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体验新版 GitCode,发现更多精彩内容 >>
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fe978b15
编写于
7月 26, 2018
作者:
李
李滨
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'master' into 'master'
Refactor quantization utils See merge request !684
上级
7a9ee4ca
de518dfc
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
172 addition
and
356 deletion
+172
-356
mace/core/tensor.h
mace/core/tensor.h
+27
-3
mace/kernels/quantize.h
mace/kernels/quantize.h
+101
-116
mace/ops/ops_register.cc
mace/ops/ops_register.cc
+0
-2
mace/ops/quantize.cc
mace/ops/quantize.cc
+0
-8
mace/ops/quantize.h
mace/ops/quantize.h
+12
-68
mace/ops/quantize_test.cc
mace/ops/quantize_test.cc
+32
-159
未找到文件。
mace/core/tensor.h
浏览文件 @
fe978b15
...
...
@@ -106,21 +106,27 @@ class Tensor {
buffer_
(
nullptr
),
is_buffer_owner_
(
true
),
unused_
(
false
),
name_
(
""
)
{}
name_
(
""
),
scale_
(
0.
f
),
zero_point_
(
0
)
{}
Tensor
(
BufferBase
*
buffer
,
DataType
dtype
)
:
dtype_
(
dtype
),
buffer_
(
buffer
),
is_buffer_owner_
(
false
),
unused_
(
false
),
name_
(
""
)
{}
name_
(
""
),
scale_
(
0.
f
),
zero_point_
(
0
)
{}
Tensor
(
const
BufferSlice
&
buffer_slice
,
DataType
dtype
)
:
dtype_
(
dtype
),
buffer_slice_
(
buffer_slice
),
is_buffer_owner_
(
false
),
unused_
(
false
),
name_
(
""
)
{
name_
(
""
),
scale_
(
0.
f
),
zero_point_
(
0
)
{
buffer_
=
&
buffer_slice_
;
}
...
...
@@ -363,6 +369,22 @@ class Tensor {
MACE_DISABLE_COPY_AND_ASSIGN
(
MappingGuard
);
};
inline
float
scale
()
const
{
return
scale_
;
}
inline
int32_t
zero_point
()
const
{
return
zero_point_
;
}
inline
void
SetScale
(
float
scale
)
{
scale_
=
scale
;
}
inline
void
SetZeroPoint
(
int32_t
zero_point
)
{
zero_point_
=
zero_point
;
}
private:
Allocator
*
allocator_
;
DataType
dtype_
;
...
...
@@ -373,6 +395,8 @@ class Tensor {
bool
is_buffer_owner_
;
bool
unused_
;
std
::
string
name_
;
float
scale_
;
int32_t
zero_point_
;
MACE_DISABLE_COPY_AND_ASSIGN
(
Tensor
);
};
...
...
mace/kernels/quantize.h
浏览文件 @
fe978b15
...
...
@@ -28,31 +28,47 @@ namespace kernels {
template
<
typename
T
>
inline
void
AdjustRange
(
const
float
in_min_data
,
const
float
in_max_data
,
float
*
out_min_data
,
float
*
out_max_data
)
{
const
bool
non_zero
,
float
*
scale
,
int32_t
*
zero_point
)
{
// re-range to make range include zero float and
// make zero float as integer u8
const
float
quantized_max
=
std
::
numeric_limits
<
uint8_t
>::
max
();
float
out_min
=
fminf
(
0.
f
,
in_min_data
);
float
out_max
=
fmaxf
(
0.
f
,
in_max_data
);
if
(
out_min
<
0.
f
)
{
float
stepsize
=
(
in_max_data
-
in_min_data
)
/
quantized_max
;
float
quantized_zero
=
-
in_min_data
/
stepsize
;
float
quantized_zero_near_int
=
roundf
(
quantized_zero
);
if
(
fabs
(
quantized_zero
-
quantized_zero_near_int
)
>
1e-6
)
{
if
(
quantized_zero
<
quantized_zero_near_int
)
{
const
T
quantized_min
=
std
::
numeric_limits
<
T
>::
lowest
();
const
T
quantized_max
=
std
::
numeric_limits
<
T
>::
max
();
if
(
quantized_min
<
0
)
{
MACE_ASSERT
(
!
non_zero
,
"Cannot nudge to non_zero quantize value."
