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体验新版 GitCode,发现更多精彩内容 >>
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a508e725
编写于
7月 26, 2021
作者:
Z
zhiboniu
提交者:
GitHub
7月 26, 2021
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
unset fluid api in nn.functional (#34114)
上级
1c95631f
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
92 addition
and
92 deletion
+92
-92
python/paddle/nn/functional/common.py
python/paddle/nn/functional/common.py
+32
-30
python/paddle/nn/functional/conv.py
python/paddle/nn/functional/conv.py
+37
-36
python/paddle/nn/functional/extension.py
python/paddle/nn/functional/extension.py
+3
-2
python/paddle/nn/functional/input.py
python/paddle/nn/functional/input.py
+2
-1
python/paddle/nn/functional/loss.py
python/paddle/nn/functional/loss.py
+11
-17
python/paddle/nn/functional/norm.py
python/paddle/nn/functional/norm.py
+3
-4
python/paddle/nn/functional/pooling.py
python/paddle/nn/functional/pooling.py
+2
-1
python/paddle/nn/functional/vision.py
python/paddle/nn/functional/vision.py
+2
-1
未找到文件。
python/paddle/nn/functional/common.py
浏览文件 @
a508e725
...
...
@@ -16,18 +16,21 @@ import warnings
import
paddle
from
...fluid.framework
import
in_dygraph_mode
,
default_main_program
from
paddle.fluid.layer_helper
import
LayerHelper
from
paddle.fluid.layers.tensor
import
Variable
,
fill_constant
,
zeros
,
concat
from
paddle.fluid.layers.tensor
import
fill_constant
from
...tensor
import
concat
from
...tensor.creation
import
zeros
from
paddle.static
import
Variable
from
...fluid.layers
import
core
from
...fluid
import
dygraph_utils
# TODO: define the common functions to build a neural network
from
...fluid.layers
import
unfold
# noqa: F401
from
...
fluid.layers
import
squeeze
from
...
fluid.layers
import
unsqueeze
from
...
tensor.manipulation
import
squeeze
from
...
tensor.manipulation
import
unsqueeze
from
...tensor
import
clip
from
...tensor
import
sum
from
...tensor
import
sqrt
from
...fluid.data_feeder
import
check_variable_and_dtype
,
check_dtype
from
...fluid.framework
import
Variable
,
in_dygraph_mode
,
_varbase_creator
from
...fluid.framework
import
in_dygraph_mode
,
_varbase_creator
from
...fluid.framework
import
in_dygraph_mode
from
...fluid
import
core
,
dygraph_utils
...
...
@@ -927,9 +930,9 @@ def dropout(x,
keep_prob
=
1
-
p
if
training
:
if
p
==
1.
:
return
layers
.
scale
(
x
,
scale
=
0.
)
return
paddle
.
scale
(
x
,
scale
=
0.
)
scale_input
=
layers
.
scale
(
scale_input
=
paddle
.
scale
(
x
,
scale
=
1
/
keep_prob
)
if
mode
==
'upscale_in_train'
else
x
#get mask shape
...
...
@@ -947,17 +950,17 @@ def dropout(x,
mask_shape
[
i
]
=
input_shape
[
i
]
#get mask
random_tensor
=
layers
.
uniform_rando
m
(
random_tensor
=
paddle
.
unifor
m
(
mask_shape
,
dtype
=
'float32'
,
min
=
0.
,
max
=
1.0
)
p
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
p
)
keep_mask
=
layers
.
greater_equal
(
random_tensor
,
p
)
keep_mask
=
paddle
.
greater_equal
(
random_tensor
,
p
)
scale_input
=
layers
.
cast
(
scale_input
,
dtype
)
keep_mask
=
layers
.
cast
(
keep_mask
,
dtype
)
scale_input
=
paddle
.
cast
(
scale_input
,
dtype
)
keep_mask
=
paddle
.
cast
(
keep_mask
,
dtype
)
ret
=
paddle
.
multiply
(
scale_input
,
keep_mask
,
name
=
name
)
return
ret
else
:
# test
ret
=
layers
.
scale
(
ret
=
paddle
.
scale
(
x
,
scale
=
keep_prob
)
if
mode
==
'downscale_in_infer'
else
x
return
ret
...
