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
b8f557f2
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
694
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
体验新版 GitCode,发现更多精彩内容 >>
提交
b8f557f2
编写于
11月 09, 2017
作者:
D
Dong Zhihong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
"add elementwise_add more type"
上级
e34e1293
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
194 addition
and
58 deletion
+194
-58
paddle/operators/accuracy_op.h
paddle/operators/accuracy_op.h
+2
-2
paddle/operators/elementwise_add_op.cc
paddle/operators/elementwise_add_op.cc
+8
-2
python/paddle/v2/framework/evaluator.py
python/paddle/v2/framework/evaluator.py
+151
-39
python/paddle/v2/framework/framework.py
python/paddle/v2/framework/framework.py
+1
-1
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+8
-2
python/paddle/v2/framework/tests/test_accuracy_op.py
python/paddle/v2/framework/tests/test_accuracy_op.py
+2
-2
python/paddle/v2/framework/tests/test_recognize_digits_conv.py
...n/paddle/v2/framework/tests/test_recognize_digits_conv.py
+22
-10
未找到文件。
paddle/operators/accuracy_op.h
浏览文件 @
b8f557f2
...
...
@@ -45,9 +45,9 @@ class AccuracyKernel : public framework::OpKernel<T> {
auto
*
correct
=
ctx
.
Output
<
Tensor
>
(
"Correct"
);
auto
*
total
=
ctx
.
Output
<
Tensor
>
(
"Total"
);
float
*
correct_data
=
correct
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
int
*
accuracy_data
=
accuracy
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
correct_data
=
correct
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
total_data
=
total
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
float
*
accuracy_data
=
accuracy
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
const
int64_t
*
indices_data
=
indices
->
data
<
int64_t
>
();
const
int64_t
*
label_data
=
label
->
data
<
int64_t
>
();
...
...
paddle/operators/elementwise_add_op.cc
浏览文件 @
b8f557f2
...
...
@@ -34,7 +34,13 @@ REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker,
elementwise_add_grad
,
ops
::
ElementwiseOpGrad
);
REGISTER_OP_CPU_KERNEL
(
elementwise_add
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
elementwise_add_grad
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
python/paddle/v2/framework/evaluator.py
浏览文件 @
b8f557f2
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
unique_name
from
paddle.v2.framework.layer_helper
import
LayerHelper
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
unique_name
,
Variable
import
paddle.v2.framework.core
as
core
def
_clone_var_in_block_
(
block
,
var
):
assert
isinstance
(
var
,
Variable
)
return
block
.
create_var
(
name
=
var
.
name
,
shape
=
var
.
shape
,
dtype
=
var
.
data_type
,
type
=
var
.
type
,
lod_level
=
var
.
lod_level
,
persistable
=
True
)
class
Evaluator
(
object
):
"""
Evalutor Base class.
...
...
@@ -13,33 +23,49 @@ class Evaluator(object):
"""
def
__init__
(
self
,
name
,
**
kwargs
):
"""
init the global states
"""
self
.
_states
=
{}
if
kwargs
.
has_key
(
"program"
):
self
.
_program
=
kwargs
.
get
(
"program"
)
if
kwargs
.
has_key
(
"main_program"
):
self
.
_main_program
=
kwargs
.
get
(
"main_program"
)
else
:
self
.
_main_program
=
g_main_program
if
kwargs
.
has_key
(
"eval_program"
):
self
.
_eval_program
=
kwargs
.
get
(
"eval_program"
)
else
:
self
.
_program
=
g_main_program
self
.
_eval_program
=
Program
()
def
_update_ops
(
self
):
"""
append update ops to the global states
"""
raise
NotImplementedError
()
def
reset
(
self
,
executor
,
program
=
None
):
"""
Clear metric states at the begin of each pass/user specified batch
"""
Clear metric states at the begin of each pass/user specified batch
"""
if
program
==
None
:
reset_program
=
Program
()
else
:
reset_program
=
program
block
=
reset_program
.
global_block
()
for
k
,
var
in
self
.
