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06c7c8c8
编写于
10月 26, 2017
作者:
Y
yangyaming
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电子邮件补丁
差异文件
Add CPU kernel.
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paddle/operators/precision_recall_op.cc
paddle/operators/precision_recall_op.cc
+118
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paddle/operators/precision_recall_op.h
paddle/operators/precision_recall_op.h
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paddle/operators/precision_recall_op.cc
0 → 100644
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06c7c8c8
/* 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. */
namespace
paddle
{
namespace
operators
{
class
PrecisionRecallOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
// may contains weights and StatesInfo
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Predictions"
),
"Input(Predictions) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Labels"
),
"Input(Labels) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchMetrics"
),
"Output(BatchMetrics) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"AccumMetrics"
),
"Output(AccumMetrics) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"AccumStatesInfo"
),
"Output(AccumStatesInfo) should not be null."
);
auto
predictions_dims
=
ctx
->
GetInputDim
(
"Predictions"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Labels"
);
if
(
ctx
->
HasInput
(
"Weights"
))
{
auto
weights_dims
=
ctx
->
GetInputDim
(
"Weights"
);
PADDLE_ENFORCE_EQ
(
weights_dims
,
{
predictions_dims
[
0
],
1
},
"The shape of Input(Weights) should be "
"[batch_size, 1]."
);
}
if
(
ctx
->
HasInput
(
"StatesInfo"
))
{
auto
states_dims
=
ctx
->
GetInputDim
(
"StatesInfo"
);
PADDLE_ENFORCE_EQ
(
states_dims
,
{
predictions_dims
[
1
],
4
},
"The shape of Input(StatesInfo) should be "
"[class_number, 4]."
);
}
PADDLE_ENFORCE_EQ
(
predictions_dims
[
0
],
labels_dims
[
0
],
"The 1st dimension of Input(Predictions) and "
"Input(Labels) both are batch_size and the shape should "
"be the same."
);
PADDLE_ENFORCE_EQ
(
labels_dims
[
1
],
1
,
"The 2nd dimension of Input(Labels) "
"contains instance label and the shape should be equal "
"to 1"
);
PADDLE_ENFORCE_GE
(
predictions_dims
[
1
],
1
,
"The shape of Input(Predictions)'s 2nd dimension is "
"equal to class number and should be at least 1."
);
// Layouts of BatchMetrics and AccumMetrics both are:
// [
// macro average precision, macro average recall, macro average F1 score,
// micro average precision, micro average recall, micro average F1 score
// ]
ctx
->
SetOutputDim
(
"BatchMetrics"
,
{
6
});
ctx
->
SetOutputDim
(
"AccumMetrics"
,
{
6
});
// Shape of AccumStatesInfo is [class_number, 4]
// The layout of each row is:
// [ TP, FP, TN, FN ]
ctx
->
SetOutputDim
(
"AccumStatesInfo"
,
{
predictions_dims
[
1
],
4
});
}
};
class
PrecisionRecallOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
PrecisionRecallOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Predictions"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
"where N is the batch size and D is the number of classes. "
"Each row contains probabilities for an instance which computed "
"by the previous operator."
);
AddInput
(
"Labels"
,
"(Tensor, default Tensor<int>), a 2-D tensor with shape N x 1, "
"where N is the batch size. Each element is a label and the "
"value should be in [0, class_number - 1]."
);
AddInput
(
"Weights"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x 1, "
"where N is the batch size. This input is optional. If provided, "
"weight of instance would be considered when computing metrics."
)
.
AsDispensable
();
AddInput
(
"StatesInfo"
,
"(Tensor, default Tensor<int>), a 2-D tensor with shape D x 4, "
"where D is the number of classes. This input is optional. If "
"provided, current state will be accumulated to this state and "
"the accumulation state will be as the output state."
)
.
AsDispensable
();
AddComment
(
R"DOC(
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
precision_recall
,
ops
::
PrecisionRecallOp
,
ops
::
PrecisionRecallOpMaker
);
REGISTER_OP_CPU_KERNEL
(
precision_recall
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
,
paddle/operators/precision_recall_op.h
0 → 100644
浏览文件 @
06c7c8c8
/* 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. */
#pragma once
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
enum
StateVariable
{
TP
=
0
,
FP
,
TN
,
FN
};
template
<
typename
Place
,
typename
T
>
class
PrecisionRecallKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in0
=
ctx
.
Input
<
Tensor
>
(
"Predictions"
);
auto
*
in1
=
ctx
.
Input
<
Tensor
>
(
"Labels"
);
auto
*
in2
=
ctx
.
Input
<
Tensor
>
(
"Weights"
);
auto
*
in3
=
ctx
.
Input
<
Tensor
>
(
"StatesInfo"
);
auto
*
out0
=
ctx
.
Output
<
Tensor
>
(
"BatchMetrics"
);
auto
*
out1
=
ctx
.
Output
<
Tensor
>
(
"AccumMetrics"
);
auto
*
out2
=
ctx
.
Output
<
Tensor
>
(
"AccumStatesInfo"
);
const
T
*
predictions_data
=
in0
->
data
<
T
>
();
const
T
*
labels_data
=
in1
->
data
<
T
>
();
const
T
*
weights_data
=
in2
?
in2
->
data
<
T
>
()
:
nullptr
;
const
T
*
states_data
=
in3
?
in3
->
data
<
T
>
()
:
nullptr
;
T
*
batch_metrics_data
=
out0
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
accum_metrics_data
=
out1
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
out2
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
accum_states
=
EigenMatrix
<
T
>::
From
(
*
out2
);
accum_states
.
setZero
();
T
*
accum_states_data
=
out2
->
data
<
T
>
(
ctx
.
