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体验新版 GitCode,发现更多精彩内容 >>
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f8f633b9
编写于
8月 13, 2019
作者:
C
channingss
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电子邮件补丁
差异文件
support model Face_cyclegan
上级
35b0c3ed
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
24 addition
and
39 deletion
+24
-39
x2paddle/op_mapper/onnx_op_mapper.py
x2paddle/op_mapper/onnx_op_mapper.py
+24
-39
未找到文件。
x2paddle/op_mapper/onnx_op_mapper.py
浏览文件 @
f8f633b9
...
...
@@ -21,6 +21,7 @@ from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDat
from
x2paddle.op_mapper.onnx_directly_map
import
default_op_mapping_field_values
from
x2paddle.op_mapper.onnx_directly_map
import
default_op_mapping
from
x2paddle.op_mapper.onnx_directly_map
import
default_ioa_constraint
from
x2paddle.op_mapper.onnx_custom_layer
import
*
import
numpy
as
np
import
onnx.numpy_helper
as
numpy_helper
import
logging
as
_logging
...
...
@@ -53,12 +54,12 @@ class ONNXOpMapper(OpMapper):
self
.
input_shapes
=
[]
self
.
weights
=
dict
()
self
.
omit_nodes
=
list
()
self
.
used_custom_layers
=
dict
()
if
not
self
.
op_checker
():
raise
Exception
(
"Model are not supported yet."
)
#mapping op
print
(
"Total nodes: {}"
.
format
(
sum
([
isinstance
(
node
,
ONNXGraphNode
)
...
...
@@ -67,19 +68,22 @@ class ONNXOpMapper(OpMapper):
for
node_name
in
self
.
graph
.
topo_sort
:
node
=
self
.
graph
.
get_node
(
node_name
)
op
=
node
.
layer_type
# print('translate{} layer_type is {}'.format(node_name, op))
if
hasattr
(
self
,
op
):
func
=
getattr
(
self
,
op
)
func
(
node
)
elif
op
in
default_op_mapping
:
self
.
directly_map
(
node
)
elif
op
in
custom_layers
:
self
.
deal_custom_layer
(
node
)
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
,
op
)
and
op
not
in
default_op_mapping
:
if
not
hasattr
(
self
,
op
)
and
op
not
in
default_op_mapping
and
op
not
in
custom_layers
:
unsupported_ops
.
add
(
op
)
if
len
(
unsupported_ops
)
==
0
:
return
True
...
...
@@ -135,6 +139,23 @@ class ONNXOpMapper(OpMapper):
output
=
val_outs
[
0
],
param_attr
=
attr
)
def
deal_custom_layer
(
self
,
node
):
op
=
node
.
layer_type
val_x
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
custom_code
,
func
=
make_custom_layer
(
node
)
params
=
get_params
(
node
.
layer
,
node
.
layer_type
)
arg_names
,
kwargs
=
set_args
(
func
,
params
)
kwargs
[
'name'
]
=
string
(
node
.
layer_name
)
inputs_node
=
[]
inputs_node
.
append
(
node
.
inputs
[
0
])
node
.
fluid_code
.
add_layer
(
func
.
__code__
.
co_name
,
inputs
=
inputs_node
[
0
],
output
=
node
,
param_attr
=
kwargs
,
is_custom_layer
=
True
)
if
op
not
in
self
.
used_custom_layers
:
self
.
used_custom_layers
[
op
]
=
custom_code
def
place_holder
(
self
,
node
):
self
.
input_shapes
.
append
(
node
.
out_shapes
[
0
])
attr
=
{
...
...
@@ -576,42 +597,6 @@ class ONNXOpMapper(OpMapper):
output
=
node
,
param_attr
=
attr
)
def
InstanceNormalization
(
self
,
node
):
'''
y = scale * (x - mean) / sqrt(variance + epsilon) + B
'''
val_x
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
val_scale
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
1
],
copy
=
True
)
val_b
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
2
],
copy
=
True
)
epsilon
=
node
.
get_attr
(
'epsilon'
,
1e-5
)
num_out_channels
=
val_scale
.
out_shapes
[
0
][
0
]
attr
=
{
"groups"
:
num_out_channels
,
"epsilon"
:
epsilon
,
"param_attr"
:
string
(
val_scale
.
layer_name
),
"bias_attr"
:
string
(
val_b
.
layer_name
),
"name"
:
string
(
node
.
layer_name
)
}
if
val_scale
.
layer_type
==
'Constant'
:
self
.
weights
[
val_scale
.
layer_name
]
=
val_scale
.
get_attr
(
'value'
)
if
val_b
.
layer_type
==
'Constant'
:
self
.
weights
[
val_b
.
layer_name
]
=
val_b
.
get_attr
(
'value'
)
# node_data_norm = node.layer_name +'data_norm'
node
.
fluid_code
.
add_layer
(
"group_norm"
,
inputs
=
val_x
,
output
=
node
,
param_attr
=
attr
)
# node.fluid_code.add_layer("elementwise_add",
# val_x.layer_name +','+ node_data_norm,
# output=node,
# param_attr=attr)
def
Softmax
(
self
,
node
):
val_x
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
attr
=
{
"name"
:
string
(
node
.
layer_name
)}
...
...
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