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体验新版 GitCode,发现更多精彩内容 >>
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3bb1cea3
编写于
8月 18, 2020
作者:
S
songhonglei413
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差异文件
add op_batchnorm_int8 and testcase
上级
1ca715c7
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
490 addition
and
8 deletion
+490
-8
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.cc
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.cc
+2
-0
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.h
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.h
+1
-0
mindspore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.cc
...spore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.cc
+2
-0
mindspore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.h
mindspore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.h
+1
-1
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.cc
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.cc
+168
-0
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.h
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.h
+54
-0
mindspore/lite/src/runtime/kernel/arm/nnacl/batchnorm_parameter.h
...e/lite/src/runtime/kernel/arm/nnacl/batchnorm_parameter.h
+29
-0
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.c
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.c
+1
-0
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.h
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.h
+1
-7
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.c
...e/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.c
+31
-0
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.h
...e/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.h
+34
-0
mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc
...st/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc
+59
-0
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc
...est/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc
+107
-0
未找到文件。
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.cc
浏览文件 @
3bb1cea3
...
...
@@ -19,6 +19,8 @@
#include "src/kernel_registry.h"
#include "include/errorcode.h"
#include "src/runtime/runtime_api.h"
#include "src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
#include "src/runtime/kernel/arm/nnacl/fp32/batchnorm.h"
using
mindspore
::
kernel
::
KERNEL_ARCH
::
kCPU
;
using
mindspore
::
lite
::
KernelRegistrar
;
...
...
mindspore/lite/src/runtime/kernel/arm/fp32/batchnorm.h
浏览文件 @
3bb1cea3
...
...
@@ -21,6 +21,7 @@
#include "src/lite_kernel.h"
#include "include/context.h"
#include "src/runtime/kernel/arm/nnacl/fp32/batchnorm.h"
#include "src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
using
mindspore
::
lite
::
Context
;
...
...
mindspore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.cc
浏览文件 @
3bb1cea3
...
...
@@ -19,6 +19,8 @@
#include "src/kernel_registry.h"
#include "include/errorcode.h"
#include "src/runtime/runtime_api.h"
#include "src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
#include "src/runtime/kernel/arm/nnacl/fp32/batchnorm.h"
using
mindspore
::
kernel
::
KERNEL_ARCH
::
kCPU
;
using
mindspore
::
lite
::
KernelRegistrar
;
...
...
mindspore/lite/src/runtime/kernel/arm/fp32/fused_batchnorm.h
浏览文件 @
3bb1cea3
...
...
@@ -19,7 +19,7 @@
#include <vector>
#include "src/lite_kernel.h"
#include "src/runtime/kernel/arm/nnacl/
fp32/batchnorm
.h"
#include "src/runtime/kernel/arm/nnacl/
batchnorm_parameter
.h"
namespace
mindspore
::
kernel
{
class
FusedBatchnormCPUKernel
:
public
LiteKernel
{
...
...
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.cc
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#include "src/runtime/kernel/arm/int8/batchnorm_int8.h"
#include <math.h>
#include "schema/model_generated.h"
#include "src/kernel_registry.h"
#include "include/errorcode.h"
#include "src/runtime/runtime_api.h"
#include "src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
using
mindspore
::
kernel
::
KERNEL_ARCH
::
kCPU
;
using
mindspore
::
lite
::
KernelRegistrar
;
using
mindspore
::
lite
::
RET_ERROR
;
using
mindspore
::
lite
::
RET_OK
;
using
mindspore
::
schema
::
PrimitiveType_BatchNorm
;
namespace
mindspore
::
kernel
{
BatchnormInt8CPUKernel
::~
BatchnormInt8CPUKernel
()
{
if
(
alpha_addr_
!=
nullptr
)
{
free
(
alpha_addr_
);
alpha_addr_
=
nullptr
;
}
if
(
beta_addr_
!=
nullptr
)
{
free
(
beta_addr_
);
beta_addr_
=
nullptr
;
}
}
int
BatchnormInt8CPUKernel
::
InitConstTensor
()
{
auto
input
=
in_tensors_
[
0
];
auto
mean
=
in_tensors_
[
1
];
auto
variance
=
in_tensors_
[
2
];
auto
output
=
out_tensors_
[
0
];
auto
mean_ptr
=
reinterpret_cast
<
int8_t
*>
(
mean
->
Data
());
auto
var_ptr
=
reinterpret_cast
<
int8_t
*>
(
variance
->
Data
());
alpha_addr_
=
reinterpret_cast
<
float
*>
(
malloc
(
mean
->
ElementsNum
()
*
sizeof
(
float
)));
if
(
alpha_addr_
==
nullptr
)
{
MS_LOG
(
ERROR
)
<<
"Malloc buffer failed."
