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78d58e75
Mace
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78d58e75
编写于
12月 08, 2017
作者:
W
wuchenghui
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix dsp convert tool to support nn_graph
上级
99e56653
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
78 addition
and
87 deletion
+78
-87
mace/dsp/test/quantized_add_test.cc
mace/dsp/test/quantized_add_test.cc
+0
-5
mace/dsp/test/quantized_maxpool_test.cc
mace/dsp/test/quantized_maxpool_test.cc
+0
-4
mace/dsp/test/quantized_relu_test.cc
mace/dsp/test/quantized_relu_test.cc
+0
-4
mace/dsp/test/quantized_resize_bilinear_test.cc
mace/dsp/test/quantized_resize_bilinear_test.cc
+0
-4
mace/dsp/test/supernode_test.cc
mace/dsp/test/supernode_test.cc
+0
-5
mace/proto/mace.proto
mace/proto/mace.proto
+1
-1
mace/python/tools/BUILD
mace/python/tools/BUILD
+3
-1
mace/python/tools/convert_util.py
mace/python/tools/convert_util.py
+29
-0
mace/python/tools/tf_converter_lib.py
mace/python/tools/tf_converter_lib.py
+24
-56
mace/python/tools/tf_dsp_converter_lib.py
mace/python/tools/tf_dsp_converter_lib.py
+21
-7
未找到文件。
mace/dsp/test/quantized_add_test.cc
浏览文件 @
78d58e75
...
...
@@ -17,7 +17,6 @@ static NetDef BuildNetDef() {
input_op
->
set_type
(
"INPUT"
);
input_op
->
set_node_id
(
0
);
input_op
->
set_padding
(
0
);
input_op
->
add_out_max_byte_size
(
1000
);
// add op
OperatorDef
*
add_op
=
net
.
add_op
();
...
...
@@ -59,10 +58,6 @@ static NetDef BuildNetDef() {
input_node_input
->
set_node_id
(
16
);
input_node_input
->
set_output_port
(
0
);
add_op
->
add_out_max_byte_size
(
1000
);
add_op
->
add_out_max_byte_size
(
1000
);
add_op
->
add_out_max_byte_size
(
1000
);
// output op
OperatorDef
*
output_op
=
net
.
add_op
();
output_op
->
set_name
(
"__output__"
);
...
...
mace/dsp/test/quantized_maxpool_test.cc
浏览文件 @
78d58e75
...
...
@@ -24,7 +24,6 @@ static NetDef BuildNetDef(const vector<index_t> &input_shape,
input_op
->
set_type
(
"INPUT"
);
input_op
->
set_node_id
(
0
);
input_op
->
set_padding
(
0
);
input_op
->
add_out_max_byte_size
(
1000
);
// maxpool op
OperatorDef
*
maxpool_op
=
net
.
add_op
();
...
...
@@ -58,9 +57,6 @@ static NetDef BuildNetDef(const vector<index_t> &input_shape,
input_node_input
=
maxpool_op
->
add_node_input
();
input_node_input
->
set_node_id
(
13
);
input_node_input
->
set_output_port
(
0
);
maxpool_op
->
add_out_max_byte_size
(
1000
);
maxpool_op
->
add_out_max_byte_size
(
1000
);
maxpool_op
->
add_out_max_byte_size
(
1000
);
// output op
OperatorDef
*
output_op
=
net
.
add_op
();
...
...
mace/dsp/test/quantized_relu_test.cc
浏览文件 @
78d58e75
...
...
@@ -16,7 +16,6 @@ static NetDef BuildNetDef() {
input_op
->
set_type
(
"INPUT"
);
input_op
->
set_node_id
(
0
);
input_op
->
set_padding
(
0
);
input_op
->
add_out_max_byte_size
(
1000
);
// relu op
OperatorDef
*
relu_op
=
net
.
add_op
();
...
...
