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715f40a4
编写于
6月 25, 2018
作者:
D
Dmitry Kurtaev
浏览文件
操作
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电子邮件补丁
差异文件
Use layers consumers to predict data layout
上级
70d6b877
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
87 addition
and
15 deletion
+87
-15
modules/dnn/src/tensorflow/tf_importer.cpp
modules/dnn/src/tensorflow/tf_importer.cpp
+85
-14
modules/dnn/test/test_tf_importer.cpp
modules/dnn/test/test_tf_importer.cpp
+2
-1
未找到文件。
modules/dnn/src/tensorflow/tf_importer.cpp
浏览文件 @
715f40a4
...
...
@@ -18,6 +18,7 @@ Implementation of Tensorflow models parser
#include <fstream>
#include <algorithm>
#include <string>
#include <queue>
#include "tf_graph_simplifier.hpp"
#endif
...
...
@@ -558,9 +559,7 @@ static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& cons
}
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static
int
predictOutputDataLayout
(
const
tensorflow
::
NodeDef
&
layer
,
const
std
::
map
<
String
,
int
>&
data_layouts
)
static
int
getDataLayout
(
const
tensorflow
::
NodeDef
&
layer
)
{
if
(
hasLayerAttr
(
layer
,
"data_format"
))
{
...
...
@@ -572,27 +571,48 @@ static int predictOutputDataLayout(const tensorflow::NodeDef& layer, const std::
else
CV_Error
(
Error
::
StsParseError
,
"Unknown data_format value: "
+
format
);
}
return
DATA_LAYOUT_UNKNOWN
;
}
static
inline
std
::
string
getNodeName
(
const
std
::
string
&
tensorName
)
{
return
tensorName
.
substr
(
0
,
tensorName
.
rfind
(
':'
));
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static
int
predictOutputDataLayout
(
const
tensorflow
::
GraphDef
&
net
,
const
tensorflow
::
NodeDef
&
layer
,
const
std
::
map
<
String
,
int
>&
data_layouts
)
{
int
layout
=
getDataLayout
(
layer
);
if
(
layout
!=
DATA_LAYOUT_UNKNOWN
)
return
layout
;
// Determine layout by layer's inputs
int
layout
=
DATA_LAYOUT_UNKNOWN
;
std
::
map
<
String
,
int
>::
const_iterator
it
;
for
(
int
i
=
0
,
n
=
layer
.
input_size
();
i
<
n
;
++
i
)
{
it
=
data_layouts
.
find
(
layer
.
input
(
i
).
substr
(
0
,
layer
.
input
(
i
).
rfind
(
':'
)));
it
=
data_layouts
.
find
(
getNodeName
(
layer
.
input
(
i
)));
if
(
it
!=
data_layouts
.
end
())
{
if
(
it
->
second
==
DATA_LAYOUT_UNKNOWN
)
return
DATA_LAYOUT_UNKNOWN
;
else
if
(
it
->
second
!=
layout
)
if
(
layout
!=
DATA_LAYOUT_UNKNOWN
)
{
if
(
layout
==
DATA_LAYOUT_UNKNOWN
)
layout
=
it
->
second
;
else
if
(
it
->
second
!=
layout
&&
it
->
second
!=
DATA_LAYOUT_UNKNOWN
)
return
DATA_LAYOUT_UNKNOWN
;
}
else
layout
=
it
->
second
;
}
}
return
layout
;
if
(
layout
!=
DATA_LAYOUT_UNKNOWN
)
return
layout
;
// Determine layout by layer's consumers recursively.
it
=
data_layouts
.
find
(
layer
.
name
());
CV_Assert
(
it
!=
data_layouts
.
end
());
return
it
->
second
;
}
void
TFImporter
::
populateNet
(
Net
dstNet
)
...
...
@@ -610,6 +630,52 @@ void TFImporter::populateNet(Net dstNet)
int
layersSize
=
net
.
node_size
();
std
::
map
<
String
,
int
>
data_layouts
;
// Pre-fill data layouts where they are set explicitly.