;
return
RET_ERROR
;
}
beta_addr_
=
reinterpret_cast
<
float
*>
(
malloc
(
variance
->
ElementsNum
()
*
sizeof
(
float
)));
if
(
beta_addr_
==
nullptr
)
{
MS_LOG
(
ERROR
)
<<
"Malloc buffer failed."
;
return
RET_ERROR
;
}
// compute alpha, beta;
// 0. tmp = (S4 * Sqrt(e + S3 * (q3 - Z3)));
// 1. A = S1 / tmp;
// 2. B = Z4 - (A1 * Z1) -((S2 * (q2 - Z2)) / tmp;
auto
eps
=
batchnorm_param_
->
epsilon_
;
auto
zp_in
=
input
->
GetQuantParams
().
front
().
zeroPoint
;
auto
zp_mean
=
mean
->
GetQuantParams
().
front
().
zeroPoint
;
auto
zp_var
=
variance
->
GetQuantParams
().
front
().
zeroPoint
;
auto
zp_out
=
output
->
GetQuantParams
().
front
().
zeroPoint
;
auto
s_in
=
input
->
GetQuantParams
().
front
().
scale
;
auto
s_mean
=
mean
->
GetQuantParams
().
front
().
scale
;
auto
s_var
=
variance
->
GetQuantParams
().
front
().
scale
;
auto
s_out
=
output
->
GetQuantParams
().
front
().
scale
;
for
(
int
i
=
0
;
i
<
batchnorm_param_
->
channel_
;
++
i
)
{
float
tmp
=
s_out
*
sqrt
(
eps
+
s_var
*
(
var_ptr
[
i
]
-
zp_var
));
float
tmp_a
=
s_in
/
tmp
;
float
tmp_b
=
zp_out
-
tmp_a
*
zp_in
-
(
s_mean
*
(
mean_ptr
[
i
]
-
zp_mean
))
/
tmp
;
alpha_addr_
[
i
]
=
tmp_a
;
beta_addr_
[
i
]
=
tmp_b
;
}
return
RET_OK
;
}
int
BatchnormInt8CPUKernel
::
Init
()
{
auto
input_shapes
=
in_tensors_
[
0
]
->
shape
();
auto
n_dim
=
input_shapes
.
size
();
batchnorm_param_
->
channel_
=
input_shapes
[
n_dim
-
1
];
batchnorm_param_
->
unit_
=
1
;
for
(
int
i
=
0
;
i
<
n_dim
-
1
;
i
++
)
{
batchnorm_param_
->
unit_
*=
input_shapes
[
i
];
}
batchnorm_param_
->
op_parameter_
.
thread_num_
=
MSMIN
(
batchnorm_param_
->
op_parameter_
.
thread_num_
,
batchnorm_param_
->
channel_
);
auto
ret
=
InitConstTensor
();
if
(
ret
!=
0
)
{
MS_LOG
(
ERROR
)
<<
"Batchnorm fp32 InitConstTensor failed."
;
return
RET_ERROR
;
}
return
RET_OK
;
}
int
BatchnormInt8CPUKernel
::
ReSize
()
{
auto
input_shapes
=
in_tensors_
[
0
]
->
shape
();
batchnorm_param_
->
unit_
=
1
;
for
(
int
i
=
0
;
i
<
input_shapes
.