);
}
float
out_max
=
std
::
max
(
0.
f
,
in_max_data
);
float
out_min
=
std
::
min
(
0.
f
,
in_min_data
);
// make in_min_data quantize as greater than 1
if
(
non_zero
)
{
out_min
=
std
::
min
(
out_min
,
in_min_data
-
(
out_max
-
in_min_data
)
/
(
quantized_max
-
quantized_min
-
1
));
}
*
scale
=
(
out_max
-
out_min
)
/
(
quantized_max
-
quantized_min
);
const
float
kEps
=
1e-6
;
if
(
out_min
<
-
kEps
&&
out_max
>
kEps
)
{
float
quantized_zero
=
-
out_min
/
*
scale
;
int32_t
quantized_zero_near_int
=
static_cast
<
int32_t
>
(
roundf
(
quantized_zero
));
*
zero_point
=
quantized_zero_near_int
;
if
(
fabs
(
quantized_zero
-
quantized_zero_near_int
)
>
kEps
)
{
if
(
quantized_zero
<
quantized_zero_near_int
||
non_zero
)
{
// keep out_max fixed, and move out_min
stepsize
=
out_max
/
(
quantized_max
-
quantized_zero_near_int
);
out_min
=
out_max
-
quantized_max
*
stepsize
;
*
scale
=
out_max
/
(
quantized_max
-
quantized_zero_near_int
);
}
else
{
// keep out_min fixed, and move out_max
stepsize
=
-
out_min
/
quantized_zero_near_int
;
out_max
=
out_min
+
quantized_max
*
stepsize
;
*
scale
=
-
out_min
/
quantized_zero_near_int
;
}
}
}
else
if
(
out_min
>
-
kEps
)
{
*
zero_point
=
quantized_min
;
}
else
{
*
zero_point
=
quantized_max
;
}
*
out_min_data
=
out_min
;
*
out_max_data
=
out_max
;
}
template
<
typename
T
>
...
...
@@ -67,6 +83,50 @@ inline T Saturate(float value) {
}
}
inline
void
FindMinMax
(
const
float
*
input
,
const
index_t
size
,
float
*
min_val
,
float
*
max_val
)
{
float
max_v
=
std
::
numeric_limits
<
float
>::
lowest
();
float
min_v
=
std
::
numeric_limits
<
float
>::
max
();
for
(
index_t
i
=
0
;
i
<
size
;
++
i
)
{
max_v
=
std
::
max
(
max_v
,
input
[
i
]);
min_v
=
std
::
min
(
min_v
,
input
[
i
]);
}
*
min_val
=
min_v
;
*
max_val
=
max_v
;
}
template
<
typename
T
>
inline
void
Quantize
(
const
float
*
input
,
const
index_t
size
,
bool
non_zero
,
T
*
output
,
float
*
scale
,
int32_t
*
zero_point
)
{
float
in_min_data
;
float
in_max_data
;
FindMinMax
(
input
,
size
,
&
in_min_data
,
&
in_max_data
);
AdjustRange
<
T
>
(
in_min_data
,
in_max_data
,
non_zero
,
scale
,
zero_point
);
float
recip_scale
=
1
/
*
scale
;
for
(
int
i
=
0
;
i
<
size
;
++
i
)
{
output
[
i
]
=
Saturate
<
T
>
(
roundf
(
*
zero_point
+
recip_scale
*
input
[
i
]));
}
}
template
<
typename
T
>
inline
void
Dequantize
(
const
T
*
input
,
const
index_t
size
,
const
float
scale
,
const
int32_t
zero_point
,
float
*
output
)
{
for
(
int
i
=
0
;
i
<
size
;
++
i
)
{
output
[
i
]
=
scale
*
(
input
[
i
]
-
zero_point
);
}
}
template
<
DeviceType
D
,
typename
T
>
struct
QuantizeFunctor
;
...
...
