdamOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
AdamOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
AdamOpKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
paddle/operators/adam_op.h
浏览文件 @
822f2834
...
...
@@ -31,9 +31,9 @@ class AdamOpKernel : public framework::OpKernel<T> {
moment1_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
moment2_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
float
beta1
=
ctx
.
Attr
<
float
>
(
"beta1"
);
float
beta2
=
ctx
.
Attr
<
float
>
(
"beta2"
);
float
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
T
beta1
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta1"
)
);
T
beta2
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta2"
)
);
T
epsilon
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
)
);
auto
param
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
));
...
...
paddle/operators/adamax_op.cc
浏览文件 @
822f2834
...
...
@@ -126,4 +126,5 @@ division by 0 error.
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
adamax
,
ops
::
AdamaxOp
,
ops
::
AdamaxOpMaker
);
REGISTER_OP_CPU_KERNEL
(
adamax
,
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/adamax_op.cu
浏览文件 @
822f2834
...
...
@@ -17,4 +17,5 @@
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
adamax
,
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
AdamaxOpKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
paddle/operators/adamax_op.h
浏览文件 @
822f2834
...
...
@@ -31,9 +31,9 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
moment_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
inf_norm_out_tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
float
beta1
=
ctx
.
Attr
<
float
>
(
"beta1"
);
float
beta2
=
ctx
.
Attr
<
float
>
(
"beta2"
);
float
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
T
beta1
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta1"
)
);
T
beta2
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta2"
)
);
T
epsilon
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
)
);
auto
param
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
));
...
...
paddle/operators/sequence_conv_op.cc
浏览文件 @
822f2834
...
...
@@ -179,7 +179,9 @@ REGISTER_OP(sequence_conv, ops::SequenceConvOp, ops::SequenceConvOpMaker,
sequence_conv_grad
,
ops
::
SequenceConvGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_conv_grad
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/sequence_conv_op.cu.cc
浏览文件 @
822f2834
...
...
@@ -16,7 +16,9 @@
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
sequence_conv_grad
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
python/paddle/v2/fluid/layers.py
浏览文件 @
822f2834
...
...
@@ -248,7 +248,7 @@ def data(name,
stop_gradient
=
stop_gradient
)
def
create_tensor
(
dtype
,
name
=
None
,
main_program
=
None
):
def
create_tensor
(
dtype
,
name
=
None
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
"create_tensor"
,
**
locals
())
return
helper
.
create_variable
(
name
=
helper
.
name
,
dtype
=
dtype
)
...
...
@@ -412,30 +412,12 @@ _create_op_func_('mul')
_create_op_func_
(
'elementwise_add'
)
_create_op_func_
(
'dropout'
)
_create_op_func_
(
'reshape'
)
_create_op_func_
(
'elementwise_add'
)
_create_op_func_
(
'sigmoid'
)
_create_op_func_
(
'scale'
)
_create_op_func_
(
'reshape'
)
_create_op_func_
(
'transpose'
)
def
fill_constant
(
data_type
,
shape
,
value
=
None
,
program
=
None
):
"""
This function creates a tensor , with shape as mentioned in the input and
specified data_type and fills this up with a constant value that
comes in the input.
"""
helper
=
LayerHelper
(
'fill_constant'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
data_type
)
helper
.
append_op
(
type
=
'fill_constant'
,
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'data_type'
:
data_type
,
'shape'
:
shape
,
'value'
:
value
})
return
out
def
cast
(
x
,
data_type
,
main_program
=
None
):
"""
This function takes in the input with input_data_type
...
...
@@ -478,7 +460,7 @@ def sums(input, main_program=None, startup_program=None):
return
out
def
assign
(
input
,
output
,
main_program
=
None
):
def
assign
(
input
,
output
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
'assign'
,
**
locals
())
helper
.
append_op
(
type
=
'scale'
,
...
...
@@ -490,7 +472,7 @@ def assign(input, output, main_program=None):
def
split_lod_tensor
(
input
,
mask
,
level
,
level
=
0
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
'split_lod_tensor'
,
**
locals
())
...
