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027bfe06
编写于
7月 15, 2020
作者:
C
Channingss
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
paddle2onnx support opset:9,10,11
上级
bbc964e8
变更
12
展开全部
隐藏空白更改
内联
并排
Showing
12 changed file
with
4 addition
and
4013 deletion
+4
-4013
x2paddle/convert.py
x2paddle/convert.py
+4
-4
x2paddle/op_mapper/onnx_op_mapper.py
x2paddle/op_mapper/onnx_op_mapper.py
+0
-92
x2paddle/op_mapper/onnx_opsets/__init__.py
x2paddle/op_mapper/onnx_opsets/__init__.py
+0
-0
x2paddle/op_mapper/onnx_opsets/custom_layer/InstanceNormalization.py
..._mapper/onnx_opsets/custom_layer/InstanceNormalization.py
+0
-56
x2paddle/op_mapper/onnx_opsets/custom_layer/__init__.py
x2paddle/op_mapper/onnx_opsets/custom_layer/__init__.py
+0
-115
x2paddle/op_mapper/onnx_opsets/custom_layer/register.py
x2paddle/op_mapper/onnx_opsets/custom_layer/register.py
+0
-55
x2paddle/op_mapper/onnx_opsets/opset9.py
x2paddle/op_mapper/onnx_opsets/opset9.py
+0
-1523
x2paddle/op_mapper/paddle_custom_layer/__init__.py
x2paddle/op_mapper/paddle_custom_layer/__init__.py
+0
-0
x2paddle/op_mapper/paddle_custom_layer/im2sequence.py
x2paddle/op_mapper/paddle_custom_layer/im2sequence.py
+0
-80
x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py
x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py
+0
-416
x2paddle/op_mapper/paddle_custom_layer/yolo_box.py
x2paddle/op_mapper/paddle_custom_layer/yolo_box.py
+0
-822
x2paddle/op_mapper/paddle_op_mapper.py
x2paddle/op_mapper/paddle_op_mapper.py
+0
-850
未找到文件。
x2paddle/convert.py
浏览文件 @
027bfe06
...
...
@@ -178,7 +178,7 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
return
print
(
"Now translating model from onnx to paddle."
)
from
x2paddle.op_mapper.onnx_op_mapper
import
ONNXOpMapper
from
x2paddle.op_mapper.onnx
2paddle.onnx
_op_mapper
import
ONNXOpMapper
from
x2paddle.decoder.onnx_decoder
import
ONNXDecoder
from
x2paddle.optimizer.onnx_optimizer
import
ONNXOptimizer
model
=
ONNXDecoder
(
model_path
)
...
...
@@ -192,12 +192,12 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
print
(
"Paddle model and code generated."
)
def
paddle2onnx
(
model_path
,
save_dir
,
opset
):
def
paddle2onnx
(
model_path
,
save_dir
,
opset
_number
):
from
x2paddle.decoder.paddle_decoder
import
PaddleDecoder
from
x2paddle.op_mapper.paddle_op_mapper
import
PaddleOpMapper
from
x2paddle.op_mapper.paddle
2onnx.paddle
_op_mapper
import
PaddleOpMapper
model
=
PaddleDecoder
(
model_path
,
'__model__'
,
'__params__'
)
mapper
=
PaddleOpMapper
()
mapper
.
convert
(
model
.
program
,
save_dir
,
opset
)
mapper
.
convert
(
model
.
program
,
save_dir
,
opset
_number
=
opset_number
)
def
main
():
...
...
x2paddle/op_mapper/onnx_op_mapper.py
已删除
100644 → 0
浏览文件 @
bbc964e8
# Copyright (c) 2019 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.
from
x2paddle.op_mapper.onnx_opsets.opset9
import
OpSet9
from
x2paddle.core.op_mapper
import
OpMapper
from
x2paddle.op_mapper.onnx_opsets.custom_layer
import
*
from
x2paddle.decoder.onnx_decoder
import
ONNXGraph
,
ONNXGraphNode
,
ONNXGraphDataNode
class
ONNXOpMapper
(
OpMapper
):
def
__init__
(
self
,
decoder
):
super
(
ONNXOpMapper
,
self
).
__init__
()
self
.
support_op_sets
=
[
9
,
]
self
.
default_op_set
=
9
self
.
graph
=
decoder
.
graph
self
.
opset
=
self
.
create_opset
(
decoder
)
if
not
self
.
op_checker
():
raise
Exception
(
"Model are not supported yet."
)
#mapping op
print
(
"Total nodes: {}"
.
format
(
sum
([
isinstance
(
node
,
ONNXGraphNode
)
for
name
,
node
in
self
.
graph
.
node_map
.
items
()
])))
print
(
"Nodes converting ..."
