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体验新版 GitCode,发现更多精彩内容 >>
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bdad0cef
编写于
10月 16, 2020
作者:
W
WenmuZhou
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
检测和识别的resnet使用paddleclass里的
上级
8f1b9c8b
变更
2
显示空白变更内容
内联
并排
Showing
2 changed file
with
387 addition
and
468 deletion
+387
-468
ppocr/modeling/backbones/det_resnet_vd.py
ppocr/modeling/backbones/det_resnet_vd.py
+188
-240
ppocr/modeling/backbones/rec_resnet_vd.py
ppocr/modeling/backbones/rec_resnet_vd.py
+199
-228
未找到文件。
ppocr/modeling/backbones/det_resnet_vd.py
浏览文件 @
bdad0cef
...
...
@@ -16,143 +16,30 @@ from __future__ import absolute_import
from
__future__
import
division
from
__future__
import
print_function
from
paddle
import
nn
from
paddle.nn
import
functional
as
F
import
paddle
from
paddle
import
ParamAttr
import
paddle.nn
as
nn
__all__
=
[
"ResNet"
]
class
ResNet
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
=
3
,
layers
=
50
,
**
kwargs
):
"""
the Resnet backbone network for detection module.
Args:
params(dict): the super parameters for network build
"""
super
(
ResNet
,
self
).
__init__
()
supported_layers
=
{
18
:
{
'depth'
:
[
2
,
2
,
2
,
2
],
'block_class'
:
BasicBlock
},
34
:
{
'depth'
:
[
3
,
4
,
6
,
3
],
'block_class'
:
BasicBlock
},
50
:
{
'depth'
:
[
3
,
4
,
6
,
3
],
'block_class'
:
BottleneckBlock
},
101
:
{
'depth'
:
[
3
,
4
,
23
,
3
],
'block_class'
:
BottleneckBlock
},
152
:
{
'depth'
:
[
3
,
8
,
36
,
3
],
'block_class'
:
BottleneckBlock
},
200
:
{
'depth'
:
[
3
,
12
,
48
,
3
],
'block_class'
:
BottleneckBlock
}
}
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
.
keys
(),
layers
)
is_3x3
=
True
depth
=
supported_layers
[
layers
][
'depth'
]
block_class
=
supported_layers
[
layers
][
'block_class'
]
num_filters
=
[
64
,
128
,
256
,
512
]
conv
=
[]
if
is_3x3
==
False
:
conv
.
append
(
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
64
,
kernel_size
=
7
,
stride
=
2
,
act
=
'relu'
))
else
:
conv
.
append
(
ConvBNLayer
(
in_channels
=
3
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
2
,
act
=
'relu'
,
name
=
'conv1_1'
))
conv
.
append
(
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'conv1_2'
))
conv
.
append
(
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'conv1_3'
))
self
.
conv1
=
nn
.
Sequential
(
*
conv
)
self
.
pool
=
nn
.
MaxPool2d
(
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
self
.
stages
=
[]
self
.
out_channels
=
[]
in_ch
=
64
for
block_index
in
range
(
len
(
depth
)):
block_list
=
[]
for
i
in
range
(
depth
[
block_index
]):
if
layers
>=
50
:
if
layers
in
[
101
,
152
,
200
]
and
block_index
==
2
:
if
i
==
0
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
"a"
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
chr
(
97
+
i
)
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
chr
(
97
+
i
)
block_list
.
append
(
block_class
(
in_channels
=
in_ch
,
out_channels
=
num_filters
[
block_index
],
stride
=
2
if
i
==
0
and
block_index
!=
0
else
1
,
if_first
=
block_index
==
i
==
0
,
name
=
conv_name
))
in_ch
=
block_list
[
-
1
].
out_channels
self
.
out_channels
.
append
(
in_ch
)
self
.
stages
.
append
(
nn
.
Sequential
(
*
block_list
))
for
i
,
stage
in
enumerate
(
self
.
stages
):
self
.
add_sublayer
(
sublayer
=
stage
,
name
=
"stage{}"
.
format
(
i
))
def
forward
(
self
,
x
):
x
=
self
.
conv1
(
x
)
x
=
self
.
pool
(
x
)
out_list
=
[]
for
stage
in
self
.
stages
:
x
=
stage
(
x
)
out_list
.
append
(
x
)
return
out_list
class
ConvBNLayer
(
nn
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
in_channels
,
out_channels
,
kernel_size
,
stride
=
1
,
groups
=
1
,
is_vd_mode
=
False
,
act
=
None
,
name
=
None
):
name
=
None
,
):
super
(
ConvBNLayer
,
self
).