// Assuming that nodes are in topological order
for
(
int
i
=
net
.
node_size
()
-
1
;
i
>=
0
;
--
i
)
{
const
tensorflow
::
NodeDef
&
layer
=
net
.
node
(
i
);
std
::
string
name
=
layer
.
name
();
int
layout
=
getDataLayout
(
layer
);
std
::
map
<
String
,
int
>::
iterator
it
=
data_layouts
.
find
(
name
);
if
(
it
!=
data_layouts
.
end
())
{
if
(
layout
!=
DATA_LAYOUT_UNKNOWN
)
{
if
(
it
->
second
==
DATA_LAYOUT_UNKNOWN
)
it
->
second
=
layout
;
else
if
(
it
->
second
!=
layout
)
{
it
->
second
=
DATA_LAYOUT_UNKNOWN
;
layout
=
DATA_LAYOUT_UNKNOWN
;
}
}
else
layout
=
it
->
second
;
}
else
data_layouts
[
name
]
=
layout
;
// Specify input layers to have the same data layout.
for
(
int
j
=
0
;
j
<
layer
.
input_size
();
++
j
)
{
name
=
getNodeName
(
layer
.
input
(
j
));
it
=
data_layouts
.
find
(
name
);
if
(
it
!=
data_layouts
.
end
())
{
if
(
layout
!=
DATA_LAYOUT_UNKNOWN
)
{
if
(
it
->
second
==
DATA_LAYOUT_UNKNOWN
)
it
->
second
=
layout
;
else
if
(
it
->
second
!=
layout
)
it
->
second
=
DATA_LAYOUT_UNKNOWN
;
}
}
else
data_layouts
[
name
]
=
layout
;
}
}
// find all Const layers for params
std
::
map
<
String
,
int
>
value_id
;
...
...
@@ -628,7 +694,8 @@ void TFImporter::populateNet(Net dstNet)
if
(
layers_to_ignore
.
find
(
name
)
!=
layers_to_ignore
.
end
())
continue
;
data_layouts
[
name
]
=
predictOutputDataLayout
(
layer
,
data_layouts
);
int
predictedLayout
=
predictOutputDataLayout
(
net
,
layer
,
data_layouts
);
data_layouts
[
name
]
=
predictedLayout
;
if
(
type
==
"Conv2D"
||
type
==
"SpaceToBatchND"
||
type
==
"DepthwiseConv2dNative"
)
{
...
...
@@ -885,6 +952,7 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect
(
layer_id
,
dstNet
,
inpId
,
id
,
0
);
data_layouts
[
name
]
=
DATA_LAYOUT_UNKNOWN
;
}
else
if
(
type
==
"Flatten"
||
type
==
"Squeeze"
)
{
...
...
@@ -1013,7 +1081,10 @@ void TFImporter::populateNet(Net dstNet)
{
int
axisId
=
(
type
==
"Concat"
?
0
:
layer
.
input_size
()
-
1
);
int
axis
=
getConstBlob
(
layer
,
value_id
,
axisId
).
int_val
().
Get
(
0
);
layerParams
.
set
(
"axis"
,
0
<=
axis
&&
axis
<
4
?
toNCHW
(
axis
)
:
axis
);
if
(
data_layouts
[
name
]
==
DATA_LAYOUT_NHWC
)
axis
=
toNCHW
(
axis
);
layerParams
.
set
(
"axis"
,
axis
);
int
id
=
dstNet
.
addLayer
(
name
,
"Concat"
,
layerParams
);
layer_id
[
name
]
=
id
;
...
...
modules/dnn/test/test_tf_importer.cpp
浏览文件 @
715f40a4
...
...
@@ -142,9 +142,10 @@ TEST_P(Test_TensorFlow_layers, eltwise_add_mul)
runTensorFlowNet
(
"eltwise_add_mul"
,
GetParam
());
}
TEST_P
(
Test_TensorFlow_layers
,
pad_and_
concat
)
TEST_P
(
Test_TensorFlow_layers
,
concat
)
{
runTensorFlowNet
(
"pad_and_concat"
,
GetParam
());
runTensorFlowNet
(
"concat_axis_1"
,
GetParam
());
}
TEST_P
(
Test_TensorFlow_layers
,
batch_norm
)
...
...
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