)
for
node_name
in
self
.
graph
.
topo_sort
:
node
=
self
.
graph
.
get_node
(
node_name
)
op
=
node
.
layer_type
if
hasattr
(
self
.
opset
,
op
):
func
=
getattr
(
self
.
opset
,
op
)
func
(
node
)
elif
op
in
self
.
opset
.
default_op_mapping
:
self
.
opset
.
directly_map
(
node
)
elif
op
in
custom_layers
:
self
.
opset
.
deal_custom_layer
(
node
)
elif
op
in
self
.
opset
.
elementwise_ops
:
self
.
opset
.
elementwise_map
(
node
)
print
(
"Nodes converted."
)
self
.
weights
=
self
.
opset
.
weights
self
.
omit_nodes
=
self
.
opset
.
omit_nodes
self
.
used_custom_layers
=
self
.
opset
.
used_custom_layers
def
op_checker
(
self
):
unsupported_ops
=
set
()
for
node_name
in
self
.
graph
.
topo_sort
:
node
=
self
.
graph
.
get_node
(
node_name
)
op
=
node
.
layer_type
if
not
hasattr
(
self
.
opset
,
op
)
and
\
op
not
in
self
.
opset
.
default_op_mapping
and
\
op
not
in
custom_layers
and
\
op
not
in
self
.
opset
.
elementwise_ops
:
unsupported_ops
.
add
(
op
)
if
len
(
unsupported_ops
)
==
0
:
return
True
else
:
print
(
"There are {} ops not supported yet, list as below"
.
format
(
len
(
unsupported_ops
)))
for
op
in
unsupported_ops
:
print
(
op
)
return
False
def
create_opset
(
self
,
decoder
):
run_op_set
=
self
.
default_op_set
opset
=
''
if
decoder
.
op_set
in
self
.
support_op_sets
:
opset
=
'OpSet'
+
str
(
decoder
.
op_set
)
elif
decoder
.
op_set
<
self
.
default_op_set
:
opset
=
'OpSet'
+
str
(
self
.
default_op_set
)
else
:
for
op_set
in
self
.
support_op_sets
:
if
decoder
.
op_set
>
op_set
:
run_op_set
=
op_set
else
:
break
opset
=
'OpSet'
+
str
(
run_op_set
)
print
(
'Now, onnx2paddle support convert onnx model opset_verison {},'
'opset_verison of your onnx model is {}, automatically treated as op_set: {}.'
.
format
(
self
.
support_op_sets
,
decoder
.
op_set
,
run_op_set
))
return
eval
(
opset
)(
decoder
)
x2paddle/op_mapper/onnx_opsets/__init__.py
已删除
100644 → 0
浏览文件 @
bbc964e8
x2paddle/op_mapper/onnx_opsets/custom_layer/InstanceNormalization.py
已删除
100644 → 0
浏览文件 @
bbc964e8
# Copyright (c) 2019 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.
from
.register
import
register
def
InstanceNormalization_shape
(
input_shape
):
return
input_shape
def
InstanceNormalization_layer
(
inputs
,
name
=
None
):
# TODO(lvmengsi@baidu.com): Check the accuracy when using fluid.layers.layer_norm.
epsilon
=
1e-5
input_
=
inputs
[
0
]
mean
=
fluid
.
layers
.
reduce_mean
(
input_
,
dim
=
[
2
,
3
],
keep_dim
=
True
)
var
=
fluid
.
layers
.
reduce_mean
(
fluid
.
layers
.
square
(
input_
-
mean
),
dim
=
[
2
,
3
],
keep_dim
=
True
)
if
name
is
not
None
:
scale_name
=
name
+
"_scale"
offset_name
=
name
+
"_offset"
scale_param
=
inputs
[
1
]
offset_param
=
inputs
[
2
]
scale
=
fluid
.
layers
.
create_parameter
(
name
=
scale_param
.
name
,
shape
=
input_
.
shape
[
1
:
2
],
dtype
=
"float32"
)
offset
=
fluid
.
layers
.
create_parameter
(
name
=
offset_param
.
name
,
shape
=
input_
.
shape
[
1
:
2
],
dtype
=
"float32"
)
tmp
=
fluid
.
layers
.
elementwise_mul
(
x
=
(
input_
-
mean
),
y
=
scale
,
axis
=
1
)
tmp
=
tmp
/
fluid
.
layers
.
sqrt
(
var
+
epsilon
)
tmp
=
fluid
.
layers
.
