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8fd11dda
编写于
1月 07, 2021
作者:
S
SunAhong1993
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
for pad
上级
16df89ef
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
315 addition
and
105 deletion
+315
-105
x2paddle/core/program.py
x2paddle/core/program.py
+1
-1
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py
..._mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py
+2
-1
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
...h/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
+32
-0
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
+10
-4
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/__init__.py
...p_mapper/static/onnx2paddle/onnx_custom_layer/__init__.py
+4
-1
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/one_hot.py
...op_mapper/static/onnx2paddle/onnx_custom_layer/one_hot.py
+10
-23
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim2.py
...pper/static/onnx2paddle/onnx_custom_layer/pad_all_dim2.py
+30
-0
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4.py
...pper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4.py
+36
-0
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
...c/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
+30
-0
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_two_input.py
...per/static/onnx2paddle/onnx_custom_layer/pad_two_input.py
+7
-6
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
+153
-69
未找到文件。
x2paddle/core/program.py
浏览文件 @
8fd11dda
...
...
@@ -354,7 +354,7 @@ class PaddleGraph(object):
remove_default_attrs
(
layer
.
kernel
,
layer
.
attrs
)
edges_in
=
self
.
edges_in
.
get
(
layer_id
,
[])
edges_out
=
self
.
edges_out
.
get
(
layer_id
,
[])
if
len
(
edges_in
)
==
0
and
len
(
edges_out
)
==
0
:
if
len
(
edges_in
)
==
0
and
len
(
edges_out
)
==
0
and
layer
.
outputs
[
0
]
not
in
self
.
outputs
:
continue
line
=
""
...
...
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py
浏览文件 @
8fd11dda
...
...
@@ -16,4 +16,5 @@
from
.one_hot
import
OneHot
from
.pad_two_input
import
PadWithTwoInput
from
.pad_all_dim2
import
PadAllDim2
from
.pad_all_dim4
import
PadAllDim4
\ No newline at end of file
from
.pad_all_dim4
import
PadAllDim4
from
.pad_all_dim4_one_input
import
PadAllDim4WithOneInput
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
0 → 100644
浏览文件 @
8fd11dda
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
paddle
from
x2paddle.core.util
import
*
class
PadAllDim4WithOneInput
(
object
):
def
__init__
(
self
,
pad
,
value
,
mode
):
self
.
layer_attrs
=
{}
self
.
layer_attrs
[
'mode'
]
=
mode
self
.
layer_attrs
[
'data_format'
]
=
'NCHW'
self
.
layer_attrs
[
'value'
]
=
value
self
.
pad1
=
pad
[
0
:
4
]
self
.
pad2
=
pad
[
4
:
9
]
def
__call__
(
self
,
x
):
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
self
.
pad1
,
**
self
.
layer_attrs
)
x
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
self
.
pad2
,
**
self
.
layer_attrs
)
out
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
return
out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
浏览文件 @
8fd11dda
...
...
@@ -415,7 +415,7 @@ class OpSet9():
paddle_op
=
'paddle.nn.Pad{}D'
.
format
(
len
(
output_shape
)
-
2
)
paddings
=
np
.
array
(
pads
).
reshape
(
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
)
paddings
=
np
.
flip
(
paddings
).
flatten
().
tolist
()
paddings
=
np
.
flip
(
paddings
,
axis
=
0
).
flatten
().
tolist
()
layer_attrs
[
'padding'
]
=
paddings
else
:
if
data_shape
:
...
...
@@ -435,10 +435,16 @@ class OpSet9():
if
output_shape
:
assume_pad
|=
output_shape
and
2
*
len
(
output_shape
)
==
len
(
pads
)
# NCHW
if
assume_pad
:
paddle_op
=
'paddle.nn.
functional.pad
'
paddle_op
=
'paddle.nn.