@@ -39,9 +38,6 @@ static NetDef BuildNetDef() {
input_node_input
=
relu_op
->
add_node_input
();
input_node_input
->
set_node_id
(
11
);
input_node_input
->
set_output_port
(
0
);
relu_op
->
add_out_max_byte_size
(
1000
);
relu_op
->
add_out_max_byte_size
(
1000
);
relu_op
->
add_out_max_byte_size
(
1000
);
// output op
OperatorDef
*
output_op
=
net
.
add_op
();
...
...
mace/dsp/test/quantized_resize_bilinear_test.cc
浏览文件 @
78d58e75
...
...
@@ -18,7 +18,6 @@ static NetDef BuildNetDef() {
input_op
->
set_type
(
"INPUT"
);
input_op
->
set_node_id
(
0
);
input_op
->
set_padding
(
0
);
input_op
->
add_out_max_byte_size
(
1200
);
// relu op
OperatorDef
*
resize_bilinear_op
=
net
.
add_op
();
...
...
@@ -46,9 +45,6 @@ static NetDef BuildNetDef() {
input_node_input
=
resize_bilinear_op
->
add_node_input
();
input_node_input
->
set_node_id
(
12
);
input_node_input
->
set_output_port
(
0
);
resize_bilinear_op
->
add_out_max_byte_size
(
1200
);
resize_bilinear_op
->
add_out_max_byte_size
(
1000
);
resize_bilinear_op
->
add_out_max_byte_size
(
1000
);
// output op
OperatorDef
*
output_op
=
net
.
add_op
();
...
...
mace/dsp/test/supernode_test.cc
浏览文件 @
78d58e75
...
...
@@ -17,7 +17,6 @@ static NetDef BuildNetDef() {
input_op
->
set_type
(
"INPUT"
);
input_op
->
set_node_id
(
0
);
input_op
->
set_padding
(
0
);
input_op
->
add_out_max_byte_size
(
1000
);
// add op
OperatorDef
*
supernode_op
=
net
.
add_op
();
...
...
@@ -77,10 +76,6 @@ static NetDef BuildNetDef() {
input_node_input
->
set_node_id
(
20
);
input_node_input
->
set_output_port
(
0
);
supernode_op
->
add_out_max_byte_size
(
1000
);
supernode_op
->
add_out_max_byte_size
(
1000
);
supernode_op
->
add_out_max_byte_size
(
1000
);
// output op
OperatorDef
*
output_op
=
net
.
add_op
();
output_op
->
set_name
(
"__output__"
);
...
...
mace/proto/mace.proto
浏览文件 @
78d58e75
...
...
@@ -83,6 +83,7 @@ message OperatorDef {
optional
string
type
=
4
;
repeated
Argument
arg
=
5
;
repeated
OutputShape
output_shape
=
6
;
repeated
DataType
output_type
=
7
;
// Memory optimization: only support one single output op
optional
int32
mem_id
=
10
[
default
=
-
1
];
...
...
@@ -92,7 +93,6 @@ message OperatorDef {
optional
uint32
op_id
=
101
;
optional
uint32
padding
=
102
;
repeated
NodeInput
node_input
=
103
;
repeated
int32
out_max_byte_size
=
104
;
// only support 32-bit len
}
// for memory optimization
...
...
mace/python/tools/BUILD
浏览文件 @
78d58e75
py_library
(
name
=
"tf_converter_lib"
,
srcs
=
[
"convert_util.py"
,
"graph_util.py"
,
"tf_converter_lib.py"
,
"tf_dsp_converter_lib.py"
,
"graph_util.py"
],
],
srcs_version
=
"PY2AND3"
,
deps
=
[
"//mace/proto:mace_py"
,
...
...
mace/python/tools/convert_util.py
0 → 100644
浏览文件 @
78d58e75
import
tensorflow
as
tf
from
mace.proto
import
mace_pb2
TF_DTYPE_2_MACE_DTYPE_MAP
=
{
tf
.
float32
:
mace_pb2
.