elementwise_add
(
tmp
,
offset
,
axis
=
1
)
return
tmp
def
InstanceNormalization_weights
(
name
,
data
=
None
):
weights_name
=
[
name
+
'_scale'
]
return
weights_name
register
(
kind
=
'InstanceNormalization'
,
shape
=
InstanceNormalization_shape
,
layer
=
InstanceNormalization_layer
,
child_func
=
None
,
weights
=
InstanceNormalization_weights
)
x2paddle/op_mapper/onnx_opsets/custom_layer/__init__.py
已删除
100644 → 0
浏览文件 @
bbc964e8
# Copyright (c) 2019 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.
from
.register
import
get_registered_layers
#custom layer import begins
from
.
import
InstanceNormalization
#custom layer import ends
custom_layers
=
get_registered_layers
()
def
set_args
(
f
,
params
):
""" set args for function 'f' using the parameters in node.layer.param
Args:
f (function): a python function object
params (object): a object contains attributes needed by f's arguments
Returns:
arg_names (list): a list of argument names
kwargs (dict): a dict contains needed arguments
"""
argc
=
f
.
__code__
.
co_argcount
arg_list
=
f
.
__code__
.
co_varnames
[
0
:
argc
]
kwargs
=
{}
for
arg_name
in
arg_list
:
if
hasattr
(
params
,
arg_name
)
and
params
is
not
None
:
kwargs
[
arg_name
]
=
getattr
(
params
,
arg_name
)
return
arg_list
,
kwargs
def
has_layer
(
layer_type
):
""" test whether this layer exists in custom layer
"""
return
layer_type
in
custom_layers
def
get_params
(
layer
,
layer_type
):
import
re
if
layer_type
.
lower
()
==
"deconvolution"
or
layer_type
.
lower
(
)
==
"convolutiondepthwise"
:
param_name
=
'_'
.
join
((
'convolution'
,
'param'
))
elif
layer_type
.
lower
()
==
"normalize"
:
param_name
=
'_'
.
join
((
'norm'
,
'param'
))
elif
len
(
layer_type
)
-
len
(
re
.
sub
(
"[A-Z]"
,
""
,
layer_type
))
>=
2
:
s
=
''
tmp_name
=
''
for
i
,
ch
in
enumerate
(
layer_type
):
if
i
==
0
:
s
+=
ch
.
lower
()
continue
elif
ch
.
isupper
()
and
layer_type
[
i
-
1
].
islower
():
tmp_name
+=
(
s
+
'_'
)
s
=
''
s
+=
ch
.
lower
()
tmp_name
+=
s
param_name
=
'_'
.
join
((
tmp_name
,
'param'
))
else
:
param_name
=
'_'
.
join
((
layer_type
.
lower
(),
'param'
))
return
getattr
(
layer
,
param_name
,
None
)
def
compute_output_shape
(
node
):
""" compute the output shape of custom layer
"""
layer_type
=
node
.
layer_type
assert
layer_type
in
custom_layers
,
"layer[%s] not exist in custom layers"
%
(
layer_type
)
shape_func
=
custom_layers
[
layer_type
][
'shape'
]
layer
=
node
.
layer
params
=
get_params
(
layer
,
layer_type
)
arg_names
,
kwargs
=
set_args
(
shape_func
,
params
)
input_shape
=
node
.
input_shape
return
shape_func
(
input_shape
,
**
kwargs
)
def
make_custom_layer
(
node
):
""" get the code which implement the custom layer function
"""
layer_type
=
node
.
layer_type
assert
layer_type
in
custom_layers
,
"layer[%s] not exist in custom layers"
%
(
layer_type
)
layer_func
=
custom_layers
[
layer_type
][
'layer'
]
import
inspect
return
inspect
.
getsource
(
layer_func
),
layer_func
def
make_custom_child_func
(
node
):
""" get the code which implement the custom layer function
"""
layer_type
=
node
.
layer_type
child_func
=
custom_layers
[
layer_type
][
'child_func'
]
if
child_func
is
None
:
return
None
,
child_func
import
inspect
return
inspect
.
getsource
(
child_func
),
child_func
def
deal_weights
(
node
,
data
=
None
):
""" deal the weights of the custom layer
"""
layer_type
=
node
.
layer_type
weights_func
=
custom_layers
[
layer_type
][
'weights'
]
name
=
node
.
layer_name
return
weights_func
(
name
,
data
)
x2paddle/op_mapper/onnx_opsets/custom_layer/register.py
已删除
100644 → 0
浏览文件 @
bbc964e8
# Copyright (c) 2019 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.
""" this module provides 'register' for registering customized layers
"""
g_custom_layers
=
{}
def
register
(
kind
,
shape
,
layer
,
child_func
,
weights
):
""" register a custom layer or a list of custom layers
Args:
@kind (str or list): type name of the layer
@shape (function): a function to generate the shape of layer's output
@layer (function): a function to generate the paddle code of layer
@weights (function): a function to deal with weights data
Returns:
None
"""
assert
type
(
shape
).
