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d4b42a38
编写于
9月 01, 2020
作者:
S
shippingwang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix
上级
994d198e
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
178 addition
and
319 deletion
+178
-319
ppcls/modeling/architectures/evonorm.py
ppcls/modeling/architectures/evonorm.py
+0
-86
ppcls/modeling/architectures/resnet.py
ppcls/modeling/architectures/resnet.py
+178
-233
未找到文件。
ppcls/modeling/architectures/evonorm.py
已删除
100644 → 0
浏览文件 @
994d198e
import
paddle
import
torch
import
numpy
as
np
import
paddle.fluid
as
fluid
def
instance_std_paddle
(
input
,
epsilon
=
1e-5
):
v
=
paddle
.
var
(
input
,
axis
=
[
2
,
3
],
keepdim
=
True
)
v
=
paddle
.
expand_as
(
v
,
input
)
return
paddle
.
sqrt
(
v
+
epsilon
)
def
instance_std
(
x
,
eps
=
1e-5
):
var
=
torch
.
var
(
x
,
dim
=
(
2
,
3
),
keepdim
=
True
).
expand_as
(
x
)
if
torch
.
isnan
(
var
).
any
():
var
=
torch
.
zeros
(
var
.
shape
)
return
torch
.
sqrt
(
var
+
eps
)
def
group_std_paddle
(
input
,
groups
=
32
,
epsilon
=
1e-5
):
#N, C, H, W = paddle.shape(input)
N
,
C
,
H
,
W
=
input
.
shape
#print(N,C,H,W)
input
=
paddle
.
reshape
(
input
,
[
N
,
groups
,
C
//
groups
,
H
,
W
])
v
=
paddle
.
var
(
input
,
axis
=
[
2
,
3
,
4
],
keepdim
=
True
)
v
=
paddle
.
expand_as
(
v
,
input
)
return
paddle
.
reshape
(
paddle
.
sqrt
(
v
+
epsilon
),(
N
,
C
,
H
,
W
))
def
group_std
(
x
,
groups
=
32
,
eps
=
1e-5
):
N
,
C
,
H
,
W
=
x
.
size
()
x
=
torch
.
reshape
(
x
,
(
N
,
groups
,
C
//
groups
,
H
,
W
))
var
=
torch
.
var
(
x
,
dim
=
(
2
,
3
,
4
),
keepdim
=
True
).
expand_as
(
x
)
return
torch
.
reshape
(
torch
.
sqrt
(
var
+
eps
),
(
N
,
C
,
H
,
W
))
class
EvoNorm
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
channels
,
version
=
'B0'
,
affine
=
True
,
non_linear
=
True
,
groups
=
32
,
epsilon
=
1e-5
,
momentum
=
0.9
,
training
=
True
):
super
(
EvoNorm
,
self
).
__init__
()
self
.
channels
=
channels
self
.
affine
=
affine
self
.
version
=
version
self
.
non_linear
=
non_linear
self
.
groups
=
groups
self
.
epsilon
=
epsilon
self
.
training
=
training
self
.
momentum
=
momentum
if
self
.
affine
:
self
.
gamma
=
self
.
create_parameter
([
1
,
self
.
channels
,
1
,
1
],
default_initializer
=
fluid
.
initializer
.
Constant
(
value
=
1.0
))
self
.
beta
=
self
.
create_parameter
([
1
,
self
.
channels
,
1
,
1
],
default_initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.0
))
if
self
.
non_linear
:
self
.
v
=
self
.
create_parameter
([
1
,
self
.
channels
,
1
,
1
],
default_initializer
=
fluid
.
initializer
.
Constant
(
value
=
1.0
))
else
:
self
.
register_parameter
(
'gamma'
,
None
)
self
.
register_parameter
(
'beta'
,
None
)
self
.
register_buffer
(
'v'
,
None
)
#self.running_var = self.create_parameter([1, self.channels, 1, 1],
# default_initializer=fluid.initializer.Constant(value=0.0))
#self.running_var.stop_gradient = True
#self.register_buffer('running_var', self.create_parameter([1, self.channels, 1, 1],
# default_initializer=fluid.initializer.Constant(value=1.0)))
self
.
register_buffer
(
'running_var'
,
paddle
.
fluid
.
layers
.
ones
(
shape
=
[
1
,
self
.
channels
,
1
,
1
],
dtype
=
'float32'
))
def
forward
(
self
,
input
):
if
self
.
version
==
'S0'
:
if
self
.
non_linear
:
num
=
input
*
paddle
.
fluid
.
layers
.
sigmoid
(
self
.
