Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
e768721c
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
695
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
e768721c
编写于
2月 15, 2017
作者:
L
Liang Zhao
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix calculating totalScore2_ bug
上级
413cbb84
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
85 addition
and
45 deletion
+85
-45
paddle/gserver/evaluators/Evaluator.cpp
paddle/gserver/evaluators/Evaluator.cpp
+57
-38
paddle/gserver/tests/test_Evaluator.cpp
paddle/gserver/tests/test_Evaluator.cpp
+1
-0
proto/ModelConfig.proto
proto/ModelConfig.proto
+4
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+3
-0
python/paddle/trainer_config_helpers/evaluators.py
python/paddle/trainer_config_helpers/evaluators.py
+10
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+10
-7
未找到文件。
paddle/gserver/evaluators/Evaluator.cpp
浏览文件 @
e768721c
...
...
@@ -39,6 +39,13 @@ void Evaluator::eval(const NeuralNetwork& nn) {
*/
class
ClassificationErrorEvaluator
:
public
Evaluator
{
public:
ClassificationErrorEvaluator
()
:
totalScore2_
(
0
)
{}
virtual
void
start
()
{
Evaluator
::
start
();
totalScore2_
=
0
;
}
virtual
void
updateSamplesNum
(
const
std
::
vector
<
Argument
>&
arguments
)
{
if
(
3
==
arguments
.
size
())
{
numSamples_
+=
arguments
[
2
].
value
->
getSum
();
...
...
@@ -85,45 +92,47 @@ public:
if
(
label
!=
nullptr
)
{
errorMat
->
classificationError
(
*
output
,
*
label
);
// top-1 error
size_t
height
=
output
->
getHeight
();
size_t
width
=
5
;
IVector
::
resizeOrCreate
(
maxIds_
,
height
*
width
,
useGpu
(
arguments
[
0
].
deviceId
));
Matrix
::
resizeOrCreate
(
maxValues_
,
height
,
width
,
false
,
useGpu
(
arguments
[
0
].
deviceId
));
output
->
rowMax
(
*
maxIds_
,
*
maxValues_
);
// top-5 values
int
*
ids
=
nullptr
;
int
*
lbl
=
nullptr
;
IVectorPtr
dest
=
IVector
::
create
(
maxIds_
->
getSize
(),
false
);
IVectorPtr
dest2
=
IVector
::
create
(
label
->
getSize
(),
false
);
if
(
useGpu
(
arguments
[
0
].
deviceId
))
{
hl_memcpy_device2host
((
void
*
)
dest
->
getData
(),
(
void
*
)
maxIds_
->
getData
(),
sizeof
(
int
)
*
maxIds_
->
getSize
());
ids
=
dest
->
getData
();
hl_memcpy_device2host
((
void
*
)
dest2
->
getData
(),
(
void
*
)
label
->
getData
(),
sizeof
(
int
)
*
label
->
getSize
());
lbl
=
dest2
->
getData
();
}
else
{
ids
=
maxIds_
->
getData
();
lbl
=
label
->
getData
();
}
if
(
config_
.
top_k
()
>
1
)
{
size_t
height
=
output
->
getHeight
();
size_t
width
=
config_
.
top_k
();
IVector
::
resizeOrCreate
(
maxIds_
,
height
*
width
,
useGpu
(
arguments
[
0
].
deviceId
));
Matrix
::
resizeOrCreate
(
maxValues_
,
height
,
width
,
false
,
useGpu
(
arguments
[
0
].
deviceId
));
output
->
rowMax
(
*
maxIds_
,
*
maxValues_
);
// top-k values
int
*
ids
=
nullptr
;
int
*
lbl
=
nullptr
;
IVectorPtr
dest
=
IVector
::
create
(
maxIds_
->
getSize
(),
false
);
IVectorPtr
dest2
=
IVector
::
create
(
label
->
getSize
(),
false
);
if
(
useGpu
(
arguments
[
0
].