...
@@ -512,11 +494,11 @@ def merge_lod_tensor(in_true,
in_false
,
x
,
mask
,
level
,
level
=
0
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
'merge_lod_tensor'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
data_type
)
out
=
helper
.
create_tmp_variable
(
dtype
=
in_true
.
data_type
)
helper
.
append_op
(
type
=
'merge_lod_tensor'
,
inputs
=
{
'X'
:
x
,
...
...
@@ -1366,7 +1348,7 @@ def array_to_lod_tensor(x, table, main_program=None):
return
tmp
def
fill_constant
(
shape
,
dtype
,
value
,
main_program
=
None
):
def
fill_constant
(
shape
,
dtype
,
value
,
main_program
=
None
,
startup_program
=
None
):
"""
This function creates a tensor , with shape as mentioned in the input and
specified data_type and fills this up with a constant value that
...
...
@@ -1387,6 +1369,31 @@ def fill_constant(shape, dtype, value, main_program=None):
return
out
def
fill_constant_batch_size_like
(
input
,
shape
,
dtype
,
value
,
input_dim_idx
=
0
,
output_dim_idx
=
0
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
"fill_constant_batch_size_like"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
dtype
)
helper
.
append_op
(
type
=
'fill_constant_batch_size_like'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'shape'
:
shape
,
'data_type'
:
out
.
data_type
,
'value'
:
float
(
value
),
'input_dim_idx'
:
input_dim_idx
,
'output_dim_idx'
:
output_dim_idx
})
out
.
stop_gradient
=
True
return
out
def
ones
(
shape
,
dtype
,
main_program
=
None
):
"""
This function performs the same function as fill_constant() declared above
...
...
@@ -1449,7 +1456,7 @@ def create_array(dtype, main_program=None):
dtype
=
dtype
)
def
less_than
(
x
,
y
,
cond
=
None
,
main_program
=
None
):
def
less_than
(
x
,
y
,
cond
=
None
,
main_program
=
None
,
**
ignored
):
helper
=
LayerHelper
(
"less_than"
,
**
locals
())
if
cond
is
None
:
cond
=
helper
.
create_tmp_variable
(
dtype
=
'bool'
)
...
...
@@ -1527,13 +1534,20 @@ class ConditionalBlockGuard(BlockGuard):
class
ConditionalBlock
(
object
):
def
__init__
(
self
,
inputs
,
name
=
None
,
main_program
=
None
):
def
__init__
(
self
,
inputs
,
name
=
None
,
main_program
=
None
,
startup_program
=
None
):
for
each_input
in
inputs
:
if
not
isinstance
(
each_input
,
Variable
):
raise
TypeError
(
"Each input should be variable"
)
self
.
inputs
=
inputs
self
.
helper
=
LayerHelper
(
'conditional_block'
,
name
=
name
,
main_program
=
main_program
)
'conditional_block'
,
name
=
name
,
main_program
=
main_program
,
startup_program
=
startup_program
)
def
block
(
self
):
return
ConditionalBlockGuard
(
self
)
...
...
@@ -1578,3 +1592,148 @@ class ConditionalBlock(object):
outputs
=
{
'Out'
:
out_list
,
'Scope'
:
[
step_scope
]},
attrs
=
{
'block'
:
inside_block
})
class
IfElseBlockGuard
(
object
):
def
__init__
(
self
,
is_true
,
ifelse
):
if
not
isinstance
(
ifelse
,
IfElse
):
raise
TypeError
(
"ifelse must be an instance of IfElse class"
)
if
ifelse
.
status
!=
IfElse
.
OUT_IF_ELSE_BLOCKS
:
raise
ValueError
(
"You cannot invoke IfElse.block() inside a block"
)
self
.
is_true
=
is_true
self
.
ie
=
ifelse
if
is_true
:
self
.
cond_block
=
ifelse
.
conditional_true_block
else
:
self
.
cond_block
=
ifelse
.
conditional_false_block
if
not
isinstance
(
self
.
cond_block
,
ConditionalBlock
):
raise
TypeError
(
"Unexpected situation"
)
self
.
cond_block
=
self
.
cond_block
.
block
()
def
__enter__
(
self
):
self
.
ie
.
status
=
IfElse
.
