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体验新版 GitCode,发现更多精彩内容 >>
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a68dc2f3
编写于
7月 23, 2018
作者:
李
李寅
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix tensorflow dsp converter
上级
915d38c2
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
203 addition
and
181 deletion
+203
-181
docs/user_guide/basic_usage.rst
docs/user_guide/basic_usage.rst
+22
-0
mace/python/tools/BUILD
mace/python/tools/BUILD
+1
-1
mace/python/tools/converter.py
mace/python/tools/converter.py
+30
-32
mace/python/tools/converter_tool/tf_dsp_converter.py
mace/python/tools/converter_tool/tf_dsp_converter.py
+150
-70
mace/python/tools/dsp_ops.py
mace/python/tools/dsp_ops.py
+0
-78
未找到文件。
docs/user_guide/basic_usage.rst
浏览文件 @
a68dc2f3
...
...
@@ -122,6 +122,28 @@ MACE now supports models from TensorFlow and Caffe (more frameworks will be supp
strip_unused_nodes
sort_by_execution_order'
Usage for DSP,
.. code:: bash
# DSP:
./transform_graph \
--in_graph=/path/to/your/tf_model.pb \
--out_graph=/path/to/your/output/tf_model_opt.pb \
--inputs='input node name' \
--outputs='output node name' \
--transforms='strip_unused_nodes(type=float, shape="1,64,64,3")
strip_unused_nodes(type=float, shape="1,64,64,3")
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms
backport_concatv2
quantize_weights(minimum_size=2)
quantize_nodes
strip_unused_nodes
sort_by_execution_order'
- Caffe
Caffe 1.0+ models are supported in MACE converter tool.
...
...
mace/python/tools/BUILD
浏览文件 @
a68dc2f3
...
...
@@ -3,7 +3,7 @@ py_library(
srcs
=
[
"convert_util.py"
,
"graph_util.py"
,
"
tf_dsp_converter_lib
.py"
,
"
converter_tool/tf_dsp_converter
.py"
,
"converter_tool/base_converter.py"
,
"converter_tool/shape_inference.py"
,
"converter_tool/tensorflow_converter.py"
,
...
...
mace/python/tools/converter.py
浏览文件 @
a68dc2f3
...
...
@@ -96,39 +96,38 @@ def main(unused_args):
print
(
"runtime %s is not supported."
%
FLAGS
.
runtime
)
sys
.
exit
(
-
1
)
if
FLAGS
.
graph_optimize_options
:
option
=
cvt
.
ConverterOption
(
FLAGS
.
graph_optimize_options
.
split
(
','
))
else
:
option
=
cvt
.
ConverterOption
()
option
.
winograd
=
FLAGS
.
winograd
input_node_names
=
FLAGS
.
input_node
.
split
(
','
)
input_node_shapes
=
FLAGS
.
input_shape
.
split
(
':'
)
if
len
(
input_node_names
)
!=
len
(
input_node_shapes
):
raise
Exception
(
'input node count and shape count do not match.'
)
for
i
in
xrange
(
len
(
input_node_names
)):
input_node
=
cvt
.
NodeInfo
()
input_node
.
name
=
input_node_names
[
i
]
input_node
.
shape
=
parse_int_array_from_str
(
input_node_shapes
[
i
])
option
.
add_input_node
(
input_node
)
output_node_names
=
FLAGS
.
output_node
.
split
(
','
)
for
i
in
xrange
(
len
(
output_node_names
)):
output_node
=
cvt
.
NodeInfo
()
output_node
.
name
=
output_node_names
[
i
]
option
.
add_output_node
(
output_node
)
print
(
"Transform model to one that can better run on device"
)
if
FLAGS
.
runtime
==
'dsp'
:
if
FLAGS
.
platform
==
'tensorflow'
:
from
mace.python.tools
import
tf_dsp_converter_lib
output_graph_def
=
tf_dsp_converter_lib
.
convert_to_mace_pb
(
FLAGS
.
model_file
,
FLAGS
.
input_node
,
FLAGS
.
output_node
,
FLAGS
.
dsp_mode
)
else
:
print
(
"%s does not support dsp runtime yet."
