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体验新版 GitCode,发现更多精彩内容 >>
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9a89b041
编写于
12月 11, 2017
作者:
G
guosheng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add ChunkEvaluator for multi-batches
上级
06a3a887
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
143 addition
and
24 deletion
+143
-24
paddle/operators/chunk_eval_op.cc
paddle/operators/chunk_eval_op.cc
+18
-0
paddle/operators/chunk_eval_op.h
paddle/operators/chunk_eval_op.h
+29
-13
python/paddle/v2/fluid/evaluator.py
python/paddle/v2/fluid/evaluator.py
+72
-1
python/paddle/v2/fluid/layers.py
python/paddle/v2/fluid/layers.py
+10
-4
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
...n/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
+7
-5
python/paddle/v2/fluid/tests/test_chunk_eval_op.py
python/paddle/v2/fluid/tests/test_chunk_eval_op.py
+7
-1
未找到文件。
paddle/operators/chunk_eval_op.cc
浏览文件 @
9a89b041
...
...
@@ -32,6 +32,13 @@ class ChunkEvalOp : public framework::OperatorWithKernel {
"Output(Recall) of ChunkEvalOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"F1-Score"
),
"Output(F1-Score) of ChunkEvalOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"NumInferChunks"
),
"Output(NumInferChunks) of ChunkEvalOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"NumLabelChunks"
),
"Output(NumLabelChunks) of ChunkEvalOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"NumCorrectChunks"
),
"Output(NumCorrectChunks) of ChunkEvalOp should not be null."
);
auto
inference_dim
=
ctx
->
GetInputDim
(
"Inference"
);
auto
label_dim
=
ctx
->
GetInputDim
(
"Label"
);
...
...
@@ -42,6 +49,9 @@ class ChunkEvalOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
"Precision"
,
{
1
});
ctx
->
SetOutputDim
(
"Recall"
,
{
1
});
ctx
->
SetOutputDim
(
"F1-Score"
,
{
1
});
ctx
->
SetOutputDim
(
"NumInferChunks"
,
{
1
});
ctx
->
SetOutputDim
(
"NumLabelChunks"
,
{
1
});
ctx
->
SetOutputDim
(
"NumCorrectChunks"
,
{
1
});
}
protected:
...
...
@@ -70,6 +80,14 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
"sensitivity) of chunks on the given mini-batch."
);
AddOutput
(
"F1-Score"
,
"(float). The evaluated F1-Score on the given mini-batch."
);
AddOutput
(
"NumInferChunks"
,
"(int). The number of chunks in Inference on the given mini-batch."
);
AddOutput
(
"NumLabelChunks"
,
"(int). The number of chunks in Label on the given mini-batch."
);
AddOutput
(
"NumCorrectChunks"
,
"(int). The number of chunks both in Inference and Label on the "
"given mini-batch."
);
AddAttr
<
int
>
(
"num_chunk_types"
,
"(int). The number of chunk type. See below for details."
);
AddAttr
<
std
::
string
>
(
...
...
paddle/operators/chunk_eval_op.h
浏览文件 @
9a89b041
...
...
@@ -111,9 +111,7 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
std
::
vector
<
Segment
>
label_segments
;
std
::
vector
<
Segment
>
output_segments
;
std
::
set
<
int
>
excluded_chunk_types
;
int64_t
num_output_segments
=
0
;
int64_t
num_label_segments
=
0
;
int64_t
num_correct
=
0
;
if
(
context
.
Attr
<
std
::
string
>
(
"chunk_scheme"
)
==
"IOB"
)
{
num_tag_types
=
2
;
tag_begin
=
0
;
...
...
@@ -151,12 +149,24 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
auto
*
precision
=
context
.
Output
<
Tensor
>
(
"Precision"
);
auto
*
recall
=
context
.
Output
<
Tensor
>
(
"Recall"
);
auto
*
f1
=
context
.
Output
<
Tensor
>
(
"F1-Score"
);
auto
*
num_infer_chunks
=
context
.