GetPlace
());
size_t
sample_num
=
in0
->
dims
()[
0
];
size_t
class_dim
=
in0
->
dims
()[
1
];
size_t
state_var_num
=
4
;
// TP FP TN FN
// get states info for current batch
for
(
size_t
i
=
0
;
i
<
sample_num
;
++
i
)
{
size_t
max_idx
=
0
;
T
max_val
=
predictions_data
[
i
*
class_dim
];
for
(
size_t
j
=
1
;
j
<
class_dim
;
++
j
)
{
if
(
max_val
<
predictions_data
[
i
*
class_dim
+
j
])
{
max_idx
=
j
;
max_val
=
predictions_data
[
i
*
class_dim
+
j
];
}
}
T
w
=
weights_data
?
weights_data
[
i
]
:
1.0
;
if
(
max_idx
==
labels_data
[
i
])
{
accum_states_data
[
max_idx
*
state_var_num
+
TP
]
+=
w
;
for
(
size_t
j
=
0
;
j
<
class_dim
;
++
j
)
{
accum_states_data
[
j
*
state_var_num
+
TN
]
+=
w
;
}
accum_states_data
[
max_idx
*
state_var_num
+
TN
]
-=
w
;
}
else
{
accum_states_data
[
labels_data
[
i
]
*
state_var_num
+
FN
]
+=
w
;
accum_states_data
[
max_idx
*
state_var_num
+
FP
]
+=
w
;
for
(
size_t
j
=
0
;
j
<
class_dim
;
++
j
)
{
accum_states_data
[
j
*
state_var_num
+
TN
]
+=
w
;
}
accum_states_data
[
max_idx
*
state_var_num
+
TN
]
-=
w
;
accum_states_data
[
labels_data
[
j
]
*
state_var_num
+
TN
]
-=
w
;
}
}
ComputeMetrics
(
accum_states_data
,
batch_metrics_data
,
state_var_num
,
class_dim
);
if
(
states_data
)
{
for
(
size_t
i
=
0
;
i
<
class_dim
;
++
i
)
{
for
(
size_t
j
=
0
;
j
<
state_var_num
;
++
j
)
{
size_t
idx
=
i
*
state_var_num
+
j
;
accum_states_data
[
idx
]
+=
states_data
[
idx
];
}
}
}
ComputeMetrics
(
accum_states_data
,
accum_metrics_data
,
state_var_num
,
class_dim
);
}
// expose to be reused
static
inline
T
CalcPrecision
(
T
tp_count
,
T
fp_count
)
{
if
(
tp_count
>
0.0
||
fp_count
>
0.0
)
{
return
tp_count
/
(
tp_count
+
fp_count
);
}
return
1.0
;
}
static
inline
T
CalcRecall
(
T
tp_count
,
T
fn_count
)
{
if
(
tp_count
>
0.0
||
fn_count
>
0.0
)
{
return
tp_count
/
(
tp_count
+
fn_count
);
}
return
1.0
}
static
inline
T
CalcF1Score
(
T
precision
,
T
recall
)
{
if
(
precision
>
0.0
||
recall
>
0.0
)
{
return
2
*
precision
*
recall
/
(
precision
+
recall
);
}
return
0.0
;
}
protected:
void
ComputeMetrics
(
const
T
*
states_data
,
T
*
metrics_data
,
size_t
state_var_num
,
size_t
class_dim
)
{
T
total_tp_count
=
0
;
T
total_fp_count
=
0
;
T
total_fn_count
=
0
;
T
macro_avg_precision
=
0.0
;
T
macro_avg_recall
=
0.0
;
for
(
size_t
i
=
0
;
i
<
class_dim
;
++
i
)
{
T
tp_count
=
states_data
[
i
*
state_var_num
+
TP
];
T
fp_count
=
states_data
[
i
*
state_var_num
+
FP
];
T
fn_count
=
states_data
[
i
*
state_var_num
+
FN
];
total_tp_count
+=
tp_count
;
total_fp_count
+=
fp_count
;
total_fn_count
+=
fn_count
;
macro_avg_precision
+=
CalcPrecision
(
tp_count
,
fp_count
);
macro_avg_recall
+=
CalcRecall
(
tp_count
,
fn_count
);
}
macro_avg_precision
/=
class_dim
;
macro_avg_recall
/=
class_dim
;
T
macro_f1_score
=
CalcF1Score
(
macro_avg_precision
,
macro_avg_recall
);
T
micro_avg_precision
=
CalcPrecision
(
total_tp_count
,
total_fp_count
);
T
micro_avg_recall
=
CalcRecall
(
total_tp_count
,
total_fn_count
);
T
micro_f1_score
=
CalcRecall
(
micro_avg_precision
,
micro_avg_recall
);
// fill metrics data
metrics_data
[
0
]
=
macro_avg_precision
;
metrics_data
[
1
]
=
macro_avg_recall
;
metrics_data
[
2
]
=
macro_f1_score
;
metrics_data
[
3
]
=
micro_avg_precision
;
metrics_data
[
4
]
=
micro_avg_recall
;
metrics_data
[
5
]
=
micro_f1_score
;
}
};
}
// namespace operators
}
// namespace paddle
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