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体验新版 GitCode,发现更多精彩内容 >>
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12416a24
编写于
2月 21, 2019
作者:
D
dengkaipeng
提交者:
ceci3
3月 06, 2019
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add doc and test_layers. test=develop
上级
63d322f0
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
92 addition
and
43 deletion
+92
-43
paddle/fluid/operators/spectral_norm_op.cc
paddle/fluid/operators/spectral_norm_op.cc
+24
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+55
-41
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+13
-0
未找到文件。
paddle/fluid/operators/spectral_norm_op.cc
浏览文件 @
12416a24
...
...
@@ -109,10 +109,32 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
.
SetDefault
(
1e-12
);
AddComment
(
R"DOC(
This operator samples input X to given output shape by using specified
This layer calculate the spectral normalize value of weight of
fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
tensor.
Spectral normalization stabilizes the training of critis in GANs
(Generative Adversarial Networks). This layers rescaling weight tensor
wiht spectral normalize value.
For spectral normalization calculations, we rescaling weight
tensor with \sigma, while \sigma{\mathbf{W}} is
\sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2}
We calculate \sigma{\mathbf{W}} through power iterations as
\mathbf{v} = \mathbf{W}^{T} \mathbf{u}
\mathbf{v} = \frac{\mathbf{v}}{\|\mathbf{v}\|_2}
\mathbf{u} = \mathbf{W}^{T} \mathbf{v}
\mathbf{u} = \frac{\mathbf{u}}{\|\mathbf{u}\|_2}
And \sigma should be
\sigma{\mathbf{W}} = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
For details of spectral normalization, please refer to paper:
`Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
)DOC"
);
}
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
12416a24
...
...
@@ -3349,28 +3349,42 @@ def group_norm(input,
@
templatedoc
()
def
spectral_norm
(
weight
,
dim
=
0
,
power_iters
=
1
,
eps
=
1e-12
,
u_attr
=
None
,
v_attr
=
None
,
name
=
None
):
def
spectral_norm
(
weight
,
dim
=
0
,
power_iters
=
1
,
eps
=
1e-12
,
name
=
None
):
"""
**Spectral Normalization Layer**
This layer calculate the spectral normalize value of weight parameters of
fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
Parameters. Calculations are showed as followings.
.. code-block:: text
Step 1:
Generate vector u in shape of [h], and v in shape of [w].
While h is the attr:`dim`th dimension of the input weights,
and w is the product result of remain dimensions.
Step 2:
While attr:`power_iters` is a positive interger, do following
iteration calculations with u and v for attr:`power_iters`
round.
\mathbf{v} = \mathbf{W}^{T} \mathbf{u}
\mathbf{v} =
\f
rac{\mathbf{v}}{\|\mathbf{v}\|_2}
\mathbf{u} = \mathbf{W}^{T} \mathbf{v}
\mathbf{u} =
\f
rac{\mathbf{u}}{\|\mathbf{u}\|_2}
Step 3:
Calculate \sigma{W} and scale weight values.
\sigma{\mathbf{W}} = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
\mathbf{W} :=
\f
rac{\mathbf{W}}{\sigma{\mathbf{W}}}
Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
Args:
weight(${weight_type}): ${weight_comment}
dim(${dim_type}): ${dim_comment}
eps(${eps_type}): ${eps_comment}
u_attr(ParamAttr|None): The parameter attribute for vector u in
spectral calculatings, set None to use default attribute, which
generates random values in normal distribution N(0, 1). Default: None.
v_attr(ParamAttr|None): The parameter attribute for vector v in
spectral calculatings, set None to use default attribute, which
generates random values in normal distribution N(0, 1). Default: None.
name (str): The name of this layer. It is optional.
Returns:
...
...
@@ -3383,43 +3397,43 @@ def spectral_norm(weight,
>>> x = fluid.layers.spectral_norm(weight=data, dim=1, power_iters=2)
"""
helper
=
LayerHelper
(
'spectral_norm'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
dtype
=
weight
.
dtype
# create intput and parameters
inputs
=
{
'Weight'
:
weight
}
input_shape
=
input
.
shape
if
data_layout
!=
'NCHW'
:
raise
ValueError
(
"unsupported data layout:"
+
data_layout
)
param_shape
=
[
input_shape
[
1
]]
if
param_attr
:
scale
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
default_initializer
=
Constant
(
1.0
))
inputs
[
'Scale'
]
=
scale
if
bias_attr
:
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
True
)
inputs
[
'Bias'
]
=
bias
input_shape
=
weight
.
shape
h
=
input_shape
[
dim
]
w
=
np
.
prod
(
input_shape
)
//
h
u
=
helper
.
create_parameter
(
attr
=
ParamAttr
(),
shape
=
[
h
],
dtype
=
dtype
,
default_initializer
=
Normal
(
0.
,
1.
))
u
.
stop_gradient
=
True
inputs
[
'U'
]
=
u
v
=
helper
.
create_parameter
(
attr
=
ParamAttr
(),
shape
=
[
w
],
dtype
=
dtype
,
default_initializer
=
Normal
(
0.
,
1.
))
inputs
[
'V'
]
=
v
v
.
stop_gradient
=
True
# create output
mean_out
=
helper
.
create_variable
(
dtype
=
dtype
,
stop_gradient
=
True
)
variance_out
=
helper
.
create_variable
(
dtype
=
dtype
,
stop_gradient
=
True
)
group_norm_out
=
helper
.
create_variable
(
dtype
=
dtype
)
out
=
helper
.
create_variable
(
dtype
=
dtype
)
helper
.
append_op
(
type
=
"
group
_norm"
,
type
=
"
spectral
_norm"
,
inputs
=
inputs
,
outputs
=
{
"Y"
:
group_norm_out
,
"Mean"
:
mean_out
,
"Variance"
:
variance_out
,
},
attrs
=
{
"epsilon"
:
epsilon
,
"groups"
:
groups
})
outputs
=
{
"Out"
:
out
,
},
attrs
=
{
"dim"
:
dim
,
"power_iters"
:
power_iters
,
"eps"
:
eps
,
})
return
helper
.
append_activation
(
group_norm_out
)
return
out
def
conv2d_transpose
(
input
,
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
12416a24
...
...
@@ -1035,6 +1035,19 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
def
test_spectral_norm
(
self
):
program
=
Program
()
with
program_guard
(
program
):
weight
=
layers
.
data
(
name
=
'weight'
,
shape
=
[
2
,
3
,
32
,
32
],
dtype
=
"float32"
,
append_batch_size
=
False
)
out
=
layers
.
spectral_norm
(
weight
,
dim
=
1
,
power_iters
=
1
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_shuffle_channel
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
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