DT_FLOAT
,
tf
.
double
:
mace_pb2
.
DT_DOUBLE
,
tf
.
half
:
mace_pb2
.
DT_HALF
,
tf
.
int64
:
mace_pb2
.
DT_INT64
,
tf
.
int32
:
mace_pb2
.
DT_INT32
,
tf
.
qint32
:
mace_pb2
.
DT_INT32
,
tf
.
int16
:
mace_pb2
.
DT_INT16
,
tf
.
qint16
:
mace_pb2
.
DT_INT16
,
tf
.
int8
:
mace_pb2
.
DT_INT8
,
tf
.
qint8
:
mace_pb2
.
DT_INT8
,
tf
.
quint16
:
mace_pb2
.
DT_UINT16
,
tf
.
uint16
:
mace_pb2
.
DT_UINT16
,
tf
.
quint8
:
mace_pb2
.
DT_UINT8
,
tf
.
uint8
:
mace_pb2
.
DT_UINT8
,
tf
.
string
:
mace_pb2
.
DT_STRING
,
tf
.
bool
:
mace_pb2
.
DT_BOOL
,
}
def
tf_dtype_2_mace_dtype
(
tf_dtype
):
mace_dtype
=
TF_DTYPE_2_MACE_DTYPE_MAP
.
get
(
tf_dtype
,
None
)
if
not
mace_dtype
:
raise
Exception
(
"Not supported tensorflow dtype: "
+
tf_dtype
)
return
mace_dtype
mace/python/tools/tf_converter_lib.py
浏览文件 @
78d58e75
from
mace.proto
import
mace_pb2
import
tensorflow
as
tf
import
numpy
as
np
from
mace.python.tools.convert_util
import
tf_dtype_2_mace_dtype
# TODO: support NCHW formt, now only support NHWC.
padding_mode
=
{
...
...
@@ -110,6 +111,19 @@ def add_output_transform(name, net_def):
epsilon_arg
.
name
=
'buffer_type'
epsilon_arg
.
i
=
buffer_type_map
[
'IN_OUT'
]
def
convert_op_outputs
(
mace_op_def
,
tf_op
):
mace_op_def
.
output
.
extend
([
output
.
name
for
output
in
tf_op
.
outputs
])
mace_op_def
.
output_type
.
extend
([
tf_dtype_2_mace_dtype
(
output
.
dtype
)
for
output
in
tf_op
.
outputs
])
output_shapes
=
[]
for
output
in
tf_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
mace_op_def
.
output_shape
.
extend
(
output_shapes
)
def
convert_ops
(
unresolved_ops
,
dt
,
net_def
,
device
):
ops_count
=
len
(
unresolved_ops
)
resolved_count
=
1
...
...
@@ -171,13 +185,7 @@ def convert_ops(unresolved_ops, dt, net_def, device):
final_op
=
relu_op
resolved_count
=
4
op_def
.
output
.
extend
([
output
.
name
for
output
in
final_op
.
outputs
])
output_shapes
=
[]
for
output
in
final_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
final_op
)
elif
first_op
.
type
==
'FusedBatchNorm'
:
op_def
.
name
=
first_op
.
name
...
...
@@ -225,26 +233,15 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
name
=
first_op
.
name
[:
-
4
]
# remove /add
op_def
.
type
=
'BatchNorm'
op_def
.
input
.
extend
([
input_name
,
gamma
,
beta
,
mean
,
variance
,
epsilon
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
add_1_op
.
outputs
])
output_shapes
=
[]
for
output
in
add_1_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
add_1_op
)
resolved_count
=
7
elif
first_op
.
type
==
'Relu6'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
'Relu'
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
max_limit_arg
=
op_def
.
arg
.
add
()
max_limit_arg
.
name
=
'max_limit'
max_limit_arg
.
f
=
6
...
...
