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
Paddle
提交
4558d395
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
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
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
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 >>
未验证
提交
4558d395
编写于
9月 08, 2020
作者:
myq406450149
提交者:
GitHub
9月 08, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix Norm op error (#26771)
* fix frobenius_norm error, rm p=0 2-axis support. test=develop
上级
4d7d6612
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
187 addition
and
89 deletion
+187
-89
paddle/fluid/operators/p_norm_op.cc
paddle/fluid/operators/p_norm_op.cc
+6
-0
python/paddle/fluid/tests/unittests/test_norm_all.py
python/paddle/fluid/tests/unittests/test_norm_all.py
+152
-34
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+29
-55
未找到文件。
paddle/fluid/operators/p_norm_op.cc
浏览文件 @
4558d395
...
...
@@ -105,6 +105,12 @@ class PnormOp : public framework::OperatorWithKernel {
bool
asvector
=
ctx
->
Attrs
().
Get
<
bool
>
(
"asvector"
);
if
(
asvector
)
{
reduce_dims
.
emplace_back
(
1
);
if
(
keepdim
)
{
for
(
int
i
=
1
;
i
<
x_dim
.
size
();
++
i
)
{
reduce_dims
.
emplace_back
(
1
);
}
x_dim
=
framework
::
make_ddim
(
reduce_dims
);
}
}
else
{
if
(
axis
<
0
)
axis
=
x_dim
.
size
()
+
axis
;
for
(
int
i
=
0
;
i
<
x_dim
.
size
();
++
i
)
{
...
...
python/paddle/fluid/tests/unittests/test_norm_all.py
浏览文件 @
4558d395
...
...
@@ -26,11 +26,11 @@ def p_norm(x, axis, porder, keepdims=False):
if
axis
is
None
:
x
=
x
.
flatten
()
if
porder
==
np
.
inf
:
r
=
np
.
amax
(
np
.
abs
(
x
))
r
=
np
.
amax
(
np
.
abs
(
x
)
,
keepdims
=
keepdims
)
elif
porder
==
-
np
.
inf
:
r
=
np
.
amin
(
np
.
abs
(
x
))
r
=
np
.
amin
(
np
.
abs
(
x
)
,
keepdims
=
keepdims
)
else
:
r
=
np
.
linalg
.
norm
(
x
,
ord
=
porder
)
r
=
np
.
linalg
.
norm
(
x
,
ord
=
porder
,
keepdims
=
keepdims
)
elif
isinstance
(
axis
,
list
or
tuple
)
and
len
(
axis
)
==
2
:
if
porder
==
np
.
inf
:
axis
=
tuple
(
axis
)
...
...
@@ -41,10 +41,10 @@ def p_norm(x, axis, porder, keepdims=False):
elif
porder
==
0
:
axis
=
tuple
(
axis
)
r
=
x
.
astype
(
bool
)
r
=
np
.
sum
(
r
,
axis
)
r
=
np
.
sum
(
r
,
axis
,
keepdims
=
keepdims
)
elif
porder
==
1
:
axis
=
tuple
(
axis
)
r
=
np
.
sum
(
np
.
abs
(
x
),
axis
)
r
=
np
.
sum
(
np
.
abs
(
x
),
axis
,
keepdims
=
keepdims
)
else
:
axis
=
tuple
(
axis
)
xp
=
np
.
power
(
np
.
abs
(
x
),
porder
)
...
...
@@ -61,7 +61,7 @@ def p_norm(x, axis, porder, keepdims=False):
def
frobenius_norm
(
x
,
axis
=
None
,
keepdims
=
False
):
if
isinstance
(
axis
,
list
):
axis
=
tuple
(
axis
)
if
axis
is
None
:
axis
=
(
-
2
,
-
1
)
if
axis
is
None
:
x
=
x
.
reshape
(
1
,
x
.
size
)
r
=
np
.
linalg
.
norm
(
x
,
ord
=
'fro'
,
axis
=
axis
,
keepdims
=
keepdims
).
astype
(
x
.
dtype
)
return
r
...
...
