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体验新版 GitCode,发现更多精彩内容 >>
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提交
1816f57f
编写于
1月 13, 2020
作者:
Z
zhupengyang
提交者:
GitHub
1月 13, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[NPU] enhance conv2d ut (#2753)
上级
91f0ef0b
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
462 addition
and
294 deletion
+462
-294
lite/core/arena/framework.h
lite/core/arena/framework.h
+4
-1
lite/kernels/npu/bridges/conv_op.cc
lite/kernels/npu/bridges/conv_op.cc
+26
-10
lite/kernels/npu/bridges/conv_op_test.cc
lite/kernels/npu/bridges/conv_op_test.cc
+0
-283
lite/tests/kernels/CMakeLists.txt
lite/tests/kernels/CMakeLists.txt
+1
-0
lite/tests/kernels/conv_compute_test.cc
lite/tests/kernels/conv_compute_test.cc
+431
-0
未找到文件。
lite/core/arena/framework.h
浏览文件 @
1816f57f
...
...
@@ -107,7 +107,8 @@ class TestCase {
void
SetCommonTensor
(
const
std
::
string
&
var_name
,
const
DDim
&
ddim
,
const
T
*
data
,
const
LoD
&
lod
=
{})
{
const
LoD
&
lod
=
{},
bool
is_persistable
=
false
)
{
auto
*
tensor
=
scope_
->
NewTensor
(
var_name
);
tensor
->
Resize
(
ddim
);
auto
*
d
=
tensor
->
mutable_data
<
T
>
();
...
...
@@ -115,6 +116,8 @@ class TestCase {
// set lod
if
(
!
lod
.
empty
())
*
tensor
->
mutable_lod
()
=
lod
;
// set persistable
tensor
->
set_persistable
(
is_persistable
);
}
// Prepare for the operator.
...
...
lite/kernels/npu/bridges/conv_op.cc
浏览文件 @
1816f57f
...
...
@@ -38,18 +38,21 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
CHECK
(
input_type
->
layout
()
==
DATALAYOUT
(
kNCHW
));
auto
input
=
scope
->
FindMutableTensor
(
input_name
);
auto
input_dims
=
input
->
dims
();
auto
filter_name
=
op_info
->
Input
(
"Filter"
).
front
();
auto
filter_type
=
kernel
->
GetInputDeclType
(
"Filter"
);
CHECK
(
filter_type
->
precision
()
==
PRECISION
(
kFloat
));
CHECK
(
filter_type
->
layout
()
==
DATALAYOUT
(
kNCHW
));
auto
filter
=
scope
->
FindMutableTensor
(
filter_name
);
auto
filter_dims
=
filter
->
dims
();
auto
output_name
=
op_info
->
Output
(
"Output"
).
front
();
auto
output_type
=
kernel
->
GetOutputDeclType
(
"Output"
);
CHECK
(
output_type
->
precision
()
==
PRECISION
(
kFloat
));
CHECK
(
output_type
->
layout
()
==
DATALAYOUT
(
kNCHW
));
auto
output
=
scope
->
FindMutableTensor
(
output_name
);
auto
output_dims
=
output
->
dims
();
auto
bs
=
input_dims
[
0
];
auto
ic
=
input_dims
[
1
];
auto
oc
=
filter_dims
[
0
];
...
...
@@ -62,8 +65,13 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
auto
paddings
=
op_info
->
GetAttr
<
std
::
vector
<
int
>>
(
"paddings"
);
auto
groups
=
op_info
->
GetAttr
<
int
>
(
"groups"
);
auto
dilations
=
op_info
->
GetAttr
<
std
::
vector
<
int
>>
(
"dilations"
);
auto
fuse_relu
=
op_info
->
HasAttr
(
"fuse_relu"
)
&&
op_info
->
GetAttr
<
bool
>
(
"fuse_relu"
);
bool
with_act
=
op_info
->
HasAttr
(
"with_act"
)
&&
op_info
->
GetAttr
<
bool
>
(
"with_act"
);
std
::
string
act_type
=
with_act
?
op_info
->
GetAttr
<
std
::
string
>
(
"act_type"
)
:
""
;
float
leaky_relu_alpha
=
act_type
==
"leaky_relu"
?
op_info
->
GetAttr
<
float
>
(
"leaky_relu_alpha"
)
:
0.
f
;
CHECK_EQ
(
strides
.
size
(),
2L
);
CHECK_EQ
(
dilations
.
size
(),
2L
);
...
...
