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829c47d1
编写于
7月 17, 2020
作者:
C
Channingss
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
delete onnx2paddle InstanceBatchNormalization custom layer
上级
bda59849
变更
4
隐藏空白更改
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并排
Showing
4 changed file
with
411 addition
and
63 deletion
+411
-63
x2paddle/op_mapper/onnx2paddle/opsets/custom_layer/InstanceNormalization.py
.../onnx2paddle/opsets/custom_layer/InstanceNormalization.py
+0
-56
x2paddle/op_mapper/onnx2paddle/opsets/custom_layer/__init__.py
...dle/op_mapper/onnx2paddle/opsets/custom_layer/__init__.py
+0
-4
x2paddle/op_mapper/paddle2onnx/opset11/paddle_custom_layer/multiclass_nms.py
...paddle2onnx/opset11/paddle_custom_layer/multiclass_nms.py
+410
-2
x2paddle/op_mapper/paddle2onnx/paddle_op_mapper.py
x2paddle/op_mapper/paddle2onnx/paddle_op_mapper.py
+1
-1
未找到文件。
x2paddle/op_mapper/onnx2paddle/opsets/custom_layer/InstanceNormalization.py
已删除
100644 → 0
浏览文件 @
bda59849
# 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/onnx2paddle/opsets/custom_layer/__init__.py
浏览文件 @
829c47d1
...
...
@@ -13,10 +13,6 @@
# 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
()
...
...
x2paddle/op_mapper/paddle2onnx/opset11/paddle_custom_layer/multiclass_nms.py
浏览文件 @
829c47d1
...
...
@@ -21,7 +21,6 @@ import paddle.fluid as fluid
import
onnx
import
warnings
from
onnx
import
helper
,
onnx_pb
from
x2paddle.op_mapper.paddle2onnx.opset10.paddle_custom_layer.multiclass_nms
import
multiclass_nms
as
multiclass_nms10
def
multiclass_nms
(
op
,
block
):
...
...
@@ -29,4 +28,413 @@ def multiclass_nms(op, block):
Convert the paddle multiclass_nms to onnx op.
This op is get the select boxes from origin boxes.
"""
return
multiclass_nms10
(
op
,
block
)
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
)
# In 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_sort_by_socre_results
=
[
result_name
+
"@concat_topk_scores"
]
node_sort_by_socre_results
=
onnx
.
helper
.
make_node
(
'Concat'
,
inputs
=
inputs_concat_final_results
,
outputs
=
outputs_sort_by_socre_results
,
axis
=
2
)
node_list
.
append
(
node_sort_by_socre_results
)
# select topk classes indices
outputs_squeeze_cast_topk_class
=
[
result_name
+
"@squeeze_cast_topk_class"
]
node_squeeze_cast_topk_class
=
onnx
.
helper
.
make_node
(
'Squeeze'
,
inputs
=
outputs_cast_topk_class
,
outputs
=
outputs_squeeze_cast_topk_class
,
axes
=
[
0
,
2
])
node_list
.
append
(
node_squeeze_cast_topk_class
)
outputs_topk_select_classes_indices
=
[
result_name
+
"@topk_select_topk_classes_scores"
,
\
result_name
+
"@topk_select_topk_classes_indices"
]
node_topk_select_topk_indices
=
onnx
.
helper
.
make_node
(
'TopK'
,
inputs
=
outputs_squeeze_cast_topk_class
+
outputs_cast_topk_indices
,
outputs
=
outputs_topk_select_classes_indices
,
largest
=
0
)
node_list
.
append
(
node_topk_select_topk_indices
)
outputs_concat_final_results
=
outputs
[
'Out'
]
node_concat_final_results
=
onnx
.
helper
.
make_node
(
'Gather'
,
inputs
=
outputs_sort_by_socre_results
+
[
outputs_topk_select_classes_indices
[
1
]],
outputs
=
outputs_concat_final_results
,
axis
=
1
)
node_list
.
append
(
node_concat_final_results
)
return
node_list
x2paddle/op_mapper/paddle2onnx/paddle_op_mapper.py
浏览文件 @
829c47d1
...
...
@@ -96,7 +96,7 @@ class PaddleOpMapper(object):
run_opset
=
opset_number
else
:
for
support_opset_number
in
self
.
support_opsets
:
if
support_opset_number
>
opset_number
:
if
support_opset_number
<
opset_number
:
run_opset
=
support_opset_number
else
:
break
...
...
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