IN_IF_ELSE_TRUE_BLOCKS
if
self
.
is_true
else
IfElse
.
IN_IF_ELSE_FALSE_BLOCKS
self
.
cond_block
.
__enter__
()
def
__exit__
(
self
,
exc_type
,
exc_val
,
exc_tb
):
if
not
self
.
cond_block
.
__exit__
(
exc_type
,
exc_val
,
exc_tb
):
# re-raise inside exception
return
False
if
len
(
self
.
ie
.
output_table
[
1
if
self
.
is_true
else
0
])
==
0
:
raise
ValueError
(
"Must set output inside block"
)
self
.
ie
.
status
=
IfElse
.
OUT_IF_ELSE_BLOCKS
class
IfElse
(
object
):
OUT_IF_ELSE_BLOCKS
=
0
IN_IF_ELSE_TRUE_BLOCKS
=
1
IN_IF_ELSE_FALSE_BLOCKS
=
2
def
__init__
(
self
,
cond
,
name
=
None
,
main_program
=
None
,
startup_program
=
None
):
if
not
isinstance
(
cond
,
Variable
):
raise
TypeError
(
"cond must be a Variable"
)
self
.
helper
=
LayerHelper
(
'ifelse'
,
name
=
name
,
main_program
=
main_program
,
startup_program
=
startup_program
)
self
.
cond
=
cond
self
.
input_table
=
{}
self
.
status
=
IfElse
.
OUT_IF_ELSE_BLOCKS
self
.
conditional_true_block
=
ConditionalBlock
(
inputs
=
[
self
.
cond
])
self
.
conditional_false_block
=
ConditionalBlock
(
inputs
=
[
self
.
cond
])
self
.
output_table
=
([],
[])
# (true_outs, false_outs)
def
input
(
self
,
x
):
if
self
.
status
==
IfElse
.
OUT_IF_ELSE_BLOCKS
:
raise
ValueError
(
"input must in true/false blocks"
)
if
id
(
x
)
not
in
self
.
input_table
:
parent_block
=
self
.
parent_block
()
out_true
=
parent_block
.
create_var
(
name
=
unique_name
(
'ifelse_input'
+
self
.
helper
.
name
),
dtype
=
x
.
data_type
)
out_false
=
parent_block
.
create_var
(
name
=
unique_name
(
'ifelse_input'
+
self
.
helper
.
name
),
dtype
=
x
.
data_type
)
parent_block
.
append_op
(
type
=
'split_lod_tensor'
,
inputs
=
{
'X'
:
x
,
'Mask'
:
self
.
cond
,
},
outputs
=
{
'OutTrue'
:
out_true
,
'OutFalse'
:
out_false
},
attrs
=
{
'level'
:
0
})
self
.
input_table
[
id
(
x
)]
=
(
out_true
,
out_false
)
else
:
out_true
,
out_false
=
self
.
input_table
[
id
(
x
)]
if
self
.
status
==
IfElse
.
IN_IF_ELSE_TRUE_BLOCKS
:
return
out_true
else
:
return
out_false
def
parent_block
(
self
):
current_block
=
self
.
helper
.
main_program
.
current_block
()
return
self
.
helper
.
main_program
.
block
(
current_block
.
parent_idx
)
def
true_block
(
self
):
return
IfElseBlockGuard
(
True
,
self
)
def
false_block
(
self
):
return
IfElseBlockGuard
(
False
,
self
)
def
output
(
self
,
*
outs
):
if
self
.
status
==
self
.
OUT_IF_ELSE_BLOCKS
:
raise
ValueError
(
"output can only be invoked in the sub-block"
)
out_table
=
self
.
output_table
[
1
if
self
.
status
==
self
.