@@ -75,26 +135,24 @@ struct QuantizeFunctor<CPU, uint8_t> {
QuantizeFunctor
()
{}
MaceStatus
operator
()(
const
Tensor
*
input
,
const
Tensor
*
in_min
,
const
Tensor
*
in_max
,
Tensor
*
output
,
Tensor
*
out_min
,
Tensor
*
out_max
,
StatsFuture
*
future
)
{
const
bool
non_zero
,
Tensor
*
output
,
StatsFuture
*
future
)
{
MACE_UNUSED
(
future
);
Tensor
::
MappingGuard
input_guard
(
input
);
Tensor
::
MappingGuard
output_guard
(
output
);
const
float
*
input_data
=
input
->
data
<
float
>
();
const
float
in_min_data
=
in_min
->
data
<
float
>
()[
0
];
const
float
in_max_data
=
in_max
->
data
<
float
>
()[
0
];
uint8_t
*
output_data
=
output
->
mutable_data
<
uint8_t
>
();
float
*
out_min_data
=
out_min
->
mutable_data
<
float
>
();
float
*
out_max_data
=
out_max
->
mutable_data
<
float
>
();
AdjustRange
<
uint8_t
>
(
in_min_data
,
in_max_data
,
out_min_data
,
out_max_data
);
float
recip_stepsize
=
255.
f
/
(
out_max_data
[
0
]
-
out_min_data
[
0
]);
for
(
int
i
=
0
;
i
<
input
->
size
();
++
i
)
{
output_data
[
i
]
=
Saturate
<
uint8_t
>
(
roundf
(
(
input_data
[
i
]
-
in_min_data
)
*
recip_stepsize
));
}
float
scale
;
int32_t
zero_point
;
Quantize
(
input_data
,
input
->
size
(),
non_zero
,
output_data
,
&
scale
,
&
zero_point
);
output
->
SetScale
(
scale
);
output
->
SetZeroPoint
(
zero_point
);
return
MACE_SUCCESS
;
}
...
...
@@ -108,91 +166,18 @@ struct DequantizeFunctor<CPU, uint8_t> {
DequantizeFunctor
()
{}
MaceStatus
operator
()(
const
Tensor
*
input
,
const
Tensor
*
in_min
,
const
Tensor
*
in_max
,
Tensor
*
output
,
StatsFuture
*
future
)
{
Tensor
*
output
,
StatsFuture
*
future
)
{
MACE_UNUSED
(
future
);
Tensor
::
MappingGuard
input_guard
(
input
);
Tensor
::
MappingGuard
output_guard
(
output
);
const
uint8_t
*
input_data
=
input
->
data
<
uint8_t
>
();
const
float
in_min_data
=
in_min
->
data
<
float
>
()[
0
];
const
float
in_max_data
=
in_max
->
data
<
float
>
()[
0
];
float
*
output_data
=
output
->
mutable_data
<
float
>
();
float
stepsize
=
(
in_max_data
-
in_min_data
)
/
255.0
;
for
(
int
i
=
0
;
i
<
input
->
size
();
++
i
)
{
output_data
[
i
]
=
in_min_data
+
stepsize
*
input_data
[
i
];
}
return
MACE_SUCCESS
;
}
};
template
<
DeviceType
D
,
typename
T
>
struct
RequantizeFunctor
;
template
<
>
struct
RequantizeFunctor
<
CPU
,
uint8_t
>
{
RequantizeFunctor
()
{}
MaceStatus
operator
()(
const
Tensor
*
input
,
const
Tensor
*
in_min
,
const
Tensor
*
in_max
,
const
Tensor
*
rerange_min
,
const
Tensor
*
rerange_max
,
Tensor
*
output
,
Tensor
*
out_min
,
Tensor
*
out_max
,
StatsFuture
*
future
)
{
MACE_UNUSED
(
future
);
const
int
*
input_data
=
input
->
data
<
int
>
();
const
float
in_min_data
=
in_min
->
data
<
float
>
()[
0
];
const
float
in_max_data
=
in_max
->
data
<
float
>
()[
0
];
float
rerange_min_data
;
float
rerange_max_data
;
int
min_val
=
std
::
numeric_limits
<
int
>::
max
();
int
max_val
=
std
::
numeric_limits