v
*
input
)
return
num
/
group_std_paddle
(
input
,
groups
=
self
.
groups
,
epsilon
=
self
.
epsilon
)
*
self
.
gamma
+
self
.
beta
else
:
return
input
*
self
.
gamma
+
self
.
beta
if
self
.
version
==
'B0'
:
if
self
.
training
:
var
=
paddle
.
var
(
input
,
axis
=
[
0
,
2
,
3
],
unbiased
=
False
,
keepdim
=
True
)
self
.
running_var
=
self
.
running_var
*
self
.
momentum
self
.
running_var
=
self
.
running_var
+
(
1
-
self
.
momentum
)
*
var
else
:
var
=
self
.
running_var
if
self
.
non_linear
:
den
=
paddle
.
elementwise_max
(
paddle
.
sqrt
((
var
+
self
.
epsilon
)),
self
.
v
*
input
+
instance_std_paddle
(
input
,
epsilon
=
self
.
epsilon
))
return
input
/
den
*
self
.
gamma
+
self
.
beta
else
:
return
input
*
self
.
gamma
+
self
.
beta
ppcls/modeling/architectures/resnet.py
浏览文件 @
d4b42a38
#
copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#
Licensed under the Apache License, Version 2.0 (the "License");
#
you may not use this file except in compliance with the License.
#
You may obtain a copy of the License at
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
Unless required by applicable law or agreed to in writing, software
#
distributed under the License is distributed on an "AS IS" BASIS,
#
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
See the License for the specific language governing permissions and
#
limitations under the License.
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
numpy
as
np
import
math
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.param_attr
import
ParamAttr
from
paddle.fluid.layer_helper
import
LayerHelper
from
paddle.fluid.dygraph.nn
import
Conv2D
,
Pool2D
,
BatchNorm
,
Linear
,
Dropout
import
math
__all__
=
[
"ResNet18"
,
"ResNet34"
,
"ResNet50"
,
"ResNet101"
,
"ResNet152"
]
class
ConvBNLayer
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
filter_size
,
stride
=
1
,
groups
=
1
,
act
=
None
,
name
=
None
):
super
(
ConvBNLayer
,
self
).
__init__
()
self
.
_conv
=
Conv2D
(
num_channels
=
num_channels
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
(
filter_size
-
1
)
//
2
,
groups
=
groups
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
False
)
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
self
.
_batch_norm
=
BatchNorm
(
num_filters
,
act
=
act
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
"_scale"
),
bias_attr
=
ParamAttr
(
bn_name
+
"_offset"
),
moving_mean_name
=
bn_name
+
"_mean"
,
moving_variance_name
=
bn_name
+
"_variance"
)
def
forward
(
self
,
inputs
):
y
=
self
.
_conv
(
inputs
)
y
=
self
.
_batch_norm
(
y
)
return
y
__all__
=
[
"ResNet"
,
"ResNet18"
,
"ResNet34"
,
"ResNet50"
,
"ResNet101"
,
"ResNet152"
]
class
BottleneckBlock
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
stride
,
shortcut
=
True
,
name
=
None
):
super
(
BottleneckBlock
,
self
).
__init__
()
self
.
conv0
=
ConvBNLayer
(
num_channels
=
num_channels
,
num_filters
=
num_filters
,
filter_size
=
1
,
act
=
"relu"
,
name
=
name
+
"_branch2a"
)
self
.
conv1
=
ConvBNLayer
(
num_channels
=
num_filters
,
num_filters
=
num_filters
,
filter_size
=
3
,
stride
=
stride
,
act
=
"relu"
,
name
=
name
+
"_branch2b"
)
self
.
conv2
=
ConvBNLayer
(
num_channels
=
num_filters
,
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
act
=
None
,
name
=
name
+
"_branch2c"
)
self
.
shortcut
=
shortcut
if
not
self
.
shortcut
:
self
.
short
=
ConvBNLayer
(
num_channels
=
num_channels
,
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
stride
=
stride
,
name
=
name
+
"_branch1"
)
self
.
_num_channels_out
=
num_filters
*
4
def
forward
(
self
,
inputs
):
y
=
self
.
conv0
(
inputs
)
conv1
=
self
.
conv1
(
y
)
conv2
=
self
.
conv2
(
conv1
)
if
self
.
shortcut
:
short
=
inputs
else
:
short
=
self
.
short
(
inputs
)
y
=
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
)
layer_helper
=
LayerHelper
(
self
.
full_name
(),
act
=
"relu"
)
return
layer_helper
.
append_activation
(
y
)
class
BasicBlock
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
stride
,
shortcut
=
True
,
name
=
None
):
super
(
BasicBlock
,
self
).