@@ -187,10 +195,15 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
conv_op
->
set_input_x
(
*
input_node
->
data
());
conv_op
->
set_input_w
(
*
filter_node
->
data
());
conv_op
->
set_attr_mode
(
1
);
conv_op
->
set_attr_pad_mode
(
0
);
// NOTSET
// when padding_algorithm=="SAME", NPU is different from lite
if
(
padding_algorithm
==
"VALID"
)
{
conv_op
->
set_attr_pad_mode
(
5
);
}
else
{
conv_op
->
set_attr_pad_mode
(
0
);
}
conv_op
->
set_attr_group
(
groups
);
conv_op
->
set_attr_pad
(
ge
::
AttrValue
::
LIST_INT
(
{
paddings
[
0
],
paddings
[
0
],
paddings
[
2
],
paddings
[
2
]}));
{
paddings
[
0
],
paddings
[
1
],
paddings
[
2
],
paddings
[
3
]}));
conv_op
->
set_attr_dilation
(
ge
::
AttrValue
::
LIST_INT
({
dilations
[
0
],
dilations
[
1
]}));
conv_op
->
set_attr_stride
(
ge
::
AttrValue
::
LIST_INT
({
strides
[
0
],
strides
[
1
]}));
...
...
@@ -212,13 +225,16 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
}
CHECK
(
conv_node
);
if
(
fuse_relu
)
{
// Append relu node if fuse_relu is true
auto
relu_node
=
graph
->
Add
<
ge
::
op
::
Activation
>
(
output_name
);
auto
relu_op
=
relu_node
->
data
<
ge
::
op
::
Activation
>
();
relu_op
->
set_input_x
(
*
conv_node
->
data
());
relu_op
->
set_attr_mode
(
CvtActMode
(
"relu"
));
if
(
!
act_type
.
empty
())
{
auto
act_node
=
graph
->
Add
<
ge
::
op
::
Activation
>
(
output_name
);
auto
act_op
=
act_node
->
data
<
ge
::
op
::
Activation
>
();
act_op
->
set_input_x
(
*
conv_node
->
data
());
act_op
->
set_attr_mode
(
CvtActMode
(
act_type
));
if
(
act_type
==
"leaky_relu"
)
{
act_op
->
set_attr_negative_slope
(
leaky_relu_alpha
);
}
}
return
REBUILD_WHEN_SHAPE_CHANGED
;
}
...
...
lite/kernels/npu/bridges/conv_op_test.cc
已删除
100644 → 0
浏览文件 @
91f0ef0b
// Copyright (c) 2019 PaddlePaddle Authors. 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.
#include "lite/operators/conv_op.h"
#include <gtest/gtest.h>
#include <random>
#include "lite/core/op_registry.h"
#include "lite/kernels/npu/bridges/registry.h"
#include "lite/kernels/npu/bridges/test_helper.h"
namespace
paddle
{
namespace
lite
{
namespace
kernels
{
namespace
npu
{
namespace
bridges
{
void
conv_ref
(
const
std
::
shared_ptr
<
operators
::
ConvOpLite
>
op
)
{
Scope
*
scope
=
op
->
scope
();
const
OpInfo
*
op_info
=
op
->
op_info
();
auto
input
=
scope
->
FindVar
(
op_info
->
Input
(
"Input"
).
front
())
->
GetMutable
<
Tensor
>
();
auto
filter
=
scope
->
FindVar
(
op_info
->
Input
(
"Filter"
).
front
())
->
GetMutable
<
Tensor
>
();
auto
output
=
scope
->
FindVar
(
op_info
->
Output
(
"Output"
).