deviceId
))
{
hl_memcpy_device2host
((
void
*
)
dest
->
getData
(),
(
void
*
)
maxIds_
->
getData
(),
sizeof
(
int
)
*
maxIds_
->
getSize
());
ids
=
dest
->
getData
();
hl_memcpy_device2host
((
void
*
)
dest2
->
getData
(),
(
void
*
)
label
->
getData
(),
sizeof
(
int
)
*
label
->
getSize
());
lbl
=
dest2
->
getData
();
}
else
{
ids
=
maxIds_
->
getData
();
lbl
=
label
->
getData
();
}
real
*
result2
=
errorMat2
->
getData
();
for
(
size_t
i
=
0
;
i
<
height
;
++
i
)
{
result2
[
i
]
=
(
ids
[
i
*
width
]
!=
lbl
[
i
]);
// initialize top-5 error
for
(
size_t
j
=
1
;
j
<
width
;
++
j
)
{
if
(
result2
[
i
]
==
0.0
)
{
break
;
real
*
result2
=
errorMat2
->
getData
();
for
(
size_t
i
=
0
;
i
<
height
;
++
i
)
{
result2
[
i
]
=
(
ids
[
i
*
width
]
!=
lbl
[
i
]);
// initialize top-k error
for
(
size_t
j
=
1
;
j
<
width
;
++
j
)
{
if
(
result2
[
i
]
==
0.0
)
{
break
;
}
result2
[
i
]
=
(
ids
[
i
*
width
+
j
]
!=
lbl
[
i
]);
// top-k error
}
result2
[
i
]
=
(
ids
[
i
*
width
+
j
]
!=
lbl
[
i
]);
// top-5 error
}
totalScore2_
+=
errorMat2
->
getSum
();
}
totalScore2_
=
errorMat2
->
getSum
();
}
else
if
(
dynamic_cast
<
CpuSparseMatrix
*>
(
multiBinaryLabel
.
get
())
||
dynamic_cast
<
GpuSparseMatrix
*>
(
multiBinaryLabel
.
get
()))
{
errorMat
->
classificationErrorMulti
(
...
...
@@ -140,8 +149,14 @@ public:
}
void
printStats
(
std
::
ostream
&
os
)
const
{
os
<<
"top_1_error="
<<
(
numSamples_
?
totalScore_
/
numSamples_
:
0
)
<<
" top_5_error="
<<
(
numSamples_
?
totalScore2_
/
numSamples_
:
0
);
if
(
config_
.
top_k
()
==
1
)
{
os
<<
config_
.
name
()
<<
"="
<<
(
numSamples_
?
totalScore_
/
numSamples_
:
0
);
}
else
{
os
<<
"top_1_error="
<<
(
numSamples_
?
totalScore_
/
numSamples_
:
0
)
<<
" top_"
<<
config_
.
top_k
()
<<
"_error="
<<
(
numSamples_
?
totalScore2_
/
numSamples_
:
0
);
}
}
virtual
real
evalImp
(
std
::
vector
<
Argument
>&
arguments
)
{
...
...
@@ -150,7 +165,11 @@ public:
}
virtual
void
distributeEval
(
ParameterClient2
*
client
)
{
mergeResultsOfAllClients
(
client
);
double
data
[
3
]
=
{
totalScore_
,
totalScore2_
,
numSamples_
};
client
->
reduce
(
data
,
data
,
3
,
FLAGS_trainer_id
,
0
);
totalScore_
=
data
[
0
];
totalScore2_
=
data
[
1
];
numSamples_
=
data
[
2
];
}
private:
...
...
paddle/gserver/tests/test_Evaluator.cpp
浏览文件 @
e768721c
...
...
@@ -129,6 +129,7 @@ void testEvaluatorAll(TestConfig testConf,
TEST
(
Evaluator
,
classification_error
)
{
TestConfig
config
;
config
.
evaluatorConfig
.
set_type
(
"classification_error"
);
config
.
evaluatorConfig
.
set_top_k
(
5
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"output"
,
50
});
config
.
inputDefs
.
push_back
({
INPUT_LABEL
,
"label"
,
50
});
...
...
proto/ModelConfig.proto
浏览文件 @
e768721c
...
...
@@ -475,6 +475,10 @@ message EvaluatorConfig {
// Used by ChunkEvaluator
// chunk of these types are not counted
repeated
int32
excluded_chunk_types
=
12
;
// Used by ClassificationErrorEvaluator
// top # classification error
optional
int32
top_k
=
13
[
default
=
1
];
}
message
LinkConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
e768721c
...
...
@@ -1253,6 +1253,7 @@ def Evaluator(
dict_file
=
None
,
result_file
=
None
,
num_results
=
None
,
top_k
=
None
,
delimited
=
None
,
excluded_chunk_types
=
None
,
):
evaluator
=
g_config
.
model_config
.
evaluators
.
add
()
...
...
@@ -1280,6 +1281,8 @@ def Evaluator(
evaluator
.
result_file
=
result_file
if
num_results
is
not
None
:
evaluator
.
num_results
=
num_results
if
top_k
is
not
None
:
evaluator
.
top_k
=
top_k
if
delimited
is
not
None
:
evaluator
.
delimited
=
delimited
...
