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31140398
编写于
9月 19, 2019
作者:
J
Jason
提交者:
GitHub
9月 19, 2019
浏览文件
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差异文件
Merge pull request #150 from jiangjiajun/develop
support new situation
上级
4c29d6df
b482fc35
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
108 addition
and
1 deletion
+108
-1
x2paddle/convert.py
x2paddle/convert.py
+1
-0
x2paddle/decoder/tf_decoder.py
x2paddle/decoder/tf_decoder.py
+31
-0
x2paddle/op_mapper/tf_op_mapper.py
x2paddle/op_mapper/tf_op_mapper.py
+22
-1
x2paddle/optimizer/tf_optimizer.py
x2paddle/optimizer/tf_optimizer.py
+54
-0
未找到文件。
x2paddle/convert.py
浏览文件 @
31140398
...
...
@@ -104,6 +104,7 @@ def tf2paddle(model_path,
# neccesary optimization
optimizer
.
delete_redundance_code
()
# optimizer below is experimental
optimizer
.
optimize_elementwise_op
()
optimizer
.
merge_activation
()
optimizer
.
merge_bias
()
optimizer
.
optimize_sub_graph
()
...
...
x2paddle/decoder/tf_decoder.py
浏览文件 @
31140398
...
...
@@ -60,6 +60,15 @@ class TFGraphNode(GraphNode):
raise
Exception
(
"Dtype[{}] not in dtype_map"
.
format
(
dtype
))
return
self
.
dtype_map
[
dtype
]
@
property
def
raw_dtype
(
self
):
keys
=
[
'dtype'
,
'Tidx'
,
'T'
,
'DstT'
]
for
k
in
keys
:
dtype
=
self
.
layer
.
attr
[
k
].
type
if
dtype
>
0
:
break
return
dtype
@
property
def
value
(
self
):
assert
self
.
layer_type
==
"Const"
,
"Only Const node has value."
...
...
@@ -120,6 +129,7 @@ class TFGraph(Graph):
# tensorflow graph optimize
self
.
_remove_isolated_node
()
self
.
_remove_identity_node
()
self
.
_remove_cast_node
()
def
get_node
(
self
,
node_name
,
copy
=
False
):
items
=
node_name
.
strip
().
split
(
':'
)
...
...
@@ -190,6 +200,27 @@ class TFGraph(Graph):
idx
=
self
.
output_nodes
.
index
(
node_name
)
self
.
output_nodes
[
idx
]
=
input_node
.
layer_name
def
_remove_cast_node
(
self
):
cast_node
=
list
()
for
node_name
,
node
in
self
.
node_map
.
items
():
if
node
.
layer_type
==
"Cast"
:
input
=
self
.
get_node
(
node
.
inputs
[
0
])
if
input
.
layer_type
!=
"Placeholder"
or
len
(
input
.
outputs
)
!=
1
:
continue
cast_node
.
append
(
node_name
)
for
node_name
in
cast_node
:
node
=
self
.
get_node
(
node_name
)
input_node
=
self
.
get_node
(
node
.
inputs
[
0
])
input_node
.
layer
.
attr
[
"dtype"
].
type
=
node
.
raw_dtype
self
.
remove_node
(
node_name
)
self
.
identity_map
[
node_name
]
=
input_node
.
layer_name
if
node_name
in
self
.
output_nodes
:
idx
=
self
.
output_nodes
.
index
(
node_name
)
self
.
output_nodes
[
idx
]
=
input_node
.
layer_name
def
data_format_propagation
(
self
,
node
):
current_node
=
self
.
node_map
[
node
.
layer_name
]
current_node
=
node
.
tf_data_format
...
...
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @
31140398
...
...