size
()
-
1
;
i
++
)
{
batchnorm_param_
->
unit_
*=
input_shapes
[
i
];
}
return
RET_OK
;
}
int
BatchnormInt8CPUKernel
::
DoExecute
(
int
task_id
)
{
BatchNormInt8
(
out_addr_
,
in_addr_
,
alpha_addr_
,
beta_addr_
,
task_id
,
batchnorm_param_
);
return
RET_OK
;
}
int
BatchNormInt8Run
(
int
task_id
,
LiteParallelGroupEnv
*
penv
,
void
*
cdata
)
{
auto
g_kernel
=
reinterpret_cast
<
BatchnormInt8CPUKernel
*>
(
cdata
);
auto
ret
=
g_kernel
->
DoExecute
(
task_id
);
if
(
ret
!=
RET_OK
)
{
MS_LOG
(
ERROR
)
<<
"BatchnormRun error task_id["
<<
task_id
<<
"] error_code["
<<
ret
<<
"]"
;
return
ret
;
}
return
RET_OK
;
}
int
BatchnormInt8CPUKernel
::
Run
()
{
auto
prepare_ret
=
Prepare
();
if
(
prepare_ret
!=
RET_OK
)
{
MS_LOG
(
ERROR
)
<<
"Prepare fail! Ret error code: "
<<
prepare_ret
;
return
prepare_ret
;
}
in_addr_
=
reinterpret_cast
<
int8_t
*>
(
in_tensors_
.
at
(
0
)
->
Data
());
out_addr_
=
reinterpret_cast
<
int8_t
*>
(
out_tensors_
.
at
(
0
)
->
Data
());
int
ret
=
LiteBackendParallelLaunch
(
BatchNormInt8Run
,
this
,
batchnorm_param_
->
op_parameter_
.
thread_num_
);
if
(
ret
!=
RET_OK
)
{
MS_LOG
(
ERROR
)
<<
"BatchnormRun error error_code["
<<
ret
<<
"]"
;
return
ret
;
}
return
RET_OK
;
}
kernel
::
LiteKernel
*
CpuBatchnormInt8KernelCreator
(
const
std
::
vector
<
lite
::
tensor
::
Tensor
*>
&
inputs
,
const
std
::
vector
<
lite
::
tensor
::
Tensor
*>
&
outputs
,
OpParameter
*
opParameter
,
const
lite
::
Context
*
ctx
,
const
kernel
::
KernelKey
&
desc
,
const
mindspore
::
lite
::
PrimitiveC
*
primitive
)
{
MS_ASSERT
(
opParameter
!=
nullptr
);
MS_ASSERT
(
desc
.
type
==
schema
::
PrimitiveType_BatchNorm
);
auto
*
kernel
=
new
(
std
::
nothrow
)
BatchnormInt8CPUKernel
(
opParameter
,
inputs
,
outputs
,
ctx
,
primitive
);
if
(
kernel
==
nullptr
)
{
MS_LOG
(
ERROR
)
<<
"new BatchnormInt8CPUKernel fail!"
;
return
nullptr
;
}
auto
ret
=
kernel
->
Init
();
if
(
ret
!=
RET_OK
)
{
MS_LOG
(
ERROR
)
<<
"Init kernel failed, name: "
<<
opParameter
->
name_
<<
", type: "
<<
schema
::
EnumNamePrimitiveType
(
static_cast
<
schema
::
PrimitiveType
>
(
opParameter
->
type_
));
delete
kernel
;
return
nullptr
;
}
return
kernel
;
}
REG_KERNEL
(
kCPU
,
kNumberTypeInt8
,
PrimitiveType_BatchNorm
,
CpuBatchnormInt8KernelCreator
)
}
// namespace mindspore::kernel
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.h
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_BATCHNORM_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_BATCHNORM_H_
#include <vector>
#include "src/lite_kernel.h"
#include "include/context.h"
#include "src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.h"
#include "src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
using
mindspore
::
lite
::
Context
;
namespace
mindspore
::
kernel
{
class
BatchnormInt8CPUKernel
:
public
LiteKernel
{
public:
BatchnormInt8CPUKernel
(
OpParameter
*
parameter
,
const
std
::
vector
<
lite
::
tensor
::
Tensor
*>
&
inputs
,
const
std
::
vector
<
lite
::
tensor
::
Tensor
*>
&
outputs
,
const
Context
*
ctx
,
const
mindspore
::
lite
::
PrimitiveC
*
primitive
)
:
LiteKernel
(
parameter
,
inputs
,
outputs
,
ctx
,
primitive
)
{
batchnorm_param_
=
reinterpret_cast
<
BatchNormParameter
*>
(
parameter
);
}
~
BatchnormInt8CPUKernel
()
override
;
int
Init
()
override
;
int
ReSize
()
override
;
int
Run
()
override
;
int
InitConstTensor
();
int
DoExecute
(
int
tid
);
private:
int8_t
*
in_addr_
=
nullptr
;
int8_t
*
out_addr_
=
nullptr
;
float
*
alpha_addr_
=
nullptr
;
float
*
beta_addr_
=
nullptr
;
BatchNormParameter
*
batchnorm_param_
;
};
}
// namespace mindspore::kernel
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_BATCHNORM_H_
mindspore/lite/src/runtime/kernel/arm/nnacl/batchnorm_parameter.h
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_BATCHNORM_PARAMETER_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_BATCHNORM_PARAMETER_H_
#include "nnacl/op_base.h"
typedef
struct
BatchNormParameter
{
OpParameter
op_parameter_
;
float
epsilon_
;
int
unit_
;
int
channel_
;
}
BatchNormParameter
;
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_BATCHNORM_PARAMETER_H_
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.c
浏览文件 @
3bb1cea3
...
