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88c9819e
编写于
5月 21, 2018
作者:
L
liuqi
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add flatten atrous convolution transformer for GPU.
上级
dbbf8596
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
209 addition
and
96 deletion
+209
-96
mace/python/tools/converter_tool/base_converter.py
mace/python/tools/converter_tool/base_converter.py
+10
-8
mace/python/tools/converter_tool/tensorflow_converter.py
mace/python/tools/converter_tool/tensorflow_converter.py
+137
-88
mace/python/tools/converter_tool/transformer.py
mace/python/tools/converter_tool/transformer.py
+62
-0
未找到文件。
mace/python/tools/converter_tool/base_converter.py
浏览文件 @
88c9819e
...
...
@@ -147,14 +147,15 @@ class TransformerRule(Enum):
TRANSFORM_GPU_WINOGRAD
=
8
TRANSFORM_ADD_TO_BIASADD
=
9
FOLD_BIASADD
=
10
FOLD_ACTIVATION
=
11
TRANSPOSE_FILTERS
=
12
RESHAPE_FC_WEIGHT
=
13
TRANSPOSE_DATA_FORMAT
=
14
TRANSFORM_GLOBAL_CONV_TO_FC
=
15
TRANSFORM_BUFFER_IMAGE
=
16
ADD_DEVICE_AND_DATA_TYPE
=
17
SORT_BY_EXECUTION
=
18
FLATTEN_ATROUS_CONV
=
11
FOLD_ACTIVATION
=
12
TRANSPOSE_FILTERS
=
13
RESHAPE_FC_WEIGHT
=
14
TRANSPOSE_DATA_FORMAT
=
15
TRANSFORM_GLOBAL_CONV_TO_FC
=
16
TRANSFORM_BUFFER_IMAGE
=
17
ADD_DEVICE_AND_DATA_TYPE
=
18
SORT_BY_EXECUTION
=
19
class
ConverterInterface
(
object
):
...
...
@@ -212,6 +213,7 @@ class ConverterOption(object):
TransformerRule
.
TRANSFORM_GPU_WINOGRAD
,
TransformerRule
.
TRANSFORM_ADD_TO_BIASADD
,
TransformerRule
.
FOLD_BIASADD
,
TransformerRule
.
FLATTEN_ATROUS_CONV
,
TransformerRule
.
FOLD_ACTIVATION
,
TransformerRule
.
TRANSPOSE_FILTERS
,
TransformerRule
.
TRANSPOSE_DATA_FORMAT
,
...
...
mace/python/tools/converter_tool/tensorflow_converter.py
浏览文件 @
88c9819e
...
...
@@ -16,6 +16,7 @@
import
math
import
numpy
as
np
import
tensorflow
as
tf
from
enum
import
Enum
from
mace.proto
import
mace_pb2
from
mace.python.tools.converter_tool
import
base_converter
...
...
@@ -41,6 +42,50 @@ tf_epsilon_str = 'epsilon'
tf_align_corners
=
'align_corners'
tf_block_size
=
'block_size'
TFSupportedOps
=
[
'Conv2D'
,
'DepthwiseConv2dNative'
,
'Conv2DBackpropInput'
,
'BiasAdd'
,
'Add'
,
'Sub'
,
'Mul'
,
'Div'
,
'Min'
,
'Max'
,
'Neg'
,
'Abs'
,
'RealDiv'
,
'SquaredDifference'
,
'Pow'
,
'Relu'
,
'Relu6'
,
'Tanh'
,
'Sigmoid'
,
'FusedBatchNorm'
,
'AvgPool'
,
'MaxPool'
,
'Squeeze'
,
'MatMul'
,
'Identity'
,
'Reshape'
,
'Shape'
,
'Transpose'
,
'Softmax'
,
'ResizeBilinear'
,
'Placeholder'
,
'SpaceToBatchND'
,
'BatchToSpaceND'
,
'DepthToSpace'
,
'SpaceToDepth'
,
'Pad'
,
'ConcatV2'
,
'Mean'
,
'Const'
,
]
TFOpType
=
Enum
(
'TFOpType'
,
[(
op
,
op
)
for
op
in
TFSupportedOps
],
type
=
str
)
class
TensorflowConverter
(
base_converter
.