@@ -170,7 +170,28 @@ class TFOpMapper(OpMapper):
x_shape
=
y
.
out_shapes
[
0
]
y_shape
=
x
.
out_shapes
[
0
]
else
:
raise
Exception
(
"Unexpected situation happend"
)
if
len
(
x_shape
)
==
1
and
len
(
y_shape
)
==
4
and
x_shape
[
0
]
==
y_shape
[
-
1
]
and
y_shape
.
count
(
-
1
)
<
1
:
shape
=
[
1
,
x_shape
[
0
],
1
,
1
]
attr
=
{
"shape"
:
shape
}
node
.
fluid_code
.
add_layer
(
"reshape"
,
inputs
=
x_input
,
output
=
"reshape_x"
,
param_attr
=
attr
)
if
y_shape
[
0
]
!=
1
:
attr
=
{
"expand_times"
:
[
y_shape
[
0
],
1
,
1
,
1
]}
node
.
fluid_code
.
add_layer
(
"expand"
,
inputs
=
"reshape_x"
,
output
=
"reshape_x"
,
param_attr
=
attr
)
inputs
=
{
"x"
:
"reshape_x"
,
"y"
:
y_input
}
node
.
fluid_code
.
add_layer
(
op_type
,
inputs
=
inputs
,
output
=
node
,
param_attr
=
None
)
return
else
:
raise
Exception
(
"Unexpected situation happend"
)
if
len
(
x_shape
)
==
4
and
len
(
y_shape
)
==
1
:
if
x_input
.
tf_data_format
==
"NHWC"
:
...
...
x2paddle/optimizer/tf_optimizer.py
浏览文件 @
31140398
...
...
@@ -16,6 +16,7 @@
from
x2paddle.op_mapper.tf_op_mapper
import
TFOpMapper
from
x2paddle.core.fluid_code
import
Layer
from
x2paddle.core.util
import
*
import
six
import
numpy
import
copy
as
cp
...
...
@@ -104,6 +105,59 @@ class TFOptimizer(object):
del
out_node
.
inputs
[
index
]
del
self
.
graph
.
node_map
[
node_name
]
def
optimize_elementwise_op
(
self
):
elementwise_ops
=
[
'Sub'
,
'Add'
,
'RealDiv'
,
'Maximum'
,
'Mul'
,
'FloorDiv'
,
'GreaterEqual'
]
revertable_ops
=
[
'Add'
,
'Mul'
]
for
node_name
in
self
.
graph
.
topo_sort
:
node
=
self
.
graph
.
get_node
(
node_name
)
if
node
is
None
:
continue
if
node
.
layer_type
in
elementwise_ops
:
if
len
(
node
.
fluid_code
.
layers
)
!=
2
:
continue
if
node
.
fluid_code
.
layers
[
0
].
op
!=
"expand"
:
continue
expand_out
=
node
.
fluid_code
.
layers
[
0
].
output
expand_in
=
node
.
fluid_code
.
layers
[
0
].
inputs
expand_times
=
node
.
fluid_code
.
layers
[
0
].
param_attr
[
"expand_times"
]
x
=
node
.
fluid_code
.
layers
[
1
].
inputs
[
"x"
]
y
=
node
.
fluid_code
.
layers
[
1
].
inputs
[
"y"
]
if
isinstance
(
x
,
six
.
string_types
)
and
node
.
layer_type
in
revertable_ops
:
node
.
fluid_code
.
layers
[
1
].
inputs
[
"y"
]
=
x
node
.
fluid_code
.
layers
[
1
].
inputs
[
"x"
]
=
y
x
=
node
.
fluid_code
.
layers
[
1
].
inputs
[
"x"
]
y
=
expand_in
elif
isinstance
(
y
,
six
.
string_types
):
y
=
expand_in
else
:
continue
x_shape
=
x
.
out_shapes
[
0
]
y_shape
=
y
.
out_shapes
[
0
]
if
len
(
x_shape
)
!=
len
(
y_shape
):
continue
if
len
(
x_shape
)
==
4
:
x_shape
=
[
x_shape
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
y_shape
=
[
y_shape
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
continue_flag
=
True
for
i
in
range
(
len
(
x_shape
)):
if
y_shape
[
-
1
*
(
i
+
1
)]
==
1
and
continue_flag
:
expand_times
[
-
1
*
(
i
+
1
)]
=
1
else
:
continue_flag
=
False
if
expand_times
.
count
(
1
)
==
len
(
expand_times
):
node
.
fluid_code
.
layers
[
1
].
inputs
[
"y"
]
=
expand_in
del
node
.
fluid_code
.
layers
[
0
]
def
merge_activation
(
self
):
act_nodes
=
list
()
for
node_name
in
self
.
graph
.
topo_sort
:
...
...
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