...
@@ -16,6 +16,7 @@
#include "nnacl/fp32/batchnorm.h"
#include <math.h>
#include "nnacl/batchnorm_parameter.h"
void
BatchNorm
(
float
*
output_ptr
,
const
float
*
input_ptr
,
const
float
*
mean_ptr
,
const
float
*
variance_ptr
,
int
task_id
,
BatchNormParameter
*
param
)
{
...
...
mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/batchnorm.h
浏览文件 @
3bb1cea3
...
...
@@ -18,13 +18,7 @@
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_FP32_BATCHNORM_H_
#include "nnacl/op_base.h"
typedef
struct
BatchNormParameter
{
OpParameter
op_parameter_
;
float
epsilon_
;
int
unit_
;
int
channel_
;
}
BatchNormParameter
;
#include "nnacl/batchnorm_parameter.h"
#ifdef __cplusplus
extern
"C"
{
...
...
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.c
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#include "nnacl/int8/batchnorm_int8.h"
#include <math.h>
#include "nnacl/batchnorm_parameter.h"
void
BatchNormInt8
(
int8_t
*
output_ptr
,
const
int8_t
*
input_ptr
,
const
float
*
alpha_ptr
,
const
float
*
beta_ptr
,
int
task_id
,
BatchNormParameter
*
param
)
{
for
(
int
c
=
task_id
;
c
<
param
->
channel_
;
c
+=
param
->
op_parameter_
.
thread_num_
)
{
for
(
int
u
=
0
;
u
<
param
->
unit_
;
u
++
)
{
int32_t
output_tmp
=
round
(
input_ptr
[
u
*
param
->
channel_
+
c
]
*
alpha_ptr
[
c
]
+
beta_ptr
[
c
]);
output_tmp
=
output_tmp
>
127
?
127
:
output_tmp
;
output_tmp
=
output_tmp
<
-
128
?
-
128
:
output_tmp
;
output_ptr
[
u
*
param
->
channel_
+
c
]
=
(
int8_t
)
output_tmp
;
}
}
}
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.h
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_BATCHNORM_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_BATCHNORM_H_
#include "nnacl/op_base.h"
#include "nnacl/batchnorm_parameter.h"
#ifdef __cplusplus
extern
"C"
{
#endif
void
BatchNormInt8
(
int8_t
*
output_ptr
,
const
int8_t
*
input_ptr
,
const
float
*
alpha_ptr
,
const
float
*
beta_ptr
,
int
task_id
,
BatchNormParameter
*
param
);
#ifdef __cplusplus
}
#endif
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_BATCHNORM_H_
mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc
浏览文件 @
3bb1cea3
...
...
@@ -147,4 +147,63 @@ TEST_F(TestBatchnormFp32, FusedBNTest) {
output0_tensor
.
SetData
(
nullptr
);
MS_LOG
(
INFO
)
<<
"TestFusedBathNormFp32 accuracy passed"
;
}
TEST_F
(
TestBatchnormFp32
,
easyTest
)
{
std
::
vector
<
float
>
in_data
=
{
1
,
4
,
2
,
5
,
3
,
6
,
-
1
,
-
4
,
-
2
,
-
5
,
-
3
,
-
6
};
std
::
vector
<
float
>
in_data1
=
{
0.1
,
0.6
};
std
::
vector
<
float
>
in_data2
=
{
3
,
4
};
std
::
vector
<
lite
::
tensor
::
Tensor
*>
inputs_tensor
;
std
::
vector
<
lite
::
tensor
::
Tensor
*>
outputs_tensor
;
BatchNormParameter
op_param
;
op_param
.
op_parameter_
.
type_
=
schema
::
PrimitiveType_BatchNorm
;
op_param
.
epsilon_
=
0.001
f
;
std
::
vector
<
int
>
shape
=
{
1
,
1
,
6
,
2
};
lite
::
tensor
::
Tensor
input0_tensor
;
lite
::
tensor
::
Tensor
input1_tensor
;
lite
::
tensor
::
Tensor
input2_tensor
;
inputs_tensor
.
push_back
(
&
input0_tensor
);
inputs_tensor
.
push_back
(
&
input1_tensor
);
inputs_tensor
.
push_back
(
&
input2_tensor
);
input0_tensor
.
