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2168ff38
编写于
4月 20, 2020
作者:
H
huzhiqiang
提交者:
GitHub
4月 20, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[Framework][InferShape] accelerate op infer_shape period (#3434)
上级
212c3227
变更
20
隐藏空白更改
内联
并排
Showing
20 changed file
with
109 addition
and
80 deletion
+109
-80
lite/core/op_lite.cc
lite/core/op_lite.cc
+9
-7
lite/core/op_lite.h
lite/core/op_lite.h
+6
-2
lite/operators/batch_norm_op.cc
lite/operators/batch_norm_op.cc
+1
-0
lite/operators/concat_op.cc
lite/operators/concat_op.cc
+1
-0
lite/operators/conv_op.h
lite/operators/conv_op.h
+1
-0
lite/operators/elementwise_ops.cc
lite/operators/elementwise_ops.cc
+2
-0
lite/operators/fc_op.cc
lite/operators/fc_op.cc
+2
-0
lite/operators/matmul_op.cc
lite/operators/matmul_op.cc
+1
-0
lite/operators/mul_op.h
lite/operators/mul_op.h
+2
-1
lite/operators/op_params.h
lite/operators/op_params.h
+73
-70
lite/operators/pool_op.h
lite/operators/pool_op.h
+1
-0
lite/operators/reshape_op.cc
lite/operators/reshape_op.cc
+1
-0
lite/operators/scale_op.cc
lite/operators/scale_op.cc
+1
-0
lite/operators/sequence_softmax_op.cc
lite/operators/sequence_softmax_op.cc
+1
-0
lite/operators/slice_op.cc
lite/operators/slice_op.cc
+1
-0
lite/operators/softmax_op.cc
lite/operators/softmax_op.cc
+2
-0
lite/operators/split_op.cc
lite/operators/split_op.cc
+1
-0
lite/operators/squeeze_op.cc
lite/operators/squeeze_op.cc
+1
-0
lite/operators/transpose_op.cc
lite/operators/transpose_op.cc
+1
-0
lite/operators/unsqueeze_op.cc
lite/operators/unsqueeze_op.cc
+1
-0
未找到文件。
lite/core/op_lite.cc
浏览文件 @
2168ff38
...
...
@@ -25,16 +25,16 @@ namespace lite {
bool
OpLite
::
InferShape
()
{
// if input_tensor_ptrs and output_tensor_ptrs are overloaded in param_
// InferShapeByMemoryInternal will be applied.
if
(
param_
.
input_tensor_ptrs
()
&&
param_
.
output_tensor_ptrs
())
{
if
(
op_param_
&&
op_param_
->
input_tensor_ptrs
()
&&
op_param_
->
output_tensor_ptrs
())
{
return
this
->
InferShapeWithCache
();
}
else
{
// otherwise, InferShapeImpl is applied directly.
return
this
->
InferShapeImpl
();
}
}
bool
OpLite
::
InferShapeWithCache
()
{
// 1. Get vector of current input tensors
auto
*
current_inputs
=
param_
.
input_tensor_ptrs
();
auto
*
current_inputs
=
op_param_
->
input_tensor_ptrs
();
// 2. Get hash value of current inputs shape and lod
size_t
new_hash
=
0
;
for
(
auto
iter
=
current_inputs
->
begin
();
iter
!=
current_inputs
->
end
();
...
...
@@ -59,7 +59,7 @@ bool OpLite::InferShapeWithCache() {
if
(
new_hash
==
io_shape_lod_hash_
&&
new_hash
!=
0
)
{
// if current hash value is consistent with io_shape_lod_hash_,
// previous outputs shape and lod are reused.
auto
*
current_outputs
=
param_
.
output_tensor_ptrs
();
auto
*
current_outputs
=
op_param_
->
output_tensor_ptrs
();
for
(
size_t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
current_outputs
->
at
(
i
)
->
Resize
(
last_output_shapes
[
i
]);
current_outputs
->
at
(
i
)
->
set_lod
(
last_output_lods
[
i
]);
...
...
@@ -68,10 +68,12 @@ bool OpLite::InferShapeWithCache() {
// otherwise, current hash value is changed, InferShapeImpl will apply.
io_shape_lod_hash_
=
new_hash
;
this
->
InferShapeImpl
();
auto
*
current_outputs
=
param_
.
output_tensor_ptrs
();
auto
*
current_outputs
=
op_param_
->
output_tensor_ptrs
();
last_output_shapes
.
clear
();
last_output_lods
.
clear
();
for
(
size_t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
last_output_shapes
[
i
]
=
current_outputs
->
at
(
i
)
->
dims
(
);
last_output_lods
[
i
]
=
current_outputs
->
at
(
i
)
->
lod
(
);
last_output_shapes
.
push_back
(
current_outputs
->
at
(
i
)
->
dims
()
);
last_output_lods
.
push_back
(
current_outputs
->
at
(
i
)
->
lod
()
);
}
}
return
true
;
...