__init__
()
self
.
conv
=
nn
.
Conv2d
(
self
.
is_vd_mode
=
is_vd_mode
self
.
_pool2d_avg
=
nn
.
AvgPool2d
(
kernel_size
=
2
,
stride
=
2
,
padding
=
0
,
ceil_mode
=
True
)
self
.
_conv
=
nn
.
Conv2d
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
kernel_size
,
...
...
@@ -165,87 +52,32 @@ class ConvBNLayer(nn.Layer):
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
self
.
bn
=
nn
.
BatchNorm
(
num_channels
=
out_channels
,
self
.
_batch_norm
=
nn
.
BatchNorm
(
out_channels
,
act
=
act
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
"_scale"
),
bias_attr
=
ParamAttr
(
name
=
bn_name
+
"_offset"
),
moving_mean_name
=
bn_name
+
"_mean"
,
moving_variance_name
=
bn_name
+
"_variance"
)
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
__call__
(
self
,
x
):
x
=
self
.
conv
(
x
)
x
=
self
.
bn
(
x
)
return
x
def
forward
(
self
,
inputs
):
if
self
.
is_vd_mode
:
inputs
=
self
.
_pool2d_avg
(
inputs
)
y
=
self
.
_conv
(
inputs
)
y
=
self
.
_batch_norm
(
y
)
return
y
class
ConvBNLayerNew
(
nn
.
Layer
):
class
BottleneckBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
kernel_size
,
stride
=
1
,
groups
=
1
,
act
=
None
,
stride
,
shortcut
=
True
,
if_first
=
False
,
name
=
None
):
super
(
ConvBNLayerNew
,
self
).
__init__
()
self
.
pool
=
nn
.
AvgPool2d
(
kernel_size
=
2
,
stride
=
2
,
padding
=
0
,
ceil_mode
=
True
)
self
.
conv
=
nn
.
Conv2d
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
kernel_size
,
stride
=
1
,
padding
=
(
kernel_size
-
1
)
//
2
,
groups
=
groups
,
weight_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
False
)
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
self
.
bn
=
nn
.
BatchNorm
(
num_channels
=
out_channels
,
act
=
act
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
"_scale"
),
bias_attr
=
ParamAttr
(
name
=
bn_name
+
"_offset"
),
moving_mean_name
=
bn_name
+
"_mean"
,
moving_variance_name
=
bn_name
+
"_variance"
)
def
__call__
(
self
,
x
):
x
=
self
.
pool
(
x
)
x
=
self
.
conv
(
x
)
x
=
self
.
bn
(
x
)
return
x
class
ShortCut
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
=
False
):
super
(
ShortCut
,
self
).
__init__
()
self
.
use_conv
=
True
if
in_channels
!=
out_channels
or
stride
!=
1
:
if
if_first
:
self
.
conv
=
ConvBNLayer
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
else
:
self
.
conv
=
ConvBNLayerNew
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
elif
if_first
:
self
.
conv
=
ConvBNLayer
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
else
:
self
.
use_conv
=
False
def
forward
(
self
,
x
):
if
self
.
use_conv
:
x
=
self
.
conv
(
x
)
return
x
class
BottleneckBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
):
super
(
BottleneckBlock
,
self
).
__init__
()
self
.
conv0
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
...
...
@@ -266,32 +98,46 @@ class BottleneckBlock(nn.Layer):
act
=
None
,
name
=
name
+
"_branch2c"
)
self
.
short
=
ShortCut
(
if
not
shortcut
:
self
.
short
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
*
4
,
stride
=
stride
,
if_first
=
if_first
,
kernel_size
=
1
,
stride
=
1
,
is_vd_mode
=
False
if
if_first
else
True
,
name
=
name
+
"_branch1"
)
self
.
out_channels
=
out_channels
*
4
def
forward
(
self
,
x
):
y
=
self
.
conv0
(
x
)
y
=
self
.
conv1
(
y
)
y
=
self
.
conv2
(
y
)
y
=
y
+
self
.
short
(
x
)
y
=
F
.
relu
(
y
)
self
.
shortcut
=
shortcut
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
=
paddle
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
)
return
y
class
BasicBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
shortcut
=
True
,
if_first
=
False
,
name
=
None
):
super
(
BasicBlock
,
self
).
