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正统之独孤求败
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69f6a1d6
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体验新版 GitCode,发现更多精彩内容 >>
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69f6a1d6
编写于
4月 26, 2020
作者:
M
mindspore-ci-bot
提交者:
Gitee
4月 26, 2020
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差异文件
!685 fix groupnorm bug and change globalbn parameter name
Merge pull request !685 from JichenZhao/normfix
上级
ee9c8a41
b4df04b7
变更
1
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1 changed file
with
10 addition
and
10 deletion
+10
-10
mindspore/nn/layer/normalization.py
mindspore/nn/layer/normalization.py
+10
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未找到文件。
mindspore/nn/layer/normalization.py
浏览文件 @
69f6a1d6
...
...
@@ -41,7 +41,7 @@ class _BatchNorm(Cell):
moving_mean_init
=
'zeros'
,
moving_var_init
=
'ones'
,
use_batch_statistics
=
True
,
group
=
1
):
device_num_each_
group
=
1
):
super
(
_BatchNorm
,
self
).
__init__
()
if
num_features
<
1
:
raise
ValueError
(
"num_features must be at least 1"
)
...
...
@@ -60,7 +60,7 @@ class _BatchNorm(Cell):
gamma_init
,
num_features
),
name
=
"gamma"
,
requires_grad
=
affine
)
self
.
beta
=
Parameter
(
initializer
(
beta_init
,
num_features
),
name
=
"beta"
,
requires_grad
=
affine
)
self
.
group
=
check_int_positive
(
group
)
self
.
group
=
check_int_positive
(
device_num_each_
group
)
if
self
.
group
!=
1
:
self
.
rank_id
=
get_rank
()
self
.
rank_size
=
get_group_size
()
...
...
@@ -324,7 +324,7 @@ class GlobalBatchNorm(_BatchNorm):
Args:
num_features (int): `C` from an expected input of size (N, C, H, W).
group (int): The number of device in each group.
device_num_each_
group (int): The number of device in each group.
eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
momentum (float): A floating hyperparameter of the momentum for the
running_mean and running_var computation. Default: 0.9.
...
...
@@ -364,7 +364,7 @@ class GlobalBatchNorm(_BatchNorm):
moving_mean_init
=
'zeros'
,
moving_var_init
=
'ones'
,
use_batch_statistics
=
True
,
group
=
1
):
device_num_each_
group
=
1
):
super
(
GlobalBatchNorm
,
self
).
__init__
(
num_features
,
eps
,
momentum
,
...
...
@@ -374,8 +374,8 @@ class GlobalBatchNorm(_BatchNorm):
moving_mean_init
,
moving_var_init
,
use_batch_statistics
,
group
)
self
.
group
=
check_int_positive
(
group
)
device_num_each_
group
)
self
.
group
=
check_int_positive
(
device_num_each_
group
)
if
self
.
group
<=
1
:
raise
ValueError
(
"the number of group must be greater than 1."
)
def
_check_data_dim
(
self
,
x
):
...
...
@@ -482,17 +482,17 @@ class GroupNorm(Cell):
>>> x = Tensor(np.ones([1, 64, 256, 256], np.float32))
>>> goup_norm_op(x)
"""
def
__init__
(
self
,
num_groups
,
num_channels
,
eps
=
1e-05
,
affine
=
True
):
def
__init__
(
self
,
num_groups
,
num_channels
,
eps
=
1e-05
,
affine
=
True
,
gamma_init
=
'ones'
,
beta_init
=
'zeros'
):
super
(
GroupNorm
,
self
).
__init__
()
self
.
num_groups
=
check_int_positive
(
num_groups
)
self
.
num_channels
=
check_int_positive
(
num_channels
)
if
num_channels
%
num_groups
!=
0
:
raise
ValueError
(
"num_channels should be divided by num_groups"
)
self
.
eps
=
Tensor
(
check_typename
(
'eps'
,
eps
,
(
float
,)),
mstype
.
float32
)
self
.
eps
=
check_typename
(
'eps'
,
eps
,
(
float
,)
)
self
.
affine
=
check_bool
(
affine
)
gamma
=
initializer
(
'ones'
,
[
num_channels
,
1
,
1
],
mstype
.
float32
)
beta
=
initializer
(
'zeros'
,
[
num_channels
,
1
,
1
],
mstype
.
float32
)
gamma
=
initializer
(
gamma_init
,
[
num_channels
,
1
,
1
]
)
beta
=
initializer
(
beta_init
,
[
num_channels
,
1
,
1
]
)
if
self
.
affine
:
self
.
gamma
=
Parameter
(
gamma
,
name
=
'gamma'
)
self
.
beta
=
Parameter
(
beta
,
name
=
'beta'
)
...
...
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