Pad2D
'
paddings
=
np
.
array
(
pads
).
reshape
(
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
).
flatten
().
tolist
()
layer_attrs
[
'pad'
]
=
paddings
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
)
paddings
=
np
.
flip
(
paddings
,
axis
=
0
).
flatten
().
tolist
()
if
sum
(
paddings
[:
4
])
==
0
:
paddings
=
paddings
[
4
:]
layer_attrs
[
'padding'
]
=
paddings
else
:
layer_attrs
[
"pad"
]
=
paddings
paddle_op
=
"custom_layer:PadAllDim4WithOneInput"
else
:
raise
Exception
(
"The padding value {} is wrong!"
.
format
(
pads
))
self
.
paddle_graph
.
add_layer
(
...
...
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/__init__.py
浏览文件 @
8fd11dda
...
...
@@ -14,4 +14,7 @@
from
.one_hot
import
one_hot
from
.pad
import
custom_pad
\ No newline at end of file
from
.pad_two_input
import
pad_with_two_input
from
.pad_all_dim2
import
pad_all_dim2
from
.pad_all_dim4
import
pad_all_dim4
from
.pad_all_dim4_one_input
import
pad_all_dim4_one_input
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/one_hot.py
浏览文件 @
8fd11dda
...
...
@@ -14,31 +14,18 @@
import
paddle
def
one_hot
(
self
,
indices
,
depth
,
values
,
axis
):
indices_shape
=
paddle
.
shape
(
indices
)
tmp
=
paddle
.
ones_like
(
indices_shape
,
dtype
=
"int32"
)
rank
=
paddle
.
sum
(
tmp
)
def
one_hot
(
indices
,
depth
,
values
,
axis
):
indices_shape
=
indices
.
shape
rank
=
len
(
indices
.
shape
)
real_axis
=
axis
if
axis
<
0
:
real_axis
=
axis
+
rank
+
1
depth_range
=
paddle
.
arange
(
end
=
depth
)
zero
=
paddle
.
zeros
([
1
],
dtype
=
"int32"
)
one
=
paddle
.
ones
([
1
],
dtype
=
"int32"
)
axis
=
axis
*
one
new_axis
=
axis
+
rank
+
1
cond
=
paddle
.
less_than
(
axis
,
zero
)
real_axis
=
paddle
.
where
(
cond
,
new_axis
,
axis
)
ls
=
paddle
.
slice
(
indices_shape
,
axes
=
[
0
],
starts
=
[
0
],
ends
=
real_axis
)
rs
=
paddle
.
slice
(
indices_shape
,
axes
=
[
0
],
starts
=
real_axis
,
ends
=
rank
)
tmp
=
paddle
.
ones_like
(
ls
,
dtype
=
"int32"
)
ls_len
=
paddle
.
sum
(
tmp
)
ls_list
=
paddle
.
ones
(
ls_len
,
dtype
=
"int32"
)
tmp
=
paddle
.
ones_like
(
rs
,
dtype
=
"int32"
)
rs_len
=
paddle
.
sum
(
tmp
)
rs_list
=
paddle
.
ones
(
rs_len
,
dtype
=
"int32"
)
depth_range_shape
=
paddle
.
shape
(
depth_range
)
targets_shape
=
paddle
.
concat
([
ls_list
,
depth_range_shape
,
rs_list
],
axis
=
0
)
targets
=
paddle
.
reshape
(
depth_range
,
targets_shape
)
ls
=
tuple
(
indices_shape
[
0
:
real_axis
])
rs
=
tuple
(
indices_shape
[
real_axis
:
rank
])
targets
=
paddle
.
reshape
(
depth_range
,
(
1
,)
*
(
real_axis
-
0
)
+
tuple
(
depth_range
.
shape
)
+
(
1
,)
*
(
rank
-
real_axis
))
mod
=
paddle
.
mod
(
indices
,
depth
)
v_shape
=
paddle
.
concat
([
ls
,
paddle
.
shape
(
one
),
rs
],
axis
=
0
)
v
=
paddle
.
reshape
(
mod
,
v_shape
)
v
=
paddle
.
reshape
(
mod
,
ls
+
(
1
,)
+
rs
)
out
=
targets
==
v
out
=
paddle
.
cast
(
out
,
"float32"
)
on_value
=
paddle
.
slice
(
values
,
axes
=
[
0
],
starts
=
[
1
],
ends
=
[
2
])
...
