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体验新版 GitCode,发现更多精彩内容 >>
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ca9ea622
编写于
10月 20, 2020
作者:
W
WenmuZhou
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电子邮件补丁
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添加im2seq实现
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bdad0cef
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1 changed file
with
4 addition
and
56 deletion
+4
-56
ppocr/modeling/necks/rnn.py
ppocr/modeling/necks/rnn.py
+4
-56
未找到文件。
ppocr/modeling/necks/rnn.py
浏览文件 @
ca9ea622
...
...
@@ -21,18 +21,6 @@ from paddle import nn
from
ppocr.modeling.heads.rec_ctc_head
import
get_para_bias_attr
class
EncoderWithReshape
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
**
kwargs
):
super
().
__init__
()
self
.
out_channels
=
in_channels
def
forward
(
self
,
x
):
B
,
C
,
H
,
W
=
x
.
shape
x
=
x
.
reshape
((
B
,
C
,
-
1
))
x
=
x
.
transpose
([
0
,
2
,
1
])
# (NTC)(batch, width, channels)
return
x
class
Im2Seq
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
**
kwargs
):
super
().
__init__
()
...
...
@@ -40,9 +28,8 @@ class Im2Seq(nn.Layer):
def
forward
(
self
,
x
):
B
,
C
,
H
,
W
=
x
.
shape
assert
H
==
1
x
=
x
.
transpose
((
0
,
2
,
3
,
1
))
x
=
x
.
reshape
((
-
1
,
C
))
x
=
x
.
reshape
((
B
,
-
1
,
W
))
x
=
x
.
transpose
((
0
,
2
,
1
))
# (NTC)(batch, width, channels)
return
x
...
...
@@ -50,49 +37,10 @@ class EncoderWithRNN(nn.Layer):
def
__init__
(
self
,
in_channels
,
hidden_size
):
super
(
EncoderWithRNN
,
self
).
__init__
()
self
.
out_channels
=
hidden_size
*
2
# self.lstm1_fw = nn.LSTMCell(
# in_channels,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st1_fc1_w'),
# bias_ih_attr=ParamAttr(name='lstm_st1_fc1_b'),
# weight_hh_attr=ParamAttr(name='lstm_st1_out1_w'),
# bias_hh_attr=ParamAttr(name='lstm_st1_out1_b'),
# )
# self.lstm1_bw = nn.LSTMCell(
# in_channels,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st1_fc2_w'),
# bias_ih_attr=ParamAttr(name='lstm_st1_fc2_b'),
# weight_hh_attr=ParamAttr(name='lstm_st1_out2_w'),
# bias_hh_attr=ParamAttr(name='lstm_st1_out2_b'),
# )
# self.lstm2_fw = nn.LSTMCell(
# hidden_size,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st2_fc1_w'),
# bias_ih_attr=ParamAttr(name='lstm_st2_fc1_b'),
# weight_hh_attr=ParamAttr(name='lstm_st2_out1_w'),
# bias_hh_attr=ParamAttr(name='lstm_st2_out1_b'),
# )
# self.lstm2_bw = nn.LSTMCell(
# hidden_size,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st2_fc2_w'),
# bias_ih_attr=ParamAttr(name='lstm_st2_fc2_b'),
# weight_hh_attr=ParamAttr(name='lstm_st2_out2_w'),
# bias_hh_attr=ParamAttr(name='lstm_st2_out2_b'),
# )
self
.
lstm
=
nn
.
LSTM
(
in_channels
,
hidden_size
,
direction
=
'bidirectional'
,
num_layers
=
2
)
def
forward
(
self
,
x
):
# fw_x, _ = self.lstm1_fw(x)
# fw_x, _ = self.lstm2_fw(fw_x)
#
# # bw
# bw_x, _ = self.lstm1_bw(x)
# bw_x, _ = self.lstm2_bw(bw_x)
# x = paddle.concat([fw_x, bw_x], axis=2)
x
,
_
=
self
.
lstm
(
x
)
return
x
...
...
@@ -118,13 +66,13 @@ class EncoderWithFC(nn.Layer):
class
SequenceEncoder
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
encoder_type
,
hidden_size
=
48
,
**
kwargs
):
super
(
SequenceEncoder
,
self
).
__init__
()
self
.
encoder_reshape
=
EncoderWithReshape
(
in_channels
)
self
.
encoder_reshape
=
Im2Seq
(
in_channels
)
self
.
out_channels
=
self
.
encoder_reshape
.
out_channels
if
encoder_type
==
'reshape'
:
self
.
only_reshape
=
True
else
:
support_encoder_dict
=
{
'reshape'
:
EncoderWithReshape
,
'reshape'
:
Im2Seq
,
'fc'
:
EncoderWithFC
,
'rnn'
:
EncoderWithRNN
}
...
...
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