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d92c671d
编写于
10月 10, 2017
作者:
C
caoying03
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差异文件
add python forward unittest.
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6 changed file
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268 addition
and
66 deletion
+268
-66
paddle/operators/crf_op.cc
paddle/operators/crf_op.cc
+0
-48
paddle/operators/linear_chain_crf_op.cc
paddle/operators/linear_chain_crf_op.cc
+141
-0
paddle/operators/linear_chain_crf_op.h
paddle/operators/linear_chain_crf_op.h
+2
-2
paddle/operators/softmax_with_cross_entropy_op.cc
paddle/operators/softmax_with_cross_entropy_op.cc
+3
-3
python/paddle/v2/framework/tests/test_crf_op.py
python/paddle/v2/framework/tests/test_crf_op.py
+0
-13
python/paddle/v2/framework/tests/test_linear_chain_crf_op.py
python/paddle/v2/framework/tests/test_linear_chain_crf_op.py
+122
-0
未找到文件。
paddle/operators/crf_op.cc
已删除
100644 → 0
浏览文件 @
dcfbbd3f
/* 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 "paddle/operators/crf_op.h"
namespace
paddle
{
namespace
operators
{
class
CrfOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
CrfOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{}
};
class
CrfOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{}
};
class
CrfGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
crf
,
ops
::
CrfOp
,
ops
::
CrfOpMaker
,
crf_grad
,
ops
::
CrfGradOp
);
REGISTER_OP_CPU_KERNEL
(
crf
,
ops
::
CrfOpKernel
<
float
>
);
REGISTER_OP_CPU_KERNEL
(
crf_grad
,
ops
::
CrfGradOpKernel
<
float
>
);
paddle/operators/linear_chain_crf_op.cc
0 → 100644
浏览文件 @
d92c671d
/* 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 "paddle/operators/linear_chain_crf_op.h"
namespace
paddle
{
namespace
operators
{
class
LinearChainCrfOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
LinearChainCrfOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Emission"
,
"(LoDTensor, default: LoDTensor<float>). "
"The unscaled emission weight matrix for the linear chain CRF. "
"This input is a LoDTensor with shape [N x D] where N is the total "
"element number of all input squences in a mini-batch, "
"and D is the total tag number."
);
AddInput
(
"Transition"
,
"(Tensor, default: Tensor<float>). A Tensor with shape [(D + 2) x D]. "
"The learnable parameter for linear_chain_crf operator. "
"See more details in the operator's comments."
);
AddInput
(
"Label"
,
"(LoDTensor, default: LoDTensor<int>). The ground truth which is a 2-D "
"LoDTensor with shape [N x 1], where N is the total element number in "
"a mini-batch."
);
AddOutput
(
"Alpha"
,
"Tensor, default: Tensor<float>. The forward vectors for the entire "
"batch. A two dimensional tensor with shape [N x D], "
"denoted as
\f
$
\a
lpha
\f
$.
\f
$
\a
lpha$
\f
is a memo table used to "
"calculate the normalization factor in CRF.
\f
$
\a
lpha[k, v]$
\f
stores "
"the unnormalized probabilites of all possible unfinished sequences of "
"tags that end at position
\f
$k$
\f
with tag
\f
$v$
\f
. For each
\f
$k$
\f
, "
"
\f
$
\a
lpha[k, v]$
\f
is a vector of length
\f
$D$
\f
with a component for "
"each tag value
\f
$v$
\f
. This vector is called a forward vecotr and "
"will also be used in backward computations."
)
.
AsIntermediate
();
AddOutput
(
"LogLikelihood"
,
"(Tensor, default: Tensor<float>). The logarithm of the conditional "
"likelihood of each training sample in a mini-batch. This is a 2-D "
"tensor with shape [S x 1], where S is the sequence number in a "
"mini-batch. "
"Note: S is equal to the sequence number in a mini-batch. The output "
"is no longer a LoDTensor."
);
AddComment
(
R"DOC(
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability \f$P(Y|X)\f$, where
\f$X = (x_1, x_2, ... , x_n)\f$ are structured inputs and
\f$Y = (y_1, y_2, ... , y_n)\f$ are labels for the inputs.
Linear chain CRF is a special case of CRF that is useful for sequence labeling
task. Sequence labeling tasks do not assume a lot of conditional
independences among inputs. They only concern about the input and the output
being linear sequences. Thus, the graph model of CRF is a simple chain or
a line, which results in a linear chain CRF.
This operator implements the Forward-Backward algorithm for linear chain CRF.
Please see http://www.cs.columbia.edu/~mcollins/fb.pdf for reference.
Equation:
- Denote the first input of this operator (Emission) as \f$x\f$ here.