__init__
()
self
.
stride
=
stride
self
.
conv0
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
3
,
act
=
'relu'
,
stride
=
stride
,
act
=
'relu'
,
name
=
name
+
"_branch2a"
)
self
.
conv1
=
ConvBNLayer
(
in_channels
=
out_channels
,
...
...
@@ -299,31 +145,133 @@ class BasicBlock(nn.Layer):
kernel_size
=
3
,
act
=
None
,
name
=
name
+
"_branch2b"
)
self
.
short
=
ShortCut
(
if
not
shortcut
:
self
.
short
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
stride
=
stride
,
if_first
=
if_first
,
kernel_size
=
1
,
stride
=
1
,
is_vd_mode
=
False
if
if_first
else
True
,
name
=
name
+
"_branch1"
)
self
.
out_channels
=
out_channels
def
forward
(
self
,
x
):
y
=
self
.
conv0
(
x
)
y
=
self
.
conv1
(
y
)
y
=
y
+
self
.
short
(
x
)
return
F
.
relu
(
y
)
self
.
shortcut
=
shortcut
def
forward
(
self
,
inputs
):
y
=
self
.
conv0
(
inputs
)
conv1
=
self
.
conv1
(
y
)
if
__name__
==
'__main__'
:
import
paddle
if
self
.
shortcut
:
short
=
inputs
else
:
short
=
self
.
short
(
inputs
)
y
=
paddle
.
elementwise_add
(
x
=
short
,
y
=
conv1
,
act
=
'relu'
)
return
y
class
ResNet
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
=
3
,
layers
=
50
,
**
kwargs
):
super
(
ResNet
,
self
).
__init__
()
paddle
.
disable_static
()
x
=
paddle
.
zeros
([
1
,
3
,
640
,
640
])
x
=
paddle
.
to_variable
(
x
)
print
(
x
.
shape
)
net
=
ResNet
(
layers
=
18
)
y
=
net
(
x
)
self
.
layers
=
layers
supported_layers
=
[
18
,
34
,
50
,
101
,
152
,
200
]
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
if
layers
==
18
:
depth
=
[
2
,
2
,
2
,
2
]
elif
layers
==
34
or
layers
==
50
:
depth
=
[
3
,
4
,
6
,
3
]
elif
layers
==
101
:
depth
=
[
3
,
4
,
23
,
3
]
elif
layers
==
152
:
depth
=
[
3
,
8
,
36
,
3
]
elif
layers
==
200
:
depth
=
[
3
,
12
,
48
,
3
]
num_channels
=
[
64
,
256
,
512
,
1024
]
if
layers
>=
50
else
[
64
,
64
,
128
,
256
]
num_filters
=
[
64
,
128
,
256
,
512
]
self
.
conv1_1
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
2
,
act
=
'relu'
,
name
=
"conv1_1"
)
self
.
conv1_2
=
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv1_2"
)
self
.
conv1_3
=
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv1_3"
)
self
.
pool2d_max
=
nn
.
MaxPool2d
(
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
self
.
stages
=
[]
self
.
out_channels
=
[]
if
layers
>=
50
:
for
block
in
range
(
len
(
depth
)):
block_list
=
[]
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
if
layers
in
[
101
,
152
]
and
block
==
2
:
if
i
==
0
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"a"
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
bottleneck_block
=
self
.
add_sublayer
(
'bb_%d_%d'
%
(
block
,
i
),
BottleneckBlock
(
in_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
]
*
4
,
out_channels
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
shortcut
=
shortcut
,
if_first
=
block
==
i
==
0
,
name
=
conv_name
))
shortcut
=
True
block_list
.
append
(
bottleneck_block
)
self
.
out_channels
.
append
(
num_filters
[
block
]
*
4
)
self
.
stages
.
append
(
nn
.
Sequential
(
*
block_list
))
else
:
for
block
in
range
(
len
(
depth
)):
block_list
=
[]
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
basic_block
=
self
.
add_sublayer
(
'bb_%d_%d'
%
(
block
,
i
),
BasicBlock
(
in_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
],
out_channels
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
shortcut
=
shortcut
,
if_first
=
block
==
i
==
0
,
name
=
conv_name
))
shortcut
=
True
block_list
.
append
(
basic_block
)
self
.
out_channels
.
append
(
num_filters
[
block
])
self
.
stages
.
append
(
nn
.