__init__
()
self
.
stride
=
stride
self
.
conv0
=
ConvBNLayer
(
num_channels
=
num_channels
,
num_filters
=
num_filters
,
filter_size
=
3
,
stride
=
stride
,
act
=
"relu"
,
name
=
name
+
"_branch2a"
)
self
.
conv1
=
ConvBNLayer
(
num_channels
=
num_filters
,
num_filters
=
num_filters
,
filter_size
=
3
,
act
=
None
,
name
=
name
+
"_branch2b"
)
if
not
shortcut
:
self
.
short
=
ConvBNLayer
(
num_channels
=
num_channels
,
num_filters
=
num_filters
,
filter_size
=
1
,
stride
=
stride
,
name
=
name
+
"_branch1"
)
self
.
shortcut
=
shortcut
def
forward
(
self
,
inputs
):
y
=
self
.
conv0
(
inputs
)
conv1
=
self
.
conv1
(
y
)
if
self
.
shortcut
:
short
=
inputs
else
:
short
=
self
.
short
(
inputs
)
y
=
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv1
)
layer_helper
=
LayerHelper
(
self
.
full_name
(),
act
=
"relu"
)
return
layer_helper
.
append_activation
(
y
)
class
ResNet
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
layers
=
50
,
class_dim
=
1000
):
super
(
ResNet
,
self
).
__init__
()
class
ResNet
():
def
__init__
(
self
,
layers
=
50
):
self
.
layers
=
layers
def
net
(
self
,
input
,
class_dim
=
1000
,
data_format
=
"NCHW"
):
layers
=
self
.
layers
supported_layers
=
[
18
,
34
,
50
,
101
,
152
]
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
if
layers
==
18
:
depth
=
[
2
,
2
,
2
,
2
]
...
...
@@ -189,25 +45,25 @@ class ResNet(fluid.dygraph.Layer):
depth
=
[
3
,
4
,
23
,
3
]
elif
layers
==
152
:
depth
=
[
3
,
8
,
36
,
3
]
else
:
raise
ValueError
(
'Input layer is not supported'
)
num_channels
=
[
64
,
256
,
512
,
1024
]
if
layers
>=
50
else
[
64
,
64
,
128
,
256
]
num_filters
=
[
64
,
128
,
256
,
512
]
self
.
conv
=
ConvBNL
ayer
(
num_channels
=
3
,
conv
=
self
.
conv_bn_l
ayer
(
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
"relu"
,
name
=
"conv1"
)
self
.
pool2d_max
=
Pool2D
(
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
"max"
)
self
.
block_list
=
[]
act
=
'relu'
,
name
=
"conv1"
,
data_format
=
data_format
)
conv
=
fluid
.
layers
.
pool2d
(
input
=
conv
,
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
'max'
,
data_format
=
data_format
)
if
layers
>=
50
:
for
block
in
range
(
len
(
depth
)):
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
if
layers
in
[
101
,
152
]
and
block
==
2
:
if
i
==
0
:
...
...
@@ -216,80 +72,169 @@ class ResNet(fluid.dygraph.Layer):
conv_name
=
"res"
+
str
(
block
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
bottleneck_block
=
self
.
add_sublayer
(
conv_name
,
BottleneckBlock
(
num_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
]
*
4
,
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
shortcut
=
shortcut
,
name
=
conv_name
))
self
.
block_list
.
append
(
bottleneck_block
)
shortcut
=
True
conv
=
self
.
bottleneck_block
(
input
=
conv
,
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
name
=
conv_name
,
data_format
=
data_format
)
else
:
for
block
in
range
(
len
(
depth
)):
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
basic_block
=
self
.
add_sublayer
(
conv_name
,
BasicBlock
(
num_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
],
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
shortcut
=
shortcut
,
name
=
conv_name
))
self
.
block_list
.
append
(
basic_block
)
shortcut
=
True
self
.
pool2d_avg
=
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg_channels
=
num_channels
[
-
1
]
*
2
stdv
=
1.0
/
math
.
sqrt
(
self
.
pool2d_avg_channels
*
1.0
)
self
.
out
=
Linear
(
self
.
pool2d_avg_channels
,
class_dim
,
param_attr
=
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
),
name
=
"fc_0.w_0"
),
conv
=
self
.
basic_block
(
input
=
conv
,
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
is_first
=
block
==
i
==
0
,
name
=
conv_name
,
data_format
=
data_format
)
pool
=
fluid
.
layers
.