ConverterInterface
):
"""A class for convert tensorflow frozen model to mace model.
...
...
@@ -53,71 +98,70 @@ class TensorflowConverter(base_converter.ConverterInterface):
'FULL'
:
PaddingMode
.
FULL
}
pooling_type_mode
=
{
'AvgPool'
:
PoolingType
.
AVG
,
'MaxPool'
:
PoolingType
.
MAX
TFOpType
.
AvgPool
.
name
:
PoolingType
.
AVG
,
TFOpType
.
MaxPool
.
name
:
PoolingType
.
MAX
}
eltwise_type
=
{
'Add'
:
EltwiseType
.
SUM
,
'Sub'
:
EltwiseType
.
SUB
,
'Mul'
:
EltwiseType
.
PROD
,
'Div'
:
EltwiseType
.
DIV
,
'Min'
:
EltwiseType
.
MIN
,
'Max'
:
EltwiseType
.
MAX
,
'Neg'
:
EltwiseType
.
NEG
,
'Abs'
:
EltwiseType
.
ABS
,
'RealDiv'
:
EltwiseType
.
DIV
,
'SquaredDifference'
:
EltwiseType
.
SQR_DIFF
,
'Pow'
:
EltwiseType
.
POW
TFOpType
.
Add
.
name
:
EltwiseType
.
SUM
,
TFOpType
.
Sub
.
name
:
EltwiseType
.
SUB
,
TFOpType
.
Mul
.
name
:
EltwiseType
.
PROD
,
TFOpType
.
Div
.
name
:
EltwiseType
.
DIV
,
TFOpType
.
Min
.
name
:
EltwiseType
.
MIN
,
TFOpType
.
Max
.
name
:
EltwiseType
.
MAX
,
TFOpType
.
Neg
.
name
:
EltwiseType
.
NEG
,
TFOpType
.
Abs
.
name
:
EltwiseType
.
ABS
,
TFOpType
.
RealDiv
.
name
:
EltwiseType
.
DIV
,
TFOpType
.
SquaredDifference
.
name
:
EltwiseType
.
SQR_DIFF
,
TFOpType
.
Pow
.
name
:
EltwiseType
.
POW
}
activation_type
=
{
'Relu'
:
ActivationType
.
RELU
,
'Relu6'
:
ActivationType
.
RELUX
,
'Tanh'
:
ActivationType
.
TANH
,
'Sigmoid'
:
ActivationType
.
SIGMOID
TFOpType
.
Relu
.
name
:
ActivationType
.
RELU
,
TFOpType
.
Relu6
.
name
:
ActivationType
.
RELUX
,
TFOpType
.
Tanh
.
name
:
ActivationType
.
TANH
,
TFOpType
.
Sigmoid
.
name
:
ActivationType
.
SIGMOID
}
def
__init__
(
self
,
option
,
src_model_file
):
self
.
_op_converters
=
{
'Conv2D'
:
self
.
convert_conv2d
,
'DepthwiseConv2dNative'
:
self
.
convert_conv2d
,
'Conv2DBackpropInput'
:
self
.
convert_conv2d
,
'BiasAdd'
:
self
.
convert_biasadd
,
'Add'
:
self
.
convert_add
,
'Sub'
:
self
.
convert_elementwise
,
'Mul'
:
self
.
convert_elementwise
,
'Div'
:
self
.
convert_elementwise
,
'Min'
:
self
.
convert_elementwise
,
'Max'
:
self
.
convert_elementwise
,
'Neg'
:
self
.
convert_elementwise
,
'Abs'
:
self
.
convert_elementwise
,
'RealDiv'
:
self
.
convert_elementwise
,
'SquaredDifference'
:
self
.
convert_elementwise
,
'Pow'
:
self
.
convert_elementwise
,
'Relu'
:
self
.
convert_activation
,
'Relu6'
:
self
.