Output
<
Tensor
>
(
"NumInferChunks"
);
auto
*
num_label_chunks
=
context
.
Output
<
Tensor
>
(
"NumLabelChunks"
);
auto
*
num_correct_chunks
=
context
.
Output
<
Tensor
>
(
"NumCorrectChunks"
);
const
int64_t
*
inference_data
=
inference
->
data
<
int64_t
>
();
const
int64_t
*
label_data
=
label
->
data
<
int64_t
>
();
T
*
precision_data
=
precision
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
racall_data
=
recall
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
f1_data
=
f1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
*
num_infer_chunks_data
=
num_infer_chunks
->
mutable_data
<
int64_t
>
(
context
.
GetPlace
());
int64_t
*
num_label_chunks_data
=
num_label_chunks
->
mutable_data
<
int64_t
>
(
context
.
GetPlace
());
int64_t
*
num_correct_chunks_data
=
num_correct_chunks
->
mutable_data
<
int64_t
>
(
context
.
GetPlace
());
*
num_infer_chunks_data
=
0
;
*
num_label_chunks_data
=
0
;
*
num_correct_chunks_data
=
0
;
auto
lod
=
label
->
lod
();
PADDLE_ENFORCE_EQ
(
lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
...
...
@@ -166,17 +176,23 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
for
(
int
i
=
0
;
i
<
num_sequences
;
++
i
)
{
int
seq_length
=
lod
[
0
][
i
+
1
]
-
lod
[
0
][
i
];
EvalOneSeq
(
inference_data
+
lod
[
0
][
i
],
label_data
+
lod
[
0
][
i
],
seq_length
,
output_segments
,
label_segments
,
num_output_segments
,
num_label_segments
,
num_correct
,
num_chunk_types
,
num_
tag_types
,
other_chunk_type
,
tag_begin
,
tag_inside
,
tag_end
,
tag_single
,
excluded_chunk_types
);
output_segments
,
label_segments
,
*
num_infer_chunks_data
,
*
num_label_chunks_data
,
*
num_correct_chunks_data
,
num_
chunk_types
,
num_tag_types
,
other_chunk_type
,
tag_begin
,
tag_
inside
,
tag_
end
,
tag_single
,
excluded_chunk_types
);
}
*
precision_data
=
!
num_output_segments
?
0
:
static_cast
<
T
>
(
num_correct
)
/
num_output_segments
;
*
racall_data
=
!
num_label_segments
?
0
:
static_cast
<
T
>
(
num_correct
)
/
num_label_segments
;
*
f1_data
=
!
num_correct
?
0
:
2
*
(
*
precision_data
)
*
(
*
racall_data
)
/
((
*
precision_data
)
+
(
*
racall_data
));
*
precision_data
=
!
(
*
num_infer_chunks_data
)
?
0
:
static_cast
<
T
>
(
*
num_correct_chunks_data
)
/
(
*
num_infer_chunks_data
);
*
racall_data
=
!
(
*
num_label_chunks_data
)
?
0
:
static_cast
<
T
>
(
*
num_correct_chunks_data
)
/
(
*
num_label_chunks_data
);
*
f1_data
=
!
(
*
num_correct_chunks_data
)
?
0
:
2
*
(
*
precision_data
)
*
(
*
racall_data
)
/
((
*
precision_data
)
+
(
*
racall_data
));
}
void
EvalOneSeq
(
const
int64_t
*
output
,
const
int64_t
*
label
,
int
length
,
...
...
python/paddle/v2/fluid/evaluator.py
浏览文件 @
9a89b041
...
...
@@ -4,7 +4,7 @@ import layers
from
framework
import
Program
,
unique_name
,
Variable
from
layer_helper
import
LayerHelper
__all__
=
[
'Accuracy'
]
__all__
=
[
'Accuracy'
,
'ChunkEvaluator'
]
def
_clone_var_
(
block
,
var
):
...