pool2d
(
input
=
conv
,
pool_type
=
'avg'
,
global_pooling
=
True
,
data_format
=
data_format
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
out
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
class_dim
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
name
=
"fc_0.w_0"
,
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
name
=
"fc_0.b_0"
))
return
out
def
conv_bn_layer
(
self
,
input
,
num_filters
,
filter_size
,
stride
=
1
,
groups
=
1
,
act
=
None
,
name
=
None
,
data_format
=
'NCHW'
):
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
(
filter_size
-
1
)
//
2
,
groups
=
groups
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
False
,
name
=
name
+
'.conv2d.output.1'
,
data_format
=
data_format
)
def
forward
(
self
,
inputs
):
y
=
self
.
conv
(
inputs
)
y
=
self
.
pool2d_max
(
y
)
for
block
in
self
.
block_list
:
y
=
block
(
y
)
y
=
self
.
pool2d_avg
(
y
)
y
=
fluid
.
layers
.
reshape
(
y
,
shape
=
[
-
1
,
self
.
pool2d_avg_channels
])
y
=
self
.
out
(
y
)
return
y
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
return
fluid
.
layers
.
batch_norm
(
input
=
conv
,
act
=
act
,
name
=
bn_name
+
'.output.1'
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
'_scale'
),
bias_attr
=
ParamAttr
(
bn_name
+
'_offset'
),
moving_mean_name
=
bn_name
+
'_mean'
,
moving_variance_name
=
bn_name
+
'_variance'
,
data_layout
=
data_format
)
def
shortcut
(
self
,
input
,
ch_out
,
stride
,
is_first
,
name
,
data_format
):
if
data_format
==
'NCHW'
:
ch_in
=
input
.
shape
[
1
]
else
:
ch_in
=
input
.
shape
[
-
1
]
if
ch_in
!=
ch_out
or
stride
!=
1
or
is_first
==
True
:
return
self
.
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
,
name
=
name
,
data_format
=
data_format
)
else
:
return
input
def
bottleneck_block
(
self
,
input
,
num_filters
,
stride
,
name
,
data_format
):
conv0
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
1
,
act
=
'relu'
,
name
=
name
+
"_branch2a"
,
data_format
=
data_format
)
conv1
=
self
.
conv_bn_layer
(
input
=
conv0
,
num_filters
=
num_filters
,
filter_size
=
3
,
stride
=
stride
,
act
=
'relu'
,
name
=
name
+
"_branch2b"
,
data_format
=
data_format
)
conv2
=
self
.
conv_bn_layer
(
input
=
conv1
,
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
act
=
None
,
name
=
name
+
"_branch2c"
,
data_format
=
data_format
)
short
=
self
.
shortcut
(
input
,
num_filters
*
4
,
stride
,
is_first
=
False
,
name
=
name
+
"_branch1"
,
data_format
=
data_format
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
,
name
=
name
+
".add.output.5"
)
def
basic_block
(
self
,
input
,
num_filters
,
stride
,
is_first
,
name
,
data_format
):
conv0
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
3
,
act
=
'relu'
,
stride
=
stride
,
name
=
name
+
"_branch2a"
,
data_format
=
data_format
)
conv1
=
self
.
conv_bn_layer
(
input
=
conv0
,
num_filters
=
num_filters
,
filter_size
=
3
,
act
=
None
,
name
=
name
+
"_branch2b"
,
data_format
=
data_format
)
short
=
self
.
shortcut
(
input
,
num_filters
,
stride
,
is_first
,
name
=
name
+
"_branch1"
,
data_format
=
data_format
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv1
,
act
=
'relu'
)
def
ResNet18
(
**
args
):
model
=
ResNet
(
layers
=
18
,
**
args
)
def
ResNet18
():
model
=
ResNet
(
layers
=
18
)
return
model
def
ResNet34
(
**
args
):
model
=
ResNet
(
layers
=
34
,
**
args
)
def
ResNet34
():
model
=
ResNet
(
layers
=
34
)
return
model
def
ResNet50
(
**
args
):
model
=
ResNet
(
layers
=
50
,
**
args
)
def
ResNet50
():
model
=
ResNet
(
layers
=
50
)
return
model
def
ResNet101
(
**
args
):
model
=
ResNet
(
layers
=
101
,
**
args
)
def
ResNet101
():
model
=
ResNet
(
layers
=
101
)
return
model
def
ResNet152
(
**
args
):
model
=
ResNet
(
layers
=
152
,
**
args
)
def
ResNet152
():
model
=
ResNet
(
layers
=
152
)
return
model
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