...
lite/core/op_lite.h
浏览文件 @
2168ff38
...
...
@@ -77,6 +77,11 @@ class OpLite : public Registry {
// Link the external execution environ to internal context.
bool
Attach
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
);
template
<
typename
T
>
inline
void
AttachParam
(
T
*
param
)
{
op_param_
=
static_cast
<
T
*>
(
param
);
}
const
OpInfo
*
op_info
()
const
{
return
op_info_
.
get
();
}
OpInfo
*
mutable_op_info
()
{
return
op_info_
.
get
();
}
...
...
@@ -167,11 +172,10 @@ class OpLite : public Registry {
std
::
vector
<
Place
>
valid_places_
;
Place
kernel_place_
{
TARGET
(
kHost
),
PRECISION
(
kFloat
)};
std
::
unique_ptr
<
OpInfo
>
op_info_
;
std
::
vector
<
DDimLite
>
last_output_shapes
{};
std
::
vector
<
std
::
vector
<
std
::
vector
<
uint64_t
>>>
last_output_lods
{};
size_t
io_shape_lod_hash_
{};
mutable
operators
::
ParamBase
param_
;
mutable
operators
::
ParamBase
*
op_param_
{
nullptr
}
;
private:
// Infer Shape according to memory, if current input shapes are consistent
...
...
lite/operators/batch_norm_op.cc
浏览文件 @
2168ff38
...
...
@@ -73,6 +73,7 @@ bool BatchNormOp::InferShapeImpl() const {
}
bool
BatchNormOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
x
=
scope
->
FindVar
(
op_desc
.
Input
(
"X"
).
front
())
->
GetMutable
<
Tensor
>
();
param_
.
bias
=
scope
->
FindVar
(
op_desc
.
Input
(
"Bias"
).
front
())
->
GetMutable
<
Tensor
>
();
...
...
lite/operators/concat_op.cc
浏览文件 @
2168ff38
...
...
@@ -66,6 +66,7 @@ bool ConcatOpLite::InferShapeImpl() const {
// TODO(Superjomn) replace framework::OpDesc with a lite one.
bool
ConcatOpLite
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
inputs
=
op_desc
.
Input
(
"X"
);
auto
out
=
op_desc
.
Output
(
"Out"
).
front
();
...
...
lite/operators/conv_op.h
浏览文件 @
2168ff38
...
...
@@ -38,6 +38,7 @@ class ConvOpLite : public OpLite {
// TODO(Superjomn) replace framework::OpDesc with a lite one.
bool
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
override
{
AttachParam
(
&
param_
);
auto
X
=
op_desc
.
Input
(
"Input"
).
front
();
auto
Filter
=
op_desc
.
Input
(
"Filter"
).
front
();
auto
Out
=
op_desc
.
Output
(
"Output"
).
front
();
...
...
lite/operators/elementwise_ops.cc
浏览文件 @
2168ff38
...
...
@@ -87,6 +87,8 @@ bool ElementwiseOp::InferShapeImpl() const {
}
bool
ElementwiseOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
X_name
=
opdesc
.
Input
(
"X"
).
front
();
auto
Y_name
=
opdesc
.
Input
(
"Y"
).
front
();
auto
Out_name
=
opdesc
.
Output
(
"Out"
).
front
();
...
...
lite/operators/fc_op.cc
浏览文件 @
2168ff38
...
...
@@ -69,6 +69,8 @@ bool FcOpLite::InferShapeImpl() const {
}
bool
FcOpLite
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
input
=
op_desc
.
Input
(
"Input"
).
front
();
auto
W
=
op_desc
.
Input
(
"W"
).
front
();
auto
out
=
op_desc
.
Output
(
"Out"
).
front
();
...
...
lite/operators/matmul_op.cc
浏览文件 @
2168ff38
...
...
@@ -132,6 +132,7 @@ bool MatMulOpLite::InferShapeImpl() const {
}
bool
MatMulOpLite
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
CHECK
(
!
op_desc
.
Input
(
"X"
).
empty
());
CHECK
(
!
op_desc
.
Input
(
"Y"
).
empty
());
CHECK
(
!
op_desc
.
Output
(
"Out"
).
empty
());
...
...
lite/operators/mul_op.h
浏览文件 @
2168ff38
...
...
@@ -38,6 +38,8 @@ class MulOpLite : public OpLite {
void
AttachKernel
(
KernelBase
*
kernel
)
override
{
kernel
->
SetParam
(
param_
);
}
// TODO(Superjomn) replace framework::OpDesc with a lite one.
bool
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
override
{
AttachParam
(
&
param_
);
CHECK
(
!
op_desc
.