<
int
>::
lowest
();
double
si
=
(
in_max_data
-
in_min_data
)
/
std
::
numeric_limits
<
uint32_t
>::
max
();
if
(
rerange_min
==
nullptr
&&
rerange_max
==
nullptr
)
{
for
(
int
i
=
0
;
i
<
input
->
size
();
++
i
)
{
min_val
=
std
::
min
(
min_val
,
input_data
[
i
]);
max_val
=
std
::
max
(
max_val
,
input_data
[
i
]);
}
rerange_min_data
=
min_val
*
si
;
rerange_max_data
=
max_val
*
si
;
}
else
{
rerange_min_data
=
rerange_min
->
data
<
float
>
()[
0
];
rerange_max_data
=
rerange_max
->
data
<
float
>
()[
0
];
}
uint8_t
*
output_data
=
output
->
mutable_data
<
uint8_t
>
();
float
*
out_min_data
=
out_min
->
mutable_data
<
float
>
();
float
*
out_max_data
=
out_max
->
mutable_data
<
float
>
();
AdjustRange
<
uint8_t
>
(
rerange_min_data
,
rerange_max_data
,
out_min_data
,
out_max_data
);
/**
* f = qi * si = min_o + qo * so
* => qo = (qi * si - min_o) / so
* = qi * (si/so) - min_o / so
* = qi * (si / so) + zo
*
* zo = -min_o / so
*
*/
float
so
=
(
out_max_data
[
0
]
-
out_min_data
[
0
])
/
std
::
numeric_limits
<
uint8_t
>::
max
();
double
step_ratio
=
si
/
so
;
float
quantized_out_zero
=
-
out_min_data
[
0
]
/
so
;
for
(
int
i
=
0
;
i
<
output
->
size
();
++
i
)
{
output_data
[
i
]
=
Saturate
<
uint8_t
>
(
roundf
(
quantized_out_zero
+
input_data
[
i
]
*
step_ratio
));
}
Dequantize
(
input_data
,
input
->
size
(),
input
->
scale
(),
input
->
zero_point
(),
output_data
);
return
MACE_SUCCESS
;
}
...
...
mace/ops/ops_register.cc
浏览文件 @
fe978b15
...
...
@@ -45,7 +45,6 @@ extern void Register_Pooling(OperatorRegistryBase *op_registry);
extern
void
Register_Proposal
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_Quantize
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_ReduceMean
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_Requantize
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_Reshape
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_ResizeBilinear
(
OperatorRegistryBase
*
op_registry
);
extern
void
Register_Shape
(
OperatorRegistryBase
*
op_registry
);
...
...
@@ -96,7 +95,6 @@ OperatorRegistry::OperatorRegistry() : OperatorRegistryBase() {
ops
::
Register_Proposal
(
this
);
ops
::
Register_Quantize
(
this
);
ops
::
Register_ReduceMean
(
this
);
ops
::
Register_Requantize
(
this
);
ops
::
Register_Reshape
(
this
);
ops
::
Register_ResizeBilinear
(
this
);
ops
::
Register_Shape
(
this
);
...
...
mace/ops/quantize.cc
浏览文件 @
fe978b15
...
...
@@ -33,13 +33,5 @@ void Register_Dequantize(OperatorRegistryBase *op_registry) {
DequantizeOp
<
DeviceType
::
CPU
,
uint8_t
>
);
}
void
Register_Requantize
(
OperatorRegistryBase
*
op_registry
)
{
MACE_REGISTER_OPERATOR
(
op_registry
,
OpKeyBuilder
(
"Requantize"
)
.
Device
(
DeviceType
::
CPU
)
.
TypeConstraint
<
uint8_t
>
(
"T"
)
.
Build
(),
RequantizeOp
<
DeviceType
::
CPU
,
uint8_t
>
);
}
}
// namespace ops
}
// namespace mace
mace/ops/quantize.h
浏览文件 @
fe978b15
...
...