@@ -217,28 +217,37 @@ class TestPnormOp5(TestPnormOp):
self
.
check_grad
([
'X'
],
'Out'
,
user_defined_grads
=
self
.
gradient
)
def
run_fro
(
self
,
p
,
axis
,
shape_x
,
dtype
):
def
run_fro
(
self
,
p
,
axis
,
shape_x
,
dtype
,
keep_dim
,
check_dim
=
False
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
data
=
fluid
.
data
(
name
=
"X"
,
shape
=
shape_x
,
dtype
=
dtype
)
out
=
paddle
.
norm
(
x
=
data
,
p
=
p
,
axis
=
axis
)
out
=
paddle
.
norm
(
x
=
data
,
p
=
p
,
axis
=
axis
,
keepdim
=
keep_dim
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
np_input
=
(
np
.
random
.
rand
(
*
shape_x
)
+
1.0
).
astype
(
dtype
)
expected_result
=
frobenius_norm
(
np_input
,
axis
=
axis
)
expected_result
=
frobenius_norm
(
np_input
,
axis
=
axis
,
keepdims
=
keep_dim
)
result
,
=
exe
.
run
(
feed
=
{
"X"
:
np_input
},
fetch_list
=
[
out
])
self
.
assertEqual
((
np
.
abs
(
result
-
expected_result
)
<
1e-6
).
all
(),
True
)
if
keep_dim
and
check_dim
:
self
.
assertEqual
(
(
np
.
abs
(
np
.
array
(
result
.
shape
)
-
np
.
array
(
expected_result
.
shape
))
<
1e-6
).
all
(),
True
)
def
run_pnorm
(
self
,
p
,
axis
,
shape_x
,
dtype
):
def
run_pnorm
(
self
,
p
,
axis
,
shape_x
,
dtype
,
keep_dim
,
check_dim
=
False
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
data
=
fluid
.
data
(
name
=
"X"
,
shape
=
shape_x
,
dtype
=
dtype
)
out
=
paddle
.
norm
(
x
=
data
,
p
=
p
,
axis
=
axis
)
out
=
paddle
.
norm
(
x
=
data
,
p
=
p
,
axis
=
axis
,
keepdim
=
keep_dim
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
np_input
=
(
np
.
random
.
rand
(
*
shape_x
)
+
1.0
).
astype
(
dtype
)
expected_result
=
p_norm
(
np_input
,
porder
=
p
,
axis
=
axis
).
astype
(
dtype
)
expected_result
=
p_norm
(
np_input
,
porder
=
p
,
axis
=
axis
,
keepdims
=
keep_dim
).
astype
(
dtype
)
result
,
=
exe
.
run
(
feed
=
{
"X"
:
np_input
},
fetch_list
=
[
out
])
self
.
assertEqual
((
np
.
abs
(
result
-
expected_result
)
<
1e-6
).
all
(),
True
)
self
.
assertEqual
((
np
.
abs
(
result
-
expected_result
)
<
1e-6
).
all
(),
True
)
if
keep_dim
and
check_dim
:
self
.
assertEqual
(
(
np
.
abs
(
np
.
array
(
result
.
shape
)
-
np
.
array
(
expected_result
.
shape
))
<
1e-6
).
all
(),
True
)
def
run_graph
(
self
,
p
,
axis
,
shape_x
,
dtype
):
...
...
@@ -253,6 +262,7 @@ def run_graph(self, p, axis, shape_x, dtype):
# compute frobenius norm along last two dimensions.
out_fro
=
paddle
.
norm
(
x
,
p
=
'fro'
)
out_fro
=
paddle
.
norm
(
x
,
p
=
'fro'
,
axis
=
0
)
out_fro
=
paddle
.
norm
(
x
,
p
=
'fro'
,
axis
=
[
0
,
1
])
# compute 2-order norm along [0,1] dimension.
out_pnorm
=
paddle
.
norm
(
x
,
p
=
2
,
axis
=
[
0
,
1
])
...
...
@@ -274,27 +284,133 @@ def run_graph(self, p, axis, shape_x, dtype):
class
API_NormTest
(
unittest
.