Input
(
"X"
).
empty
());
CHECK
(
!
op_desc
.
Input
(
"Y"
).
empty
());
CHECK
(
!
op_desc
.
Output
(
"Out"
).
empty
());
...
...
@@ -56,7 +58,6 @@ class MulOpLite : public OpLite {
param_
.
output
=
var
->
GetMutable
<
Tensor
>
();
param_
.
x_num_col_dims
=
op_desc
.
GetAttr
<
int
>
(
"x_num_col_dims"
);
param_
.
y_num_col_dims
=
op_desc
.
GetAttr
<
int
>
(
"y_num_col_dims"
);
return
true
;
}
...
...
lite/operators/op_params.h
浏览文件 @
2168ff38
...
...
@@ -35,8 +35,11 @@ namespace operators {
struct
ParamBase
{
public:
const
std
::
vector
<
Tensor
*>*
input_tensor_ptrs
()
const
{
return
nullptr
;
}
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
return
nullptr
;
}
virtual
~
ParamBase
()
{}
virtual
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
return
nullptr
;
}
virtual
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
return
nullptr
;
}
protected:
std
::
shared_ptr
<
std
::
vector
<
const
Tensor
*>>
input_tensor_ptrs_cache_
{
nullptr
};
...
...
@@ -108,15 +111,15 @@ struct FcParam : ParamBase {
WITH_INT8_CONFIG
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
input
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -160,15 +163,15 @@ struct MulParam : ParamBase {
WITH_INT8_CONFIG
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
,
y
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -243,15 +246,15 @@ struct ScaleParam : ParamBase {
bool
bias_after_scale
{
true
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -265,15 +268,15 @@ struct SoftmaxParam : ParamBase {
int
axis
{
-
1
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -292,15 +295,15 @@ struct ReshapeParam : ParamBase {
bool
inplace
{
false
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -314,8 +317,8 @@ struct ConcatParam : ParamBase {
int
axis
{
0
};
lite
::
Tensor
*
axis_tensor
{};
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
std
::
vector
<
const
Tensor
*>
vec
;
for
(
auto
in
:
x
)
{
vec
.
push_back
(
in
);
...
...
@@ -325,8 +328,8 @@ struct ConcatParam : ParamBase {
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -406,15 +409,15 @@ struct ConvParam : ParamBase {
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -440,15 +443,15 @@ struct BatchNormParam : ParamBase {
DataLayoutType
data_layout
{
DATALAYOUT
(
kNCHW
)};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
y
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -479,15 +482,15 @@ struct PoolParam : ParamBase {
WITH_INT8_CONFIG
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -518,15 +521,15 @@ struct SplitParam : ParamBase {
std
::
vector
<
int
>
sections
;
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -544,15 +547,15 @@ struct TransposeParam : ParamBase {
std
::
string
data_format
{
"AnyLayout"
};
///////////////////////////////////////////////////////////////////////////////////
// // get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
x
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
output
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -571,15 +574,15 @@ struct ElementwiseParam : ParamBase {
float
y_input_scale
{
1.0
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
,
Y
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -884,15 +887,15 @@ struct SequenceSoftmaxParam : ParamBase {
lite
::
Tensor
*
Out
{};
///////////////////////////////////////////////////////////////////////////////////
// // get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -1135,15 +1138,15 @@ struct SliceParam : ParamBase {
lite
::
Tensor
*
EndsTensor
{
nullptr
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -1197,15 +1200,15 @@ struct SqueezeParam : ParamBase {
std
::
vector
<
int
>
axes
{};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -1221,15 +1224,15 @@ struct UnsqueezeParam : ParamBase {
std
::
vector
<
const
lite
::
Tensor
*>
axes_tensor_vct
{};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
@@ -1253,15 +1256,15 @@ struct MatMulParam : ParamBase {
float
alpha
{
1.0
f
};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
{
if
(
UNLIKELY
(
input_tensor_ptrs_cache_
)
)
{
const
std
::
vector
<
const
Tensor
*>*
input_tensor_ptrs
()
override
{
if
(
!
input_tensor_ptrs_cache_
)
{
input_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
const
Tensor
*>
({
X
,
Y
}));
}
return
input_tensor_ptrs_cache_
.
get
();
}
// get a vector of output tensors
const
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
{
if
(
UNLIKELY
(
output_tensor_ptrs_cache_
)
)
{
std
::
vector
<
Tensor
*>*
output_tensor_ptrs
()
override
{
if
(
!
output_tensor_ptrs_cache_
)
{
output_tensor_ptrs_cache_
.
reset
(
new
std
::
vector
<
lite
::
Tensor
*>
({
Out
}));
}
return
output_tensor_ptrs_cache_
.
get
();
...