...
@@ -132,3 +132,74 @@ class Accuracy(Evaluator):
correct
=
layers
.
cast
(
correct
,
dtype
=
'float32'
,
**
kwargs
)
out
=
layers
.
elementwise_div
(
x
=
correct
,
y
=
total
,
**
kwargs
)
return
np
.
array
(
executor
.
run
(
eval_program
,
fetch_list
=
[
out
])[
0
])
class
ChunkEvaluator
(
Evaluator
):
"""
Accumulate counter numbers output by chunk_eval from mini-batches and
compute the precision recall and F1-score using the accumulated counter
numbers.
"""
def
__init__
(
self
,
input
,
label
,
chunk_scheme
,
num_chunk_types
,
excluded_chunk_types
=
None
,
**
kwargs
):
super
(
ChunkEvaluator
,
self
).
__init__
(
"chunk_eval"
,
**
kwargs
)
main_program
=
self
.
helper
.
main_program
if
main_program
.
current_block
().
idx
!=
0
:
raise
ValueError
(
"You can only invoke Evaluator in root block"
)
self
.
num_infer_chunks
=
self
.
create_state
(
dtype
=
'int64'
,
shape
=
[
1
],
suffix
=
'num_infer_chunks'
)
self
.
num_label_chunks
=
self
.
create_state
(
dtype
=
'int64'
,
shape
=
[
1
],
suffix
=
'num_label_chunks'
)
self
.
num_correct_chunks
=
self
.
create_state
(
dtype
=
'int64'
,
shape
=
[
1
],
suffix
=
'num_correct_chunks'
)
kwargs
=
{
'main_program'
:
main_program
}
precision
,
recall
,
f1_score
,
num_infer_chunks
,
num_label_chunks
,
num_correct_chunks
=
layers
.
chunk_eval
(
input
=
input
,
label
=
label
,
chunk_scheme
=
chunk_scheme
,
num_chunk_types
=
num_chunk_types
,
excluded_chunk_types
=
excluded_chunk_types
,
**
kwargs
)
layers
.
sums
(
input
=
[
self
.
num_infer_chunks
,
num_infer_chunks
],
out
=
self
.
num_infer_chunks
,
**
kwargs
)
layers
.
sums
(
input
=
[
self
.
num_label_chunks
,
num_label_chunks
],
out
=
self
.
num_label_chunks
,
**
kwargs
)
layers
.
sums
(
input
=
[
self
.
num_correct_chunks
,
num_correct_chunks
],
out
=
self
.
num_correct_chunks
,
**
kwargs
)
self
.
metrics
.
extend
([
precision
,
recall
,
f1_score
])
def
eval
(
self
,
executor
,
eval_program
=
None
):
if
eval_program
is
None
:
eval_program
=
Program
()
block
=
eval_program
.
current_block
()
kwargs
=
{
'main_program'
:
eval_program
}
num_infer_chunks
,
num_label_chunks
,
num_correct_chunks
=
executor
.
run
(
eval_program
,
fetch_list
=
[
_clone_var_
(
block
,
state
)
for
state
in
self
.
states
])
num_infer_chunks
=
num_infer_chunks
[
0
]
num_label_chunks
=
num_label_chunks
[
0
]
num_correct_chunks
=
num_correct_chunks
[
0
]
precision
=
float
(
num_correct_chunks
)
/
num_infer_chunks
if
num_infer_chunks
else
0
recall
=
float
(
num_correct_chunks
)
/
num_label_chunks
if
num_label_chunks
else
0
f1_score
=
float
(
2
*
precision
*
recall
)
/
(
precision
+
recall
)
if
num_correct_chunks
else
0
return
np
.
array
(
[
precision
],
dtype
=
'float32'
),
np
.
array
(
[
recall
],
dtype
=
'float32'
),
np
.
array
(
[
f1_score
],
dtype
=
'float32'
)
python/paddle/v2/fluid/layers.py
浏览文件 @
9a89b041
...