%
FLAGS
.
platform
)
sys
.
exit
(
-
1
)
mace_check
(
FLAGS
.
platform
==
'tensorflow'
,
'DSP only supports tensorflow'
)
from
mace.python.tools.converter_tool
import
tf_dsp_converter
converter
=
tf_dsp_converter
.
TensorflowDspConverter
(
option
,
FLAGS
.
model_file
)
output_graph_def
=
converter
.
run
()
else
:
if
FLAGS
.
graph_optimize_options
:
option
=
cvt
.
ConverterOption
(
FLAGS
.
graph_optimize_options
.
split
(
','
))
else
:
option
=
cvt
.
ConverterOption
()
option
.
winograd
=
FLAGS
.
winograd
input_node_names
=
FLAGS
.
input_node
.
split
(
','
)
input_node_shapes
=
FLAGS
.
input_shape
.
split
(
':'
)
if
len
(
input_node_names
)
!=
len
(
input_node_shapes
):
raise
Exception
(
'input node count and shape count do not match.'
)
for
i
in
xrange
(
len
(
input_node_names
)):
input_node
=
cvt
.
NodeInfo
()
input_node
.
name
=
input_node_names
[
i
]
input_node
.
shape
=
parse_int_array_from_str
(
input_node_shapes
[
i
])
option
.
add_input_node
(
input_node
)
output_node_names
=
FLAGS
.
output_node
.
split
(
','
)
for
i
in
xrange
(
len
(
output_node_names
)):
output_node
=
cvt
.
NodeInfo
()
output_node
.
name
=
output_node_names
[
i
]
option
.
add_output_node
(
output_node
)
if
FLAGS
.
platform
==
'tensorflow'
:
from
mace.python.tools.converter_tool
import
tensorflow_converter
converter
=
tensorflow_converter
.
TensorflowConverter
(
...
...
@@ -144,7 +143,6 @@ def main(unused_args):
output_graph_def
=
converter
.
run
()
print
(
"Transform model to one that can better run on device"
)
if
FLAGS
.
runtime
==
'cpu+gpu'
:
cpu_graph_def
=
copy
.
deepcopy
(
output_graph_def
)
...
...
mace/python/tools/
tf_dsp_converter_lib
.py
→
mace/python/tools/
converter_tool/tf_dsp_converter
.py
浏览文件 @
a68dc2f3
...
...
@@ -14,16 +14,80 @@
from
mace.proto
import
mace_pb2
from
mace.python.tools.converter_tool
import
base_converter
from
mace.python.tools
import
graph_util
from
mace.python.tools.convert_util
import
mace_check
import
tensorflow
as
tf
from
tensorflow
import
gfile
from
tensorflow
.core.framework
import
tensor_shape_pb2
from
operator
import
mul
from
dsp_ops
import
DspOps
from
mace.python.tools
import
graph_util
import
numpy
as
np
class
DspOps
(
object
):
def
__init__
(
self
):
self
.