Sequential
(
*
block_list
))
for
stage
in
y
:
print
(
stage
.
shape
)
# paddle.save(net.state_dict(),'1.pth')
def
forward
(
self
,
inputs
):
y
=
self
.
conv1_1
(
inputs
)
y
=
self
.
conv1_2
(
y
)
y
=
self
.
conv1_3
(
y
)
y
=
self
.
pool2d_max
(
y
)
out
=
[]
for
block
in
self
.
stages
:
y
=
block
(
y
)
out
.
append
(
y
)
return
out
ppocr/modeling/backbones/rec_resnet_vd.py
浏览文件 @
bdad0cef
...
...
@@ -16,144 +16,34 @@ from __future__ import absolute_import
from
__future__
import
division
from
__future__
import
print_function
from
paddle
import
nn
,
ParamAttr
from
paddle.nn
import
functional
as
F
import
paddle
from
paddle
import
ParamAttr
import
paddle.nn
as
nn
__all__
=
[
"ResNet"
]
class
ResNet
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
=
3
,
layers
=
34
):
super
(
ResNet
,
self
).
__init__
()
supported_layers
=
{
18
:
{
'depth'
:
[
2
,
2
,
2
,
2
],
'block_class'
:
BasicBlock
},
34
:
{
'depth'
:
[
3
,
4
,
6
,
3
],
'block_class'
:
BasicBlock
},
50
:
{
'depth'
:
[
3
,
4
,
6
,
3
],
'block_class'
:
BottleneckBlock
},
101
:
{
'depth'
:
[
3
,
4
,
23
,
3
],
'block_class'
:
BottleneckBlock
},
152
:
{
'depth'
:
[
3
,
8
,
36
,
3
],
'block_class'
:
BottleneckBlock
},
200
:
{
'depth'
:
[
3
,
12
,
48
,
3
],
'block_class'
:
BottleneckBlock
}
}
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
.
keys
(),
layers
)
is_3x3
=
True
num_filters
=
[
64
,
128
,
256
,
512
]
depth
=
supported_layers
[
layers
][
'depth'
]
block_class
=
supported_layers
[
layers
][
'block_class'
]
conv
=
[]
if
is_3x3
==
False
:
conv
.
append
(
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
64
,
kernel_size
=
7
,
stride
=
1
,
act
=
'relu'
))
else
:
conv
.
append
(
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'conv1_1'
))
conv
.
append
(
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'conv1_2'
))
conv
.
append
(
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'conv1_3'
))
self
.
conv1
=
nn
.
Sequential
(
*
conv
)
self
.
pool
=
nn
.
MaxPool2d
(
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
)
block_list
=
[]
in_ch
=
64
for
block_index
in
range
(
len
(
depth
)):
for
i
in
range
(
depth
[
block_index
]):
if
layers
>=
50
:
if
layers
in
[
101
,
152
,
200
]
and
block_index
==
2
:
if
i
==
0
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
"a"
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
chr
(
97
+
i
)
else
:
conv_name
=
"res"
+
str
(
block_index
+
2
)
+
chr
(
97
+
i
)
if
i
==
0
and
block_index
!=
0
:
stride
=
(
2
,
1
)
else
:
stride
=
(
1
,
1
)
block_list
.
append
(
block_class
(
in_channels
=
in_ch
,
out_channels
=
num_filters
[
block_index
],
stride
=
stride
,
if_first
=
block_index
==
i
==
0
,
name
=
conv_name
))
in_ch
=
block_list
[
-
1
].
out_channels
self
.
block_list
=
nn
.
Sequential
(
*
block_list
)
self
.
add_sublayer
(
sublayer
=
self
.
block_list
,
name
=
"block_list"
)
self
.
pool_out
=
nn
.
MaxPool2d
(
kernel_size
=
2
,
stride
=
2
,
padding
=
0
)
self
.
out_channels
=
in_ch
def
forward
(
self
,
x
):
x
=
self
.
conv1
(
x
)
x
=
self
.
pool
(
x
)
x
=
self
.
block_list
(
x
)
x
=
self
.
pool_out
(
x
)
return
x
class
ConvBNLayer
(
nn
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
in_channels
,
out_channels
,
kernel_size
,
stride
=
1
,
groups
=
1
,
is_vd_mode
=
False
,
act
=
None
,
name
=
None
):
name
=
None
,
):
super
(
ConvBNLayer
,
self
).
__init__
()
self
.
conv
=
nn
.
Conv2d
(
self
.
is_vd_mode
=
is_vd_mode
self
.
_pool2d_avg
=
nn
.
AvgPool2d
(
kernel_size
=
stride
,
stride
=
stride
,
padding
=
0
,
ceil_mode
=
True
)
self
.