...
@@ -1113,7 +1116,7 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
if
training
:
if
p
==
1
:
return
layers
.
scale
(
x
,
scale
=
0.
)
return
paddle
.
scale
(
x
,
scale
=
0.
)
#get transformation params
alpha
=
1.6732632423543772848170429916717
scale
=
1.0507009873554804934193349852946
...
...
@@ -1125,23 +1128,22 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
input_shape
=
x
.
shape
#get mask
random_tensor
=
layers
.
uniform_rando
m
(
random_tensor
=
paddle
.
unifor
m
(
input_shape
,
dtype
=
'float32'
,
min
=
0.
,
max
=
1.0
)
p
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
p
)
keep_mask
=
layers
.
greater_equal
(
random_tensor
,
p
)
keep_mask
=
layers
.
cast
(
keep_mask
,
dtype
)
drop_mask
=
layers
.
elementwise_sub
(
keep_mask
=
paddle
.
greater_equal
(
random_tensor
,
p
)
keep_mask
=
paddle
.
cast
(
keep_mask
,
dtype
)
drop_mask
=
paddle
.
subtract
(
layers
.
fill_constant
(
shape
=
input_shape
,
dtype
=
dtype
,
value
=
1.
),
keep_mask
)
#apply mask
b
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
dtype
,
value
=
b
)
y
=
layers
.
elementwise_add
(
paddle
.
multiply
(
x
,
keep_mask
),
layers
.
scale
(
drop_mask
,
scale
=
alpha_p
))
res
=
layers
.
elementwise_add
(
layers
.
scale
(
y
,
scale
=
a
),
b
,
name
=
name
)
y
=
paddle
.
add
(
paddle
.
multiply
(
x
,
keep_mask
),
paddle
.
scale
(
drop_mask
,
scale
=
alpha_p
))
res
=
paddle
.
add
(
paddle
.
scale
(
y
,
scale
=
a
),
b
,
name
=
name
)
return
res
else
:
# test
return
x
...
...
@@ -1277,42 +1279,42 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
if
x_dim
==
3
:
pad
=
concat
([
zeros
((
4
,
),
dtype
=
"int32"
),
pad
],
axis
=
0
)
unsqueezed_dim
=
[
3
,
4
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
x_dim
==
4
:
pad
=
concat
([
pad
,
zeros
((
2
,
),
dtype
=
"int32"
)],
axis
=
0
)
unsqueezed_dim
=
[
2
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
data_format
in
[
"NLC"
,
"NHWC"
,
"NDHWC"
]:
data_format
=
"NDHWC"
if
x_dim
==
3
:
pad
=
concat
([
zeros
((
4
,
),
dtype
=
"int32"
),
pad
],
axis
=
0
)
unsqueezed_dim
=
[
2
,
3
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
x_dim
==
4
:
pad
=
concat
([
pad
,
zeros
((
2
,
),
dtype
=
"int32"
)],
axis
=
0
)
unsqueezed_dim
=
[
1
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
else
:
if
data_format
in
[
"NCL"
,
"NCHW"
,
"NCDHW"
]:
data_format
=
"NCDHW"
if
x_dim
==
3
:
pad
=
[
0
,
0
,
0
,
0
]
+
pad
unsqueezed_dim
=
[
3
,
4
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
x_dim
==
4
:
pad
=
pad
+
[
0
,
0
]
unsqueezed_dim
=
[
2
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
data_format
in
[
"NLC"
,
"NHWC"
,
"NDHWC"
]:
data_format
=
"NDHWC"
if
x_dim
==
3
:
pad
=
[
0
,
0
,
0
,
0
]
+
pad
unsqueezed_dim
=
[
2
,
3
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
elif
x_dim
==
4
:
pad
=
pad
+
[
0
,
0
]
unsqueezed_dim
=
[
1
]
x
=
unsqueeze
(
x
,
ax
e
s
=
unsqueezed_dim
)
x
=
unsqueeze
(
x
,
ax
i
s
=
unsqueezed_dim
)
if
in_dygraph_mode
():
if
isinstance
(
pad
,
Variable
):
...