__name__
==
'function'
,
'shape should be a function'
assert
type
(
layer
).
__name__
==
'function'
,
'layer should be a function'
if
type
(
kind
)
is
str
:
kind
=
[
kind
]
else
:
assert
type
(
kind
)
is
list
,
'invalid param "kind" for register, not a list or str'
for
k
in
kind
:
assert
type
(
k
)
is
str
,
'invalid param "kind" for register, not a list of str'
assert
k
not
in
g_custom_layers
,
'this type[%s] has already been registered'
%
(
k
)
g_custom_layers
[
k
]
=
{
'shape'
:
shape
,
'layer'
:
layer
,
'child_func'
:
child_func
,
'weights'
:
weights
}
def
get_registered_layers
():
return
g_custom_layers
x2paddle/op_mapper/onnx_opsets/opset9.py
已删除
100644 → 0
浏览文件 @
bbc964e8
此差异已折叠。
点击以展开。
x2paddle/op_mapper/paddle_custom_layer/__init__.py
已删除
100644 → 0
浏览文件 @
bbc964e8
x2paddle/op_mapper/paddle_custom_layer/im2sequence.py
已删除
100644 → 0
浏览文件 @
bbc964e8
import
onnx
import
numpy
as
np
from
onnx
import
onnx_pb
,
helper
im2seq_counter
=
0
def
im2sequence
(
op
,
block
):
global
im2sequence_counter
n
,
c
,
h
,
w
=
block
.
var
(
op
.
input
(
'X'
)[
0
]).
shape
assert
h
>
0
and
w
>
0
,
"Only supported fixed input shape for im2sequence operator."
stride_h
,
stride_w
=
op
.
attr
(
'strides'
)
paddings
=
op
.
attr
(
'paddings'
)
assert
op
.
attr
(
'out_stride'
)
!=
1
,
"Only out_stride==1 is supported for im2sequence operator."
h
=
h
+
paddings
[
0
]
+
paddings
[
1
]
w
=
w
+
paddings
[
1
]
+
paddings
[
2
]
kernel_h
,
kernel_w
=
op
.
attr
(
'kernels'
)
out_h
=
1
+
(
h
-
kernel_h
+
stride_h
-
1
)
//
stride_h
out_w
=
1
+
(
w
-
kernel_w
+
stride_w
-
1
)
//
stride_w
h_steps
=
list
()
for
i
in
range
(
out_h
):
h_steps
.
append
([
i
*
stride_h
,
i
*
stride_h
+
kernel_h
])
w_steps
=
list
()
for
i
in
range
(
out_w
):
w_steps
.
append
([
i
*
stride_w
,
i
*
stride_w
+
kernel_w
])
nodes
=
list
()
slice_blocks
=
list
()
for
i
in
range
(
out_h
):
for
j
in
range
(
out_w
):
starts_name
=
"im2sequence.starts.{}.{}.{}"
.
format
(
im2seq_counter
,
i
,
j
)
starts_tensor
=
helper
.
make_tensor
(
name
=
starts_name
,
data_type
=
onnx_pb
.
TensorProto
.
INT64
,
dims
=
[
4
],
vals
=
[
0
,
0
,
h_steps
[
i
][
0
],
w_steps
[
j
][
0
]])
ends_name
=
"im2sequence.ends.{}.{}.{}"
.
format
(
im2seq_counter
,
i
,
j
)
ends_tensor
=
helper
.
make_tensor
(
name
=
ends_name
,
data_type
=
onnx_pb
.
TensorProto
.
INT64
,
dims
=
[
4
],
vals
=
[
999999
,
999999
,
h_steps
[
i
][
1
],
w_steps
[
j
][
1
]])
starts_node
=
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
[
starts_name
],
value
=
starts_tensor
)
ends_node
=
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
[
ends_name
],
value
=
ends_tensor
)
nodes
.
extend
([
starts_node
,
ends_node
])
slice_block_name
=
"im2sequence.slice.{}.{}.{}"
.
format
(
im2seq_counter
,
i
,
j
)
slice_block_node
=
helper
.
make_node
(
'Slice'
,
inputs
=
[
op
.
input
(
'X'
)[
0
],
starts_name
,
ends_name
],
outputs
=
[
slice_block_name
])
flatten_block_name
=
"im2sequence.flatten.{}.{}.{}"
.
format
(
im2seq_counter
,
i
,
j
)
flatten_block_node
=
helper
.