front
())
->
GetMutable
<
Tensor
>
();
std
::
vector
<
int32_t
>
strides
=
op_info
->
GetAttr
<
std
::
vector
<
int32_t
>>
(
"strides"
);
std
::
vector
<
int32_t
>
paddings
=
op_info
->
GetAttr
<
std
::
vector
<
int32_t
>>
(
"paddings"
);
int32_t
groups
=
op_info
->
GetAttr
<
int32_t
>
(
"groups"
);
std
::
vector
<
int32_t
>
dilations
=
op_info
->
GetAttr
<
std
::
vector
<
int32_t
>>
(
"dilations"
);
bool
fuse_relu
=
op_info
->
GetAttr
<
bool
>
(
"fuse_relu"
);
auto
input_dims
=
input
->
dims
();
auto
filter_dims
=
filter
->
dims
();
auto
output_dims
=
output
->
dims
();
auto
input_data
=
input
->
mutable_data
<
float
>
();
auto
filter_data
=
filter
->
mutable_data
<
float
>
();
auto
output_data
=
output
->
mutable_data
<
float
>
();
int
kernel_w
=
filter_dims
[
3
];
int
kernel_h
=
filter_dims
[
2
];
int
stride_w
=
strides
[
1
];
int
stride_h
=
strides
[
0
];
int
dila_w
=
dilations
[
1
];
int
dila_h
=
dilations
[
0
];
int
pad_w
=
paddings
[
2
];
int
pad_h
=
paddings
[
0
];
int
batch_size
=
input_dims
[
0
];
int
in_ch_size
=
input_dims
[
1
];
int
in_h
=
input_dims
[
2
];
int
in_w
=
input_dims
[
3
];
int
out_ch_size
=
output_dims
[
1
];
int
out_h
=
output_dims
[
2
];
int
out_w
=
output_dims
[
3
];
int
out_c_group
=
out_ch_size
/
groups
;
int
in_c_group
=
in_ch_size
/
groups
;
Tensor
*
bias
=
nullptr
;
float
*
bias_data
=
nullptr
;
bool
is_channel_bias
=
false
;
if
(
op_info
->
HasInput
(
"Bias"
))
{
auto
bias_var_names
=
op_info
->
Input
(
"Bias"
);
if
(
bias_var_names
.
size
()
>
0
)
{
auto
bias_var_name
=
bias_var_names
.
front
();
bias
=
scope
->
FindVar
(
bias_var_name
)
->
GetMutable
<
lite
::
Tensor
>
();
auto
bias_dims
=
bias
->
dims
();
is_channel_bias
=
bias_dims
.
production
()
==
out_ch_size
;
bias_data
=
bias
->
mutable_data
<
float
>
();
}
}
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
g
=
0
;
g
<
groups
;
++
g
)
{
for
(
int
oc
=
0
;
oc
<
out_c_group
;
++
oc
)
{
for
(
int
oh
=
0
;
oh
<
out_h
;
++
oh
)
{
for
(
int
ow
=
0
;
ow
<
out_w
;
++
ow
)
{
int
out_idx
=
n
*
groups
*
out_c_group
*
out_h
*
out_w
+
g
*
out_c_group
*
out_h
*
out_w
+
oc
*
out_h
*
out_w
+
oh
*
out_w
+
ow
;
float
out_value
=
bias_data
!=
nullptr
?
(
is_channel_bias
?
bias_data
[
g
*
out_c_group
+
oc
]
:
bias_data
[
out_idx
])
:
0
;
// + out_value *= beta;
for
(
int
ic
=
0
;
ic
<
in_c_group
;
++
ic
)
{
for
(
int
kh
=
0
;
kh
<
kernel_h
;
++
kh
)
{
for
(
int
kw
=
0
;
kw
<
kernel_w
;
++
kw
)
{
int
iw
=
ow
*
stride_w
-
pad_w
+
kw
*
(
dila_w
);
int
ih
=
oh
*
stride_h
-
pad_h
+
kh
*
(
dila_h
);
if
(
iw
<
0
||
iw
>=
in_w
)
continue
;
if
(
ih
<
0
||
ih
>=
in_h
)
continue
;
int
in_idx
=
n
*
in_ch_size
*
in_h
*
in_w
+
g
*
in_c_group
*
in_h
*
in_w
+
ic
*
in_h
*
in_w
+
ih
*
in_w
+
iw
;
int
filter_idx
=
g
*
out_c_group
*
in_c_group
*
kernel_h
*
kernel_w
+
oc
*
in_c_group
*
kernel_h
*
kernel_w
+
ic
*
kernel_h
*
kernel_w
+
kh
*
kernel_w
+
kw
;
out_value
+=
input_data
[
in_idx
]
*
filter_data
[
filter_idx
];
}
}
}
if
(
fuse_relu
)
{
out_value
=
out_value
>
0
?
out_value
:
0
;
}
output_data
[
out_idx
]
=
out_value
;
}
}
}
}
}
}
void
test_conv
(
int
bs
,
int
ic
,
int
oc
,
int
ih
,
int
iw
,
bool
has_bias
,
bool
is_channel_bias
,
bool
fuse_relu
,
bool
depthwise
,
int
dilation
,
int
stride
,
int
padding
,
int
kernel
)
{
// prepare input&output variables
Scope
scope
;
std
::
string
input_var_name
(
"input"
);
std
::
string
filter_var_name
(
"filter"
);
std
::
string
bias_var_name
(
"bias"
);
std
::
string
output_var_name
(
"output"
);
std
::
string
output_ref_var_name
(
"output_ref"
);
auto
*
input
=
scope
.