...
python/paddle/trainer_config_helpers/evaluators.py
浏览文件 @
e768721c
...
...
@@ -71,6 +71,7 @@ def evaluator_base(
result_file
=
None
,
num_results
=
None
,
delimited
=
None
,
top_k
=
None
,
excluded_chunk_types
=
None
,
):
"""
Evaluator will evaluate the network status while training/testing.
...
...
@@ -104,12 +105,15 @@ def evaluator_base(
:param weight: An input layer which is a weight for each sample.
Each evaluator may calculate differently to use this weight.
:type weight: LayerOutput.
:param top_k: number k in top-k error rate
:type top_k: int
"""
# inputs type assertions.
assert
classification_threshold
is
None
or
isinstance
(
classification_threshold
,
float
)
assert
positive_label
is
None
or
isinstance
(
positive_label
,
int
)
assert
num_results
is
None
or
isinstance
(
num_results
,
int
)
assert
top_k
is
None
or
isinstance
(
top_k
,
int
)
if
not
isinstance
(
input
,
list
):
input
=
[
input
]
...
...
@@ -130,6 +134,8 @@ def evaluator_base(
dict_file
=
dict_file
,
result_file
=
result_file
,
delimited
=
delimited
,
num_results
=
num_results
,
top_k
=
top_k
,
excluded_chunk_types
=
excluded_chunk_types
,
)
...
...
@@ -139,6 +145,7 @@ def classification_error_evaluator(input,
label
,
name
=
None
,
weight
=
None
,
top_k
=
None
,
threshold
=
None
):
"""
Classification Error Evaluator. It will print error rate for classification.
...
...
@@ -167,6 +174,8 @@ def classification_error_evaluator(input,
then means not set weight. The larger weight it is, the more
important this sample is.
:type weight: LayerOutput
:param top_k: number k in top-k error rate
:type top_k: int
:param threshold: The classification threshold.
:type threshold: float
:return: None.
...
...
@@ -178,6 +187,7 @@ def classification_error_evaluator(input,
input
=
input
,
label
=
label
,
weight
=
weight
,
top_k
=
top_k
,
classification_threshold
=
threshold
,
)
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
e768721c
...
...
@@ -2870,8 +2870,8 @@ def gru_step_layer(input,
:param name:
:param gate_act:
:param bias_attr:
:param param_attr: the parameter_attribute for transforming the output_mem
from previous step.
:param param_attr: the parameter_attribute for transforming the output_mem
from previous step.
:param layer_attr:
:return: LayerOutput object.
:rtype: LayerOutput
...
...
@@ -2882,10 +2882,10 @@ def gru_step_layer(input,
Layer
(
name
=
name
,
type
=
LayerType
.
GRU_STEP_LAYER
,
# The parameter here is for transforming the output_mem. The input has
# already been transformed outside this module so it does not need
# parameter associated with it.
# The parameter here is instead grouped with input is due to
# The parameter here is for transforming the output_mem. The input has
# already been transformed outside this module so it does not need
# parameter associated with it.
# The parameter here is instead grouped with input is due to
# backward model compatibility.
inputs
=
[
Input
(
input
.
name
,
**
param_attr
.
attr
),
output_mem
.
name
],
bias
=
ParamAttr
.
to_bias
(
bias_attr
),
...
...
@@ -3536,6 +3536,7 @@ def classification_cost(input,
label
,
weight
=
None
,
name
=
None
,
top_k
=
None
,
evaluator
=
classification_error_evaluator
,
layer_attr
=
None
):
"""
...
...
@@ -3550,6 +3551,8 @@ def classification_cost(input,
:param weight: The weight affects the cost, namely the scale of cost.
It is an optional argument.
:type weight: LayerOutput
:param top_k: number k in top-k error rate
:type top_k: int
:param evaluator: Evaluator method.
:param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute
...
...
@@ -3577,7 +3580,7 @@ def classification_cost(input,
assert
isinstance
(
e
.
for_classification
,
bool
)
assert
e
.
for_classification
e
(
name
=
e
.
__name__
,
input
=
input
,
label
=
label
,
weight
=
weight
)
e
(
name
=
e
.
__name__
,
input
=
input
,
label
=
label
,
weight
=
weight
,
top_k
=
top_k
)
if
not
isinstance
(
evaluator
,
collections
.
Sequence
):
evaluator
=
[
evaluator
]
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录