convert_activation
,
'Tanh'
:
self
.
convert_activation
,
'Sigmoid'
:
self
.
convert_activation
,
'FusedBatchNorm'
:
self
.
convert_fused_batchnorm
,
'AvgPool'
:
self
.
convert_pooling
,
'MaxPool'
:
self
.
convert_pooling
,
'Squeeze'
:
self
.
convert_identity
,
'MatMul'
:
self
.
convert_matmul
,
'Identity'
:
self
.
convert_identity
,
'Reshape'
:
self
.
convert_reshape
,
'Shape'
:
self
.
convert_nop
,
'Transpose'
:
self
.
convert_transpose
,
'Softmax'
:
self
.
convert_softmax
,
'ResizeBilinear'
:
self
.
convert_resize_bilinear
,
'Placeholder'
:
self
.
convert_nop
,
'SpaceToBatchND'
:
self
.
convert_space_batch
,
'BatchToSpaceND'
:
self
.
convert_space_batch
,
'DepthToSpace'
:
self
.
convert_space_depth
,
'SpaceToDepth'
:
self
.
convert_space_depth
,
'Pad'
:
self
.
convert_pad
,
'ConcatV2'
:
self
.
convert_concat
,
'Mean'
:
self
.
convert_mean
,
# Const converter_tool should be placed at the end
'Const'
:
self
.
convert_tensor
,
TFOpType
.
Conv2D
.
name
:
self
.
convert_conv2d
,
TFOpType
.
DepthwiseConv2dNative
.
name
:
self
.
convert_conv2d
,
TFOpType
.
Conv2DBackpropInput
.
name
:
self
.
convert_conv2d
,
TFOpType
.
BiasAdd
.
name
:
self
.
convert_biasadd
,
TFOpType
.
Add
.
name
:
self
.
convert_add
,
TFOpType
.
Sub
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Mul
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Div
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Min
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Max
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Neg
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Abs
.
name
:
self
.
convert_elementwise
,
TFOpType
.
RealDiv
.
name
:
self
.
convert_elementwise
,
TFOpType
.
SquaredDifference
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Pow
.
name
:
self
.
convert_elementwise
,
TFOpType
.
Relu
.
name
:
self
.
convert_activation
,
TFOpType
.
Relu6
.
name
:
self
.
convert_activation
,
TFOpType
.
Tanh
.
name
:
self
.
convert_activation
,
TFOpType
.
Sigmoid
.
name
:
self
.
convert_activation
,
TFOpType
.
FusedBatchNorm
.
name
:
self
.
convert_fused_batchnorm
,
TFOpType
.
AvgPool
.
name
:
self
.
convert_pooling
,
TFOpType
.
MaxPool
.
name
:
self
.
convert_pooling
,
TFOpType
.
Squeeze
.
name
:
self
.
convert_identity
,
TFOpType
.
MatMul
.
name
:
self
.
convert_matmul
,
TFOpType
.
Identity
.
name
:
self
.
convert_identity
,
TFOpType
.
Reshape
.
name
:
self
.
convert_reshape
,
TFOpType
.
Shape
.
name
:
self
.
convert_nop
,
TFOpType
.
Transpose
.
name
:
self
.
convert_transpose
,
TFOpType
.
Softmax
.
name
:
self
.
convert_softmax
,
TFOpType
.
ResizeBilinear
.
name
:
self
.
convert_resize_bilinear
,
TFOpType
.
Placeholder
.
name
:
self
.
convert_nop
,
TFOpType
.
SpaceToBatchND
.
name
:
self
.
convert_space_batch
,
TFOpType
.
BatchToSpaceND
.
name
:
self
.
convert_space_batch
,
TFOpType
.
DepthToSpace
.
name
:
self
.
convert_space_depth
,
TFOpType
.
SpaceToDepth
.
name
:
self
.
convert_space_depth
,
TFOpType
.
Pad
.
name
:
self
.
convert_pad
,
TFOpType
.
ConcatV2
.
name
:
self
.
convert_concat
,
TFOpType
.