Var
(
input_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
filter
=
scope
.
Var
(
filter_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
bias
=
scope
.
Var
(
bias_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
output
=
scope
.
Var
(
output_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
output_ref
=
scope
.
Var
(
output_ref_var_name
)
->
GetMutable
<
Tensor
>
();
// get group size and input&filter shape
int
groups
=
1
;
if
(
depthwise
)
{
// depthwise convolution ?
groups
=
oc
=
ic
;
}
std
::
vector
<
int64_t
>
input_shape
=
{
bs
,
ic
,
ih
,
iw
};
std
::
vector
<
int64_t
>
filter_shape
=
{
oc
,
ic
/
groups
,
kernel
,
kernel
};
std
::
vector
<
int64_t
>
output_shape
({
bs
,
oc
});
for
(
size_t
i
=
0
;
i
<
2
;
i
++
)
{
const
int
dkernel
=
dilation
*
(
kernel
-
1
)
+
1
;
int
output_size
=
(
input_shape
[
i
+
2
]
+
2
*
padding
-
dkernel
)
/
stride
+
1
;
output_shape
.
push_back
(
output_size
);
}
input
->
Resize
(
input_shape
);
filter
->
Resize
(
filter_shape
);
// initialize input&output data
FillTensor
<
float
,
int
>
(
input
);
FillTensor
<
float
,
int
>
(
filter
);
// initialize op desc
cpp
::
OpDesc
opdesc
;
opdesc
.
SetType
(
depthwise
?
"depthwise_conv2d"
:
"conv2d"
);
opdesc
.
SetInput
(
"Input"
,
{
input_var_name
});
opdesc
.
SetInput
(
"Filter"
,
{
filter_var_name
});
opdesc
.
SetOutput
(
"Output"
,
{
output_var_name
});
opdesc
.
SetAttr
(
"dilations"
,
std
::
vector
<
int32_t
>
({
dilation
,
dilation
}));
opdesc
.
SetAttr
(
"strides"
,
std
::
vector
<
int32_t
>
({
stride
,
stride
}));
opdesc
.
SetAttr
(
"paddings"
,
std
::
vector
<
int32_t
>
({
padding
,
padding
,
padding
,
padding
}));
opdesc
.
SetAttr
(
"groups"
,
groups
);
opdesc
.
SetAttr
(
"fuse_relu"
,
static_cast
<
bool
>
(
fuse_relu
));
if
(
has_bias
)
{
if
(
is_channel_bias
)
{
bias
->
Resize
({
1
,
oc
,
1
,
1
});
}
else
{
bias
->
Resize
({
output_shape
});
}
FillTensor
<
float
,
int
>
(
bias
);
opdesc
.
SetInput
(
"Bias"
,
{
bias_var_name
});
}
// create and convert op to NPU model, then run it on NPU
auto
op
=
CreateOp
<
operators
::
ConvOpLite
>
(
opdesc
,
&
scope
);
LauchOp
(
op
,
{
input_var_name
},
{
output_var_name
});
output_ref
->
CopyDataFrom
(
*
output
);
// execute reference implementation and save to output tensor('out')
conv_ref
(
op
);
// compare results
auto
*
output_data
=
output
->
mutable_data
<
float
>
();
auto
*
output_ref_data
=
output_ref
->
mutable_data
<
float
>
();
for
(
int
i
=
0
;
i
<
output
->
dims
().
production
();
i
++
)
{
VLOG
(
5
)
<<
i
;
EXPECT_NEAR
(
output_data
[
i
],
output_ref_data
[
i
],
1e-5
);
}
}
TEST
(
NPUBridges
,
conv
)
{
#if 1
for
(
auto
bs
:
{
1
,
2
})
{
for
(
auto
ic
:
{
3
,
6
})
{
for
(
auto
oc
:
{
6
,
9
})
{
for
(
auto
ih
:
{
14
,
28
})
{
for
(
auto
iw
:
{
14
,
28
})
{
for
(
auto
has_bias
:
{
false
,
true
})
{
for
(
auto
is_channel_bias
:
{
false
,
true
})
{
for
(
auto
fuse_relu
:
{
false
,
true
})
{
for
(
auto
depthwise
:
{
false
,
true
})
{
for
(
auto
dilation
:
{
1
,
2
})
{
for
(
auto
stride
:
{
1
,
2
})
{
for
(
auto
kernel
:
{
1
,
3
,
5
})
{
std
::
vector
<
int
>
paddings
=
{
kernel
/
2
};
if
(
kernel
/
2
!=
0
)
{
paddings
.