_conv
=
nn
.
Conv2d
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
kernel_size
,
stride
=
stride
,
stride
=
1
if
is_vd_mode
else
stride
,
padding
=
(
kernel_size
-
1
)
//
2
,
groups
=
groups
,
weight_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
...
...
@@ -162,88 +52,32 @@ class ConvBNLayer(nn.Layer):
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
self
.
bn
=
nn
.
BatchNorm
(
num_channels
=
out_channels
,
self
.
_batch_norm
=
nn
.
BatchNorm
(
out_channels
,
act
=
act
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
"_scale"
),
bias_attr
=
ParamAttr
(
name
=
bn_name
+
"_offset"
),
moving_mean_name
=
bn_name
+
"_mean"
,
moving_variance_name
=
bn_name
+
"_variance"
)
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
__call__
(
self
,
x
):
x
=
self
.
conv
(
x
)
x
=
self
.
bn
(
x
)
return
x
def
forward
(
self
,
inputs
):
if
self
.
is_vd_mode
:
inputs
=
self
.
_pool2d_avg
(
inputs
)
y
=
self
.
_conv
(
inputs
)
y
=
self
.
_batch_norm
(
y
)
return
y
class
ConvBNLayerNew
(
nn
.
Layer
):
class
BottleneckBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
kernel_size
,
stride
=
1
,
groups
=
1
,
act
=
None
,
stride
,
shortcut
=
True
,
if_first
=
False
,
name
=
None
):
super
(
ConvBNLayerNew
,
self
).
__init__
()
self
.
pool
=
nn
.
AvgPool2d
(
kernel_size
=
stride
,
stride
=
stride
,
padding
=
0
,
ceil_mode
=
True
)
self
.
conv
=
nn
.
Conv2d
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
kernel_size
,
stride
=
1
,
padding
=
(
kernel_size
-
1
)
//
2
,
groups
=
groups
,
weight_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
False
)
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
self
.
bn
=
nn
.
BatchNorm
(
num_channels
=
out_channels
,
act
=
act
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
"_scale"
),
bias_attr
=
ParamAttr
(
name
=
bn_name
+
"_offset"
),
moving_mean_name
=
bn_name
+
"_mean"
,
moving_variance_name
=
bn_name
+
"_variance"
)
def
__call__
(
self
,
x
):
x
=
self
.
pool
(
x
)
x
=
self
.
conv
(
x
)
x
=
self
.
bn
(
x
)
return
x
class
ShortCut
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
=
False
):
super
(
ShortCut
,
self
).
__init__
()
self
.
use_conv
=
True
if
in_channels
!=
out_channels
or
stride
[
0
]
!=
1
:
if
if_first
:
self
.
conv
=
ConvBNLayer
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
else
:
self
.
conv
=
ConvBNLayerNew
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
elif
if_first
:
self
.
conv
=
ConvBNLayer
(
in_channels
,
out_channels
,
1
,
stride
,
name
=
name
)
else
:
self
.
use_conv
=
False
def
forward
(
self
,
x
):
if
self
.
use_conv
:
x
=
self
.
conv
(
x
)
return
x
class
BottleneckBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
):
super
(
BottleneckBlock
,
self
).
__init__
()
self
.
conv0
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
...
...
@@ -264,32 +98,47 @@ class BottleneckBlock(nn.Layer):
act
=
None
,
name
=
name
+
"_branch2c"
)
self
.
short
=
ShortCut
(
if
not
shortcut
:
self
.
short
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
*
4
,
kernel_size
=
1
,
stride
=
stride
,
if_first
=
if_first
,
is_vd_mode
=
not
if_first
and
stride
[
0
]
!=
1
,
name
=
name
+
"_branch1"
)
self
.
out_channels
=
out_channels
*
4
def
forward
(
self
,
x
):
y
=
self
.
conv0
(
x
)
y
=
self
.
conv1
(
y
)
y
=
self
.
conv2
(
y
)
y
=
y
+
self
.
short
(
x
)
y
=
F
.
relu
(
y
)
self
.
shortcut
=
shortcut
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
=
paddle
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
)
return
y
class
BasicBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
name
,
if_first
):
def
__init__
(
self
,
in_channels
,
out_channels
,
stride
,
shortcut
=
True
,
if_first
=
False
,
name
=
None
):
super
(
BasicBlock
,
self
).
