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体验新版 GitCode,发现更多精彩内容 >>
提交
65dbbd57
编写于
10月 26, 2017
作者:
Y
yangyaming
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add and pass unittests.
上级
06c7c8c8
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
188 addition
and
11 deletion
+188
-11
paddle/operators/precision_recall_op.cc
paddle/operators/precision_recall_op.cc
+16
-5
paddle/operators/precision_recall_op.h
paddle/operators/precision_recall_op.h
+8
-6
python/paddle/v2/framework/tests/test_precision_recall_op.py
python/paddle/v2/framework/tests/test_precision_recall_op.py
+164
-0
未找到文件。
paddle/operators/precision_recall_op.cc
浏览文件 @
65dbbd57
...
@@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
@@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include "paddle/operators/precision_recall_op.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -37,13 +39,15 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
...
@@ -37,13 +39,15 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
if
(
ctx
->
HasInput
(
"Weights"
))
{
if
(
ctx
->
HasInput
(
"Weights"
))
{
auto
weights_dims
=
ctx
->
GetInputDim
(
"Weights"
);
auto
weights_dims
=
ctx
->
GetInputDim
(
"Weights"
);
PADDLE_ENFORCE_EQ
(
weights_dims
,
{
predictions_dims
[
0
],
1
},
PADDLE_ENFORCE_EQ
(
weights_dims
,
framework
::
make_ddim
({
predictions_dims
[
0
],
1
}),
"The shape of Input(Weights) should be "
"The shape of Input(Weights) should be "
"[batch_size, 1]."
);
"[batch_size, 1]."
);
}
}
if
(
ctx
->
HasInput
(
"StatesInfo"
))
{
if
(
ctx
->
HasInput
(
"StatesInfo"
))
{
auto
states_dims
=
ctx
->
GetInputDim
(
"StatesInfo"
);
auto
states_dims
=
ctx
->
GetInputDim
(
"StatesInfo"
);
PADDLE_ENFORCE_EQ
(
states_dims
,
{
predictions_dims
[
1
],
4
},
PADDLE_ENFORCE_EQ
(
states_dims
,
framework
::
make_ddim
({
predictions_dims
[
1
],
4
}),
"The shape of Input(StatesInfo) should be "
"The shape of Input(StatesInfo) should be "
"[class_number, 4]."
);
"[class_number, 4]."
);
}
}
...
@@ -71,6 +75,12 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
...
@@ -71,6 +75,12 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
// [ TP, FP, TN, FN ]
// [ TP, FP, TN, FN ]
ctx
->
SetOutputDim
(
"AccumStatesInfo"
,
{
predictions_dims
[
1
],
4
});
ctx
->
SetOutputDim
(
"AccumStatesInfo"
,
{
predictions_dims
[
1
],
4
});
}
}
protected:
framework
::
DataType
IndicateDataType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Predictions"
)
->
type
());
}
};
};
class
PrecisionRecallOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
PrecisionRecallOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
@@ -98,6 +108,9 @@ class PrecisionRecallOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -98,6 +108,9 @@ class PrecisionRecallOpMaker : public framework::OpProtoAndCheckerMaker {
"provided, current state will be accumulated to this state and "
"provided, current state will be accumulated to this state and "
"the accumulation state will be as the output state."
)
"the accumulation state will be as the output state."
)
.
AsDispensable
();
.
AsDispensable
();
AddOutput
(
"BatchMetrics"
,
""
);
AddOutput
(
"AccumMetrics"
,
""
);
AddOutput
(
"AccumStatesInfo"
,
""
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(
)DOC"
);
)DOC"
);
...
@@ -113,6 +126,4 @@ REGISTER_OP_WITHOUT_GRADIENT(precision_recall, ops::PrecisionRecallOp,
...
@@ -113,6 +126,4 @@ REGISTER_OP_WITHOUT_GRADIENT(precision_recall, ops::PrecisionRecallOp,
REGISTER_OP_CPU_KERNEL
(
REGISTER_OP_CPU_KERNEL
(
precision_recall
,
precision_recall
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
PrecisionRecallKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
,
paddle/operators/precision_recall_op.h
浏览文件 @
65dbbd57
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#pragma once
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -37,7 +39,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -37,7 +39,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
auto
*
out2
=
ctx
.
Output
<
Tensor
>
(
"AccumStatesInfo"
);
auto
*
out2
=
ctx
.
Output
<
Tensor
>
(
"AccumStatesInfo"
);
const
T
*
predictions_data
=
in0
->
data
<
T
>
();
const
T
*
predictions_data
=
in0
->
data
<
T
>
();
const
T
*
labels_data
=
in1
->
data
<
T
>
();
const
int
*
labels_data
=
in1
->
data
<
int
>
();
const
T
*
weights_data
=
in2
?
in2
->
data
<
T
>
()
:
nullptr
;
const
T
*
weights_data
=
in2
?
in2
->
data
<
T
>
()
:
nullptr
;
const
T
*
states_data
=
in3
?
in3
->
data
<
T
>
()
:
nullptr
;
const
T
*
states_data
=
in3
?
in3
->
data
<
T
>
()
:
nullptr
;
T
*
batch_metrics_data
=
out0
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batch_metrics_data
=
out0
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
...