@@ -252,13 +249,8 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
name
=
first_op
.
name
op_def
.
type
=
'Pooling'
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
pooling_type_arg
=
op_def
.
arg
.
add
()
pooling_type_arg
.
name
=
'pooling_type'
pooling_type_arg
.
i
=
pooling_type_mode
[
first_op
.
type
]
...
...
@@ -278,55 +270,31 @@ def convert_ops(unresolved_ops, dt, net_def, device):
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"AddN"
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
elif
first_op
.
type
==
'ConcatV2'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"Concat"
op_def
.
input
.
extend
([
first_op
.
inputs
[
i
].
name
for
i
in
xrange
(
2
)])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
axis_arg
=
op_def
.
arg
.
add
()
axis_arg
.
name
=
'axis'
axis_arg
.
i
=
get_input_tensor
(
first_op
,
2
).
eval
().
astype
(
np
.
int32
)
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
elif
first_op
.
type
==
'ResizeBilinear'
:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
"ResizeBilinear"
op_def
.
input
.
extend
([
first_op
.
inputs
[
0
].
name
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
size_arg
=
op_def
.
arg
.
add
()
size_arg
.
name
=
'size'
size_arg
.
ints
.
extend
(
get_input_tensor
(
first_op
,
1
).
eval
().
astype
(
np
.
int32
).
flat
)
size_arg
=
op_def
.
arg
.
add
()
size_arg
.
name
=
'align_corners'
size_arg
.
i
=
first_op
.
get_attr
(
'align_corners'
)
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
elif
first_op
.
type
in
[
'Relu'
,
'SpaceToBatchND'
,
'BatchToSpaceND'
,
'BiasAdd'
]:
op_def
.
name
=
first_op
.
name
op_def
.
type
=
first_op
.
type
op_def
.
input
.
extend
([
input
.
name
for
input
in
first_op
.
inputs
])
op_def
.
output
.
extend
([
output
.
name
for
output
in
first_op
.
outputs
])
output_shapes
=
[]
for
output
in
first_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
op_def
.
output_shape
.
extend
(
output_shapes
)
convert_op_outputs
(
op_def
,
first_op
)
else
:
raise
Exception
(
'Unknown Op: %s, type: %s'
%
(
first_op
.
name
,
first_op
.
type
))
pass
...
...
mace/python/tools/tf_dsp_converter_lib.py
浏览文件 @
78d58e75
...
...
@@ -3,6 +3,7 @@ import tensorflow as tf
from
operator
import
mul
from
dsp_ops
import
DspOps
from
mace.python.tools
import
graph_util
from
mace.python.tools.convert_util
import
tf_dtype_2_mace_dtype
# converter --input ../libcv/quantized_icnet.pb --output quantized_icnet_dsp.pb \
# --runtime dsp --input_node input_node --output_node output_node
...
...
@@ -65,6 +66,18 @@ def add_shape_const_node(net_def, op, values, name):
tensor
.
dims
.
extend
(
values
)
return
tensor
.
name
def
convert_op_outputs
(
mace_op_def
,
tf_op
):
mace_op_def
.
output_type
.
extend
([
tf_dtype_2_mace_dtype
(
output
.
dtype
)
for
output
in
tf_op
.
outputs
])
output_shapes
=
[]
for
output
in
tf_op
.
outputs
:
output_shape
=
mace_pb2
.
OutputShape
()
output_shape
.
dims
.
extend
(
output
.
shape
.
as_list
())
output_shapes
.
append
(
output_shape
)
mace_op_def
.
output_shape
.
extend
(
output_shapes
)
def
convert_ops
(
unresolved_ops
,
resolved_ops
,
net_def
,
output_node
,
dsp_ops
):
first_op
=
unresolved_ops
[
0
]
print
(
'Op: '
,
first_op
.
name
,
first_op
.
type
,
first_op
.
outputs
[
0
].
shape
)
...
...