SetData
(
in_data
.
data
());
input1_tensor
.
SetData
(
in_data1
.
data
());
input2_tensor
.
SetData
(
in_data2
.
data
());
input0_tensor
.
set_shape
(
shape
);
input1_tensor
.
set_shape
({
2
});
input2_tensor
.
set_shape
({
2
});
std
::
vector
<
float
>
output
(
12
);
std
::
vector
<
float
>
corr_out
=
{
0.519529
,
1.69979
,
1.09678
,
2.19973
,
1.67404
,
2.69966
,
-
0.63498
,
-
2.29971
,
-
1.21223
,
-
2.79965
,
-
1.78949
,
-
3.29959
};
lite
::
tensor
::
Tensor
output0_tensor
;
outputs_tensor
.
push_back
(
&
output0_tensor
);
output0_tensor
.
SetData
(
output
.
data
());
output0_tensor
.
set_shape
(
shape
);
kernel
::
KernelKey
desc
=
{
kernel
::
KERNEL_ARCH
::
kCPU
,
kNumberTypeFloat32
,
schema
::
PrimitiveType_BatchNorm
};
auto
creator
=
lite
::
KernelRegistry
::
GetInstance
()
->
GetCreator
(
desc
);
ASSERT_NE
(
creator
,
nullptr
);
lite
::
Context
ctx
;
ctx
.
thread_num_
=
1
;
kernel
::
LiteKernel
*
kernel
=
creator
(
inputs_tensor
,
outputs_tensor
,
reinterpret_cast
<
OpParameter
*>
(
&
op_param
),
&
ctx
,
desc
,
nullptr
);
ASSERT_NE
(
kernel
,
nullptr
);
auto
output_tensor_shape
=
output0_tensor
.
shape
();
kernel
->
Run
();
printf
(
"==================output data=================
\n
"
);
for
(
int
i
=
0
;
i
<
output0_tensor
.
ElementsNum
();
i
++
)
{
std
::
cout
<<
output
[
i
]
<<
" ,"
;
}
std
::
cout
<<
std
::
endl
;
CompareOutputData
(
output
.
data
(),
corr_out
.
data
(),
output0_tensor
.
ElementsNum
(),
0.001
);
input0_tensor
.
SetData
(
nullptr
);
input1_tensor
.
SetData
(
nullptr
);
input2_tensor
.
SetData
(
nullptr
);
output0_tensor
.
SetData
(
nullptr
);
MS_LOG
(
INFO
)
<<
"TestBathNormFp32 accuracy passed"
;
}
}
// namespace mindspore
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc
0 → 100644
浏览文件 @
3bb1cea3
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#include <iostream>
#include "mindspore/core/utils/log_adapter.h"
#include "common/common_test.h"
#include "mindspore/lite/src/runtime/kernel/arm/nnacl/batchnorm_parameter.h"
#include "mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.h"
#include "mindspore/lite/src/kernel_registry.h"
#include "mindspore/lite/src/lite_kernel.h"
namespace
mindspore
{
class
TestBatchnormInt8
:
public
mindspore
::
CommonTest
{
public:
TestBatchnormInt8
()
{}
};
TEST_F
(
TestBatchnormInt8
,
BNTest
)
{
std
::
vector
<
int8_t
>
in_data
=
{
11
,
41
,
21
,
51
,
31
,
61
,
-
11
,
-
41
,
-
21
,
-
51
,
-
31
,
-
61
};
std
::
vector
<
int8_t
>
in_data1
=
{
4
,
14
};
std
::
vector
<
int8_t
>
in_data2
=
{
29
,
39
};
std
::
vector
<
lite
::
tensor
::
Tensor
*>
inputs_tensor
;
std
::
vector
<
lite
::
tensor
::
Tensor
*>
outputs_tensor
;
BatchNormParameter
op_param
;
op_param
.