...
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim2.py
0 → 100644
浏览文件 @
8fd11dda
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
paddle
def
pad_all_dim2
(
x
,
pad
,
value
,
mode
):
pad
=
paddle
.
reshape
(
pad
,
shape
=
[
2
,
-
1
])
pad
=
paddle
.
transpose
(
pad
,
perm
=
[
1
,
0
])
pad
=
paddle
.
reverse
(
pad
,
axis
=
[
0
])
pad
=
paddle
.
flatten
(
pad
)
pad
=
paddle
.
cast
(
pad
,
dtype
=
"int32"
)
x
=
paddle
.
unsqueeze
(
x
,
axis
=
[
0
,
1
])
out
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad
,
mode
=
mode
,
data_format
=
'NCHW'
,
value
=
value
)
out
=
paddle
.
squeeze
(
out
,
axis
=
[
0
,
1
])
return
out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4.py
0 → 100644
浏览文件 @
8fd11dda
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
paddle
def
pad_all_dim4
(
x
,
pad
,
value
,
mode
):
pad
=
paddle
.
reshape
(
pad
,
shape
=
[
2
,
-
1
])
pad
=
paddle
.
transpose
(
pad
,
perm
=
[
1
,
0
])
pad
=
paddle
.
reverse
(
pad
,
axis
=
[
0
])
pad
=
paddle
.
flatten
(
pad
)
pad
=
paddle
.
cast
(
pad
,
dtype
=
"int32"
)
pad1
,
pad2
=
paddle
.
split
(
pad
,
num_or_sections
=
2
,
axis
=
0
)
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad1
,
mode
=
mode
,
data_format
=
'NCHW'
,
value
=
value
)
x
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad2
,
mode
=
mode
,
data_format
=
'NCHW'
,
value
=
value
)
out
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
return
out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py
0 → 100644
浏览文件 @
8fd11dda
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
paddle
def
pad_all_dim4_one_input
(
x
,
pad
,
value
,
mode
):
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad
[
0
:
4
],
mode
=
mode
,
data_format
=
'NCHW'
,
value
=
value
)
x
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
x
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad
[
4
:
9
],
mode
=
mode
,
data_format
=
'NCHW'
,
value
=
value
)
out
=
paddle
.
transpose
(
x
,
perm
=
[
2
,
3
,
0
,
1
])
return
out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad.py
→
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad
_two_input
.py
浏览文件 @
8fd11dda
...
...
@@ -14,14 +14,15 @@
import
paddle
def
custom_pad
(
self
,
x
,
pad
,
value
,
mode
):
layer_attrs
=
{}
layer_attrs
[
'mode'
]
=
string
(
mode
)
layer_attrs
[
'data_format'
]
=
string
(
'NCHW'
)
layer_attrs
[
'value'
]
=
value
def
pad_with_two_input
(
x
,
pad
,
value
,
mode
,
data_format
):
pad
=
paddle
.
reshape
(
pad
,
shape
=
[
2
,
-
1
])
pad
=
paddle
.
transpose
(
pad
,
perm
=
[
1
,
0
])
pad
=
paddle
.
reverse
(
pad
,
axis
=
[
0
])
pad
=
paddle
.
flatten
(
pad
)
out
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad
,
**
self
.
layer_attrs
)
pad
=
paddle
.
cast
(
pad
,
dtype
=
"int32"
)
out
=
paddle
.
nn
.
functional
.
pad
(
x
=
x
,
pad
=
pad
,
value
=
value
,
mode
=
mode
,
data_format
=
data_format
)
return
out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
浏览文件 @
8fd11dda
...
...