_states
.
iteritems
():
zeros
=
block
.
create_var
(
dtype
=
var
.
data_type
)
g_var
=
_clone_var_in_block_
(
block
,
var
)
zeros
=
block
.
create_var
(
dtype
=
"float32"
,
persistable
=
True
)
block
.
append_op
(
type
=
"fill_constant"
,
outputs
=
{
"Out"
:
[
zeros
]},
attrs
=
{
"shape"
:
var
.
shape
,
"value"
:
0
,
"shape"
:
g_var
.
shape
,
"value"
:
.
0
,
"data_type"
:
5
,
})
block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
zeros
},
outputs
=
{
"Out"
:
var
})
executor
.
run
(
reset_program
)
type
=
"scale"
,
inputs
=
{
"X"
:
zeros
},
outputs
=
{
"Out"
:
g_var
})
print
reset_program
executor
.
run
(
reset_program
,
fetch_list
=
self
.
_states
.
values
())
def
eval
(
self
,
executor
,
program
=
None
):
"""
...
...
@@ -53,15 +79,16 @@ class Accuracy(Evaluator):
Accuracy need two state variable Total, Correct
"""
def
__init__
(
self
,
input
,
label
,
k
=
1
,
**
kwargs
):
def
__init__
(
self
,
*
args
,
**
kwargs
):
super
(
Accuracy
,
self
).
__init__
(
"accuracy"
,
**
kwargs
)
block
=
self
.
_program
.
global_block
()
# block = self._eval_program.global_block()
block
=
self
.
_main_program
.
global_block
()
g_total
=
block
.
create_var
(
name
=
unique_name
(
"Total"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
g_correct
=
helper
.
create_global_variable
(
g_correct
=
block
.
create_var
(
name
=
unique_name
(
"Correct"
),
persistable
=
True
,
dtype
=
"int64"
,
...
...
@@ -69,6 +96,8 @@ class Accuracy(Evaluator):
self
.
_states
[
"Total"
]
=
g_total
self
.
_states
[
"Correct"
]
=
g_correct
def
_update_ops
(
self
,
input
,
label
,
k
=
1
,
**
kwargs
):
block
=
self
.
_main_program
.
global_block
()
topk_out
=
block
.
create_var
(
dtype
=
input
.
data_type
)
topk_indices
=
block
.
create_var
(
dtype
=
"int64"
)
block
.
append_op
(
...
...
@@ -77,8 +106,9 @@ class Accuracy(Evaluator):
outputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
acc_out_dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
)
acc_out
=
block
.
create_var
(
dtype
=
acc_out_dtype
)
acc_out
=
block
.
create_var
(
dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
))
correct
=
block
.
create_var
(
dtype
=
"int64"
,
persistable
=
True
)
total
=
block
.
create_var
(
dtype
=
"int64"
,
persistable
=
True
)
block
.
append_op
(
type
=
"accuracy"
,
inputs
=
{
...
...
@@ -92,39 +122,121 @@ class Accuracy(Evaluator):
"Total"
:
[
total
],
})
# block = self._eval_program.global_block()
# e_correct = _clone_var_in_block_(block, correct)
# e_total = _clone_var_in_block_(block, total)
# block.append_op(
# type="sum",
# inputs={"X": [self._states["Total"], total]},
# outputs={"Out": [self._states["Total"]]})
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Total"
]]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Total"
]]},
attrs
=
{
"in_data_type"
:
5
,
"out_data_type"
:
2
,
})
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Correct"
]]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Correct"
]]},
attrs
=
{
"in_data_type"
:
5
,
"out_data_type"
:
2
,
})
block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
[
g_total
,
total
]},
outputs
=
{
"Out"
:
[
g_total
]})
type
=
"elementwise_add"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Total"
]],
"Y"
:
[
total
]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Total"
]]})
block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
[
g_correct
,
correct
]},
outputs
=
{
"Out"
:
[
g_total
]})
type
=
"elementwise_add"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Correct"
]],
"Y"
:
[
correct
]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Correct"
]]})
# g_total = self._states["Total"]
# print g_total
# print total
# print "*" * 100
# print g_total.block.program == total.block.program
# g_total = _clone_var_in_block_(block, self._states["Total"])
# e_total = _clone_var_in_block_(block, total)
# block.append_op(
# type="sum",
# inputs={"X": [g_total, e_total]},
# outputs={"Out": [g_total]})
# block.append_op(
# type="sum",
# inputs={"X": [self._states["Correct"], correct]},
# outputs={"Out": [self._states["Correct"]]})
# print self._main_program
return
acc_out
def
eval
(
self
,
executor
,
program
=
None
):
if
program
==
None
:
eval_program
=
Program
()
else
:
eval_program
=
program
block
=
eval_program
.
global_block
()
eval_out
=
block
.
create_var
(
dtype
=
self
.