dsp_ops
=
{
'INPUT'
:
'INPUT"'
,
'OUTPUT'
:
'OUTPUT'
,
'NoOp'
:
'Nop'
,
'FLATTEN'
:
'Flatten'
,
'Identity'
:
'Nop'
,
'Placeholder'
:
'INPUT'
,
'Const'
:
'Const'
,
'QuantizedConv2D'
:
'QuantizedConv2d_8x8to32'
,
'QuantizedMatMul'
:
'QuantizedMatMul_8x8to32'
,
'QuantizeDownAndShrinkRange'
:
'QuantizeDownAndShrinkRange_32to8'
,
'QuantizedRelu'
:
'QuantizedRelu_8'
,
'QuantizedReluX'
:
'QuantizedReluX_8'
,
'QuantizedMaxPool'
:
'QuantizedMaxPool_8'
,
'QuantizedAvgPool'
:
'QuantizedAvgPool_8'
,
'QuantizedConcat'
:
'QuantizedConcat_8'
,
'QuantizedBiasAdd'
:
'QuantizedBiasAdd_8p8to32'
,
'QuantizedResizeBilinear'
:
'QuantizedResizeBilinear_8'
,
'QuantizedSpaceToBatchND'
:
'QuantizedSpaceToBatchND_8'
,
'QuantizedBatchToSpaceND'
:
'QuantizedBatchToSpaceND_8'
,
'QuantizedSoftmax'
:
'QuantizedSoftmax_8'
,
'QuantizedTanh'
:
'QuantizedTanh_8'
,
'Min'
:
'Min_f'
,
'Max'
:
'Max_f'
,
'QuantizeV2'
:
'Quantize'
,
'Dequantize'
:
'Dequantize'
,
'Softmax'
:
'Softmax_f'
,
'Reshape'
:
'Reshape'
,
'QuantizedReshape'
:
'QuantizedReshape'
,
'Sigmoid'
:
'Sigmoid_f'
,
'Slice'
:
'Slice_f'
,
'Add'
:
'Add_f'
,
'Mul'
:
'Mul_f'
,
'Requantize'
:
'Requantize_32to8'
,
'RequantizationRange'
:
'RequantizationRange_32'
,
'Sub'
:
'Sub_f'
,
'Pack'
:
'Pack_int32'
,
'StridedSlice'
:
'StridedSlice_f'
,
'ExpandDims'
:
'ExpandDims_f'
,
'QuantizedMul'
:
'QuantizedMul_8x8to32'
,
'QuantizedAdd'
:
'QuantizedAdd_8p8to32'
,
'Pad'
:
'Pad_f'
,
'SpaceToBatchND'
:
'SpaceToBatchND_f'
,
'BatchToSpaceND'
:
'BatchToSpaceND_f'
,
'ResizeBilinear'
:
'ResizeBilinear_f'
,
'ConcatV2'
:
'ConcatV2_f'
,
'Conv2DBackpropInput'
:
'Deconv_f'
,
'Tanh'
:
'Tanh_f'
,
'Split'
:
'Split_f'
,
'Transpose'
:
'Transpose_f'
,
'Concat'
:
'Concat_f'
,
'AddN'
:
'AddN_f'
,
}
def
has_op
(
self
,
tf_op
):
return
tf_op
in
self
.
dsp_ops
def
map_nn_op
(
self
,
tf_op
):
if
tf_op
not
in
self
.
dsp_ops
:
raise
Exception
(
'Could not map nn op for: '
,
tf_op
)
return
self
.
dsp_ops
[
tf_op
]
# converter --input ../libcv/quantized_model.pb \
# --output quantized_model_dsp.pb \
# --runtime dsp --input_node input_node \
# --output_node output_node
TF_DTYPE_2_MACE_DTYPE_MAP
=
{
tf
.
float32
:
mace_pb2
.
DT_FLOAT
,
...
...
@@ -101,7 +165,6 @@ def get_input_tensor(op, index):
def
add_shape_const_node
(
net_def
,
op
,
values
,
name
):
print
(
'Add const node: '
,
op
.
name
+
'/'
+
name
)
tensor
=
net_def
.
tensors
.
add
()
node_name
=
op
.
name
+
'/'
+
name
tensor
.
name
=
node_name
+
':0'
...
...
@@ -128,7 +191,7 @@ def convert_op_outputs(mace_op_def, tf_op):
mace_op_def
.
output_shape
.
extend
(
output_shapes
)
def
convert_ops
(
unresolved_ops
,
resolved_ops
,
net_def
,
output_node
,
dsp_ops
):
def
convert_ops
(
unresolved_ops
,
resolved_ops
,
net_def
,
dsp_ops
):
first_op
=
unresolved_ops
[
0
]
print
(
'Op: '
,
first_op
.
name
,
first_op
.
type
,
first_op
.
outputs
[
0
].
shape
)
...