@@ -106,6 +106,9 @@ class OpSet9():
'ReduceMax'
:
[
'paddle.max'
,
dict
(
axes
=
'axis'
,
keepdims
=
'keepdim'
),
dict
(
keepdim
=
1
)],
'ReduceProd'
:
[
'paddle.prod'
,
dict
(
axes
=
'axis'
,
keepdims
=
'keepdim'
),
dict
(
keepdim
=
1
)],
# active function
'Relu'
:
[
'paddle.nn.functional.relu'
],
'LeakyRelu'
:
[
'paddle.nn.functional.leaky_relu'
,
...
...
@@ -380,73 +383,122 @@ class OpSet9():
value
=
node
.
get_attr
(
'value'
,
0.
)
data_shape
=
val_x
.
out_shapes
[
0
]
output_shape
=
node
.
out_shapes
[
0
]
assume_pad
2d
=
False
assume_pad
=
False
layer_attrs
=
{}
layer_attrs
[
'mode'
]
=
string
(
mode
)
layer_attrs
[
'value'
]
=
value
if
not
op_independent
:
output_name
=
node
.
name
+
'_paded'
else
:
output_name
=
node
.
name
layer_outputs
=
[
output_name
]
if
is_pads_attr
:
paddings
=
[]
if
len
(
pads
)
==
4
:
assume_pad2d
|=
mode
!=
'constant'
paddle_op
=
'paddle.nn.functional.pad'
if
len
(
pads
)
in
[
2
,
4
,
6
]:
if
data_shape
:
assume_pad
2d
|=
data_shape
and
len
(
data_shape
)
==
4
# NCHW
assume_pad
|=
data_shape
and
2
*
(
len
(
data_shape
)
-
2
)
==
len
(
pads
)
# NCHW
if
output_shape
:
assume_pad2d
|=
output_shape
and
len
(
output_shape
)
==
4
# NCHW
if
assume_pad2d
:
paddle_op
=
'paddle.nn.functional.pad'
layer_attrs
[
'data_format'
]
=
string
(
'NCHW'
)
layer_attrs
[
'value'
]
=
value
else
:
paddle_op
=
'paddle.fluid.layers.pad'
layer_attrs
[
"pad_value"
]
=
value
if
len
(
pads
)
==
4
:
paddings
=
np
.
array
(
pads
).
reshape
(
(
-
1
,
2
)).
transpose
().
flatten
().
tolist
()
# SSEE -> SESE
assume_pad
|=
output_shape
and
2
*
(
len
(
output_shape
)
-
2
)
==
len
(
pads
)
# NCHW
if
assume_pad
:
if
len
(
pads
)
==
2
:
data_format
=
"NCL"
elif
len
(
pads
)
==
4
:
data_format
=
"NCHW"
else
:
data_format
=
"NCDHW"
paddings
=
np
.
array
(
pads
).
reshape
(
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
)
paddings
=
np
.
flip
(
paddings
,
axis
=
0
).
flatten
().
tolist
()
layer_attrs
[
'pad'
]
=
paddings
layer_attrs
[
'data_format'
]
=
data_format
else
:
if
data_shape
:
assume_pad
|=
data_shape
and
2
*
len
(
data_shape
)
==
len
(
pads
)
# NCHW
if
output_shape
:
assume_pad
|=
output_shape
and
2
*
len
(
output_shape
)
==
len
(
pads
)
# NCHW
if
assume_pad
:
paddings
=
np
.
array
(
pads
).
reshape
(
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
).
flatten
().
tolist
()
layer_attrs
[
'pad'
]
=
paddings
else
:
raise
Exception
(
"The padding value {} is wrong!"