@@ -21,39 +21,33 @@
namespace
mace
{
namespace
ops
{
template
<
DeviceType
D
,
class
T
>
template
<
DeviceType
D
,
class
T
>
class
QuantizeOp
:
public
Operator
<
D
,
T
>
{
public:
QuantizeOp
(
const
OperatorDef
&
operator_def
,
Workspace
*
ws
)
:
Operator
<
D
,
T
>
(
operator_def
,
ws
)
{}
:
Operator
<
D
,
T
>
(
operator_def
,
ws
),
non_zero_
(
static_cast
<
bool
>
(
OperatorBase
::
GetOptionalArg
<
int
>
(
"non_zero"
,
0
)))
{}
MaceStatus
Run
(
StatsFuture
*
future
)
override
{
const
Tensor
*
input
=
this
->
Input
(
INPUT
);
const
Tensor
*
in_min
=
this
->
Input
(
IN_MIN
);
const
Tensor
*
in_max
=
this
->
Input
(
IN_MAX
);
MACE_CHECK
(
in_min
->
size
()
==
1
,
"min val tensor has more than 1 value"
);
MACE_CHECK
(
in_max
->
size
()
==
1
,
"max val tensor has more than 1 value"
);
Tensor
*
output
=
this
->
Output
(
OUTPUT
);
Tensor
*
out_min
=
this
->
Output
(
OUT_MIN
);
Tensor
*
out_max
=
this
->
Output
(
OUT_MAX
);
MACE_RETURN_IF_ERROR
(
output
->
ResizeLike
(
input
));
MACE_RETURN_IF_ERROR
(
out_min
->
ResizeLike
(
in_min
));
MACE_RETURN_IF_ERROR
(
out_max
->
ResizeLike
(
in_max
));
return
functor_
(
input
,
in_min
,
in_max
,
output
,
out_min
,
out_max
,
future
);
return
functor_
(
input
,
non_zero_
,
output
,
future
);
}
private:
kernels
::
QuantizeFunctor
<
D
,
T
>
functor_
;
bool
non_zero_
;
protected:
MACE_OP_INPUT_TAGS
(
INPUT
,
IN_MIN
,
IN_MAX
);
MACE_OP_OUTPUT_TAGS
(
OUTPUT
,
OUT_MIN
,
OUT_MAX
);
MACE_OP_INPUT_TAGS
(
INPUT
);
MACE_OP_OUTPUT_TAGS
(
OUTPUT
);
};
template
<
DeviceType
D
,
class
T
>
template
<
DeviceType
D
,
class
T
>
class
DequantizeOp
:
public
Operator
<
D
,
T
>
{
public:
DequantizeOp
(
const
OperatorDef
&
operator_def
,
Workspace
*
ws
)
...
...
@@ -61,70 +55,20 @@ class DequantizeOp : public Operator<D, T> {
MaceStatus
Run
(
StatsFuture
*
future
)
override
{
const
Tensor
*
input
=
this
->
Input
(
INPUT
);
const
Tensor
*
in_min
=
this
->
Input
(
IN_MIN
);
const
Tensor
*
in_max
=
this
->
Input
(
IN_MAX
);
MACE_CHECK
(
in_min
->
size
()
==
1
,
"min val tensor has more than 1 value"
);
MACE_CHECK
(
in_max
->
size
()
==
1
,
"max val tensor has more than 1 value"
);
Tensor
*
output
=
this
->
Output
(
OUTPUT
);
MACE_RETURN_IF_ERROR
(
output
->
ResizeLike
(
input
));
return
functor_
(
input
,
in_min
,
in_max
,
output
,
future
);
return
functor_
(
input
,
output
,
future
);
}
private:
kernels
::
DequantizeFunctor
<
D
,
T
>
functor_
;
protected:
MACE_OP_INPUT_TAGS
(
INPUT
,
IN_MIN
,
IN_MAX
);
MACE_OP_INPUT_TAGS
(
INPUT
);
MACE_OP_OUTPUT_TAGS
(
OUTPUT
);
};
template
<
DeviceType
D
,
class
T
>
class
RequantizeOp
:
public
Operator
<
D
,
T
>
{
public:
RequantizeOp
(
const
OperatorDef
&
operator_def
,
Workspace
*
ws
)
:
Operator
<
D
,
T
>
(
operator_def
,
ws
)
{}
MaceStatus
Run
(
StatsFuture
*
future
)
override
{
const
Tensor
*
input
=
this
->
Input
(
INPUT
);
const
Tensor
*
in_min
=
this
->
Input
(
IN_MIN
);
const
Tensor
*
in_max
=
this
->
Input
(
IN_MAX
);
const
Tensor
*
rerange_min
=
nullptr
;
const
Tensor
*
rerange_max
=
nullptr
;
MACE_CHECK
(
in_min
->