- The first D values of the second input (Transition) of this operator are for
starting weights, denoted as \f$a\f$ here.
- The next D values of the second input (Transition) of this operator are for
ending weights, denoted as \f$b\f$ here.
- The remaning values of the second input (Transition) are for transition
weights, denoted as \f$w\f$ here.
- Denote the third input of this operator (Label) as \f$s\f$ here.
The probability of a sequence \f$s\f$ of length \f$L\f$ is defined as:
\f$P(s) = (1/Z) exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})\f$
where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over
all possible sequences is \f$1\f$, and \f$x\f$ is the emission feature weight
to the linear chain CRF.
Finaly, the linear chain CRF operator outputs the logarithm of the conditional
likelihood of each training sample in a mini-batch.
NOTE:
1. The feature function for a CRF is made up of the emission features and the
transition features. The emission feature weights are NOT computed in
this operator. They MUST be computed first before this operator is called.
2. Because this operator performs globally normaliztion over all possible
sequences internally, it expects UNSCALED emission feature weights.
Please do not call this op with the emission feature being output of any
nonlinear activation.
3. The 2nd dimension of the first input of this operator (Emission) MUST be
equal to the tag number.
)DOC"
);
}
};
class
LinearChainCrfOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{}
};
class
LinearChainCrfGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
linear_chain_crf
,
ops
::
LinearChainCrfOp
,
ops
::
LinearChainCrfOpMaker
,
linear_chain_crf_grad
,
ops
::
LinearChainCrfGradOp
);
REGISTER_OP_CPU_KERNEL
(
linear_chain_crf
,
ops
::
LinearChainCrfOpKernel
<
float
>
);
REGISTER_OP_CPU_KERNEL
(
linear_chain_crf_grad
,
ops
::
LinearChainCrfGradOpKernel
<
float
>
);
paddle/operators/crf_op.h
→
paddle/operators/
linear_chain_
crf_op.h
浏览文件 @
d92c671d
...
...
@@ -20,7 +20,7 @@ namespace paddle {
namespace
operators
{
template
<
typename
T
>
class
CrfOpKernel
:
public
framework
::
OpKernel
<
T
>
{
class
LinearChain
CrfOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
...
...
@@ -29,7 +29,7 @@ class CrfOpKernel : public framework::OpKernel<T> {
};
template
<
typename
T
>
class
CrfGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
class
LinearChain
CrfGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
...
...
paddle/operators/softmax_with_cross_entropy_op.cc
浏览文件 @
d92c671d
...
...
@@ -32,9 +32,9 @@ class SoftmaxWithCrossEntropyOpMaker
AddInput
(
"Label"
,
"(Tensor, default: Tensor<int>), The ground truth which is a 2-D "
"tensor. "
"If softLab
le is set to 0, Label is a Tensor<int> with shape [N x
"
"
1].
"
"If softLab
le is set to 1
, Label is a Tensor<float/double> "
"If softLab
el is set to false, Label is a Tensor<int> with shape
"
"
[N x 1].
"
"If softLab
el is set to true
, Label is a Tensor<float/double> "
"with shape [N x K]."
);
AddOutput
(
"Softmax"
,
...
...
python/paddle/v2/framework/tests/test_crf_op.py
已删除
100644 → 0
浏览文件 @
dcfbbd3f
import
unittest
import
numpy
as
np
class
TestCrfOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"crf"
batch_size
=
3
class_num
=
37
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_linear_chain_crf_op.py
0 → 100644
浏览文件 @
d92c671d
import
unittest
import
random
import
numpy
as
np
from
op_test
import
OpTest
class
LinearChainCrfForward
(
object
):
def
__init__
(
self
,
seq_start_positions
,
emission_weights
,
transition_weights
,
labels
):
self
.
tag_num
=
emission_weights
.
shape
[
1
]
self
.
seq_num
=
len
(
seq_start_positions
)
-
1
self
.
seq_start_positions
=
seq_start_positions
self
.
labels
=
labels
self
.
x
=
emission_weights
self
.
x_row_max
=
np
.
amax
(
self
.
x
,
axis
=
1
,
keepdims
=
True
)
self
.
x_exps
=
np
.
exp
(
self
.
x
-
self
.
x_row_max
)
# unnormalized logits of the transition weights for the start mark.
self
.
a
=
transition_weights
[
0
,
:]
self
.
a_exps
=
np
.
exp
(
self
.
a
)
# unnormalized logits of the transition weights for the end mark.
self
.
b
=
transition_weights
[
1
,
:]
self
.
b_exps
=
np
.
exp
(
self
.
b
)
# unnormalized logits of the transition weights for all the other tags.
self
.