.
format
(
pads
))
elif
len
(
pads
)
==
8
:
paddings
=
np
.
array
(
pads
).
reshape
(
(
-
1
,
4
)).
transpose
().
flatten
().
tolist
()
# SSEE -> SESE
if
sum
(
paddings
[:
4
])
==
0
:
paddle_op
=
'paddle.nn.functional.pad'
paddings
=
paddings
[
4
:]
layer_attrs
[
'value'
]
=
value
if
'pad_value'
in
layer_attrs
:
layer_attrs
.
pop
(
'pad_value'
)
tmp_paddings
=
copy
.
deepcopy
(
paddings
)
paddings
[
0
]
=
tmp_paddings
[
2
]
paddings
[
1
]
=
tmp_paddings
[
3
]
paddings
[
2
]
=
tmp_paddings
[
0
]
paddings
[
3
]
=
tmp_paddings
[
1
]
if
paddle_op
==
'paddle.nn.functional.pad'
:
layer_attrs
[
'pad'
]
=
paddings
else
:
layer_attrs
[
'paddings'
]
=
paddings
if
op_independent
:
self
.
paddle_graph
.
add_layer
(
paddle_op
,
inputs
=
{
'x'
:
val_x
.
name
},
outputs
=
[
node
.
name
],
**
layer_attrs
)
if
data_shape
:
assume_pad
|=
data_shape
and
2
*
len
(
data_shape
)
==
len
(
pads
)
# NCHW
if
output_shape
:
assume_pad
|=
output_shape
and
2
*
len
(
output_shape
)
==
len
(
pads
)
# NCHW
if
assume_pad
:
paddings
=
np
.
array
(
pads
).
reshape
(
(
2
,
-
1
)).
transpose
().
astype
(
"int32"
)
paddings
=
np
.
flip
(
paddings
,
axis
=
0
).
flatten
().
tolist
()
if
sum
(
paddings
[:
4
])
==
0
:
paddings
=
paddings
[
4
:]
layer_attrs
[
'pad'
]
=
paddings
else
:
layer_attrs
[
'pad'
]
=
paddings
paddle_op
=
"custom_layer:pad_all_dim4_one_input"
else
:
self
.
paddle_graph
.
add_layer
(
paddle_op
,
inputs
=
{
'x'
:
val_x
.
name
},
outputs
=
[
node
.
name
+
'_paded'
],
**
layer_attrs
)
raise
Exception
(
"The padding value {} is wrong!"
.
format
(
pads
))
self
.
paddle_graph
.
add_layer
(
paddle_op
,
inputs
=
{
'x'
:
val_x
.
name
},
outputs
=
layer_outputs
,
**
layer_attrs
)
if
not
op_independent
:
return
node
.
name
+
'_paded'
else
:
if
pad_shape
[
0
]
==
4
:
assume_pad2d
|=
mode
!=
'constant'
pads_len
=
val_pad
.
out_shapes
[
0
][
0
]
if
pads_len
in
[
2
,
4
,
6
]:
if
data_shape
:
assume_pad
2d
|=
data_shape
and
len
(
data_shape
)
==
4
# NCHW
assume_pad
|=
data_shape
and
2
*
(
len
(
data_shape
)
-
2
)
==
pads_len
# NCHW
if
output_shape
:
assume_pad2d
|=
output_shape
and
len
(
output_shape
)
==
4
# NCHW
if
pad_shape
[
0
]
==
8
or
not
assume_pad2d
:
raise
Exception
(
"When the pad shape is 8 and pad is tensor, the op is not supported yet!"
)
layer_attrs
[
'value'
]
=
value
self
.
paddle_graph
.
add_layer
(
"custom_layer:custom_pad"
,
inputs
=
{
'x'
:
val_x
.
name
,
'pad'
:
val_pad
.
name
},
outputs
=
[
node
.
name
+
'_paded'
],
**
layer_attrs
)
assume_pad
|=
output_shape
and
2
*
(
len
(
output_shape
)
-
2
)
==
pads_len
# NCHW
if
assume_pad
:
if
pads_len
==
2
:
data_format
=
"NCL"
elif
pads_len
==
4
:
data_format
=
"NCHW"
else
:
data_format
=
"NCDHW"
self
.
paddle_graph
.
add_layer
(
"custom_layer:pad_with_two_input"
,
inputs
=
{
'x'
:
val_x
.