size
()
==
1
,
"min val tensor has more than 1 value"
);
MACE_CHECK
(
in_max
->
size
()
==
1
,
"max val tensor has more than 1 value"
);
if
(
this
->
InputSize
()
>=
5
)
{
rerange_min
=
this
->
Input
(
RERANGE_MIN
);
rerange_max
=
this
->
Input
(
RERANGE_MAX
);
MACE_CHECK
(
rerange_min
->
size
()
==
1
,
"rerange min val tensor has more than 1 value"
);
MACE_CHECK
(
rerange_max
->
size
()
==
1
,
"rerange max val tensor has more than 1 value"
);
}
Tensor
*
output
=
this
->
Output
(
OUTPUT
);
Tensor
*
out_min
=
this
->
Output
(
OUT_MIN
);
Tensor
*
out_max
=
this
->
Output
(
OUT_MAX
);
MACE_RETURN_IF_ERROR
(
output
->
ResizeLike
(
input
));
MACE_RETURN_IF_ERROR
(
out_min
->
ResizeLike
(
in_min
));
MACE_RETURN_IF_ERROR
(
out_max
->
ResizeLike
(
out_max
));
return
functor_
(
input
,
in_min
,
in_max
,
rerange_min
,
rerange_max
,
output
,
out_min
,
out_max
,
future
);
}
private:
kernels
::
RequantizeFunctor
<
D
,
T
>
functor_
;
protected:
MACE_OP_INPUT_TAGS
(
INPUT
,
IN_MIN
,
IN_MAX
,
RERANGE_MIN
,
RERANGE_MAX
);
MACE_OP_OUTPUT_TAGS
(
OUTPUT
,
OUT_MIN
,
OUT_MAX
);
};
}
// namespace ops
}
// namespace mace
...
...
mace/ops/quantize_test.cc
浏览文件 @
fe978b15
...
...
@@ -19,190 +19,63 @@ namespace mace {
namespace
ops
{
namespace
test
{
class
QuantizeTest
:
public
OpsTestBase
{};
namespace
{
TEST_F
(
QuantizeTest
,
TestQuantize
)
{
// Construct graph
void
TestQuantizeDequantize
(
const
std
::
vector
<
float
>
&
input
,
bool
non_zero
)
{
OpsTestNet
net
;
// Add input data
net
.
AddInputFromArray
<
CPU
,
float
>
(
"Input"
,
{
1
,
2
,
3
,
1
},
{
-
2
,
-
1
,
1
,
2
,
3
,
4
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMin"
,
{
1
},
{
-
3
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMax"
,
{
1
},
{
5
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"Input"
,
{
static_cast
<
index_t
>
(
input
.
size
())},
input
);
OpDefBuilder
(
"Quantize"
,
"QuantizeTest"
)
.
Input
(
"Input"
)
.
Input
(
"InputMin"
)
.
Input
(
"InputMax"
)
.
Output
(
"Output"
)
.
Output
(
"OutputMin"
)
.
Output
(
"OutputMax"
)
.
OutputType
({
DT_UINT8
,
DT_FLOAT
,
DT_FLOAT
})
.
Output
(
"QuantizeOutput"
)
.
OutputType
({
DT_UINT8
})
.
AddIntArg
(
"T"
,
DT_UINT8
)
.
AddIntArg
(
"non_zero"
,
non_zero
)
.
Finalize
(
net
.
NewOperatorDef
());
// Run
net
.
RunOp
();
auto
output
=
net
.
GetTensor
(
"Output"
);
auto
output_min
=
net
.
GetTensor
(
"OutputMin"
);
auto
output_max
=
net
.
GetTensor
(
"OutputMax"
);
auto
expected_output
=
CreateTensor
<
uint8_t
>
({
1
,
2
,
3
,
1
},
{
32
,
64
,
127
,
159
,
191
,
223
});
auto
expected_min
=
CreateTensor
<
float
>
({
1
},
{
-
3.01887
});
auto
expected_max
=
CreateTensor
<
float
>
({
1
},
{
5
});
ExpectTensorNear
<
uint8_t
>
(
*
expected_output
,
*
output
);
ExpectTensorNear
<
float
>
(
*
expected_min
,
*
output_min
);
ExpectTensorNear
<
float
>
(
*
expected_max
,
*
output_max
);
}
TEST_F
(
QuantizeTest
,
TestQuantizeTrend
)
{
// Construct graph
OpsTestNet
net
;
// Add input data
net
.
AddRandomInput
<
CPU
,
float
>
(
"Input"
,
{
100
});
const
float
*
input_data
=
net
.