@@ -119,7 +132,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
op_def
.
input
.
append
(
input_tensor
.
name
)
op_def
.
input
.
extend
([
t
.
name
for
t
in
s2b_op
.
inputs
[
1
:]])
op_def
.
input
.
extend
([
min_tensor
.
name
,
max_tensor
.
name
])
op_def
.
out_max_byte_size
.
extend
([
max_elem_size
(
out
)
for
out
in
quantize_op
.
outputs
]
)
convert_op_outputs
(
op_def
,
quantize_op
)
elif
has_padding_and_strides
(
first_op
):
op_def
.
padding
=
padding_mode
[
first_op
.
get_attr
(
'padding'
)]
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
...
...
@@ -130,14 +143,14 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
strides
=
first_op
.
get_attr
(
'strides'
)
strides_tensor
=
add_shape_const_node
(
net_def
,
first_op
,
strides
,
'strides'
)
op_def
.
input
.
extend
([
strides_tensor
])
op_def
.
out_max_byte_size
.
extend
([
max_elem_size
(
out
)
for
out
in
first_op
.
outputs
]
)
convert_op_outputs
(
op_def
,
first_op
)
elif
is_node_flatten_reshape
(
first_op
):
op_def
.
type
=
'Flatten'
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
op_def
.
out_max_byte_size
.
extend
([
max_elem_size
(
out
)
for
out
in
first_op
.
outputs
]
)
convert_op_outputs
(
op_def
,
first_op
)
elif
dsp_ops
.
has_op
(
first_op
.
type
):
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
op_def
.
out_max_byte_size
.
extend
([
max_elem_size
(
out
)
for
out
in
first_op
.
outputs
]
)
convert_op_outputs
(
op_def
,
first_op
)
else
:
raise
Exception
(
'Unsupported op: '
,
first_op
)
...
...
@@ -188,11 +201,9 @@ def reverse_batch_to_space_and_biasadd(net_def):
new_biasadd_op
.
input
[
0
]
=
get_tensor_name_from_op
(
conv_requantize_op
.
name
,
0
)
new_biasadd_op
.
input
[
2
]
=
get_tensor_name_from_op
(
conv_requantize_op
.
name
,
1
)
new_biasadd_op
.
input
[
3
]
=
get_tensor_name_from_op
(
conv_requantize_op
.
name
,
2
)
new_biasadd_op
.
out_max_byte_size
[
0
]
=
conv_requantize_op
.
out_max_byte_size
[
0
]
*
4
new_biasadd_requantize_op
=
mace_pb2
.
OperatorDef
()
new_biasadd_requantize_op
.
CopyFrom
(
biasadd_requantize_op
)
new_biasadd_requantize_op
.
out_max_byte_size
[
0
]
=
new_biasadd_op
.
out_max_byte_size
[
0
]
/
4
new_b2s_op
=
mace_pb2
.
OperatorDef
()
new_b2s_op
.
CopyFrom
(
b2s_op
)
...
...
@@ -309,8 +320,11 @@ def strip_input_quantize_and_output_dequantize(net_def, input_node, output_node)
new_input_op
.
name
=
input_op
.
name
new_input_op
.
type
=
input_op
.
type
new_input_op
.
padding
=
input_op
.
padding
new_input_op
.
out_max_byte_size
.
extend
([
input_op
.
out_max_byte_size
[
0
]
/
4
,
4
,
4
])
new_ops
.
append
(
new_input_op
)
new_input_op
.
output_shape
.
extend
([
input_op
.
output_shape
[
0
],
minf_op
.
output_shape
[
0
],
maxf_op
.
output_shape
[
0
]])
new_input_op
.
output_type
.
extend
([
input_op
.
output_type
[
0
],
mace_pb2
.
DT_FLOAT
,
mace_pb2
.
DT_FLOAT
])
for
follow_op
in
consumers
[
get_tensor_name_from_op
(
quantize_op
.
name
,
0
)]:
new_follow_op
=
mace_pb2
.
OperatorDef
()
new_follow_op
.
CopyFrom
(
follow_op
)
...
...
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