name
,
'pad'
:
val_pad
.
name
},
outputs
=
layer_outputs
,
value
=
value
,
mode
=
string
(
mode
),
data_format
=
string
(
data_format
))
else
:
if
data_shape
:
assume_pad
|=
data_shape
and
2
*
len
(
data_shape
)
==
pads_len
# NCHW
if
output_shape
:
assume_pad
|=
output_shape
and
2
*
len
(
output_shape
)
==
pads_len
# NCHW
if
assume_pad
:
if
pads_len
==
4
:
self
.
paddle_graph
.
add_layer
(
"custom_layer:pad_all_dim2"
,
inputs
=
{
'x'
:
val_x
.
name
,
'pad'
:
val_pad
.
name
},
outputs
=
layer_outputs
,
value
=
value
,
mode
=
string
(
mode
))
else
:
raise
Exception
(
"The padding value is wrong!"
)
elif
pads_len
==
8
:
if
data_shape
:
assume_pad
|=
data_shape
and
2
*
len
(
data_shape
)
==
pads_len
# NCHW
if
output_shape
:
assume_pad
|=
output_shape
and
2
*
len
(
output_shape
)
==
pads_len
# NCHW
if
assume_pad
:
self
.
paddle_graph
.
add_layer
(
"custom_layer:pad_all_dim4"
,
inputs
=
{
'x'
:
val_x
.
name
,
'pad'
:
val_pad
.
name
},
outputs
=
layer_outputs
,
value
=
value
,
mode
=
string
(
mode
))
else
:
print
(
pads_len
)
raise
Exception
(
"The padding value is wrong!"
)
if
not
op_independent
:
return
node
.
name
+
'_paded'
...
...
@@ -650,15 +702,11 @@ class OpSet9():
inputs
=
{
"x"
:
indices
.
name
},
outputs
=
[
indices_cast
],
dtype
=
string
(
'int64'
))
op_name
=
name_generator
(
"embedding"
,
self
.
nn_name2id
)
output_name
=
node
.
name
layer_outputs
=
[
op_name
,
output_name
]
self
.
paddle_graph
.
add_layer
(
'paddle.nn.Embedding'
,
inputs
=
{
"x"
:
indices_cast
},
outputs
=
layer_outputs
,
param_attr
=
string
(
val_x
.
name
),
size
=
val_x
.
out_shapes
[
0
])
'paddle.nn.functional.embedding'
,
inputs
=
{
"x"
:
indices_cast
,
"weight"
:
val_x
.
name
},
outputs
=
[
node
.
name
])
else
:
from
functools
import
reduce
reshape_shape
=
reduce
(
lambda
x
,
y
:
x
*
y
,
indices_shape
)
...
...
@@ -830,14 +878,21 @@ class OpSet9():
starts
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
ends
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
starts_value
=
_const_weight_or_none
(
starts
)
if
starts_value
is
not
None
:
starts_value
=
starts_value
.
tolist
()
ends_value
=
_const_weight_or_none
(
ends
)
if
ends_value
is
not
None
:
ends_value
=
ends_value
.
tolist
()
if
len
(
node
.
inputs
)
>
2
:
s_len
=
len
(
val_x
.
out_shapes
[
0
])
axes
=
list
(
range
(
s_len
))
if
len
(
node
.
inputs
)
>
3
:
axes
=
self
.
graph
.
get_input_node
(
node
,
idx
=
3
,
copy
=
True
)
axes
=
_const_weight_or_none
(
axes
,
necessary
=
True
)
axes
_node
=
self
.
graph
.
get_input_node
(
node
,
idx
=
3
,
copy
=
True
)
axes
=
_const_weight_or_none
(
axes
_node
,
necessary
=
True
).
tolist
(
)
if
len
(
node
.
inputs
)
>
4
:
steps
=
self
.
graph
.
get_input_node
(
node
,
idx
=
4
,
copy
=
True
)
steps
=
_const_weight_or_none
(
steps
)
steps
=
_const_weight_or_none
(
steps
).
tolist
()
layer_attrs
=
{
"axes"
:
axes
,
"starts"
:
starts
.
name
,
...