make_node
(
"Flatten"
,
inputs
=
[
slice_block_name
],
outputs
=
[
flatten_block_name
],
axis
=
0
)
nodes
.
extend
([
slice_block_node
,
flatten_block_node
])
slice_blocks
.
append
(
flatten_block_name
)
concat_block_name
=
"im2sequence.concat_block.{}"
.
format
(
im2seq_counter
)
# concat_block_node = helper.make_node("Concat", inputs=slice_blocks, outputs=[concat_block_name], axis=0)
concat_block_node
=
helper
.
make_node
(
"Concat"
,
inputs
=
slice_blocks
,
outputs
=
op
.
output
(
'Out'
),
axis
=
0
)
nodes
.
append
(
concat_block_node
)
print
(
"
\n\n
==========Importance Notice==========="
)
print
(
"Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
)
print
(
"======================================
\n
"
)
return
nodes
x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py
已删除
100644 → 0
浏览文件 @
bbc964e8
# Copyright (c) 2019 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
math
import
sys
import
os
import
numpy
as
np
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
import
onnx
import
warnings
from
onnx
import
helper
,
onnx_pb
def
multiclass_nms
(
op
,
block
):
"""
Convert the paddle multiclass_nms to onnx op.
This op is get the select boxes from origin boxes.
"""
inputs
=
dict
()
outputs
=
dict
()
attrs
=
dict
()
for
name
in
op
.
input_names
:
inputs
[
name
]
=
op
.
input
(
name
)
for
name
in
op
.
output_names
:
outputs
[
name
]
=
op
.
output
(
name
)
for
name
in
op
.
attr_names
:
attrs
[
name
]
=
op
.
attr
(
name
)
result_name
=
outputs
[
'Out'
][
0
]
background
=
attrs
[
'background_label'
]
normalized
=
attrs
[
'normalized'
]
if
normalized
==
False
:
warnings
.
warn
(
'The parameter normalized of multiclass_nms OP of Paddle is False, which has diff with ONNX.
\
Please set normalized=True in multiclass_nms of Paddle'
)
#convert the paddle attribute to onnx tensor
name_score_threshold
=
[
outputs
[
'Out'
][
0
]
+
"@score_threshold"
]
name_iou_threshold
=
[
outputs
[
'Out'
][
0
]
+
"@iou_threshold"
]
name_keep_top_k
=
[
outputs
[
'Out'
][
0
]
+
'@keep_top_k'
]
name_keep_top_k_2D
=
[
outputs
[
'Out'
][
0
]
+
'@keep_top_k_1D'
]
node_score_threshold
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
name_score_threshold
,
value
=
onnx
.
helper
.
make_tensor
(
name
=
name_score_threshold
[
0
]
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
FLOAT
,
dims
=
(),
vals
=
[
float
(
attrs
[
'score_threshold'
])]))
node_iou_threshold
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
name_iou_threshold
,
value
=
onnx
.
helper
.
make_tensor
(
name
=
name_iou_threshold
[
0
]
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
FLOAT
,
dims
=
(),
vals
=
[
float
(
attrs
[
'nms_threshold'
])]))
node_keep_top_k
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
name_keep_top_k
,
value
=
onnx
.
helper
.
make_tensor
(
name
=
name_keep_top_k
[
0
]
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
INT64
,
dims
=
(),
vals
=
[
np
.
int64
(
attrs
[
'keep_top_k'
])]))
node_keep_top_k_2D
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
name_keep_top_k_2D
,
value
=
onnx
.
helper
.
make_tensor
(
name
=
name_keep_top_k_2D
[
0
]
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
INT64
,
dims
=
[
1
,
1
],
vals
=
[
np
.
int64
(
attrs
[
'keep_top_k'
])]))
# the paddle data format is x1,y1,x2,y2
kwargs
=
{
'center_point_box'
:
0
}
name_select_nms
=
[
outputs
[
'Out'
][
0
]
+
"@select_index"
]
node_select_nms
=
onnx
.
helper
.
make_node
(
'NonMaxSuppression'
,
inputs
=
inputs
[
'BBoxes'
]
+
inputs
[
'Scores'
]
+
name_keep_top_k
+
\
name_iou_threshold
+
name_score_threshold
,
outputs
=
name_select_nms
)
# step 1 nodes select the nms class
node_list
=
[
node_score_threshold
,
node_iou_threshold
,
node_keep_top_k
,
node_keep_top_k_2D
,
node_select_nms
]
# create some const value to use
name_const_value
=
[
result_name
+
"@const_0"
,
result_name
+
"@const_1"
,
\
result_name
+
"@const_2"
,
\
result_name
+
"@const_-1"
]
value_const_value
=
[
0
,
1
,
2
,
-
1
]
for
name
,
value
in
zip
(
name_const_value
,
value_const_value
):
node
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
[
name
],
value
=
onnx
.