push_back
(
0
);
}
for
(
auto
padding
:
paddings
)
{
VLOG
(
3
)
<<
"bs: "
<<
bs
<<
" ic: "
<<
ic
<<
" oc: "
<<
oc
<<
" ih: "
<<
ih
<<
" iw: "
<<
iw
<<
" has_bias: "
<<
has_bias
<<
" is_channel_bias: "
<<
is_channel_bias
<<
" fuse_relu: "
<<
fuse_relu
<<
" depthwise: "
<<
depthwise
<<
" dilation: "
<<
dilation
<<
" stride: "
<<
stride
<<
" padding: "
<<
padding
<<
" kernel: "
<<
kernel
;
test_conv
(
bs
,
ic
,
oc
,
ih
,
iw
,
has_bias
,
is_channel_bias
,
fuse_relu
,
depthwise
,
dilation
,
stride
,
padding
,
kernel
);
}
}
}
}
}
}
}
}
}
}
}
}
}
#else
test_conv
(
1
,
3
,
6
,
14
,
14
,
false
,
false
,
false
,
true
,
2
,
1
,
1
,
3
);
test_conv
(
1
,
3
,
6
,
14
,
14
,
false
,
false
,
false
,
true
,
2
,
1
,
0
,
3
);
test_conv
(
1
,
3
,
6
,
14
,
14
,
false
,
false
,
false
,
true
,
2
,
1
,
2
,
5
);
test_conv
(
1
,
3
,
6
,
14
,
14
,
false
,
false
,
false
,
true
,
2
,
1
,
0
,
5
);
#endif
}
}
// namespace bridges
}
// namespace npu
}
// namespace kernels
}
// namespace lite
}
// namespace paddle
USE_LITE_OP
(
conv2d
);
USE_NPU_BRIDGE
(
conv2d
);
USE_LITE_OP
(
depthwise_conv2d
);
USE_NPU_BRIDGE
(
depthwise_conv2d
);
lite/tests/kernels/CMakeLists.txt
浏览文件 @
1816f57f
...
...
@@ -11,6 +11,7 @@ if((NOT LITE_WITH_OPENCL AND NOT LITE_WITH_FPGA) AND (LITE_WITH_X86 OR LITE_WITH
lite_cc_test
(
test_kernel_activation_compute SRCS activation_compute_test.cc DEPS arena_framework
${
npu_kernels
}
${
xpu_kernels
}
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_argmax_compute SRCS argmax_compute_test.cc DEPS arena_framework
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_axpy_compute SRCS axpy_compute_test.cc DEPS arena_framework
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_conv_compute SRCS conv_compute_test.cc DEPS arena_framework
${
xpu_kernels
}
${
npu_kernels
}
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_conv2d_transpose_compute SRCS conv2d_transpose_compute_test.cc DEPS arena_framework
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_norm_compute SRCS norm_compute_test.cc DEPS arena_framework
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
lite_cc_test
(
test_kernel_cast_compute SRCS cast_compute_test.cc DEPS arena_framework
${
xpu_kernels
}
${
npu_kernels
}
${
x86_kernels
}
${
cuda_kernels
}
${
arm_kernels
}
${
lite_ops
}
${
host_kernels
}
)
...
...
lite/tests/kernels/conv_compute_test.cc
0 → 100644
浏览文件 @
1816f57f
// Copyright (c) 2019 PaddlePaddle Authors. 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.
#include <gtest/gtest.h>
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/core/arena/framework.h"
#include "lite/tests/utils/fill_data.h"
namespace
paddle
{
namespace
lite
{
class
ConvComputeTester
:
public
arena
::
TestCase
{
protected:
// common attributes for this op.