__init__
()
self
.
stride
=
stride
self
.
conv0
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
3
,
act
=
'relu'
,
stride
=
stride
,
act
=
'relu'
,
name
=
name
+
"_branch2a"
)
self
.
conv1
=
ConvBNLayer
(
in_channels
=
out_channels
,
...
...
@@ -297,16 +146,138 @@ class BasicBlock(nn.Layer):
kernel_size
=
3
,
act
=
None
,
name
=
name
+
"_branch2b"
)
self
.
short
=
ShortCut
(
if
not
shortcut
:
self
.
short
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
out_channels
,
kernel_size
=
1
,
stride
=
stride
,
if_first
=
if_first
,
is_vd_mode
=
not
if_first
and
stride
[
0
]
!=
1
,
name
=
name
+
"_branch1"
)
self
.
out_channels
=
out_channels
def
forward
(
self
,
x
):
y
=
self
.
conv0
(
x
)
y
=
self
.
conv1
(
y
)
y
=
y
+
self
.
short
(
x
)
return
F
.
relu
(
y
)
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
=
paddle
.
elementwise_add
(
x
=
short
,
y
=
conv1
,
act
=
'relu'
)
return
y
class
ResNet
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
=
3
,
layers
=
50
,
**
kwargs
):
super
(
ResNet
,
self
).
__init__
()
self
.
layers
=
layers
supported_layers
=
[
18
,
34
,
50
,
101
,
152
,
200
]
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
if
layers
==
18
:
depth
=
[
2
,
2
,
2
,
2
]
elif
layers
==
34
or
layers
==
50
:
depth
=
[
3
,
4
,
6
,
3
]
elif
layers
==
101
:
depth
=
[
3
,
4
,
23
,
3
]
elif
layers
==
152
:
depth
=
[
3
,
8
,
36
,
3
]
elif
layers
==
200
:
depth
=
[
3
,
12
,
48
,
3
]
num_channels
=
[
64
,
256
,
512
,
1024
]
if
layers
>=
50
else
[
64
,
64
,
128
,
256
]
num_filters
=
[
64
,
128
,
256
,
512
]
self
.
conv1_1
=
ConvBNLayer
(
in_channels
=
in_channels
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv1_1"
)
self
.
conv1_2
=
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv1_2"
)
self
.
conv1_3
=
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv1_3"
)
self
.
pool2d_max
=
nn
.
MaxPool2d
(
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
self
.
block_list
=
[]
if
layers
>=
50
:
for
block
in
range
(
len
(
depth
)):
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
if
layers
in
[
101
,
152
,
200
]
and
block
==
2
:
if
i
==
0
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"a"
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
if
i
==
0
and
block
!=
0
:
stride
=
(
2
,
1
)
else
:
stride
=
(
1
,
1
)
bottleneck_block
=
self
.
add_sublayer
(
'bb_%d_%d'
%
(
block
,
i
),
BottleneckBlock
(
in_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
]
*
4
,
out_channels
=
num_filters
[
block
],
stride
=
stride
,
shortcut
=
shortcut
,
if_first
=
block
==
i
==
0
,
name
=
conv_name
))
shortcut
=
True
self
.
block_list
.
append
(
bottleneck_block
)
self
.
out_channels
=
num_filters
[
block
]
else
:
for
block
in
range
(
len
(
depth
)):
shortcut
=
False
for
i
in
range
(
depth
[
block
]):
conv_name
=
"res"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
if
i
==
0
and
block
!=
0
:
stride
=
(
2
,
1
)
else
:
stride
=
(
1
,
1
)
basic_block
=
self
.
add_sublayer
(
'bb_%d_%d'
%
(
block
,
i
),
BasicBlock
(
in_channels
=
num_channels
[
block
]
if
i
==
0
else
num_filters
[
block
],
out_channels
=
num_filters
[
block
],
stride
=
stride
,
shortcut
=
shortcut
,
if_first
=
block
==
i
==
0
,
name
=
conv_name
))
shortcut
=
True
self
.
block_list
.
append
(
basic_block
)
self
.
out_channels
=
num_filters
[
block
]
self
.
out_pool
=
nn
.
MaxPool2d
(
kernel_size
=
2
,
stride
=
2
,
padding
=
0
)
def
forward
(
self
,
inputs
):
y
=
self
.
conv1_1
(
inputs
)
y
=
self
.
conv1_2
(
y
)
y
=
self
.
conv1_3
(
y
)
y
=
self
.
pool2d_max
(
y
)
for
block
in
self
.
block_list
:
y
=
block
(
y
)
y
=
self
.
out_pool
(
y
)
return
y
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