Mean
.
name
:
self
.
convert_mean
,
TFOpType
.
Const
.
name
:
self
.
convert_nop
,
}
self
.
_option
=
option
self
.
_mace_net_def
=
mace_pb2
.
NetDef
()
...
...
@@ -180,8 +224,12 @@ class TensorflowConverter(base_converter.ConverterInterface):
"Mace does not support tensorflow op type %s yet"
%
tf_op
.
type
)
self
.
_op_converters
[
tf_op
.
type
](
tf_op
)
self
.
convert_tensors
()
def
convert_tensor
(
self
,
tf_op
):
def
convert_tensors
(
self
):
for
tf_op
in
self
.
_tf_graph
.
get_operations
():
if
tf_op
.
type
!=
TFOpType
.
Const
.
name
:
continue
output_name
=
tf_op
.
outputs
[
0
].
name
if
output_name
not
in
self
.
_skip_tensor
:
tensor
=
self
.
_mace_net_def
.
tensors
.
add
()
...
...
@@ -197,7 +245,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
tensor
.
data_type
=
mace_pb2
.
DT_INT32
tensor
.
int32_data
.
extend
(
tf_tensor
.
astype
(
np
.
int32
).
flat
)
else
:
mace_check
(
False
,
"Not supported tensor type: %s"
%
tf_dt
.
name
)
mace_check
(
False
,
"Not supported tensor type: %s"
%
tf_dt
.
name
)
def
add_tensor
(
self
,
name
,
shape
,
data_type
,
value
):
tensor
=
self
.
_mace_net_def
.
tensors
.
add
()
...
...
@@ -229,9 +278,9 @@ class TensorflowConverter(base_converter.ConverterInterface):
def
convert_conv2d
(
self
,
tf_op
):
op
=
self
.
convert_general_op
(
tf_op
)
if
tf_op
.
type
==
'DepthwiseConv2dNative'
:
if
tf_op
.
type
==
TFOpType
.
DepthwiseConv2dNative
.
name
:
op
.
type
=
MaceOp
.
DepthwiseConv2d
.
name
elif
tf_op
.
type
==
'Conv2DBackpropInput'
:
elif
tf_op
.
type
==
TFOpType
.
Conv2DBackpropInput
.
name
:
op
.
type
=
MaceOp
.
Deconv2D
.
name
else
:
op
.
type
=
MaceOp
.
Conv2D
.
name
...
...
@@ -274,7 +323,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
type_arg
.
name
=
MaceKeyword
.
mace_activation_type_str
type_arg
.
s
=
self
.
activation_type
[
tf_op
.
type
].
name
if
tf_op
.
type
==
'Relu6'
:
if
tf_op
.
type
==
TFOpType
.
Relu6
.
name
:
limit_arg
=
op
.
arg
.
add
()
limit_arg
.
name
=
MaceKeyword
.
mace_activation_max_limit_str
limit_arg
.
f
=
6.0
...
...
@@ -335,7 +384,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
size_arg
.
name
=
MaceKeyword
.
mace_resize_size_str
size_value
=
tf_op
.
inputs
[
1
].
eval
().
astype
(
np
.
int32
)
size_arg
.
ints
.
extend
(
size_value
)
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
1
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
1
].
name
)
align_corners_arg
=
op
.
arg
.
add
()
align_corners_arg
.
name
=
MaceKeyword
.
mace_align_corners_str
align_corners_arg
.
i
=
tf_op
.
get_attr
(
tf_align_corners
)
...
...
@@ -357,7 +406,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
size_arg
.
ints
.
extend
(
size_value
)
crops_or_paddings_arg
=
op
.
arg
.
add
()
if
op
.
type
==
'BatchToSpaceND'
:
if
op
.
type
==
TFOpType
.
BatchToSpaceND
.
name
:
op
.
type
=
MaceOp
.
BatchToSpaceND
.
name
crops_or_paddings_arg
.
name
=
\
MaceKeyword
.
mace_batch_to_space_crops_str
...
...