std
::
string
op_type_
=
"conv2d"
;
std
::
string
input_
=
"input"
;
std
::
string
filter_
=
"filter"
;
std
::
string
output_
=
"output"
;
DDim
dims_
;
int
out_channels_
=
1
;
int
ksize_
=
3
;
std
::
vector
<
int
>
strides_
{
1
,
1
};
std
::
vector
<
int
>
paddings_
{
0
,
0
};
int
groups_
=
1
;
std
::
vector
<
int
>
dilations_
{
1
,
1
};
std
::
string
padding_algorithm_
;
bool
with_bias_
=
false
;
std
::
string
bias_
=
"bias"
;
bool
with_act_
=
false
;
std
::
string
act_type_
;
float
leaky_relu_alpha_
=
0.1
;
public:
ConvComputeTester
(
const
Place
&
place
,
const
std
::
string
&
alias
,
DDim
dims
,
int
out_channels
=
1
,
int
ksize
=
3
,
std
::
vector
<
int
>
strides
=
{
1
,
1
},
std
::
vector
<
int
>
paddings
=
{
0
,
0
},
int
groups
=
1
,
std
::
vector
<
int
>
dilations
=
{
1
,
1
},
std
::
string
padding_algorithm
=
""
,
bool
with_bias
=
false
,
bool
with_act
=
false
,
std
::
string
act_type
=
""
,
float
leaky_relu_alpha
=
0.1
)
:
TestCase
(
place
,
alias
),
dims_
(
dims
),
out_channels_
(
out_channels
),
ksize_
(
ksize
),
strides_
(
strides
),
paddings_
(
paddings
),
groups_
(
groups
),
dilations_
(
dilations
),
padding_algorithm_
(
padding_algorithm
),
with_bias_
(
with_bias
),
with_act_
(
with_act
),
act_type_
(
act_type
),
leaky_relu_alpha_
(
leaky_relu_alpha
)
{}
void
RunBaseline
(
Scope
*
scope
)
override
{
auto
*
input
=
scope
->
FindTensor
(
input_
);
auto
*
filter
=
scope
->
FindTensor
(
filter_
);
auto
input_dims
=
input
->
dims
();
auto
filter_dims
=
filter
->
dims
();
auto
*
output
=
scope
->
NewTensor
(
output_
);
CHECK
(
output
);
if
(
paddings_
.
size
()
==
2L
)
{
paddings_
.
insert
(
paddings_
.
begin
(),
paddings_
[
0
]);
paddings_
.
insert
(
paddings_
.
begin
()
+
2
,
paddings_
[
2
]);
}
if
(
padding_algorithm_
==
"SAME"
)
{
for
(
size_t
i
=
0
;
i
<
strides_
.
size
();
++
i
)
{
int
out_size
=
(
input_dims
[
i
+
2
]
+
strides_
[
i
]
-
1
)
/
strides_
[
i
];
int
pad_sum
=
std
::
max
((
out_size
-
1
)
*
strides_
[
i
]
+
ksize_
-
input_dims
[
i
+
2
],
(
int64_t
)
0
);
int
pad_0
=
pad_sum
/
2
;
int
pad_1
=
pad_sum
-
pad_0
;
// pad
*
(
paddings_
.
begin
()
+
i
*
2
)
=
pad_0
;
*
(
paddings_
.
begin
()
+
i
*
2
+
1
)
=
pad_1
;
// dilation
*
(
dilations_
.
begin
()
+
i
)
=
1
;
}
}
else
if
(
padding_algorithm_
==
"VALID"
)
{
for
(
auto
&
it
:
paddings_
)
{
it
=
0
;
}
}
std
::
vector
<
int64_t
>
output_shape
({
input_dims
[
0
],
filter_dims
[
0
]});
for
(
size_t
i
=
0
;
i
<
strides_
.
size
();
++
i
)
{
const
int
dkernel
=
dilations_
[
i
]
*
(
filter_dims
[
i
+
2
]
-
1
)
+
1
;
int
output_size
=
(
input_dims
[
i
+
2
]
+
(
paddings_
[
i
*
2
]
+
paddings_
[
i
*
2
+
1
])
-
dkernel
)
/
strides_
[
i
]
+
1
;
output_shape
.
push_back
(
output_size
);
}
output
->
Resize
(
DDim
(
output_shape
));
auto
output_dims
=
output
->
dims
();
auto
input_data
=
input
->
data
<
float
>
();
auto
filter_data
=
filter
->
data
<
float
>
();
auto
output_data
=
output
->
mutable_data
<
float
>
();
int
kernel_w
=
filter_dims
[
3
];
int
kernel_h
=
filter_dims
[
2
];
int
stride_w
=
strides_
[
1
];
int
stride_h
=
strides_
[
0
];
int
dila_w
=
dilations_
[
1
];
int
dila_h
=
dilations_
[
0
];
int
pad_w
=
paddings_
[
2
];
int
pad_h
=
paddings_
[
0
];
int
batch_size
=
input_dims
[
0
];
int
in_ch_size
=
input_dims
[
1
];
int
in_h
=
input_dims
[
2
];
int
in_w
=
input_dims
[
3
];
int
out_ch_size
=
output_dims
[
1
];
int
out_h
=
output_dims
[
2
];
int
out_w
=
output_dims
[
3
];
int
out_c_group
=
out_ch_size
/
groups_
;
int
in_c_group
=
in_ch_size
/
groups_
;
const
float
*
bias_data
=
nullptr
;
bool
is_channel_bias
=
true
;
if
(
with_bias_
)
{
auto
bias
=
scope
->
FindTensor
(
bias_
);
bias_data
=
bias
->
data
<
float
>
();
}
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
g
=
0
;
g
<
groups_
;
++
g
)
{
for
(
int
oc
=
0
;
oc
<
out_c_group
;
++
oc
)
{
for
(
int
oh
=
0
;
oh
<
out_h
;
++
oh
)
{
for
(
int
ow
=
0
;
ow
<
out_w
;
++
ow
)
{
int
out_idx
=
n
*
groups_
*
out_c_group
*
out_h
*
out_w
+
g
*
out_c_group
*
out_h
*
out_w
+
oc
*
out_h
*
out_w
+
oh
*
out_w
+
ow
;
float
out_value
=
bias_data
!=
nullptr
?