...
@@ -152,7 +215,8 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
first_op
.
outputs
[
0
].
dtype
==
tf
.
quint8
or
\
first_op
.
outputs
[
0
].
dtype
==
tf
.
quint16
:
tensor
.
int32_data
.
extend
(
tf_tensor
.
astype
(
int
).
flat
)
elif
first_op
.
type
==
'Shape'
:
resolved_ops
.
add
(
first_op
.
name
)
else
:
op_def
=
net_def
.
op
.
add
()
op_def
.
name
=
first_op
.
name
...
...
@@ -162,7 +226,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
if
len
(
first_op
.
outputs
)
>
0
and
first_op
.
type
==
'Dequantize'
\
and
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
\
and
(
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'SpaceToBatchND'
or
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'BatchToSpaceND'
):
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'BatchToSpaceND'
):
# noqa
input_tensor
=
first_op
.
inputs
[
0
]
min_tensor
=
first_op
.
inputs
[
1
]
max_tensor
=
first_op
.
inputs
[
2
]
...
...
@@ -183,14 +247,12 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
op_def
.
input
.
extend
([
t
.
name
for
t
in
s2b_op
.
inputs
[
1
:]])
op_def
.
input
.
extend
([
min_tensor
.
name
,
max_tensor
.
name
])
convert_op_outputs
(
op_def
,
quantize_op
)
elif
len
(
first_op
.
outputs
)
>
0
and
\
first_op
.
type
==
'QuantizedReshape'
and
\
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
\
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'Dequantize'
and
\
len
(
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
())
\
>
0
and
\
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
()[
0
].
type
\
==
'Softmax'
:
elif
(
len
(
first_op
.
outputs
)
>
0
and
first_op
.
type
==
'QuantizedReshape'
and
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'Dequantize'
and
len
(
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
())
>
0
and
# noqa
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
()[
0
].
type
==
'Softmax'
):
# noqa
input_tensor
=
first_op
.
inputs
[
0
]
min_tensor
=
first_op
.
inputs
[
2
]
max_tensor
=
first_op
.
inputs
[
3
]
...
...
@@ -216,17 +278,17 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
[
input_tensor
.
name
,
min_tensor
.
name
,
max_tensor
.
name
])
convert_op_outputs
(
op_def
,
quantize_reshape_op
)
# remove Squeeze
elif
len
(
first_op
.
outputs
)
>
0
and
\
first_op
.
type
==
'Requantize'
and
\
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
\
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'Dequantize'
and
\
len
(
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
())
\
>
0
and
\
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
()[
0
].
type
\
==
'Squeeze'
:
elif
(
len
(
first_op
.
outputs
)
>
0
and
first_op
.
type
==
'Requantize'
and
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'Dequantize'
and
len
(
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
())
>
0
and
# noqa
first_op
.
outputs
[
0
].
consumers
()[
0
].
outputs
[
0
].
consumers
()[
0
].
type
==
'Squeeze'
):
# noqa
dequantize_op
=
first_op
.
outputs
[
0
].
consumers
()[
0
]
squeeze_op
=
dequantize_op
.
outputs
[
0
].
consumers
()[
0
]
reshape_op
=
squeeze_op
.
outputs
[
0
].
consumers
()[
0
]
if
reshape_op
.
type
==
'Shape'
:
reshape_op
=
squeeze_op
.
outputs
[
0
].
consumers
()[
1
]
min_op
=
reshape_op
.
outputs
[
0
].
consumers
()[
0
]
max_op
=
reshape_op
.
outputs
[
0
].
consumers
()[
1
]
quantize_op
=
min_op
.
outputs
[
0
].
consumers
()[
0
]
...