...
@@ -640,8 +640,8 @@ def chunk_eval(input,
excluded_chunk_types
=
None
,
**
kwargs
):
"""
This function computes
the accuracy using the input and label.
The output is the top_k inputs and their indices
.
This function computes
and outputs the precision, recall and
F1-score of chunk detection
.
"""
helper
=
LayerHelper
(
"chunk_eval"
,
**
kwargs
)
...
...
@@ -649,6 +649,9 @@ def chunk_eval(input,
precision
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
recall
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
f1_score
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
num_infer_chunks
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
num_label_chunks
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
num_correct_chunks
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"chunk_eval"
,
...
...
@@ -657,14 +660,17 @@ def chunk_eval(input,
outputs
=
{
"Precision"
:
[
precision
],
"Recall"
:
[
recall
],
"F1-Score"
:
[
f1_score
]
"F1-Score"
:
[
f1_score
],
"NumInferChunks"
:
[
num_infer_chunks
],
"NumLabelChunks"
:
[
num_label_chunks
],
"NumCorrectChunks"
:
[
num_correct_chunks
]
},
attrs
=
{
"num_chunk_types"
:
num_chunk_types
,
'chunk_scheme'
:
chunk_scheme
,
'excluded_chunk_types'
:
excluded_chunk_types
or
[]
})
return
precision
,
recall
,
f1_score
return
precision
,
recall
,
f1_score
,
num_infer_chunks
,
num_label_chunks
,
num_correct_chunks
def
sequence_conv
(
input
,
...
...
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
9a89b041
...
...
@@ -150,7 +150,7 @@ def main():
crf_decode
=
fluid
.
layers
.
crf_decoding
(
input
=
feature_out
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'crfw'
))
precision
,
recall
,
f1_score
=
fluid
.
layers
.
chunk_eval
(
chunk_evaluator
=
fluid
.
evaluator
.
ChunkEvaluator
(
input
=
crf_decode
,
label
=
target
,
chunk_scheme
=
"IOB"
,
...
...
@@ -176,14 +176,16 @@ def main():
batch_id
=
0
for
pass_id
in
xrange
(
PASS_NUM
):
chunk_evaluator
.
reset
(
exe
)
for
data
in
train_data
():
outs
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
,
precision
,
recall
,
f1_score
])
fetch_list
=
[
avg_cost
]
+
chunk_evaluator
.
metrics
)
precision
,
recall
,
f1_score
=
chunk_evaluator
.
eval
(
exe
)
avg_cost_val
=
np
.
array
(
outs
[
0
])
precision_val
=
np
.
array
(
outs
[
1
]
)
recall_val
=
np
.
array
(
outs
[
2
]
)
f1_score_val
=
np
.
array
(
outs
[
3
]
)
precision_val
=
np
.
array
(
precision
)
recall_val
=
np
.
array
(
recall
)
f1_score_val
=
np
.
array
(
f1_score
)
if
batch_id
%
10
==
0
:
print
(
"avg_cost="
+
str
(
avg_cost_val
))
...
...
python/paddle/v2/fluid/tests/test_chunk_eval_op.py
浏览文件 @
9a89b041
...
...
@@ -147,7 +147,13 @@ class TestChunkEvalOp(OpTest):
'Recall'
:
np
.
asarray
(
[
recall
],
dtype
=
'float32'
),
'F1-Score'
:
np
.
asarray
(
[
f1
],
dtype
=
'float32'
)
[
f1
],
dtype
=
'float32'
),
'NumInferChunks'
:
np
.
asarray
(
[
self
.
num_infer_chunks
],
dtype
=
'int64'
),
'NumLabelChunks'
:
np
.
asarray
(
[
self
.
num_label_chunks
],
dtype
=
'int64'
),
'NumCorrectChunks'
:
np
.
asarray
(
[
self
.
num_correct_chunks
],
dtype
=
'int64'
)
}
def
setUp
(
self
):
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