w
=
transition_weights
[
2
:,
:]
self
.
w_exps
=
np
.
exp
(
self
.
w
)
# The output of linear chain crf operator.
# alpha is a memo table in dynamic programming to caculate
# nomalization factor.
self
.
alpha
=
np
.
zeros
(
(
seq_start_positions
[
-
1
],
self
.
tag_num
),
dtype
=
"float32"
)
self
.
log_likelihood
=
np
.
zeros
((
self
.
tag_num
,
1
))
def
_l1_norm
(
self
,
x
):
s
=
np
.
sum
(
x
)
x
/=
s
return
s
def
_forward_a_sequence
(
self
,
x
,
x_row_max
,
x_exps
,
label
,
alpha
):
seq_len
=
x_row_max
.
shape
[
0
]
log_likelihood
=
0.
for
i
in
range
(
self
.
tag_num
):
alpha
[
0
,
i
]
=
self
.
a_exps
[
i
]
*
x_exps
[
0
,
i
]
log_likelihood
=
-
x_row_max
[
0
]
-
np
.
log
(
self
.
_l1_norm
(
alpha
[
0
,
:]))
# calculate the unnormalized logits of the normalization factor.
for
k
in
range
(
1
,
seq_len
):
for
i
in
range
(
self
.
tag_num
):
s
=
0.
for
j
in
range
(
self
.
tag_num
):
s
+=
alpha
[
k
-
1
,
j
]
*
self
.
w_exps
[
j
,
i
]
alpha
[
k
,
i
]
=
x_exps
[
k
,
i
]
*
s
log_likelihood
-=
x_row_max
[
k
]
+
np
.
log
(
self
.
_l1_norm
(
alpha
[
k
,
:]))
s
=
0.
for
i
in
range
(
self
.
tag_num
):
s
+=
alpha
[
-
1
,
i
]
*
self
.
b_exps
[
i
]
log_likelihood
-=
np
.
log
(
s
)
# calculate the noninator part.
log_likelihood
+=
(
self
.
a
[
label
[
0
]]
+
self
.
x
[
0
,
label
[
0
]]
+
self
.
b
[
label
[
-
1
]])
for
k
in
range
(
1
,
seq_len
):
log_likelihood
+=
(
self
.
x
[
k
,
label
[
k
]]
+
self
.
w
[
label
[
k
-
1
],
label
[
k
]])
return
log_likelihood
def
crf_forward_compute
(
self
):
for
i
in
range
(
self
.
seq_num
):
start
=
self
.
seq_start_positions
[
i
]
end
=
self
.
seq_start_positions
[
i
+
1
]
self
.
log_likelihood
[
i
]
=
self
.
_forward_a_sequence
(
self
.
x
[
start
:
end
],
self
.
x_row_max
[
start
:
end
,
:],
self
.
x_exps
[
start
:
end
,
:],
self
.
labels
[
start
:
end
,
:],
self
.
alpha
[
start
:
end
,
:])
return
self
.
alpha
,
self
.
log_likelihood
class
TestLinearChainCrfOp
(
OpTest
):
def
set_test_data
(
self
):
SEQ_NUM
=
3
TAG_NUM
=
17
MAX_SEQ_LEN
=
13
# the linear_chain_crf operator only supports sequence (LoD level = 1)
lod
=
[[
0
]]
for
i
in
range
(
SEQ_NUM
):
lod
[
-
1
].
append
(
lod
[
-
1
][
-
1
]
+
random
.
randint
(
1
,
MAX_SEQ_LEN
))
emission
=
np
.
random
.
uniform
(
-
1
,
1
,
[
lod
[
-
1
][
-
1
],
TAG_NUM
]).
astype
(
"float32"
)
transition
=
np
.
random
.
uniform
(
-
0.5
,
0.5
,
[
TAG_NUM
+
2
,
TAG_NUM
]).
astype
(
"float32"
)
labels
=
np
.
random
.
randint
(
low
=
0
,
high
=
TAG_NUM
,
size
=
(
lod
[
-
1
][
-
1
],
1
),
dtype
=
"int32"
)
self
.
inputs
=
{
"Emission"
:
(
emission
,
lod
),
"Transition"
:
transition
,
"label"
:
(
labels
,
lod
)
}
crf
=
LinearChainCrfForward
(
lod
[
0
],
emission
,
transition
,
labels
)
alpha
,
log_likelihood
=
crf
.
crf_forward_compute
()
self
.
outputs
=
{
"Alpha"
:
alpha
,
"LogLikelihood"
:
log_likelihood
}
def
setUp
(
self
):
self
.
op_type
=
"linear_chain_crf"
self
.
set_test_data
()
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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