...
@@ -1336,7 +1338,7 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
type
=
'pad3d'
,
inputs
=
inputs
,
outputs
=
{
"Out"
:
out
},
attrs
=
attrs
)
if
len
(
unsqueezed_dim
)
!=
0
:
out
=
squeeze
(
out
,
ax
e
s
=
unsqueezed_dim
)
out
=
squeeze
(
out
,
ax
i
s
=
unsqueezed_dim
)
return
out
...
...
python/paddle/nn/functional/conv.py
浏览文件 @
a508e725
...
...
@@ -16,13 +16,17 @@ from paddle.fluid.framework import _global_flags
import
numpy
as
np
from
...device
import
get_cudnn_version
from
...fluid.framework
import
Variable
,
in_dygraph_mode
from
...fluid.framework
import
in_dygraph_mode
from
...static
import
Variable
from
...fluid
import
core
,
dygraph_utils
,
get_flags
from
...fluid.layers
import
nn
,
utils
from
...fluid.layers
.utils
import
convert_to_list
,
_is_symmetric_padding
from
...fluid.data_feeder
import
check_variable_and_dtype
from
...f
luid.param_attr
import
ParamAttr
from
...f
ramework
import
ParamAttr
from
...fluid.layer_helper
import
LayerHelper
from
paddle
import
_C_ops
from
...tensor.manipulation
import
unsqueeze
,
squeeze
from
...tensor.math
import
add
from
...fluid.layers
import
nn
__all__
=
[]
...
...
@@ -69,24 +73,24 @@ def _update_padding_nd(padding, channel_last, num_dims):
padding_algorithm
=
"EXPLICIT"
padding
=
_exclude_padding_in_batch_and_channel
(
padding
,
channel_last
)
if
utils
.
_is_symmetric_padding
(
padding
,
num_dims
):
if
_is_symmetric_padding
(
padding
,
num_dims
):
padding
=
padding
[
0
::
2
]
# for padding like [pad_before, pad_after, pad_before, pad_after, ...]
elif
len
(
padding
)
==
2
*
num_dims
and
isinstance
(
padding
[
0
],
int
):
padding_algorithm
=
"EXPLICIT"
padding
=
utils
.
convert_to_list
(
padding
,
2
*
num_dims
,
'padding'
)
if
utils
.
_is_symmetric_padding
(
padding
,
num_dims
):
padding
=
convert_to_list
(
padding
,
2
*
num_dims
,
'padding'
)
if
_is_symmetric_padding
(
padding
,
num_dims
):
padding
=
padding
[
0
::
2
]
# for padding like [pad_d1, pad_d2, ...]
elif
len
(
padding
)
==
num_dims
and
isinstance
(
padding
[
0
],
int
):
padding_algorithm
=
"EXPLICIT"
padding
=
utils
.
convert_to_list
(
padding
,
num_dims
,
'padding'
)
padding
=
convert_to_list
(
padding
,
num_dims
,
'padding'
)
else
:
raise
ValueError
(
"In valid padding: {}"
.
format
(
padding
))
# for integer padding
else
:
padding_algorithm
=
"EXPLICIT"
padding
=
utils
.
convert_to_list
(
padding
,
num_dims
,
'padding'
)
padding
=
convert_to_list
(
padding
,
num_dims
,
'padding'
)
if
not
all
([
p
>=
0
for
p
in
padding
]):
raise
ValueError
(
"Invalid padding, all value should be larger than or equal to 0, but received: {}"
.
...
...