...
@@ -249,7 +311,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
if
next_op
and
len
(
next_op
.
outputs
)
>
0
and
\
next_op
.
type
==
'QuantizedReshape'
and
\
len
(
next_op
.
outputs
[
0
].
consumers
())
>
0
else
None
softmax_op
=
dequantize_op
.
outputs
[
0
].
consumers
()[
0
]
\
softmax_op
=
dequantize_op
.
outputs
[
0
].
consumers
()[
0
]
\
if
dequantize_op
and
len
(
dequantize_op
.
outputs
)
>
0
and
\
dequantize_op
.
type
==
'Dequantize'
and
\
len
(
dequantize_op
.
outputs
[
0
].
consumers
())
>
0
else
None
...
...
@@ -280,7 +342,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
convert_op_outputs
(
softmax_op_def
,
quantize_reshape_op
)
elif
len
(
first_op
.
outputs
)
>
0
and
first_op
.
type
==
'Dequantize'
and
\
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
\
len
(
first_op
.
outputs
[
0
].
consumers
())
>
0
and
\
first_op
.
outputs
[
0
].
consumers
()[
0
].
type
==
'Tanh'
:
input_tensor
=
first_op
.
inputs
[
0
]
min_tensor
=
first_op
.
inputs
[
1
]
...
...
@@ -446,12 +508,10 @@ def reverse_batch_to_space_and_biasadd(net_def):
new_follow_op
.
CopyFrom
(
follow_op
)
for
i
in
xrange
(
len
(
follow_op
.
input
)):
for
k
in
xrange
(
3
):
if
new_follow_op
.
input
[
i
]
==
get_tensor_name_from_op
(
biasadd_requantize_op
.
name
,
k
):
new_follow_op
.
input
[
i
]
=
get_tensor_name_from_op
(
b2s_op
.
name
,
k
)
if
new_follow_op
.
input
[
i
]
==
get_tensor_name_from_op
(
# noqa
biasadd_requantize_op
.
name
,
k
):
new_follow_op
.
input
[
i
]
=
get_tensor_name_from_op
(
# noqa
b2s_op
.
name
,
k
)
new_ops
.
append
(
new_follow_op
)
skip_ops
.
add
(
follow_op
.
name
)
visited_ops
.
add
(
follow_op
.
name
)
...
...
@@ -518,7 +578,7 @@ def add_input_output_info(net_def, input_node, output_node, graph, dtype):
return
net_def
def
fuse_quantize
(
net_def
,
input_node
,
output_node
):
def
fuse_quantize
(
net_def
):
tensor_map
=
{}
for
tensor
in
net_def
.
tensors
:
tensor_map
[
tensor
.
name
]
=
tensor
...
...
@@ -567,51 +627,71 @@ def fuse_quantize(net_def, input_node, output_node):
return
new_net_def
def
convert_to_mace_pb
(
model_file
,
input_node
,
output_node
,
dsp_mode
):
"""
nnlib does not have batch norm, so use tensorflow optimizer to fold
batch norm with convolution. The fold optimization reorders ops, so
we sort ops first by topology.
"""
input_graph_def
=
tf
.
GraphDef
()
with
gfile
.
Open
(
model_file
,
"rb"
)
as
f
:
data
=
f
.
read
()
input_graph_def
.
ParseFromString
(
data
)
input_graph_def
=
graph_util
.
sort_tf_graph
(
input_graph_def
)
net_def
=
mace_pb2
.
NetDef
()
with
tf
.
Session
()
as
session
:
with
session
.
graph
.
as_default
()
as
graph
:
tf
.
import_graph_def
(
input_graph_def
,
name
=
""
)
ops
=
graph
.
get_operations
()
dsp_ops
=
DspOps
()
resolved_ops
=
set
()
# convert const node
unresolved_ops
=
[
op
for
op
in
ops
if
op
.
type
==
'Const'
]
class
TensorflowDspConverter
(
base_converter
.
