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体验新版 GitCode,发现更多精彩内容 >>
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a4435d23
编写于
6月 29, 2021
作者:
T
TFLM-bot
提交者:
GitHub
6月 29, 2021
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差异文件
Sync from upstream TF. (#238)
上级
d25c30df
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
54 addition
and
2 deletion
+54
-2
tensorflow/lite/kernels/internal/reference/mul.h
tensorflow/lite/kernels/internal/reference/mul.h
+2
-2
tensorflow/lite/kernels/internal/types.h
tensorflow/lite/kernels/internal/types.h
+52
-0
未找到文件。
tensorflow/lite/kernels/internal/reference/mul.h
浏览文件 @
a4435d23
...
...
@@ -51,7 +51,7 @@ inline void Mul(const ArithmeticParams& params,
GetActivationParams
(
params
,
&
output_activation_min
,
&
output_activation_max
);
const
int
flat_size
=
MatchingFlatSize
(
input1_shape
,
input2_shape
,
output_shape
);
Matching
ExtendedShape
FlatSize
(
input1_shape
,
input2_shape
,
output_shape
);
for
(
int
i
=
0
;
i
<
flat_size
;
++
i
)
{
output_data
[
i
]
=
ActivationFunctionWithMinMax
(
input1_data
[
i
]
*
input2_data
[
i
],
output_activation_min
,
...
...
@@ -66,7 +66,7 @@ inline void Mul(const ArithmeticParams& params,
TFLITE_DCHECK_LE
(
params
.
quantized_activation_min
,
params
.
quantized_activation_max
);
const
int
flat_size
=
MatchingFlatSize
(
input1_shape
,
input2_shape
,
output_shape
);
Matching
ExtendedShape
FlatSize
(
input1_shape
,
input2_shape
,
output_shape
);
MulElementwise
(
flat_size
,
params
,
input1_data
,
input2_data
,
output_data
);
}
...
...
tensorflow/lite/kernels/internal/types.h
浏览文件 @
a4435d23
...
...
@@ -602,6 +602,58 @@ inline int MatchingFlatSize(const Dims<N>& dims, const Dims<N>& check_dims_0,
return
MatchingFlatSize
(
dims
,
check_dims_1
,
check_dims_2
,
check_dims_3
);
}
// Flat size calculation, checking if their extended shapes match.
inline
int
MatchingExtendedShapeFlatSize
(
const
RuntimeShape
&
shape
,
const
RuntimeShape
&
check_shape_0
)
{
const
int
shape_dims
=
shape
.
DimensionsCount
();
const
int
check_shape_0_dims
=
check_shape_0
.
DimensionsCount
();
const
int
min_dims
=
std
::
min
(
shape_dims
,
check_shape_0_dims
);
for
(
int
i
=
0
;
i
<
min_dims
;
++
i
)
{
TFLITE_DCHECK_EQ
(
shape
.
Dims
(
shape_dims
-
1
-
i
),
check_shape_0
.
Dims
(
check_shape_0_dims
-
1
-
i
));
}
for
(
int
i
=
min_dims
;
i
<
shape_dims
;
++
i
)
{
TFLITE_DCHECK_EQ
(
shape
.
Dims
(
shape_dims
-
1
-
i
),
1
);
}
for
(
int
i
=
min_dims
;
i
<
check_shape_0_dims
;
++
i
)
{
TFLITE_DCHECK_EQ
(
check_shape_0
.
Dims
(
check_shape_0_dims
-
1
-
i
),
1
);
}
return
shape
.
FlatSize
();
}
inline
int
MatchingExtendedShapeFlatSize
(
const
RuntimeShape
&
shape
,
const
RuntimeShape
&
check_shape_0
,
const
RuntimeShape
&
check_shape_1
)
{
const
int
flat_size
=
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_0
);
TFLITE_DCHECK_EQ
(
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_1
),
flat_size
);
return
flat_size
;
}
inline
int
MatchingExtendedShapeFlatSize
(
const
RuntimeShape
&
shape
,
const
RuntimeShape
&
check_shape_0
,
const
RuntimeShape
&
check_shape_1
,
const
RuntimeShape
&
check_shape_2
)
{
const
int
flat_size
=
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_0
);
TFLITE_DCHECK_EQ
(
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_1
,
check_shape_2
),
flat_size
);
return
flat_size
;
}
inline
int
MatchingExtendedShapeFlatSize
(
const
RuntimeShape
&
shape
,
const
RuntimeShape
&
check_shape_0
,
const
RuntimeShape
&
check_shape_1
,
const
RuntimeShape
&
check_shape_2
,
const
RuntimeShape
&
check_shape_3
)
{
const
int
flat_size
=
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_0
);
TFLITE_DCHECK_EQ
(
MatchingExtendedShapeFlatSize
(
shape
,
check_shape_1
,
check_shape_2
,
check_shape_3
),
flat_size
);
return
flat_size
;
}
// Data is required to be contiguous, and so many operators can use either the
// full array flat size or the flat size with one dimension skipped (commonly
// the depth).
...
...
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