@@ -323,8 +327,8 @@ def conv1d(x,
"The size of padding's dimension should be 1 or 2. But got padding={}"
.
format
(
padding
))
stride
=
utils
.
convert_to_list
(
stride
,
1
,
'stride'
)
+
[
1
]
dilation
=
utils
.
convert_to_list
(
dilation
,
1
,
'dilation'
)
+
[
1
]
stride
=
convert_to_list
(
stride
,
1
,
'stride'
)
+
[
1
]
dilation
=
convert_to_list
(
dilation
,
1
,
'dilation'
)
+
[
1
]
l_type
=
"conv2d"
if
(
num_channels
==
groups
and
num_channels
!=
1
and
...
...
@@ -333,8 +337,8 @@ def conv1d(x,
use_cudnn
=
False
squeeze_aixs
=
-
2
if
channel_last
else
-
1
x
=
nn
.
unsqueeze
(
input
=
x
,
axe
s
=
[
squeeze_aixs
])
weight
=
nn
.
unsqueeze
(
input
=
weight
,
axe
s
=
[
-
1
])
x
=
unsqueeze
(
x
,
axi
s
=
[
squeeze_aixs
])
weight
=
unsqueeze
(
weight
,
axi
s
=
[
-
1
])
if
in_dygraph_mode
():
attrs
=
(
'strides'
,
stride
,
'paddings'
,
padding
,
'dilations'
,
dilation
,
'groups'
,
groups
,
'use_cudnn'
,
use_cudnn
,
'use_mkldnn'
,
False
,
...
...
@@ -366,7 +370,7 @@ def conv1d(x,
type
=
l_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
out
,
bias
,
axis
=
channel_dim
)
out
=
nn
.
squeeze
(
input
=
out
,
axe
s
=
[
squeeze_aixs
])
out
=
squeeze
(
out
,
axi
s
=
[
squeeze_aixs
])
return
out
...
...
@@ -530,8 +534,8 @@ def conv2d(x,
# update attrs
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
2
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
2
,
'dilation'
)
stride
=
convert_to_list
(
stride
,
2
,
'stride'
)
dilation
=
convert_to_list
(
dilation
,
2
,
'dilation'
)
l_type
=
"conv2d"
if
(
num_channels
==
groups
and
num_channels
!=
1
and
...
...
@@ -730,8 +734,8 @@ def conv1d_transpose(x,
"The size of padding's dimension should 1 or 2. But got padding={}"
.
format
(
padding
))
stride
=
utils
.
convert_to_list
(
stride
,
1
,
'stride'
)
+
[
1
]
dilation
=
utils
.
convert_to_list
(
dilation
,
1
,
'dilation'
)
+
[
1
]
stride
=
convert_to_list
(
stride
,
1
,
'stride'
)
+
[
1
]
dilation
=
convert_to_list
(
dilation
,
1
,
'dilation'
)
+
[
1
]
if
output_size
is
None
:
output_size
=
[]
...
...
@@ -740,8 +744,7 @@ def conv1d_transpose(x,
raise
ValueError
(
'output_padding option is mutually exclusive with '
'output_size'
)
if
isinstance
(
output_size
,
(
list
,
tuple
,
int
)):
output_size
=
utils
.
convert_to_list
(
output_size
,
1
,
'output_size'
)
+
[
1
]
output_size
=
convert_to_list
(
output_size
,
1
,
'output_size'
)
+
[
1
]
else
:
raise
ValueError
(
"output_size should be int, or list, tuple of ints"
)
...
...
@@ -749,8 +752,8 @@ def conv1d_transpose(x,
if
output_padding
==
0
:
output_padding
=
[]
else
:
output_padding
=
utils
.
convert_to_list
(
output_padding
,
1
,
'output_padding'
)
+
[
0
]
output_padding
=
convert_to_list
(
output_padding
,
1
,
'output_padding'
)
+
[
0
]
if
len
(
output_padding
)
>
0
and
output_padding
[
0
]
>
stride
[
0
]:
raise
ValueError
(
...
...