...
@@ -873,6 +928,8 @@ class OpSet9():
layer_attrs
[
'starts'
]
=
starts_cast
if
ends
.
dtype
!=
'int32'
:
ends_cast
=
ends
.
name
+
'_cast'
else
:
ends_cast
=
ends
.
name
self
.
paddle_graph
.
add_layer
(
'paddle.cast'
,
inputs
=
{
"x"
:
ends
.
name
},
...
...
@@ -888,6 +945,7 @@ class OpSet9():
ends
[
idx
]
=
2
**
31
-
1
layer_attrs
=
{
"axes"
:
axes
,
"starts"
:
starts
,
"ends"
:
ends
}
if
steps
is
not
None
:
layer_attrs
[
'strides'
]
=
steps
self
.
paddle_graph
.
add_layer
(
...
...
@@ -1012,6 +1070,12 @@ class OpSet9():
inputs
=
{
'x'
:
val_shape
.
name
},
outputs
=
[
val_shape
.
name
],
shape
=
val_shape
.
out_shapes
[
0
])
if
val_shape
.
dtype
!=
"int32"
:
self
.
paddle_graph
.
add_layer
(
'paddle.cast'
,
inputs
=
{
'x'
:
val_shape
.
name
},
outputs
=
[
val_shape
.
name
],
dtype
=
string
(
"int32"
))
self
.
paddle_graph
.
add_layer
(
'paddle.reshape'
,
inputs
=
{
'x'
:
val_x
.
name
,
...
...
@@ -1247,7 +1311,10 @@ class OpSet9():
@
print_mapping_info
def
Transpose
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
perm
=
node
.
get_attr
(
'perm'
)
s_len
=
len
(
val_x
.
out_shapes
[
0
])
perm_default
=
list
(
range
(
s_len
))
perm_default
.
reverse
()
perm
=
node
.
get_attr
(
'perm'
,
perm_default
)
self
.
paddle_graph
.
add_layer
(
"paddle.transpose"
,
inputs
=
{
"x"
:
val_x
.
name
},
...
...
@@ -1620,8 +1687,13 @@ class OpSet9():
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
self
.
paddle_graph
.
add_layer
(
"paddle.shape"
,
inputs
=
{
"
x
"
:
val_x
.
name
},
inputs
=
{
"
input
"
:
val_x
.
name
},
outputs
=
[
node
.
name
])
self
.
paddle_graph
.
add_layer
(
'paddle.cast'
,
inputs
=
{
"x"
:
node
.
name
},
outputs
=
[
node
.
name
],
dtype
=
string
(
'int64'
))
self
.
paddle_graph
.
add_layer
(
"paddle.prod"
,
inputs
=
{
"x"
:
node
.
name
},
...
...
@@ -1630,10 +1702,22 @@ class OpSet9():
@
print_mapping_info
def
Sign
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
if
node
.
dtype
not
in
[
"float16"
,
"float32"
,
"float64"
]:
self
.
paddle_graph
.
add_layer
(
"paddle.cast"
,
inputs
=
{
"x"
:
val_x
.
name
},
outputs
=
[
val_x
.
name
],
dtype
=
string
(
"float32"
))
self
.
paddle_graph
.
add_layer
(
"paddle.sign"
,
inputs
=
{
"x"
:
val_x
.
name
},
outputs
=
[
node
.
name
])
if
node
.
dtype
not
in
[
"float16"
,
"float32"
,
"float64"
]:
self
.
paddle_graph
.
add_layer
(
"paddle.cast"
,
inputs
=
{
"x"
:
node
.
name
},
outputs
=
[
node
.
name
],
dtype
=
string
(
node
.
dtype
))
@
print_mapping_info
def
OneHot
(
self
,
node
):
...
...
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