_helper
.
input_dtype
())
def
eval
(
self
,
executor
):
block
=
self
.
_eval_program
.
global_block
()
eval_out
=
block
.
create_var
(
dtype
=
self
.
_states
[
"Total"
].
data_type
)
e_correct
=
_clone_var_in_block_
(
block
,
correct
)
e_total
=
_clone_var_in_block_
(
block
,
total
)
# block.append_op(
# type="elementwise_div",
# inputs={"X": self._states["Total"],
# "Y": self._states["Correct"]},
# outputs={"Out": eval_out})
block
.
append_op
(
type
=
"elementwise_div"
,
inputs
=
{
"X"
:
self
.
_states
[
"Total"
]
,
"Y"
:
self
.
_states
[
"Correct"
]
},
inputs
=
{
"X"
:
e_total
,
"Y"
:
e_correct
},
outputs
=
{
"Out"
:
eval_out
})
return
executor
.
run
(
eval_program
,
fetch_list
=
[
eval_out
])
return
executor
.
run
(
self
.
_
eval_program
,
fetch_list
=
[
eval_out
])
# Demo for composing low level op to compute the F1 metric
class
F1
(
Evaluator
):
def
__init__
(
self
,
input
,
label
,
**
kwargs
):
super
(
F1
,
self
).
__init__
(
"F1"
,
**
kwargs
)
g_tp
=
helper
.
create_global_variable
(
# Demo for composing low level ops to compute the F1 metric
class
FScore
(
Evaluator
):
def
__init__
(
self
,
input
,
label
,
beta
=
1.0
,
**
kwargs
):
super
(
F1
,
self
).
__init__
(
"FScore"
,
**
kwargs
)
block
=
self
.
_program
.
global_block
()
g_tp
=
block
.
create_var
(
name
=
unique_name
(
"Tp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
g_fp
=
helper
.
create_global_variable
(
g_fn
=
block
.
create_var
(
name
=
unique_name
(
"Fn"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
g_fp
=
block
.
create_var
(
name
=
unique_name
(
"Fp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
self
.
_states
[
"Tp"
]
=
g_tp
self
.
_states
[
"Fp"
]
=
g_fp
self
.
_states
[
"Fn"
]
=
g_fn
def
_update_ops
(
self
):
block
=
self
.
_program
.
global_block
()
equal_out
=
block
.
create_var
()
block
.
append_op
(
type
=
"equal"
,
inputs
=
{
"X"
:
[
input
],
"Y"
:
[
label
]},
outputs
=
{
"Out"
:
equal_out
})
positive
=
block
.
create_var
()
block
.
append_op
(
type
=
"sequence_pool"
,
inputs
=
{
"X"
:
[
equal_out
]},
outputs
=
{
"Out"
:
positive
},
attrs
=
{
"pooltype"
:
"SUM"
})
batch
=
block
.
create_var
(
name
=
feed_var_name
,
type
=
core
.
VarDesc
.
VarType
.
FEED_MINIBATCH
,
persistable
=
True
)
# def register():
accuracy
=
Accuracy
# def accuracy(*args, **kwargs):
# acc = Accuracy(**kwargs)
# return acc._update_ops(*args, **kwargs)
python/paddle/v2/framework/framework.py
浏览文件 @
b8f557f2
...
...
@@ -550,7 +550,7 @@ class Parameter(Variable):
raise
ValueError
(
"Parameter shape should not be related with "
"batch-size"
)
super
(
Parameter
,
self
)
.
__init__
(
Variable
.
__init__
(
self
,
block
,
persistable
=
True
,
shape
=
shape
,
dtype
=
dtype
,
**
kwargs
)
self
.
trainable
=
kwargs
.
get
(
'trainable'
,
True
)
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
b8f557f2
...