...
lite/operators/pool_op.h
浏览文件 @
2168ff38
...
...
@@ -41,6 +41,7 @@ class PoolOpLite : public OpLite {
// TODO(Superjomn) replace framework::OpDesc with a lite one.
bool
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
override
{
AttachParam
(
&
param_
);
auto
x
=
op_desc
.
Input
(
"X"
).
front
();
auto
out
=
op_desc
.
Output
(
"Out"
).
front
();
...
...
lite/operators/reshape_op.cc
浏览文件 @
2168ff38
...
...
@@ -56,6 +56,7 @@ bool ReshapeOp::InferShapeImpl() const {
}
bool
ReshapeOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
x
=
scope
->
FindVar
(
opdesc
.
Input
(
"X"
).
front
())
->
GetMutable
<
lite
::
Tensor
>
();
param_
.
output
=
...
...
lite/operators/scale_op.cc
浏览文件 @
2168ff38
...
...
@@ -30,6 +30,7 @@ bool ScaleOp::InferShapeImpl() const {
}
bool
ScaleOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
x
=
op_desc
.
Input
(
"X"
).
front
();
auto
output
=
op_desc
.
Output
(
"Out"
).
front
();
param_
.
x
=
scope
->
FindVar
(
x
)
->
GetMutable
<
Tensor
>
();
...
...
lite/operators/sequence_softmax_op.cc
浏览文件 @
2168ff38
...
...
@@ -34,6 +34,7 @@ bool SequenceSoftmaxOp::InferShapeImpl() const {
bool
SequenceSoftmaxOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
X
=
scope
->
FindVar
(
opdesc
.
Input
(
"X"
).
front
())
->
GetMutable
<
lite
::
Tensor
>
();
param_
.
Out
=
...
...
lite/operators/slice_op.cc
浏览文件 @
2168ff38
...
...
@@ -87,6 +87,7 @@ bool SliceOp::InferShapeImpl() const {
}
bool
SliceOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
X
=
scope
->
FindVar
(
opdesc
.
Input
(
"Input"
).
front
())
->
GetMutable
<
lite
::
Tensor
>
();
param_
.
Out
=
...
...
lite/operators/softmax_op.cc
浏览文件 @
2168ff38
...
...
@@ -38,6 +38,8 @@ bool SoftmaxOp::InferShapeImpl() const {
}
bool
SoftmaxOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
x
=
const_cast
<
lite
::
Tensor
*>
(
&
scope
->
FindVar
(
opdesc
.
Input
(
"X"
).
front
())
->
Get
<
lite
::
Tensor
>
());
param_
.
output
=
...
...
lite/operators/split_op.cc
浏览文件 @
2168ff38
...
...
@@ -75,6 +75,7 @@ bool SplitOp::InferShapeImpl() const {
}
bool
SplitOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
param_
.
axis
=
opdesc
.
GetAttr
<
int
>
(
"axis"
);
param_
.
num
=
opdesc
.
GetAttr
<
int
>
(
"num"
);
param_
.
sections
=
opdesc
.
GetAttr
<
std
::
vector
<
int
>>
(
"sections"
);
...
...
lite/operators/squeeze_op.cc
浏览文件 @
2168ff38
...
...
@@ -84,6 +84,7 @@ bool SqueezeOp::InferShapeImpl() const {
}
bool
SqueezeOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
x_var
=
scope
->
FindVar
(
opdesc
.
Input
(
"X"
).
front
());
auto
output_var
=
scope
->
FindVar
(
opdesc
.
Output
(
"Out"
).
front
());
CHECK
(
x_var
);
...
...
lite/operators/transpose_op.cc
浏览文件 @
2168ff38
...
...
@@ -70,6 +70,7 @@ bool TransposeOp::InferShapeImpl() const {
}
bool
TransposeOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
op_desc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
x
=
op_desc
.
Input
(
"X"
).
front
();
auto
out
=
op_desc
.
Output
(
"Out"
).
front
();
...
...
lite/operators/unsqueeze_op.cc
浏览文件 @
2168ff38
...
...
@@ -89,6 +89,7 @@ bool UnsqueezeOp::InferShapeImpl() const {
}
bool
UnsqueezeOp
::
AttachImpl
(
const
cpp
::
OpDesc
&
opdesc
,
lite
::
Scope
*
scope
)
{
AttachParam
(
&
param_
);
auto
x_var
=
scope
->
FindVar
(
opdesc
.
Input
(
"X"
).
front
());
auto
output_var
=
scope
->
FindVar
(
opdesc
.
Output
(
"Out"
).
front
());
CHECK
(
x_var
);
...
...
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