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e29dcb56
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体验新版 GitCode,发现更多精彩内容 >>
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e29dcb56
编写于
7月 17, 2018
作者:
李
李寅
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差异文件
Calculate shape if it cannot be inferred
上级
fc1c855e
变更
1
隐藏空白更改
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并排
Showing
1 changed file
with
16 addition
and
7 deletion
+16
-7
mace/python/tools/converter_tool/tensorflow_converter.py
mace/python/tools/converter_tool/tensorflow_converter.py
+16
-7
未找到文件。
mace/python/tools/converter_tool/tensorflow_converter.py
浏览文件 @
e29dcb56
...
@@ -200,6 +200,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
...
@@ -200,6 +200,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
tf_graph_def
=
tf
.
GraphDef
()
tf_graph_def
=
tf
.
GraphDef
()
with
tf
.
gfile
.
Open
(
src_model_file
,
'rb'
)
as
f
:
with
tf
.
gfile
.
Open
(
src_model_file
,
'rb'
)
as
f
:
tf_graph_def
.
ParseFromString
(
f
.
read
())
tf_graph_def
.
ParseFromString
(
f
.
read
())
self
.
_placeholders
=
{}
self
.
add_shape_info
(
tf_graph_def
)
self
.
add_shape_info
(
tf_graph_def
)
with
tf
.
Session
()
as
session
:
with
tf
.
Session
()
as
session
:
...
@@ -240,6 +242,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
...
@@ -240,6 +242,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
tensor_shape_pb2
.
TensorShapeProto
.
Dim
(
size
=
i
)
for
i
in
tensor_shape_pb2
.
TensorShapeProto
.
Dim
(
size
=
i
)
for
i
in
input_node
.
shape
input_node
.
shape
])
])
self
.
_placeholders
[
node
.
name
+
':0'
]
=
\
np
.
zeros
(
shape
=
input_node
.
shape
,
dtype
=
float
)
@
staticmethod
@
staticmethod
def
get_scope
(
tensor_name
):
def
get_scope
(
tensor_name
):
...
@@ -288,13 +292,18 @@ class TensorflowConverter(base_converter.ConverterInterface):
...
@@ -288,13 +292,18 @@ class TensorflowConverter(base_converter.ConverterInterface):
# this function tries to infer tensor shape, but some dimension shape
# this function tries to infer tensor shape, but some dimension shape
# may be undefined due to variance of input length
# may be undefined due to variance of input length
@
staticmethod
def
infer_tensor_shape
(
self
,
tensor
):
def
infer_tensor_shape
(
tensor
):
inferred_tensor_shape
=
tensor
.
shape
.
as_list
()
shape
=
tensor
.
shape
.
as_list
()
inferred_success
=
True
for
_
,
dim
in
enumerate
(
inferred_tensor_shape
):
def
normalize_func
(
dim
):
if
dim
is
None
:
return
dim
if
dim
else
-
1
inferred_success
=
False
return
[
normalize_func
(
dim
)
for
dim
in
shape
]
break
if
inferred_success
:
return
inferred_tensor_shape
tensor_shape
=
tf
.
shape
(
tensor
).
eval
(
feed_dict
=
self
.
_placeholders
)
return
tensor_shape
def
convert_nop
(
self
,
tf_op
):
def
convert_nop
(
self
,
tf_op
):
pass
pass
...
...
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