@@ -768,8 +771,8 @@ def conv1d_transpose(x,
squeeze_axis
=
-
2
if
channel_last
else
-
1
conv2d_data_format
=
"NHWC"
if
channel_last
else
"NCHW"
x
=
nn
.
unsqueeze
(
input
=
x
,
axe
s
=
[
squeeze_axis
])
weight
=
nn
.
unsqueeze
(
input
=
weight
,
axe
s
=
[
-
1
])
x
=
unsqueeze
(
x
,
axi
s
=
[
squeeze_axis
])
weight
=
unsqueeze
(
weight
,
axi
s
=
[
-
1
])
if
in_dygraph_mode
():
attrs
=
(
'output_padding'
,
output_padding
,
'output_size'
,
output_size
,
...
...
@@ -803,7 +806,7 @@ def conv1d_transpose(x,
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
out
,
bias
,
axis
=
channel_dim
)
out
=
nn
.
squeeze
(
input
=
out
,
axe
s
=
[
squeeze_axis
])
out
=
squeeze
(
out
,
axi
s
=
[
squeeze_axis
])
return
out
...
...
@@ -979,8 +982,8 @@ def conv2d_transpose(x,
# update attrs
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
2
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
2
,
'dilation'
)
stride
=
convert_to_list
(
stride
,
2
,
'stride'
)
dilation
=
convert_to_list
(
dilation
,
2
,
'dilation'
)
if
output_size
is
None
:
output_size
=
[]
...
...
@@ -989,7 +992,7 @@ def conv2d_transpose(x,
raise
ValueError
(
'output_padding option is mutually exclusive with '
'output_size'
)
if
isinstance
(
output_size
,
(
list
,
tuple
,
int
)):
output_size
=
utils
.
convert_to_list
(
output_size
,
2
,
'output_size'
)
output_size
=
convert_to_list
(
output_size
,
2
,
'output_size'
)
else
:
raise
ValueError
(
"output_size should be int, or list, tuple of ints"
)
...
...
@@ -997,8 +1000,7 @@ def conv2d_transpose(x,
if
output_padding
==
0
:
output_padding
=
[]
else
:
output_padding
=
utils
.
convert_to_list
(
output_padding
,
2
,
'output_padding'
)
output_padding
=
convert_to_list
(
output_padding
,
2
,
'output_padding'
)
op_type
=
'conv2d_transpose'
num_filters
=
weight
.
shape
[
1
]
...
...
@@ -1187,8 +1189,8 @@ def conv3d(x,
cudnn_version
is
not
None
)
else
False
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
3
)
stride
=
utils
.
convert_to_list
(
stride
,
3
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
stride
=
convert_to_list
(
stride
,
3
,
'stride'
)
dilation
=
convert_to_list
(
dilation
,
3
,
'dilation'
)
op_type
=
"conv3d"
return
_conv_nd
(
x
,
weight
,
bias
,
stride
,
padding
,
padding_algorithm
,
...
...
@@ -1369,8 +1371,8 @@ def conv3d_transpose(x,
groups
))
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
3
)
stride
=
utils
.
convert_to_list
(
stride
,
3
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
stride
=
convert_to_list
(
stride
,
3
,
'stride'
)
dilation
=
convert_to_list
(
dilation
,
3
,
'dilation'
)
if
output_size
is
None
:
output_size
=
[]
else
:
...
...
@@ -1378,7 +1380,7 @@ def conv3d_transpose(x,
raise
ValueError
(
'output_padding option is mutually exclusive with '
'output_size'
)
if
isinstance
(
output_size
,
(
list
,
tuple
,
int
)):
output_size
=
utils
.
convert_to_list
(
output_size
,
3
,
'output_size'
)
output_size
=
convert_to_list
(
output_size
,
3
,
'output_size'
)
else
:
raise
ValueError
(
"output_size should be int, or list, tuple of ints"
)
...
...
@@ -1386,8 +1388,7 @@ def conv3d_transpose(x,
if
output_padding
==
0
:
output_padding
=
[]
else
:
output_padding
=
utils
.
convert_to_list
(
output_padding
,
3
,
'output_padding'
)
output_padding
=
convert_to_list
(
output_padding
,
3
,
'output_padding'
)
cudnn_version
=
get_cudnn_version
()
...
...
python/paddle/nn/functional/extension.py
浏览文件 @
a508e725
...
...