ConverterInterface
):
def
__init__
(
self
,
option
,
src_model_file
):
self
.
_option
=
option
self
.
_mace_net_def
=
mace_pb2
.
NetDef
()
# import tensorflow graph
tf_graph_def
=
tf
.
GraphDef
()
with
tf
.
gfile
.
Open
(
src_model_file
,
'rb'
)
as
f
:
tf_graph_def
.
ParseFromString
(
f
.
read
())
self
.
_placeholders
=
{}
self
.
add_shape_info
(
tf_graph_def
)
with
tf
.
Session
()
as
session
:
with
session
.
graph
.
as_default
()
as
graph
:
tf
.
import_graph_def
(
tf_graph_def
,
name
=
''
)
self
.
_tf_graph
=
graph
def
run
(
self
):
ops
=
self
.
_tf_graph
.
get_operations
()
dsp_ops
=
DspOps
()
resolved_ops
=
set
()
mace_check
(
len
(
self
.
_option
.
input_nodes
)
==
1
and
len
(
self
.
_option
.
output_nodes
)
==
1
,
'dsp only support single input and output'
)
input_node
=
self
.
_option
.
input_nodes
.
values
()[
0
].
name
output_node
=
self
.
_option
.
output_nodes
.
values
()[
0
].
name
# convert const node
unresolved_ops
=
[
op
for
op
in
ops
if
op
.
type
==
'Const'
]
with
tf
.
Session
()
as
session
:
while
len
(
unresolved_ops
)
>
0
:
convert_ops
(
unresolved_ops
,
resolved_ops
,
net_def
,
output_node
,
convert_ops
(
unresolved_ops
,
resolved_ops
,
self
.
_mace_net_def
,
dsp_ops
)
# convert op node
unresolved_ops
=
[
op
for
op
in
ops
if
op
.
type
!=
'Const'
]
while
len
(
unresolved_ops
)
>
0
:
convert_ops
(
unresolved_ops
,
resolved_ops
,
net_def
,
output_node
,
convert_ops
(
unresolved_ops
,
resolved_ops
,
self
.
_mace_net_def
,
dsp_ops
)
add_output_node
(
net_def
,
output_node
)
net_def
=
reverse_batch_to_space_and_biasadd
(
net_def
)
net_def
=
fuse_quantize
(
net_def
,
input_node
,
output_node
)
add_output_node
(
self
.
_mace_
net_def
,
output_node
)
net_def
=
reverse_batch_to_space_and_biasadd
(
self
.
_mace_
net_def
)
net_def
=
fuse_quantize
(
net_def
)
sorted_net_def
=
graph_util
.
sort_mace_graph
(
net_def
,
'__output__'
)
net_def_with_node_id
=
add_node_id
(
sorted_net_def
)
dtype
=
mace_pb2
.
DT_FLOAT
final_net_def
=
add_input_output_info
(
net_def_with_node_id
,
input_node
,
output_node
,
graph
,
dtype
)
arg
=
final_net_def
.
arg
.
add
()
arg
.
name
=
'dsp_mode'
arg
.
i
=
dsp_mode
return
final_net_def
net_def_with_node_id
,
input_node
,
output_node
,
self
.
_tf_graph
,
dtype
)
return
final_net_def
def
add_shape_info
(
self
,
tf_graph_def
):
for
node
in
tf_graph_def
.
node
:
for
input_node
in
self
.
_option
.
input_nodes
.
values
():
if
node
.
name
==
input_node
.
name
or
\
node
.
name
+
':0'
==
input_node
.
name
:
del
node
.
attr
[
'shape'
].
shape
.
dim
[:]
node
.
attr
[
'shape'
].
shape
.
dim
.
extend
([
tensor_shape_pb2
.
TensorShapeProto
.
Dim
(
size
=
i
)
for
i
in
input_node
.
shape
])
self
.