(
is_channel_bias
?
bias_data
[
g
*
out_c_group
+
oc
]
:
bias_data
[
out_idx
])
:
0
;
// + out_value *= beta;
for
(
int
ic
=
0
;
ic
<
in_c_group
;
++
ic
)
{
for
(
int
kh
=
0
;
kh
<
kernel_h
;
++
kh
)
{
for
(
int
kw
=
0
;
kw
<
kernel_w
;
++
kw
)
{
int
iw
=
ow
*
stride_w
-
pad_w
+
kw
*
(
dila_w
);
int
ih
=
oh
*
stride_h
-
pad_h
+
kh
*
(
dila_h
);
if
(
iw
<
0
||
iw
>=
in_w
)
continue
;
if
(
ih
<
0
||
ih
>=
in_h
)
continue
;
int
in_idx
=
n
*
in_ch_size
*
in_h
*
in_w
+
g
*
in_c_group
*
in_h
*
in_w
+
ic
*
in_h
*
in_w
+
ih
*
in_w
+
iw
;
int
filter_idx
=
g
*
out_c_group
*
in_c_group
*
kernel_h
*
kernel_w
+
oc
*
in_c_group
*
kernel_h
*
kernel_w
+
ic
*
kernel_h
*
kernel_w
+
kh
*
kernel_w
+
kw
;
out_value
+=
input_data
[
in_idx
]
*
filter_data
[
filter_idx
];
}
}
}
if
(
with_act_
)
{
if
(
act_type_
==
"relu"
)
{
out_value
=
out_value
>
0
?
out_value
:
0
;
}
else
if
(
act_type_
==
"leaky_relu"
)
{
out_value
=
std
::
max
(
out_value
,
out_value
*
leaky_relu_alpha_
);
}
else
{
LOG
(
FATAL
)
<<
"unsupported"
;
}
}
output_data
[
out_idx
]
=
out_value
;
}
}
}
}
}
}
void
PrepareOpDesc
(
cpp
::
OpDesc
*
op_desc
)
{
op_desc
->
SetType
(
op_type_
);
op_desc
->
SetInput
(
"Input"
,
{
input_
});
op_desc
->
SetInput
(
"Filter"
,
{
filter_
});
if
(
with_bias_
)
{
op_desc
->
SetInput
(
"Bias"
,
{
bias_
});
}
op_desc
->
SetOutput
(
"Output"
,
{
output_
});
op_desc
->
SetAttr
(
"strides"
,
strides_
);
op_desc
->
SetAttr
(
"paddings"
,
paddings_
);
op_desc
->
SetAttr
(
"groups"
,
groups_
);
op_desc
->
SetAttr
(
"dilations"
,
dilations_
);
if
(
!
padding_algorithm_
.
empty
())
{
op_desc
->
SetAttr
(
"padding_algorithm"
,
padding_algorithm_
);
}
if
(
with_act_
)
{
op_desc
->
SetAttr
(
"with_act"
,
with_act_
);
op_desc
->
SetAttr
(
"act_type"
,
act_type_
);
if
(
act_type_
==
"leaky_relu"
)
{
op_desc
->
SetAttr
(
"leaky_relu_alpha"
,
leaky_relu_alpha_
);
}
}
}
void
PrepareData
()
override
{
std
::
vector
<
float
>
din
(
dims_
.
production
());
fill_data_rand
(
din
.
data
(),
-
1.
f
,
1.
f
,
dims_
.
production
());
SetCommonTensor
(
input_
,
dims_
,
din
.
data
());
DDim
filter_dims
(
std
::
vector
<
int64_t
>
{
out_channels_
,
dims_
[
1
]
/
groups_
,
ksize_
,
ksize_
});
std
::
vector
<
float
>
dfilter
(
filter_dims
.