helper
.
make_tensor
(
name
=
name
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
INT64
,
dims
=
[
1
],
vals
=
[
value
]))
node_list
.
append
(
node
)
# Ine this code block, we will deocde the raw score data, reshape N * C * M to 1 * N*C*M
# and the same time, decode the select indices to 1 * D, gather the select_indices
outputs_gather_1
=
[
result_name
+
"@gather_1"
]
node_gather_1
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
name_select_nms
+
[
result_name
+
"@const_1"
],
outputs
=
outputs_gather_1
,
axis
=
1
)
node_list
.
append
(
node_gather_1
)
outputs_squeeze_gather_1
=
[
result_name
+
"@sequeeze_gather_1"
]
node_squeeze_gather_1
=
onnx
.
helper
.
make_node
(
'Squeeze'
,
inputs
=
outputs_gather_1
,
outputs
=
outputs_squeeze_gather_1
,
axes
=
[
1
])
node_list
.
append
(
node_squeeze_gather_1
)
outputs_gather_2
=
[
result_name
+
"@gather_2"
]
node_gather_2
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
name_select_nms
+
[
result_name
+
"@const_2"
],
outputs
=
outputs_gather_2
,
axis
=
1
)
node_list
.
append
(
node_gather_2
)
#slice the class is not 0
if
background
==
0
:
outputs_nonzero
=
[
result_name
+
"@nonzero"
]
node_nonzero
=
onnx
.
helper
.
make_node
(
'NonZero'
,
inputs
=
outputs_squeeze_gather_1
,
outputs
=
outputs_nonzero
)
node_list
.
append
(
node_nonzero
)
else
:
name_thresh
=
[
result_name
+
"@thresh"
]
node_thresh
=
onnx
.
helper
.
make_node
(
'Constant'
,
inputs
=
[],
outputs
=
name_thresh
,
value
=
onnx
.
helper
.
make_tensor
(
name
=
name_thresh
[
0
]
+
"@const"
,
data_type
=
onnx
.
TensorProto
.
INT32
,
dims
=
[
1
],
vals
=
[
-
1
]))
node_list
.
append
(
node_thresh
)
outputs_cast
=
[
result_name
+
"@cast"
]
node_cast
=
onnx
.
helper
.
make_node
(
'Cast'
,
inputs
=
outputs_squeeze_gather_1
,
outputs
=
outputs_cast
,
to
=
6
)
node_list
.
append
(
node_cast
)
outputs_greater
=
[
result_name
+
"@greater"
]
node_greater
=
onnx
.
helper
.
make_node
(
'Greater'
,
inputs
=
outputs_cast
+
name_thresh
,
outputs
=
outputs_greater
)
node_list
.
append
(
node_greater
)
outputs_nonzero
=
[
result_name
+
"@nonzero"
]
node_nonzero
=
onnx
.
helper
.
make_node
(
'NonZero'
,
inputs
=
outputs_greater
,
outputs
=
outputs_nonzero
)
node_list
.
append
(
node_nonzero
)
outputs_gather_1_nonzero
=
[
result_name
+
"@gather_1_nonzero"
]
node_gather_1_nonzero
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_gather_1
+
outputs_nonzero
,
outputs
=
outputs_gather_1_nonzero
,
axis
=
0
)
node_list
.
append
(
node_gather_1_nonzero
)
outputs_gather_2_nonzero
=
[
result_name
+
"@gather_2_nonzero"
]
node_gather_2_nonzero
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_gather_2
+
outputs_nonzero
,
outputs
=
outputs_gather_2_nonzero
,
axis
=
0
)
node_list
.
append
(
node_gather_2_nonzero
)
# reshape scores N * C * M to (N*C*M) * 1
outputs_reshape_scores_rank1
=
[
result_name
+
"@reshape_scores_rank1"
]
node_reshape_scores_rank1
=
onnx
.
helper
.
make_node
(
"Reshape"
,
inputs
=
inputs
[
'Scores'
]
+
[
result_name
+
"@const_-1"
],
outputs
=
outputs_reshape_scores_rank1
)
node_list
.
append
(
node_reshape_scores_rank1
)
# get the shape of scores
outputs_shape_scores
=
[
result_name
+
"@shape_scores"
]
node_shape_scores
=
onnx
.
helper
.
make_node
(
'Shape'
,
inputs
=
inputs
[
'Scores'
],
outputs
=
outputs_shape_scores
)
node_list
.