@@ -367,12 +416,12 @@ class TensorflowConverter(base_converter.ConverterInterface):
crops_or_paddings_value
=
tf_op
.
inputs
[
2
].
eval
().
astype
(
np
.
int32
).
flat
crops_or_paddings_arg
.
ints
.
extend
(
crops_or_paddings_value
)
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
1
].
name
)
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
2
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
1
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
2
].
name
)
def
convert_space_depth
(
self
,
tf_op
):
op
=
self
.
convert_general_op
(
tf_op
)
if
op
.
type
==
'SpaceToDepth'
:
if
op
.
type
==
TFOpType
.
SpaceToDepth
.
name
:
op
.
type
=
MaceOp
.
SpaceToDepth
.
name
else
:
op
.
type
=
MaceOp
.
DepthToSpace
.
name
...
...
@@ -390,14 +439,14 @@ class TensorflowConverter(base_converter.ConverterInterface):
paddings_arg
.
name
=
MaceKeyword
.
mace_paddings_str
paddings_value
=
tf_op
.
inputs
[
1
].
eval
().
astype
(
np
.
int32
).
flat
paddings_arg
.
ints
.
extend
(
paddings_value
)
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
1
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
1
].
name
)
if
len
(
tf_op
.
inputs
)
==
3
:
constant_value_arg
=
op
.
arg
.
add
()
constant_value_arg
.
name
=
MaceKeyword
.
mace_constant_value_str
constant_value
=
tf_op
.
inputs
[
2
].
eval
().
astype
(
np
.
int32
).
flat
[
0
]
constant_value_arg
.
i
=
constant_value
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
2
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
2
].
name
)
def
convert_concat
(
self
,
tf_op
):
op
=
self
.
convert_general_op
(
tf_op
)
...
...
@@ -412,7 +461,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
mace_check
(
axis
==
3
,
"only support concat at channel dimension"
)
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
-
1
].
name
)
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
-
1
].
name
)
def
convert_matmul
(
self
,
tf_op
):
op
=
self
.
convert_general_op
(
tf_op
)
...
...
@@ -426,13 +475,13 @@ class TensorflowConverter(base_converter.ConverterInterface):
shape_arg
=
op
.
arg
.
add
()
shape_arg
.
name
=
MaceKeyword
.
mace_shape_str
shape_value
=
[]
if
tf_op
.
inputs
[
1
].
op
.
type
==
'Const'
:
if
tf_op
.
inputs
[
1
].
op
.
type
==
TFOpType
.
Const
.
name
:
shape_value
=
list
(
tf_op
.
inputs
[
1
].
eval
().
astype
(
np
.
int32
))
for
i
in
xrange
(
len
(
shape_value
)):
if
shape_value
[
i
]
==
-
1
:
shape_value
[
i
]
=
1
self
.
_skip_tensor
.
update
(
tf_op
.
inputs
[
-
1
].
name
)
elif
tf_op
.
inputs
[
1
].
op
.
type
==
'Shape'
:
self
.
_skip_tensor
.
add
(
tf_op
.
inputs
[
-
1
].
name
)
elif
tf_op
.
inputs
[
1
].
op
.
type
==
TFOpType
.
Shape
.
name
:
shape_value
=
list
(
tf_op
.
inputs
[
1
].
op
.
inputs
[
0
].
shape
.
as_list
())
shape_arg
.
ints
.
extend
(
shape_value
)
...
...
mace/python/tools/converter_tool/transformer.py
浏览文件 @
88c9819e
...
...
@@ -65,6 +65,8 @@ class Transformer(base_converter.ConverterInterface):
TransformerRule
.
TRANSFORM_ADD_TO_BIASADD
,
TransformerRule
.
FOLD_BIASADD
,
TransformerRule
.
FOLD_ACTIVATION
,
TransformerRule
.
FLATTEN_ATROUS_CONV
,
TransformerRule
.
FOLD_ACTIVATION
,
TransformerRule
.
TRANSPOSE_FILTERS
,
TransformerRule
.
TRANSPOSE_DATA_FORMAT
,
TransformerRule
.