IN_IF_ELSE_TRUE_BLOCKS
else
0
]
parent_block
=
self
.
parent_block
()
for
each_out
in
outs
:
if
not
isinstance
(
each_out
,
Variable
):
raise
TypeError
(
"Each output should be a variable"
)
# create outside tensor
outside_out
=
parent_block
.
create_var
(
name
=
unique_name
(
"_"
.
join
([
self
.
helper
.
name
,
'output'
])),
dtype
=
each_out
.
data_type
)
out_table
.
append
(
outside_out
)
# assign local var to outside
assign
(
input
=
each_out
,
output
=
outside_out
,
main_program
=
self
.
helper
.
main_program
,
startup_program
=
self
.
helper
.
startup_program
)
def
__call__
(
self
):
if
self
.
status
!=
self
.
OUT_IF_ELSE_BLOCKS
:
raise
ValueError
(
"IfElse::__call__ must be out of sub-block"
)
false_len
,
true_len
=
map
(
len
,
self
.
output_table
)
if
false_len
==
0
and
true_len
==
0
:
raise
ValueError
(
"Must invoke true_block/false_block before "
"__call__"
)
elif
false_len
!=
true_len
and
false_len
!=
0
and
true_len
!=
0
:
raise
ValueError
(
"The output side must be same"
)
elif
false_len
==
0
or
true_len
==
0
:
return
self
.
output_table
[
0
if
false_len
!=
0
else
1
]
# else none of false_len/true_len is zero
# merge together
rlist
=
[]
for
false_var
,
true_var
in
zip
(
*
self
.
output_table
):
rlist
.
append
(
merge_lod_tensor
(
in_true
=
true_var
,
in_false
=
false_var
,
mask
=
self
.
cond
,
x
=
self
.
cond
,
level
=
0
,
main_program
=
self
.
helper
.
main_program
,
startup_program
=
self
.
helper
.
startup_program
))
return
rlist
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
0 → 100644
浏览文件 @
822f2834
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.framework
import
Program
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.optimizer
import
MomentumOptimizer
import
paddle.v2.fluid.core
as
core
import
paddle.v2
as
paddle
import
unittest
import
numpy
as
np
class
TestMNISTIfElseOp
(
unittest
.
TestCase
):
def
test_raw_api
(
self
):
kwargs
=
{
'startup_program'
:
Program
(),
'main_program'
:
Program
()}
image
=
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
data_type
=
'float32'
,
**
kwargs
)
label
=
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
data_type
=
'int64'
,
**
kwargs
)
limit
=
layers
.
fill_constant_batch_size_like
(
input
=
label
,
dtype
=
'int64'
,
shape
=
[
1
],
value
=
5.0
,
**
kwargs
)
cond
=
layers
.
less_than
(
x
=
label
,
y
=
limit
,
**
kwargs
)
true_image
,
false_image
=
layers
.
split_lod_tensor
(
input
=
image
,
mask
=
cond
,
**
kwargs
)
true_out
=
layers
.
create_tensor
(
dtype
=
'float32'
,
**
kwargs
)
true_cond
=
layers
.
ConditionalBlock
([
true_image
],
**
kwargs
)
with
true_cond
.
block
():
hidden
=
layers
.
fc
(
input
=
true_image
,
size
=
100
,
act
=
'tanh'
,
**
kwargs
)
prob
=
layers
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
'softmax'
,
**
kwargs
)
layers
.
assign
(
input
=
prob
,
output
=
true_out
,
**
kwargs
)
false_out
=
layers
.
create_tensor
(
dtype
=
'float32'
,
**
kwargs
)
false_cond
=
layers
.