append
(
node_shape_scores
)
# gather the index: 2 shape of scores
outputs_gather_scores_dim1
=
[
result_name
+
"@gather_scores_dim1"
]
node_gather_scores_dim1
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_shape_scores
+
[
result_name
+
"@const_2"
],
outputs
=
outputs_gather_scores_dim1
,
axis
=
0
)
node_list
.
append
(
node_gather_scores_dim1
)
# mul class * M
outputs_mul_classnum_boxnum
=
[
result_name
+
"@mul_classnum_boxnum"
]
node_mul_classnum_boxnum
=
onnx
.
helper
.
make_node
(
'Mul'
,
inputs
=
outputs_gather_1_nonzero
+
outputs_gather_scores_dim1
,
outputs
=
outputs_mul_classnum_boxnum
)
node_list
.
append
(
node_mul_classnum_boxnum
)
# add class * M * index
outputs_add_class_M_index
=
[
result_name
+
"@add_class_M_index"
]
node_add_class_M_index
=
onnx
.
helper
.
make_node
(
'Add'
,
inputs
=
outputs_mul_classnum_boxnum
+
outputs_gather_2_nonzero
,
outputs
=
outputs_add_class_M_index
)
node_list
.
append
(
node_add_class_M_index
)
# Squeeze the indices to 1 dim
outputs_squeeze_select_index
=
[
result_name
+
"@squeeze_select_index"
]
node_squeeze_select_index
=
onnx
.
helper
.
make_node
(
'Squeeze'
,
inputs
=
outputs_add_class_M_index
,
outputs
=
outputs_squeeze_select_index
,
axes
=
[
0
,
2
])
node_list
.
append
(
node_squeeze_select_index
)
# gather the data from flatten scores
outputs_gather_select_scores
=
[
result_name
+
"@gather_select_scores"
]
node_gather_select_scores
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_reshape_scores_rank1
+
\
outputs_squeeze_select_index
,
outputs
=
outputs_gather_select_scores
,
axis
=
0
)
node_list
.
append
(
node_gather_select_scores
)
# get nums to input TopK
outputs_shape_select_num
=
[
result_name
+
"@shape_select_num"
]
node_shape_select_num
=
onnx
.
helper
.
make_node
(
'Shape'
,
inputs
=
outputs_gather_select_scores
,
outputs
=
outputs_shape_select_num
)
node_list
.
append
(
node_shape_select_num
)
outputs_gather_select_num
=
[
result_name
+
"@gather_select_num"
]
node_gather_select_num
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_shape_select_num
+
[
result_name
+
"@const_0"
],
outputs
=
outputs_gather_select_num
,
axis
=
0
)
node_list
.
append
(
node_gather_select_num
)
outputs_unsqueeze_select_num
=
[
result_name
+
"@unsqueeze_select_num"
]
node_unsqueeze_select_num
=
onnx
.
helper
.
make_node
(
'Unsqueeze'
,
inputs
=
outputs_gather_select_num
,
outputs
=
outputs_unsqueeze_select_num
,
axes
=
[
0
])
node_list
.
append
(
node_unsqueeze_select_num
)
outputs_concat_topK_select_num
=
[
result_name
+
"@conat_topK_select_num"
]
node_conat_topK_select_num
=
onnx
.
helper
.
make_node
(
'Concat'
,
inputs
=
outputs_unsqueeze_select_num
+
name_keep_top_k_2D
,
outputs
=
outputs_concat_topK_select_num
,
axis
=
0
)
node_list
.
append
(
node_conat_topK_select_num
)
outputs_cast_concat_topK_select_num
=
[
result_name
+
"@concat_topK_select_num"
]
node_outputs_cast_concat_topK_select_num
=
onnx
.
helper
.
make_node
(
'Cast'
,
inputs
=
outputs_concat_topK_select_num
,
outputs
=
outputs_cast_concat_topK_select_num
,
to
=
6
)
node_list
.
append
(
node_outputs_cast_concat_topK_select_num
)
# get min(topK, num_select)
outputs_compare_topk_num_select
=
[
result_name
+
"@compare_topk_num_select"
]
node_compare_topk_num_select
=
onnx
.
helper
.
make_node
(
'ReduceMin'
,
inputs
=
outputs_cast_concat_topK_select_num
,
outputs
=
outputs_compare_topk_num_select
,
keepdims
=
0
)
node_list
.
append
(
node_compare_topk_num_select
)
# unsqueeze the indices to 1D tensor
outputs_unsqueeze_topk_select_indices
=
[
result_name
+
"@unsqueeze_topk_select_indices"
]
node_unsqueeze_topk_select_indices
=
onnx
.
helper
.