TestCase
):
def
test_basic
(
self
):
run_fro
(
self
,
p
=
'fro'
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
)
run_fro
(
self
,
p
=
'fro'
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
2
,
axis
=
None
,
shape_x
=
[
3
,
4
],
dtype
=
"float32"
)
run_pnorm
(
self
,
p
=
2
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
0
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
0
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
0
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
1
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
0
,
axis
=
None
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
2
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
2
,
axis
=-
1
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
1
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
0
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
)
keep_dims
=
{
False
,
True
}
for
keep
in
keep_dims
:
run_fro
(
self
,
p
=
'fro'
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
,
keep_dim
=
keep
)
run_fro
(
self
,
p
=
'fro'
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
2
,
axis
=
None
,
shape_x
=
[
3
,
4
],
dtype
=
"float32"
,
keep_dim
=
keep
)
run_pnorm
(
self
,
p
=
2
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
0
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
,
keep_dim
=
keep
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
0
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
)
run_pnorm
(
self
,
p
=
0
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
1
,
axis
=
1
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
0
,
axis
=
None
,
shape_x
=
[
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
2
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
2
,
axis
=-
1
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
1
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=
np
.
inf
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
run_pnorm
(
self
,
p
=-
np
.
inf
,
axis
=
[
0
,
1
],
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float64"
,
keep_dim
=
keep
,
check_dim
=
True
)
def
test_dygraph
(
self
):
run_graph
(
self
,
p
=
'fro'
,
axis
=
None
,
shape_x
=
[
2
,
3
,
4
],
dtype
=
"float32"
)
...
...
@@ -315,6 +431,7 @@ class API_NormTest(unittest.TestCase):
paddle
.
norm
(
data
,
p
=
p
,
out
=
out
)
self
.
assertRaises
(
TypeError
,
err_dtype
,
"fro"
,
[
2
,
2
],
"int64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
"inf"
,
[
2
],
"int64"
)
out
=
fluid
.
data
(
name
=
"out"
,
shape
=
[
1
],
dtype
=
"int64"
)
self
.
assertRaises
(
TypeError
,
err_dtype
,
"fro"
,
[
2
,
2
],
"float64"
,
out
)
...
...
@@ -325,6 +442,7 @@ class API_NormTest(unittest.TestCase):
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
data
,
p
=
"unsupport norm"
)
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
data
,
p
=
[
1
])
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
data
,
p
=
[
1
],
axis
=-
1
)
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
0
,
[
1
,
0
],
"float64"
)
data
=
fluid
.
data
(
name
=
"data_3d"
,
shape
=
[
2
,
2
,
2
],
dtype
=
"float64"
)
self
.
assertRaises
(
ValueError
,
paddle
.
norm
,
data
,
p
=
'unspport'
,
axis
=
[
-
3
,
-
2
,
-
1
])
...
...
python/paddle/tensor/linalg.py
浏览文件 @
4558d395
...
...
@@ -183,12 +183,13 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
x (Tensor): The input tensor could be N-D tensor, and the input data
type could be float32 or float64.
p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,
`inf`,`-inf` and any positive real number yielding the corresponding p-norm.
Not supported: ord < 0, nuclear norm
.
`inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.
Default value is `fro`
.
axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int
or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.
If `axis < 0`, the dimension to norm operation is rank(input) + axis.
If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.
Defalut value is `None`.
keepdim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have fewer dimension
than the :attr:`input` unless :attr:`keepdim` is true, default
...
...
@@ -197,13 +198,9 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable: Tensor,
results of norm operation on the specified axis of input tensor,
Tensor:
results of norm operation on the specified axis of input tensor,
it's data type is the same as input's Tensor.
Raises:
TypeError, if out data type is different with the input data type.
ValueError, If `p` or `axis` is invalid.
Examples:
.. code-block:: python
...
...
@@ -256,15 +253,13 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
"The dim of frobenius norm op should be None or two elements list!"