@@ -45,7 +47,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -45,7 +47,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
out2
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
out2
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
accum_states
=
EigenMatrix
<
T
>::
From
(
*
out2
);
auto
accum_states
=
EigenMatrix
<
T
>::
From
(
*
out2
);
accum_states
.
setZero
();
accum_states
.
setZero
();
T
*
accum_states_data
=
out2
->
data
<
T
>
(
ctx
.
GetPlace
()
);
T
*
accum_states_data
=
out2
->
data
<
T
>
();
size_t
sample_num
=
in0
->
dims
()[
0
];
size_t
sample_num
=
in0
->
dims
()[
0
];
size_t
class_dim
=
in0
->
dims
()[
1
];
size_t
class_dim
=
in0
->
dims
()[
1
];
...
@@ -76,7 +78,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -76,7 +78,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
accum_states_data
[
j
*
state_var_num
+
TN
]
+=
w
;
accum_states_data
[
j
*
state_var_num
+
TN
]
+=
w
;
}
}
accum_states_data
[
max_idx
*
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
;
accum_states_data
[
labels_data
[
i
]
*
state_var_num
+
TN
]
-=
w
;
}
}
}
}
...
@@ -108,7 +110,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -108,7 +110,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
if
(
tp_count
>
0.0
||
fn_count
>
0.0
)
{
if
(
tp_count
>
0.0
||
fn_count
>
0.0
)
{
return
tp_count
/
(
tp_count
+
fn_count
);
return
tp_count
/
(
tp_count
+
fn_count
);
}
}
return
1.0
return
1.0
;
}
}
static
inline
T
CalcF1Score
(
T
precision
,
T
recall
)
{
static
inline
T
CalcF1Score
(
T
precision
,
T
recall
)
{
...
@@ -120,7 +122,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -120,7 +122,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
protected:
protected:
void
ComputeMetrics
(
const
T
*
states_data
,
T
*
metrics_data
,
void
ComputeMetrics
(
const
T
*
states_data
,
T
*
metrics_data
,
size_t
state_var_num
,
size_t
class_dim
)
{
size_t
state_var_num
,
size_t
class_dim
)
const
{
T
total_tp_count
=
0
;
T
total_tp_count
=
0
;
T
total_fp_count
=
0
;
T
total_fp_count
=
0
;
T
total_fn_count
=
0
;
T
total_fn_count
=
0
;
...
@@ -143,7 +145,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
...
@@ -143,7 +145,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
T
micro_avg_precision
=
CalcPrecision
(
total_tp_count
,
total_fp_count
);
T
micro_avg_precision
=
CalcPrecision
(
total_tp_count
,
total_fp_count
);
T
micro_avg_recall
=
CalcRecall
(
total_tp_count
,
total_fn_count
);
T
micro_avg_recall
=
CalcRecall
(
total_tp_count
,
total_fn_count
);
T
micro_f1_score
=
Calc
Recall
(
micro_avg_precision
,
micro_avg_recall
);
T
micro_f1_score
=
Calc
F1Score
(
micro_avg_precision
,
micro_avg_recall
);
// fill metrics data
// fill metrics data
metrics_data
[
0
]
=
macro_avg_precision
;
metrics_data
[
0
]
=
macro_avg_precision
;
...
...
python/paddle/v2/framework/tests/test_precision_recall_op.py
0 → 100644
浏览文件 @
65dbbd57
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
calc_precision
(
tp_count
,
fp_count
):
if
tp_count
>
0.0
or
fp_count
>
0.0
:
return
tp_count
/
(
tp_count
+
fp_count
)
return
1.0
def
calc_recall
(
tp_count
,
fn_count
):
if
tp_count
>
0.0
or
fn_count
>
0.0
:
return
tp_count
/
(
tp_count
+
fn_count
)
return
1.0
def
calc_f1_score
(
precision
,
recall
):
if
precision
>
0.0
or
recall
>
0.0
:
return
2
*
precision
*
recall
/
(
precision
+
recall
)
return
0.0
def
get_states
(
predictions
,
labels
,
weights
=
None
):
ins_num
=
predictions
.
shape
[
0
]
class_num
=
predictions
.
shape
[
1
]
# TP FP TN FN
states
=
np
.
zeros
((
class_num
,
4
)).
astype
(
'float32'
)
for
i
in
xrange
(
ins_num
):
w
=
weights
[
i
]
if
weights
is
not
None
else
1.0
max_idx
=
np
.