GetTensor
(
"Input"
)
->
data
<
float
>
();
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMin"
,
{
1
},
{
*
std
::
min_element
(
input_data
,
input_data
+
net
.
GetTensor
(
"Input"
)
->
size
())});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMax"
,
{
1
},
{
*
std
::
max_element
(
input_data
,
input_data
+
net
.
GetTensor
(
"Input"
)
->
size
())});
OpDefBuilder
(
"Quantize"
,
"QuantizeTest"
)
.
Input
(
"Input"
)
.
Input
(
"InputMin"
)
.
Input
(
"InputMax"
)
.
Output
(
"Output"
)
.
Output
(
"OutputMin"
)
.
Output
(
"OutputMax"
)
.
OutputType
({
DT_UINT8
,
DT_FLOAT
,
DT_FLOAT
})
.
AddIntArg
(
"T"
,
DT_UINT8
)
.
Finalize
(
net
.
NewOperatorDef
());
// Run
net
.
RunOp
();
auto
output
=
net
.
GetTensor
(
"Output"
);
const
uint8_t
*
output_data
=
net
.
GetTensor
(
"Output"
)
->
data
<
uint8_t
>
();
for
(
int
i
=
1
;
i
<
output
->
size
();
++
i
)
{
if
(
input_data
[
i
]
>
input_data
[
i
-
1
])
{
EXPECT_GE
(
output_data
[
i
],
output_data
[
i
-
1
]);
}
else
if
(
input_data
[
i
]
==
input_data
[
i
-
1
])
{
EXPECT_EQ
(
output_data
[
i
],
output_data
[
i
-
1
]);
}
else
{
EXPECT_LE
(
output_data
[
i
],
output_data
[
i
-
1
]);
if
(
non_zero
)
{
Tensor
*
quantized_output
=
net
.
GetTensor
(
"QuantizeOutput"
);
Tensor
::
MappingGuard
guard
(
quantized_output
);
const
uint8_t
*
quantized_output_data
=
quantized_output
->
data
<
uint8_t
>
();
for
(
index_t
i
=
0
;
i
<
quantized_output
->
size
();
++
i
)
{
EXPECT_GT
(
quantized_output_data
[
i
],
0
);
}
}
}
TEST_F
(
QuantizeTest
,
TestDequantize
)
{
// Construct graph
OpsTestNet
net
;
// Add input data
net
.
AddInputFromArray
<
CPU
,
uint8_t
>
(
"Input"
,
{
1
,
2
,
3
,
1
},
{
32
,
64
,
127
,
159
,
191
,
223
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMin"
,
{
1
},
{
-
3.01887
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMax"
,
{
1
},
{
5
});
OpDefBuilder
(
"Dequantize"
,
"DequantizeTest"
)
.
Input
(
"Input"
)
.
Input
(
"InputMin"
)
.
Input
(
"InputMax"
)
OpDefBuilder
(
"Dequantize"
,
"DeQuantizeTest"
)
.
Input
(
"QuantizeOutput"
)
.
Output
(
"Output"
)
.
OutputType
({
DT_FLOAT
})
.
AddIntArg
(
"T"
,
DT_UINT8
)
.
Finalize
(
net
.
NewOperatorDef
());
// Run
net
.
RunOp
();
auto
output
=
net
.
GetTensor
(
"Output"
);
auto
expected_output
=
CreateTensor
<
float
>
({
1
,
2
,
3
,
1
},
{
-
2
,
-
1
,
1
,
2
,
3
,
4
});
auto
expected_min
=
CreateTensor
<
float
>
({
1
},
{
-
3.01887
});
auto
expected_max
=
CreateTensor
<
float
>
({
1
},
{
5
});
ExpectTensorNear
<
float
>
(
*
expected_output
,
*
output
,
0.1
,
0.01
);
}
TEST_F
(
QuantizeTest
,
TestRequantizeWithMinMax
)
{
// Construct graph
OpsTestNet
net
;
// Add input data
net
.
AddInputFromArray
<
CPU
,
int
>
(
"Input"
,
{
1
,
2
,
3
,
1
},
{
-
1073741824
,
-
536870912
,
536870912
,
1073741824
,
1610612736
,
2147483647
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMin"
,
{
1
},
{
-
3
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMax"
,
{
1
},
{
5
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"RerangeMin"
,
{
1
},
{
-
3.01887
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"RerangeMax"
,
{
1
},
{
5
});
OpDefBuilder
(
"Requantize"
,
"RequantizeTest"
)
.