)
if
in_dygraph_mode
():
if
dim
is
None
:
dim
=
[
-
1
]
return
core
.
ops
.
frobenius_norm
(
input
,
'dim'
,
dim
,
'keepdim'
,
keepdim
)
attrs
=
{
'dim'
:
dim
if
dim
!=
None
else
[
-
2
,
-
1
],
'keep_dim'
:
keepdim
,
'reduce_all'
:
False
}
if
len
(
attrs
[
'dim'
])
==
len
(
input
.
shape
):
if
dim
is
None
:
return
core
.
ops
.
frobenius_norm
(
input
,
'keep_dim'
,
keepdim
,
'reduce_all'
,
True
)
return
core
.
ops
.
frobenius_norm
(
input
,
'dim'
,
dim
,
'keep_dim'
,
keepdim
,
'reduce_all'
,
False
)
attrs
=
{
'dim'
:
dim
,
'keep_dim'
:
keepdim
,
'reduce_all'
:
False
}
if
dim
is
None
:
attrs
[
'reduce_all'
]
=
True
check_variable_and_dtype
(
input
,
'input'
,
[
'float32'
,
'float64'
],
'frobenius_norm'
)
...
...
@@ -351,42 +346,6 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
return
reduce_out
def
p0_matrix_norm
(
input
,
porder
=
0.
,
axis
=
axis
,
keepdim
=
False
,
name
=
None
):
block
=
LayerHelper
(
'norm'
,
**
locals
())
out
=
block
.
create_variable_for_type_inference
(
dtype
=
block
.
input_dtype
())
cast_out
=
block
.
create_variable_for_type_inference
(
dtype
=
bool
)
block
.
append_op
(
type
=
'cast'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
cast_out
},
attrs
=
{
'in_dtype'
:
input
.
dtype
,
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
BOOL
)
})
cast_out2
=
block
.
create_variable_for_type_inference
(
dtype
=
bool
)
block
.
append_op
(
type
=
'cast'
,
inputs
=
{
'X'
:
cast_out
},
outputs
=
{
'Out'
:
cast_out2
},
attrs
=
{
'in_dtype'
:
cast_out
.
dtype
,
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
)
})
sum_out
=
block
.
create_variable_for_type_inference
(
dtype
=
block
.
input_dtype
())
block
.
append_op
(
type
=
'reduce_sum'
,
inputs
=
{
'X'
:
cast_out2
},
outputs
=
{
'Out'
:
sum_out
},
attrs
=
{
'dim'
:
axis
,
'keep_dim'
:
keepdim
,
'reduce_all'
:
True
if
axis
is
None
else
False
})
return
sum_out
def
p_matrix_norm
(
input
,
porder
=
1.
,
axis
=
axis
,
keepdim
=
False
,
name
=
None
):
block
=
LayerHelper
(
'norm'
,
**
locals
())
out
=
block
.
create_variable_for_type_inference
(
...
...
@@ -448,7 +407,20 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
#calculate vector norm, where axis is int or list with only one integer
if
isinstance
(
axis
,
int
):
if
isinstance
(
p
,
(
int
,
float
)):
if
isinstance
(
p
,
str
):
if
p
==
"fro"
:
return
vector_norm
(
x
,
porder
=
2
,
axis
=
axis
,
keepdim
=
keepdim
,
asvector
=
False
,
name
=
name
)
else
:
raise
ValueError
(
"only valid string values are 'fro', found {}"
.
format
(
p
))
elif
isinstance
(
p
,
(
int
,
float
)):
return
vector_norm
(
x
,
axis
=
axis
,
...
...
@@ -464,10 +436,12 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
elif
isinstance
(
axis
,
list
)
and
len
(
axis
)
==
2
:
if
p
==
"fro"
:
return
frobenius_norm
(
x
,
dim
=
axis
,
keepdim
=
keepdim
,
name
=
name
)
elif
p
==
0
:
return
p0_matrix_norm
(
x
,
axis
=
axis
,
keepdim
=
keepdim
,
name
=
name
)
elif
p
==
np
.
inf
or
p
==
-
np
.
inf
:
return
inf_norm
(
x
,
porder
=
p
,
axis
=
axis
,
keepdim
=
keepdim
,
name
=
name
)
elif
p
==
0
:
raise
ValueError
(
"just suport axis type int or list (length of list <=1) if p = 0, found {}"
.
format
(
axis
))
else
:
return
p_matrix_norm
(
x
,
porder
=
p
,
axis
=
axis
,
keepdim
=
keepdim
,
name
=
name
)
...
...
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