...
@@ -263,7 +263,9 @@ def accuracy(input, label, k=1, **kwargs):
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
acc_out_dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
)
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
acc_out_dtype
)
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
correct
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
total
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"accuracy"
,
inputs
=
{
...
...
@@ -271,7 +273,11 @@ def accuracy(input, label, k=1, **kwargs):
"Indices"
:
[
topk_indices
],
"Label"
:
[
label
]
},
outputs
=
{
"Accuracy"
:
[
acc_out
]})
outputs
=
{
"Accuracy"
:
[
acc_out
],
"Correct"
:
[
correct
],
"Total"
:
[
total
],
})
return
acc_out
...
...
python/paddle/v2/framework/tests/test_accuracy_op.py
浏览文件 @
b8f557f2
...
...
@@ -19,7 +19,8 @@ class TestAccuracyOp(OpTest):
break
self
.
outputs
=
{
'Accuracy'
:
np
.
array
([
num_correct
/
float
(
n
)]).
astype
(
"float32"
),
'Correct'
:
np
.
array
([
num_correct
]).
astype
(
"int32"
)
'Correct'
:
np
.
array
([
num_correct
]).
astype
(
"int32"
),
'Total'
:
np
.
array
([
n
]).
astype
(
"int32"
)
}
def
test_check_output
(
self
):
...
...
@@ -27,5 +28,4 @@ class TestAccuracyOp(OpTest):
if
__name__
==
'__main__'
:
exit
(
0
)
unittest
.
main
()
python/paddle/v2/framework/tests/test_recognize_digits_conv.py
浏览文件 @
b8f557f2
...
...
@@ -3,6 +3,7 @@ import paddle.v2.framework.layers as layers
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
import
paddle.v2.framework.evaluator
as
evaluator
from
paddle.v2.framework.framework
import
Program
,
g_main_program
from
paddle.v2.framework.executor
import
Executor
...
...
@@ -54,17 +55,24 @@ cost = layers.cross_entropy(
main_program
=
main_program
,
startup_program
=
startup_program
)
avg_cost
=
layers
.
mean
(
x
=
cost
,
main_program
=
main_program
)
accuracy
=
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
main_program
=
main_program
,
startup_program
=
startup_program
)
# accuracy = layers.accuracy(
# input=predict,
# label=label,
# main_program=main_program,
# startup_program=startup_program)
# optimizer = optimizer.MomentumOptimizer(learning_rate=0.1 / 128.0,
# momentum=0.9)
optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.01
,
beta1
=
0.9
,
beta2
=
0.999
)
opts
=
optimizer
.
minimize
(
avg_cost
,
startup_program
)
accuracy
=
evaluator
.
accuracy
(
input
=
predict
,
label
=
label
,
main_program
=
main_program
,
startup_program
=
startup_program
)
acc_out
=
accuracy
.
_update_ops
(
input
=
predict
,
label
=
label
,
main_program
=
main_program
)
BATCH_SIZE
=
50
PASS_NUM
=
3
train_reader
=
paddle
.
batch
(
...
...
@@ -79,6 +87,7 @@ exe.run(startup_program, feed={}, fetch_list=[])
for
pass_id
in
range
(
PASS_NUM
):
count
=
0
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
img_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
].
reshape
([
1
,
28
,
28
]),
data
)).
astype
(
"float32"
)
...
...
@@ -93,11 +102,14 @@ for pass_id in range(PASS_NUM):
outs
=
exe
.
run
(
main_program
,
feed
=
{
"pixel"
:
tensor_img
,
"label"
:
tensor_y
},
fetch_list
=
[
avg_cost
,
acc
uracy
])
fetch_list
=
[
avg_cost
,
acc
_out
])
loss
=
np
.
array
(
outs
[
0
])
acc
=
np
.
array
(
outs
[
1
])
# pass_acc = accuracy.eval(exe)
# print pass_acc
print
loss
,
acc
if
loss
<
10.0
and
acc
>
0.9
:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit
(
0
)
#
if loss < 10.0 and acc > 0.9:
#
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
#
exit(0)
exit
(
1
)
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