make_node
(
'Unsqueeze'
,
inputs
=
outputs_compare_topk_num_select
,
outputs
=
outputs_unsqueeze_topk_select_indices
,
axes
=
[
0
])
node_list
.
append
(
node_unsqueeze_topk_select_indices
)
# cast the indices to INT64
outputs_cast_topk_indices
=
[
result_name
+
"@cast_topk_indices"
]
node_cast_topk_indices
=
onnx
.
helper
.
make_node
(
'Cast'
,
inputs
=
outputs_unsqueeze_topk_select_indices
,
outputs
=
outputs_cast_topk_indices
,
to
=
7
)
node_list
.
append
(
node_cast_topk_indices
)
# select topk scores indices
outputs_topk_select_topk_indices
=
[
result_name
+
"@topk_select_topk_values"
,
\
result_name
+
"@topk_select_topk_indices"
]
node_topk_select_topk_indices
=
onnx
.
helper
.
make_node
(
'TopK'
,
inputs
=
outputs_gather_select_scores
+
outputs_cast_topk_indices
,
outputs
=
outputs_topk_select_topk_indices
)
node_list
.
append
(
node_topk_select_topk_indices
)
# gather topk label, scores, boxes
outputs_gather_topk_scores
=
[
result_name
+
"@gather_topk_scores"
]
node_gather_topk_scores
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_gather_select_scores
+
[
outputs_topk_select_topk_indices
[
1
]],
outputs
=
outputs_gather_topk_scores
,
axis
=
0
)
node_list
.
append
(
node_gather_topk_scores
)
outputs_gather_topk_class
=
[
result_name
+
"@gather_topk_class"
]
node_gather_topk_class
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_gather_1_nonzero
+
[
outputs_topk_select_topk_indices
[
1
]],
outputs
=
outputs_gather_topk_class
,
axis
=
1
)
node_list
.
append
(
node_gather_topk_class
)
# gather the boxes need to gather the boxes id, then get boxes
outputs_gather_topk_boxes_id
=
[
result_name
+
"@gather_topk_boxes_id"
]
node_gather_topk_boxes_id
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_gather_2_nonzero
+
[
outputs_topk_select_topk_indices
[
1
]],
outputs
=
outputs_gather_topk_boxes_id
,
axis
=
1
)
node_list
.
append
(
node_gather_topk_boxes_id
)
# squeeze the gather_topk_boxes_id to 1 dim
outputs_squeeze_topk_boxes_id
=
[
result_name
+
"@squeeze_topk_boxes_id"
]
node_squeeze_topk_boxes_id
=
onnx
.
helper
.
make_node
(
'Squeeze'
,
inputs
=
outputs_gather_topk_boxes_id
,
outputs
=
outputs_squeeze_topk_boxes_id
,
axes
=
[
0
,
2
])
node_list
.
append
(
node_squeeze_topk_boxes_id
)
outputs_gather_select_boxes
=
[
result_name
+
"@gather_select_boxes"
]
node_gather_select_boxes
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
inputs
[
'BBoxes'
]
+
outputs_squeeze_topk_boxes_id
,
outputs
=
outputs_gather_select_boxes
,
axis
=
1
)
node_list
.
append
(
node_gather_select_boxes
)
# concat the final result
# before concat need to cast the class to float
outputs_cast_topk_class
=
[
result_name
+
"@cast_topk_class"
]
node_cast_topk_class
=
onnx
.
helper
.
make_node
(
'Cast'
,
inputs
=
outputs_gather_topk_class
,
outputs
=
outputs_cast_topk_class
,
to
=
1
)
node_list
.
append
(
node_cast_topk_class
)
outputs_unsqueeze_topk_scores
=
[
result_name
+
"@unsqueeze_topk_scores"
]
node_unsqueeze_topk_scores
=
onnx
.
helper
.
make_node
(
'Unsqueeze'
,
inputs
=
outputs_gather_topk_scores
,
outputs
=
outputs_unsqueeze_topk_scores
,
axes
=
[
0
,
2
])
node_list
.
append
(
node_unsqueeze_topk_scores
)
inputs_concat_final_results
=
outputs_cast_topk_class
+
outputs_unsqueeze_topk_scores
+
\
outputs_gather_select_boxes
outputs_concat_final_results
=
outputs
[
'Out'
]
node_concat_final_results
=
onnx
.
helper
.
make_node
(
'Concat'
,
inputs
=
inputs_concat_final_results
,
outputs
=
outputs_concat_final_results
,
axis
=
2
)
node_list
.
append
(
node_concat_final_results
)
return
node_list
x2paddle/op_mapper/paddle_custom_layer/yolo_box.py
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x2paddle/op_mapper/paddle_op_mapper.py
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