@@ -17,8 +17,9 @@
import
numpy
as
np
from
...fluid.data_feeder
import
check_dtype
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.framework
import
Variable
,
in_dygraph_mode
from
...fluid.layers.tensor
import
assign
from
...fluid.framework
import
in_dygraph_mode
from
...static
import
Variable
from
...tensor.creation
import
assign
from
...fluid
import
core
,
dygraph_utils
from
...fluid.layers.layer_function_generator
import
templatedoc
from
...fluid.layers.sequence_lod
import
sequence_mask
...
...
python/paddle/nn/functional/input.py
浏览文件 @
a508e725
...
...
@@ -14,7 +14,8 @@
from
__future__
import
print_function
import
warnings
from
...fluid.framework
import
Variable
,
in_dygraph_mode
from
...fluid.framework
import
in_dygraph_mode
from
...static
import
Variable
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.layers
import
core
from
...fluid.data_feeder
import
check_variable_and_dtype
,
check_dtype
...
...
python/paddle/nn/functional/loss.py
浏览文件 @
a508e725
...
...
@@ -27,7 +27,7 @@ from ...fluid.layers.nn import _elementwise_op_in_dygraph
from
...fluid.layers
import
dice_loss
# noqa: F401
from
...fluid.layers
import
log_loss
# noqa: F401
from
...fluid.layers
import
npair_loss
# noqa: F401
from
...
fluid.layers
import
reshape
from
...
tensor.manipulation
import
reshape
from
...fluid.layers
import
softmax_with_cross_entropy
as
fluid_softmax_with_cross_entropy
from
...fluid.layers
import
square_error_cost
# noqa: F401
...
...
@@ -36,7 +36,7 @@ from ...fluid.layers import huber_loss
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.framework
import
in_dygraph_mode
from
...fluid.framework
import
_varbase_creator
from
...
fluid.framework
import
Variable
from
...
static
import
Variable
from
paddle.utils
import
deprecated
from
paddle
import
_C_ops
...
...
@@ -291,9 +291,7 @@ def binary_cross_entropy_with_logits(logit,
pos_weight
,
'pos_weight'
,
[
'float32'
,
'float64'
],
'binary_cross_entropy_with_logits'
)
log_weight
=
paddle
.
add
(
paddle
.
multiply
(
label
,
paddle
.
fluid
.
layers
.
elementwise_sub
(
pos_weight
,
one
)),
one
)
paddle
.
multiply
(
label
,
paddle
.
subtract
(
pos_weight
,
one
)),
one
)
pos_weight_name
=
name
if
reduction
==
'none'
and
weight
is
None
else
None
out
=
paddle
.
multiply
(
out
,
log_weight
,
name
=
pos_weight_name
)
...
...
@@ -515,9 +513,9 @@ def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None):
if
reduction
==
'none'
:
return
out
elif
reduction
==
'mean'
:
return
fluid
.
layers
.
reduce_
mean
(
out
)
return
paddle
.
mean
(
out
)
elif
reduction
==
'sum'
:
return
fluid
.
layers
.
reduce_
sum
(
out
)
return
paddle
.
sum
(
out
)
def
margin_ranking_loss
(
input
,
...
...
@@ -592,7 +590,7 @@ def margin_ranking_loss(input,
fluid
.
data_feeder
.
check_variable_and_dtype
(
label
,
'label'
,
[
'float32'
,
'float64'
],
'margin_rank_loss'
)
out
=
paddle
.
fluid
.
layers
.
elementwise_sub
(
other
,
input
)
out
=
paddle
.
subtract
(
other
,
input
)
out
=
paddle
.
multiply
(
out
,
label
)
if
margin
!=
0.0
:
...
...