Input
(
"Input"
)
.
Input
(
"InputMin"
)
.
Input
(
"InputMax"
)
.
Input
(
"RerangeMin"
)
.
Input
(
"RerangeMax"
)
.
Output
(
"Output"
)
.
Output
(
"OutputMin"
)
.
Output
(
"OutputMax"
)
.
OutputType
({
DT_UINT8
,
DT_FLOAT
,
DT_FLOAT
})
.
AddIntArg
(
"T"
,
DT_UINT8
)
.
Finalize
(
net
.
NewOperatorDef
());
// Run
net
.
RunOp
();
auto
output
=
net
.
GetTensor
(
"Output"
);
auto
expected_output
=
CreateTensor
<
uint8_t
>
({
1
,
2
,
3
,
1
},
{
32
,
64
,
128
,
160
,
191
,
223
});
auto
expected_min
=
CreateTensor
<
float
>
({
1
},
{
-
3.01887
});
auto
expected_max
=
CreateTensor
<
float
>
({
1
},
{
5
});
ExpectTensorNear
<
uint8_t
>
(
*
expected_output
,
*
output
);
ExpectTensorNear
<
float
>
(
*
net
.
GetTensor
(
"Input"
),
*
net
.
GetTensor
(
"Output"
),
0.1
);
}
TEST_F
(
QuantizeTest
,
TestRequantizeWithoutMinMax
)
{
// Construct graph
OpsTestNet
net
;
// Add input data
net
.
AddInputFromArray
<
CPU
,
int
>
(
"Input"
,
{
1
,
2
,
3
,
1
},
{
-
1073741824
,
-
536870912
,
536870912
,
1073741824
,
1610612736
,
2147483647
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMin"
,
{
1
},
{
-
3
});
net
.
AddInputFromArray
<
CPU
,
float
>
(
"InputMax"
,
{
1
},
{
5
});
}
// namespace
OpDefBuilder
(
"Requantize"
,
"RequantizeTest"
)
.
Input
(
"Input"
)
.
Input
(
"InputMin"
)
.
Input
(
"InputMax"
)
.
Output
(
"Output"
)
.
Output
(
"OutputMin"
)
.
Output
(
"OutputMax"
)
.
OutputType
({
DT_UINT8
,
DT_FLOAT
,
DT_FLOAT
})
.
AddIntArg
(
"T"
,
DT_UINT8
)
.
Finalize
(
net
.
NewOperatorDef
());
// Run
net
.
RunOp
();
class
QuantizeTest
:
public
OpsTestBase
{};
auto
output
=
net
.
GetTensor
(
"Output"
);
auto
expected_output
=
CreateTensor
<
uint8_t
>
({
1
,
2
,
3
,
1
},
{
0
,
43
,
128
,
170
,
213
,
255
});
auto
expected_min
=
CreateTensor
<
float
>
({
1
},
{
-
3.01887
});
auto
expected_max
=
CreateTensor
<
float
>
({
1
},
{
5
});
ExpectTensorNear
<
uint8_t
>
(
*
expected_output
,
*
output
);
TEST_F
(
QuantizeTest
,
TestQuantize
)
{
TestQuantizeDequantize
({
-
2
,
-
1
,
0
,
1
,
2
,
3
,
4
},
false
);
TestQuantizeDequantize
({
-
2
,
-
1
,
0
,
1
,
2
,
3
,
4
},
true
);
TestQuantizeDequantize
({
0
,
1
,
2
,
3
,
4
},
false
);
TestQuantizeDequantize
({
0
,
1
,
2
,
3
,
4
},
true
);
TestQuantizeDequantize
({
2
,
3
,
4
,
5
,
6
},
false
);
TestQuantizeDequantize
({
2
,
3
,
4
,
5
,
6
},
true
);
TestQuantizeDequantize
({
2
,
4
,
6
,
8
},
false
);
TestQuantizeDequantize
({
2
,
4
,
6
,
8
},
true
);
TestQuantizeDequantize
({
-
2
,
-
4
,
-
6
,
-
8
},
false
);
TestQuantizeDequantize
({
-
2
,
-
4
,
-
6
,
-
8
},
true
);
}
}
// namespace test
...
...
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