_placeholders
[
node
.
name
+
':0'
]
=
\
np
.
zeros
(
shape
=
input_node
.
shape
,
dtype
=
float
)
mace/python/tools/dsp_ops.py
已删除
100644 → 0
浏览文件 @
915d38c2
# Copyright 2018 Xiaomi, Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class
DspOps
(
object
):
def
__init__
(
self
):
self
.
dsp_ops
=
{
'INPUT'
:
'INPUT"'
,
'OUTPUT'
:
'OUTPUT'
,
'NoOp'
:
'Nop'
,
'FLATTEN'
:
'Flatten'
,
'Identity'
:
'Nop'
,
'Placeholder'
:
'INPUT'
,
'Const'
:
'Const'
,
'QuantizedConv2D'
:
'QuantizedConv2d_8x8to32'
,
'QuantizedMatMul'
:
'QuantizedMatMul_8x8to32'
,
'QuantizeDownAndShrinkRange'
:
'QuantizeDownAndShrinkRange_32to8'
,
'QuantizedRelu'
:
'QuantizedRelu_8'
,
'QuantizedReluX'
:
'QuantizedReluX_8'
,
'QuantizedMaxPool'
:
'QuantizedMaxPool_8'
,
'QuantizedAvgPool'
:
'QuantizedAvgPool_8'
,
'QuantizedConcat'
:
'QuantizedConcat_8'
,
'QuantizedBiasAdd'
:
'QuantizedBiasAdd_8p8to32'
,
'QuantizedResizeBilinear'
:
'QuantizedResizeBilinear_8'
,
'QuantizedSpaceToBatchND'
:
'QuantizedSpaceToBatchND_8'
,
'QuantizedBatchToSpaceND'
:
'QuantizedBatchToSpaceND_8'
,
'QuantizedSoftmax'
:
'QuantizedSoftmax_8'
,
'QuantizedTanh'
:
'QuantizedTanh_8'
,
'Min'
:
'Min_f'
,
'Max'
:
'Max_f'
,
'QuantizeV2'
:
'Quantize'
,
'Dequantize'
:
'Dequantize'
,
'Softmax'
:
'Softmax_f'
,
'Reshape'
:
'Reshape'
,
'QuantizedReshape'
:
'QuantizedReshape'
,
'Sigmoid'
:
'Sigmoid_f'
,
'Slice'
:
'Slice_f'
,
'Add'
:
'Add_f'
,
'Mul'
:
'Mul_f'
,
'Requantize'
:
'Requantize_32to8'
,
'RequantizationRange'
:
'RequantizationRange_32'
,
'Sub'
:
'Sub_f'
,
'Pack'
:
'Pack_int32'
,
'StridedSlice'
:
'StridedSlice_f'
,
'ExpandDims'
:
'ExpandDims_f'
,
'QuantizedMul'
:
'QuantizedMul_8x8to32'
,
'QuantizedAdd'
:
'QuantizedAdd_8p8to32'
,
'Pad'
:
'Pad_f'
,
'SpaceToBatchND'
:
'SpaceToBatchND_f'
,
'BatchToSpaceND'
:
'BatchToSpaceND_f'
,
'ResizeBilinear'
:
'ResizeBilinear_f'
,
'ConcatV2'
:
'ConcatV2_f'
,
'Conv2DBackpropInput'
:
'Deconv_f'
,
'Tanh'
:
'Tanh_f'
,
'Split'
:
'Split_f'
,
'Transpose'
:
'Transpose_f'
,
'Concat'
:
'Concat_f'
,
'AddN'
:
'AddN_f'
,
}
def
has_op
(
self
,
tf_op
):
return
tf_op
in
self
.
dsp_ops
def
map_nn_op
(
self
,
tf_op
):
if
tf_op
not
in
self
.
dsp_ops
:
raise
Exception
(
'Could not map nn op for: '
,
tf_op
)
return
self
.
dsp_ops
[
tf_op
]
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