production
());
fill_data_rand
(
dfilter
.
data
(),
-
1.
f
,
1.
f
,
filter_dims
.
production
());
SetCommonTensor
(
filter_
,
filter_dims
,
dfilter
.
data
(),
{},
true
);
if
(
with_bias_
)
{
DDim
bias_dims
(
std
::
vector
<
int64_t
>
{
out_channels_
});
std
::
vector
<
float
>
dbias
(
bias_dims
.
production
());
fill_data_rand
(
din
.
data
(),
-
1.
f
,
1.
f
,
bias_dims
.
production
());
SetCommonTensor
(
bias_
,
bias_dims
,
dbias
.
data
(),
{},
true
);
}
}
};
void
TestConvKsize
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
7
,
8
},
{
5
,
6
,
17
,
18
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
for
(
auto
ksize
:
{
1
,
3
,
5
,
7
})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
ksize
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvGroups
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
6
,
3
,
4
},
{
5
,
12
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
2
,
3
,
6
})
{
for
(
auto
groups
:
{
2
,
3
,
6
})
{
#ifdef LITE_WITH_NPU
if
(
out_channels
%
groups
!=
0
)
continue
;
#endif
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
groups
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvDilations
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
5
,
6
},
{
5
,
6
,
9
,
10
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
for
(
auto
dilations
:
std
::
vector
<
std
::
vector
<
int
>>
{{
2
,
2
},
{
1
,
2
}})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
1
,
dilations
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvStrides
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
3
,
4
},
{
5
,
6
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
for
(
auto
strides
:
std
::
vector
<
std
::
vector
<
int
>>
{{
2
,
2
},
{
3
,
3
},
{
1
,
2
},
{
3
,
1
}})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
strides
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvPaddings
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
3
,
4
},
{
5
,
6
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
for
(
auto
paddings
:
std
::
vector
<
std
::
vector
<
int
>>
{
{
1
,
1
},
{
2
,
2
},
{
1
,
0
,
0
,
1
},
{
1
,
2
,
0
,
1
}})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
paddings
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvPaddingAlgorithm
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
3
,
4
},
{
5
,
6
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
for
(
auto
padding_algorithm
:
std
::
vector
<
std
::
string
>
{
"VALID"
,
"SAME"
})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
1
,
{
1
,
1
},
padding_algorithm
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
}
void
TestConvBias
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
3
,
4
},
{
5
,
6
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
1
,
{
1
,
1
},
""
,
true
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
abs_error
);
arena
.
TestPrecision
();
}
}
}
void
TestConvAct
(
Place
place
,
float
abs_error
=
2e-5
)
{
for
(
auto
dims
:
std
::
vector
<
std
::
vector
<
int64_t
>>
{{
1
,
2
,
3
,
4
},
{
5
,
6
,
7
,
8
}})
{
for
(
auto
out_channels
:
{
1
,
3
})
{
std
::
unique_ptr
<
arena
::
TestCase
>
tester0
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
1
,
{
1
,
1
},
""
,
false
,
true
,
"relu"
));
arena
::
Arena
arena0
(
std
::
move
(
tester0
),
place
,
abs_error
);
arena0
.
TestPrecision
();
std
::
unique_ptr
<
arena
::
TestCase
>
tester1
(
new
ConvComputeTester
(
place
,
"def"
,
DDim
(
dims
),
out_channels
,
3
,
{
1
,
1
},
{
0
,
0
},
1
,
{
1
,
1
},
""
,
false
,
true
,
"leaky_relu"
,
0.1
));
arena
::
Arena
arena1
(
std
::
move
(
tester1
),
place
,
abs_error
);
arena1
.
TestPrecision
();
}
}
}
TEST
(
Conv2d
,
precision
)
{
float
abs_error
=
2e-5
;
Place
place
;
#if defined(LITE_WITH_NPU)
place
=
TARGET
(
kNPU
);
abs_error
=
5e-2
;
// Using fp16 in NPU
#else
return
;
#endif
TestConvKsize
(
place
,
abs_error
);
TestConvGroups
(
place
,
abs_error
);
TestConvDilations
(
place
,
abs_error
);
TestConvStrides
(
place
,
abs_error
);
TestConvPaddings
(
place
,
abs_error
);
TestConvPaddingAlgorithm
(
place
,
abs_error
);
TestConvBias
(
place
,
abs_error
);
TestConvAct
(
place
,
abs_error
);
}
}
// namespace lite
}
// namespace paddle
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