TRANSFORM_GLOBAL_CONV_TO_FC
,
...
...
@@ -92,6 +94,7 @@ class Transformer(base_converter.ConverterInterface):
TransformerRule
.
TRANSFORM_ADD_TO_BIASADD
:
self
.
transform_add_to_biasadd
,
TransformerRule
.
FOLD_BIASADD
:
self
.
fold_biasadd
,
TransformerRule
.
FLATTEN_ATROUS_CONV
:
self
.
flatten_atrous_conv
,
TransformerRule
.
FOLD_ACTIVATION
:
self
.
fold_activation
,
TransformerRule
.
TRANSPOSE_FILTERS
:
self
.
transpose_filters
,
TransformerRule
.
TRANSPOSE_DATA_FORMAT
:
self
.
transpose_data_format
,
...
...
@@ -615,6 +618,65 @@ class Transformer(base_converter.ConverterInterface):
return
False
def
flatten_atrous_conv
(
self
):
if
self
.
_option
.
device
!=
mace_pb2
.
GPU
:
return
net
=
self
.
_model
for
op
in
net
.
op
:
if
(
op
.
type
==
MaceOp
.
SpaceToBatchND
.
name
and
len
(
self
.
_consumers
.
get
(
op
.
output
[
0
],
[]))
==
1
):
conv_op
=
self
.
_consumers
.
get
(
op
.
output
[
0
])[
0
]
if
(
conv_op
.
type
==
MaceOp
.
Conv2D
.
name
or
conv_op
.
type
==
MaceOp
.
DepthwiseConv2d
.
name
)
\
and
len
(
self
.
_consumers
.
get
(
conv_op
.
output
[
0
],
[]))
==
1
:
# noqa
b2s_op
=
self
.
_consumers
.
get
(
conv_op
.
output
[
0
])[
0
]
if
b2s_op
.
type
==
MaceOp
.
BatchToSpaceND
.
name
:
print
"Flatten atrous convolution"
# Add args.
padding_arg_values
=
ConverterUtil
.
get_arg
(
op
,
MaceKeyword
.
mace_paddings_str
).
ints
blocks_arg_values
=
ConverterUtil
.
get_arg
(
b2s_op
,
MaceKeyword
.
mace_space_batch_block_shape_str
).
ints
dilation_arg
=
ConverterUtil
.
get_arg
(
conv_op
,
MaceKeyword
.
mace_dilations_str
)
if
dilation_arg
is
None
:
dilation_arg
=
conv_op
.
arg
.
add
()
dilation_arg
.
name
=
MaceKeyword
.
mace_dilations_str
dilation_arg
.
ints
[:]
=
blocks_arg_values
padding_arg
=
ConverterUtil
.
get_arg
(
conv_op
,
MaceKeyword
.
mace_padding_str
)
if
padding_arg
is
None
:
padding_arg
=
conv_op
.
arg
.
add
()
padding_arg
.
name
=
MaceKeyword
.
mace_padding_str
if
len
(
padding_arg_values
)
>
0
\
and
padding_arg_values
[
0
]
>
0
:
padding_arg
.
i
=
PaddingMode
.
SAME
.
value
else
:
padding_arg
.
i
=
PaddingMode
.
VALID
.
value
strides_arg
=
ConverterUtil
.
get_arg
(
conv_op
,
MaceKeyword
.
mace_strides_str
)
if
strides_arg
is
None
:
strides_arg
=
conv_op
.
arg
.
add
()
strides_arg
.
name
=
MaceKeyword
.
mace_strides_str
strides_arg
.
ints
[:]
=
[
1
,
1
]
# update output shape
conv_op
.
output_shape
[
0
].
dims
[:]
=
\
b2s_op
.
output_shape
[
0
].
dims
[:]
self
.
safe_remove_node
(
op
,
None
)
self
.
safe_remove_node
(
b2s_op
,
conv_op
)
return
True
return
False
def
fold_activation
(
self
):
net
=
self
.
_model
for
op
in
net
.
op
:
...
...
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