@@ -898,11 +896,11 @@ def kl_div(input, label, reduction='mean', name=None):
if
fluid
.
data_feeder
.
convert_dtype
(
input
.
dtype
)
==
'float32'
and
fluid
.
data_feeder
.
convert_dtype
(
label
.
dtype
)
==
'float64'
:
input
=
fluid
.
layers
.
cast
(
input
,
'float64'
)
input
=
paddle
.
cast
(
input
,
'float64'
)
elif
fluid
.
data_feeder
.
convert_dtype
(
input
.
dtype
)
==
'float64'
and
fluid
.
data_feeder
.
convert_dtype
(
label
.
dtype
)
==
'float32'
:
label
=
fluid
.
layers
.
cast
(
label
,
'float64'
)
label
=
paddle
.
cast
(
label
,
'float64'
)
if
paddle
.
in_dynamic_mode
():
out
=
_C_ops
.
kldiv_loss
(
input
,
label
,
'reduction'
,
reduction
)
...
...
@@ -988,16 +986,12 @@ def mse_loss(input, label, reduction='mean', name=None):
label
,
'label'
,
[
'float32'
,
'float64'
],
'mse_loss'
)
if
reduction
==
'none'
:
return
paddle
.
fluid
.
layers
.
square
(
paddle
.
fluid
.
layers
.
elementwise_sub
(
input
,
label
),
name
=
name
)
return
paddle
.
square
(
paddle
.
subtract
(
input
,
label
),
name
=
name
)
elif
reduction
==
'mean'
:
return
paddle
.
mean
(
paddle
.
fluid
.
layers
.
square
(
paddle
.
fluid
.
layers
.
elementwise_sub
(
input
,
label
)),
name
=
name
)
paddle
.
square
(
paddle
.
subtract
(
input
,
label
)),
name
=
name
)
else
:
return
paddle
.
sum
(
paddle
.
fluid
.
layers
.
square
(
paddle
.
fluid
.
layers
.
elementwise_sub
(
input
,
label
)),
return
paddle
.
sum
(
paddle
.
square
(
paddle
.
subtract
(
input
,
label
)),
name
=
name
)
...
...
python/paddle/nn/functional/norm.py
浏览文件 @
a508e725
...
...
@@ -19,8 +19,8 @@ from ...fluid.data_feeder import check_variable_and_dtype, check_type
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.framework
import
in_dygraph_mode
,
core
from
...framework
import
create_parameter
from
..
.fluid.
initializer
import
Constant
from
...f
luid.param_attr
import
ParamAttr
from
..initializer
import
Constant
from
...f
ramework
import
ParamAttr
from
...fluid
import
core
,
dygraph_utils
import
numbers
from
paddle
import
_C_ops
...
...
@@ -104,8 +104,7 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
type
=
'p_norm'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
eps
=
out
.
block
.
create_var
(
dtype
=
out
.
dtype
)
paddle
.
fluid
.
layers
.
fill_constant
([
1
],
out
.
dtype
,
epsilon
,
out
=
eps
)
return
paddle
.
fluid
.
layers
.
elementwise_div
(
x
,
paddle
.
maximum
(
out
,
eps
),
name
=
name
)
return
paddle
.
divide
(
x
,
paddle
.
maximum
(
out
,
eps
),
name
=
name
)
def
batch_norm
(
x
,
...
...
python/paddle/nn/functional/pooling.py
浏览文件 @
a508e725
...
...
@@ -15,7 +15,8 @@
# TODO: define pooling functions
from
...fluid
import
core
from
...fluid.framework
import
in_dygraph_mode
from
...fluid.layers
import
utils
,
LayerHelper
,
unsqueeze
,
squeeze
from
...fluid.layers
import
utils
,
LayerHelper
from
...tensor.manipulation
import
unsqueeze
,
squeeze
from
...fluid.data_feeder
import
check_type
,
check_variable_and_dtype
from
paddle
import
_C_ops
from
paddle
import
_C_ops
...
...
python/paddle/nn/functional/vision.py
浏览文件 @
a508e725
...
...
@@ -13,7 +13,8 @@
# limitations under the License.
from
...device
import
get_cudnn_version
from
...fluid.framework
import
core
,
in_dygraph_mode
,
Variable
from
...fluid.framework
import
core
,
in_dygraph_mode
from
...static
import
Variable
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.data_feeder
import
check_variable_and_dtype
from
...fluid
import
dygraph_utils
...
...
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