op_parameter_
.
type_
=
schema
::
PrimitiveType_BatchNorm
;
op_param
.
epsilon_
=
0.001
f
;
std
::
vector
<
int
>
shape
=
{
1
,
1
,
6
,
2
};
lite
::
tensor
::
QuantArg
input_quant_arg
;
input_quant_arg
.
scale
=
0.1
;
input_quant_arg
.
zeroPoint
=
1
;
lite
::
tensor
::
QuantArg
input_quant_arg_1
;
input_quant_arg_1
.
scale
=
0.05
;
input_quant_arg_1
.
zeroPoint
=
2
;
lite
::
tensor
::
QuantArg
input_quant_arg_2
;
input_quant_arg_2
.
scale
=
0.1
;
input_quant_arg_2
.
zeroPoint
=
-
1
;
lite
::
tensor
::
QuantArg
output_quant_arg
;
output_quant_arg
.
scale
=
1
;
output_quant_arg
.
zeroPoint
=
0
;
lite
::
tensor
::
Tensor
input0_tensor
;
lite
::
tensor
::
Tensor
input1_tensor
;
lite
::
tensor
::
Tensor
input2_tensor
;
inputs_tensor
.
push_back
(
&
input0_tensor
);
inputs_tensor
.
push_back
(
&
input1_tensor
);
inputs_tensor
.
push_back
(
&
input2_tensor
);
input0_tensor
.
SetData
(
in_data
.
data
());
input1_tensor
.
SetData
(
in_data1
.
data
());
input2_tensor
.
SetData
(
in_data2
.
data
());
input0_tensor
.
set_shape
(
shape
);
input1_tensor
.
set_shape
({
2
});
input2_tensor
.
set_shape
({
2
});
input0_tensor
.
AddQuantParam
(
input_quant_arg
);
input1_tensor
.
AddQuantParam
(
input_quant_arg_1
);
input2_tensor
.
AddQuantParam
(
input_quant_arg_2
);
std
::
vector
<
int8_t
>
output
(
12
);
// std::vector<int8_t> corr_out1 = {5, 17, 11, 22, 17, 27, -6, -23, -12, -28, -18, -33};
std
::
vector
<
int8_t
>
corr_out
=
{
1
,
2
,
1
,
2
,
2
,
3
,
-
1
,
-
2
,
-
1
,
-
3
,
-
2
,
-
3
};
lite
::
tensor
::
Tensor
output0_tensor
;
outputs_tensor
.
push_back
(
&
output0_tensor
);
output0_tensor
.
SetData
(
output
.
data
());
output0_tensor
.
set_shape
(
shape
);
output0_tensor
.
AddQuantParam
(
output_quant_arg
);
kernel
::
KernelKey
desc
=
{
kernel
::
KERNEL_ARCH
::
kCPU
,
kNumberTypeInt8
,
schema
::
PrimitiveType_BatchNorm
};
auto
creator
=
lite
::
KernelRegistry
::
GetInstance
()
->
GetCreator
(
desc
);
ASSERT_NE
(
creator
,
nullptr
);
lite
::
Context
ctx
;
ctx
.
thread_num_
=
3
;
kernel
::
LiteKernel
*
kernel
=
creator
(
inputs_tensor
,
outputs_tensor
,
reinterpret_cast
<
OpParameter
*>
(
&
op_param
),
&
ctx
,
desc
,
nullptr
);
ASSERT_NE
(
kernel
,
nullptr
);
auto
output_tensor_shape
=
output0_tensor
.
shape
();
kernel
->
Run
();
printf
(
"==================output data=================
\n
"
);
for
(
int
i
=
0
;
i
<
output0_tensor
.
ElementsNum
();
i
++
)
{
printf
(
"%d, "
,
output
[
i
]);
}
std
::
cout
<<
std
::
endl
;
CompareOutputData
(
output
.
data
(),
corr_out
.
data
(),
output0_tensor
.
ElementsNum
(),
0.001
);
input0_tensor
.
SetData
(
nullptr
);
input1_tensor
.
SetData
(
nullptr
);
input2_tensor
.
SetData
(
nullptr
);
output0_tensor
.
SetData
(
nullptr
);
MS_LOG
(
INFO
)
<<
"TestBathNormFp32 accuracy passed"
;
}
}
// namespace mindspore
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