ConditionalBlock
([
false_image
],
**
kwargs
)
with
false_cond
.
block
():
hidden
=
layers
.
fc
(
input
=
false_image
,
size
=
200
,
act
=
'tanh'
,
**
kwargs
)
prob
=
layers
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
'softmax'
,
**
kwargs
)
layers
.
assign
(
input
=
prob
,
output
=
false_out
,
**
kwargs
)
prob
=
layers
.
merge_lod_tensor
(
in_true
=
true_out
,
in_false
=
false_out
,
mask
=
cond
,
x
=
image
,
**
kwargs
)
loss
=
layers
.
cross_entropy
(
input
=
prob
,
label
=
label
,
**
kwargs
)
avg_loss
=
layers
.
mean
(
x
=
loss
,
**
kwargs
)
optimizer
=
MomentumOptimizer
(
learning_rate
=
0.001
,
momentum
=
0.9
)
optimizer
.
minimize
(
avg_loss
,
kwargs
[
'startup_program'
])
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
200
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
kwargs
[
'startup_program'
])
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
x_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
],
data
)).
astype
(
"float32"
)
y_data
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
y_data
=
np
.
expand_dims
(
y_data
,
axis
=
1
)
tensor_x
=
core
.
LoDTensor
()
tensor_x
.
set
(
x_data
,
place
)
tensor_y
=
core
.
LoDTensor
()
tensor_y
.
set
(
y_data
,
place
)
outs
=
map
(
np
.
array
,
exe
.
run
(
kwargs
[
'main_program'
],
feed
=
{
'x'
:
tensor_x
,
'y'
:
tensor_y
},
fetch_list
=
[
avg_loss
]))
print
outs
[
0
]
if
outs
[
0
]
<
1.0
:
return
self
.
assertFalse
(
True
)
def
test_ifelse
(
self
):
kwargs
=
{
'startup_program'
:
Program
(),
'main_program'
:
Program
()}
image
=
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
data_type
=
'float32'
,
**
kwargs
)
label
=
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
data_type
=
'int64'
,
**
kwargs
)
limit
=
layers
.
fill_constant_batch_size_like
(
input
=
label
,
dtype
=
'int64'
,
shape
=
[
1
],
value
=
5.0
,
**
kwargs
)
cond
=
layers
.
less_than
(
x
=
label
,
y
=
limit
,
**
kwargs
)
ie
=
layers
.
IfElse
(
cond
,
**
kwargs
)
with
ie
.
true_block
():
true_image
=
ie
.
input
(
image
)
hidden
=
layers
.
fc
(
input
=
true_image
,
size
=
100
,
act
=
'tanh'
,
**
kwargs
)
prob
=
layers
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
'softmax'
,
**
kwargs
)
ie
.
output
(
prob
)
with
ie
.
false_block
():
false_image
=
ie
.
input
(
image
)
hidden
=
layers
.
fc
(
input
=
false_image
,
size
=
200
,
act
=
'tanh'
,
**
kwargs
)
prob
=
layers
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
'softmax'
,
**
kwargs
)
ie
.
output
(
prob
)
prob
=
ie
()
loss
=
layers
.
cross_entropy
(
input
=
prob
[
0
],
label
=
label
,
**
kwargs
)
avg_loss
=
layers
.
mean
(
x
=
loss
,
**
kwargs
)
optimizer
=
MomentumOptimizer
(
learning_rate
=
0.001
,
momentum
=
0.9
)
optimizer
.
minimize
(
avg_loss
,
kwargs
[
'startup_program'
])
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
200
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
kwargs
[
'startup_program'
])
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
x_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
],
data
)).
astype
(
"float32"
)
y_data
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
y_data
=
np
.
expand_dims
(
y_data
,
axis
=
1
)
tensor_x
=
core
.
LoDTensor
()
tensor_x
.
set
(
x_data
,
place
)
tensor_y
=
core
.
LoDTensor
()
tensor_y
.
set
(
y_data
,
place
)
outs
=
map
(
np
.
array
,
exe
.
run
(
kwargs
[
'main_program'
],
feed
=
{
'x'
:
tensor_x
,
'y'
:
tensor_y
},
fetch_list
=
[
avg_loss
]))
print
outs
[
0
]
if
outs
[
0
]
<
1.0
:
return
self
.
assertFalse
(
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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