argmax
(
predictions
[
i
])
if
max_idx
==
labels
[
i
][
0
]:
states
[
max_idx
][
0
]
+=
w
for
j
in
xrange
(
class_num
):
states
[
j
][
2
]
+=
w
states
[
max_idx
][
2
]
-=
w
else
:
states
[
labels
[
i
][
0
]][
3
]
+=
w
states
[
max_idx
][
1
]
+=
w
for
j
in
xrange
(
class_num
):
states
[
j
][
2
]
+=
w
states
[
labels
[
i
][
0
]][
2
]
-=
w
states
[
max_idx
][
2
]
-=
w
return
states
def
compute_metrics
(
states
):
class_num
=
states
.
shape
[
0
]
total_tp_count
=
0.0
total_fp_count
=
0.0
total_fn_count
=
0.0
macro_avg_precision
=
0.0
macro_avg_recall
=
0.0
for
i
in
xrange
(
class_num
):
total_tp_count
+=
states
[
i
][
0
]
total_fp_count
+=
states
[
i
][
1
]
total_fn_count
+=
states
[
i
][
3
]
macro_avg_precision
+=
calc_precision
(
states
[
i
][
0
],
states
[
i
][
1
])
macro_avg_recall
+=
calc_recall
(
states
[
i
][
0
],
states
[
i
][
3
])
metrics
=
[]
macro_avg_precision
/=
class_num
macro_avg_recall
/=
class_num
metrics
.
append
(
macro_avg_precision
)
metrics
.
append
(
macro_avg_recall
)
metrics
.
append
(
calc_f1_score
(
macro_avg_precision
,
macro_avg_recall
))
micro_avg_precision
=
calc_precision
(
total_tp_count
,
total_fp_count
)
metrics
.
append
(
micro_avg_precision
)
micro_avg_recall
=
calc_recall
(
total_tp_count
,
total_fn_count
)
metrics
.
append
(
micro_avg_recall
)
metrics
.
append
(
calc_f1_score
(
micro_avg_precision
,
micro_avg_recall
))
return
np
.
array
(
metrics
).
astype
(
'float32'
)
class
TestPrecisionRecallOp_0
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"precision_recall"
ins_num
=
64
class_num
=
10
predictions
=
np
.
random
.
uniform
(
0
,
1.0
,
(
ins_num
,
class_num
)).
astype
(
'float32'
)
labels
=
np
.
random
.
choice
(
xrange
(
class_num
),
ins_num
).
reshape
(
(
ins_num
,
1
)).
astype
(
'int32'
)
states
=
get_states
(
predictions
,
labels
)
metrics
=
compute_metrics
(
states
)
self
.
inputs
=
{
'Predictions'
:
predictions
,
'Labels'
:
labels
}
self
.
outputs
=
{
'BatchMetrics'
:
metrics
,
'AccumMetrics'
:
metrics
,
'AccumStatesInfo'
:
states
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestPrecisionRecallOp_1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"precision_recall"
ins_num
=
64
class_num
=
10
predictions
=
np
.
random
.
uniform
(
0
,
1.0
,
(
ins_num
,
class_num
)).
astype
(
'float32'
)
weights
=
np
.
random
.
uniform
(
0
,
1.0
,
(
ins_num
,
1
)).
astype
(
'float32'
)
predictions
=
np
.
random
.
random
((
ins_num
,
class_num
)).
astype
(
'float32'
)
labels
=
np
.
random
.
choice
(
xrange
(
class_num
),
ins_num
).
reshape
(
(
ins_num
,
1
)).
astype
(
'int32'
)
states
=
get_states
(
predictions
,
labels
,
weights
)
metrics
=
compute_metrics
(
states
)
self
.
inputs
=
{
'Predictions'
:
predictions
,
'Labels'
:
labels
,
'Weights'
:
weights
}
self
.
outputs
=
{
'BatchMetrics'
:
metrics
,
'AccumMetrics'
:
metrics
,
'AccumStatesInfo'
:
states
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestPrecisionRecallOp_2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"precision_recall"
ins_num
=
64
class_num
=
10
predictions
=
np
.
random
.
uniform
(
0
,
1.0
,
(
ins_num
,
class_num
)).
astype
(
'float32'
)
weights
=
np
.
random
.
uniform
(
0
,
1.0
,
(
ins_num
,
1
)).
astype
(
'float32'
)
predictions
=
np
.
random
.
random
((
ins_num
,
class_num
)).
astype
(
'float32'
)
labels
=
np
.
random
.
choice
(
xrange
(
class_num
),
ins_num
).
reshape
(
(
ins_num
,
1
)).
astype
(
'int32'
)
states
=
np
.
random
.
randint
(
0
,
30
,
(
class_num
,
4
)).
astype
(
'float32'
)
accum_states
=
get_states
(
predictions
,
labels
,
weights
)
batch_metrics
=
compute_metrics
(
accum_states
)
accum_states
+=
states
accum_metrics
=
compute_metrics
(
accum_states
)
self
.
inputs
=
{
'Predictions'
:
predictions
,
'Labels'
:
labels
,
'Weights'
:
weights
,
'StatesInfo'
:
states
}
self
.
outputs
=
{
'BatchMetrics'
:
batch_metrics
,
'AccumMetrics'
:
accum_metrics
,
'AccumStatesInfo'
:
accum_states
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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