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体验新版 GitCode,发现更多精彩内容 >>
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5d19f8d8
编写于
4月 21, 2021
作者:
J
jakpiase
提交者:
GitHub
4月 21, 2021
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电子邮件补丁
差异文件
Added bilinear and nearest interp v2 oneDNN FP32 kernels (#32312)
上级
4898c38d
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
465 addition
and
11 deletion
+465
-11
paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc
paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc
+3
-2
paddle/fluid/framework/ir/placement_pass_base.cc
paddle/fluid/framework/ir/placement_pass_base.cc
+2
-1
paddle/fluid/operators/interpolate_v2_op.cc
paddle/fluid/operators/interpolate_v2_op.cc
+36
-2
paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc
paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc
+24
-6
python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py
.../tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py
+2
-0
python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_v2_mkldnn_op.py
...sts/unittests/mkldnn/test_bilinear_interp_v2_mkldnn_op.py
+210
-0
python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py
...d/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py
+2
-0
python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.py
...ests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.py
+184
-0
tools/static_mode_white_list.py
tools/static_mode_white_list.py
+2
-0
未找到文件。
paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc
浏览文件 @
5d19f8d8
...
...
@@ -43,8 +43,9 @@ void InterpolateMKLDNNPass::ApplyImpl(ir::Graph* graph) const {
int
found_count
=
0
;
const
std
::
vector
<
std
::
string
>
interpolate_op_types
=
{
"bilinear_interp"
,
"nearest_interp"
,
"trilinear_interp"
,
"bicubic_interp"
,
"linear_interp"
};
"bilinear_interp"
,
"nearest_interp"
,
"trilinear_interp"
,
"bicubic_interp"
,
"linear_interp"
,
"bilinear_interp_v2"
,
"nearest_interp_v2"
};
for
(
const
Node
*
node
:
graph
->
Nodes
())
{
if
(
node
->
IsOp
()
&&
...
...
paddle/fluid/framework/ir/placement_pass_base.cc
浏览文件 @
5d19f8d8
...
...
@@ -77,7 +77,8 @@ bool PlacementPassBase::IsDefaultOpTypes(const std::string& op_type) const {
// the corresponding pass.
const
std
::
vector
<
std
::
string
>
not_default_op_types
=
{
"bilinear_interp"
,
"nearest_interp"
,
"trilinear_interp"
,
"bicubic_interp"
,
"linear_interp"
};
"bicubic_interp"
,
"linear_interp"
,
"bilinear_interp_v2"
,
"linear_interp_v2"
};
bool
is_interpolate_op
=
std
::
find
(
not_default_op_types
.
begin
(),
not_default_op_types
.
end
(),
op_type
)
!=
not_default_op_types
.
end
();
...
...
paddle/fluid/operators/interpolate_v2_op.cc
浏览文件 @
5d19f8d8
...
...
@@ -14,6 +14,9 @@
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -359,13 +362,41 @@ class InterpolateV2Op : public framework::OperatorWithKernel {
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
),
ctx
.
GetPlace
());
framework
::
DataLayout
layout
=
framework
::
DataLayout
::
kAnyLayout
;
framework
::
LibraryType
library
=
framework
::
LibraryType
::
kPlain
;
auto
data_type
=
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
);
#ifdef PADDLE_WITH_MKLDNN
auto
interp_method
=
ctx
.
Attr
<
std
::
string
>
(
"interp_method"
);
// TODO(danqing): support other interp_method
if
(
this
->
CanMKLDNNBeUsed
(
ctx
,
data_type
)
&&
(
interp_method
==
"nearest"
||
interp_method
==
"bilinear"
))
{
layout
=
framework
::
DataLayout
::
kMKLDNN
;
library
=
framework
::
LibraryType
::
kMKLDNN
;
}
#endif
return
framework
::
OpKernelType
(
data_type
,
ctx
.
GetPlace
(),
layout
,
library
);
}
framework
::
OpKernelType
GetKernelTypeForVar
(
const
std
::
string
&
var_name
,
const
Tensor
&
tensor
,
const
framework
::
OpKernelType
&
expected_kernel_type
)
const
override
{
#ifdef PADDLE_WITH_MKLDNN
if
((
expected_kernel_type
.
data_layout_
==
framework
::
DataLayout
::
kMKLDNN
)
&&
(
tensor
.
layout
()
!=
framework
::
DataLayout
::
kMKLDNN
))
{
auto
attrs
=
Attrs
();
auto
ar
=
paddle
::
framework
::
AttrReader
(
attrs
);
const
std
::
string
data_format
=
ar
.
Get
<
std
::
string
>
(
"data_layout"
);
auto
dl
=
framework
::
StringToDataLayout
(
data_format
);
// Some models may have intentionally set "AnyLayout" for pool
// op. Treat this as NCHW (default data_format value)
if
(
dl
!=
framework
::
DataLayout
::
kAnyLayout
)
{
return
framework
::
OpKernelType
(
expected_kernel_type
.
data_type_
,
tensor
.
place
(),
dl
);
}
}
#endif
if
(
var_name
==
"SizeTensor"
||
var_name
==
"Scale"
)
{
return
expected_kernel_type
;
}
...
...
@@ -436,6 +467,9 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
"can be
\'
0
\'
for src_idx = scale*(dst_indx+0.5)-0.5 , "
"can be
\'
1
\'
for src_idx = scale*dst_index ."
)
.
SetDefault
(
1
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
This operator samples input X to given output shape by using specified
interpolation method, the interpolation methods can be \"nearest\"
...
...
paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc
浏览文件 @
5d19f8d8
...
...
@@ -33,7 +33,7 @@ class InterpolateMKLDNNHandler
:
public
platform
::
MKLDNNHandlerT
<
T
,
dnnl
::
resampling_forward
>
{
public:
InterpolateMKLDNNHandler
(
const
dnnl
::
algorithm
algo
,
const
p
addle
::
p
latform
::
MKLDNNDeviceContext
&
dev_ctx
,
const
platform
::
MKLDNNDeviceContext
&
dev_ctx
,
const
dnnl
::
engine
engine
,
platform
::
Place
cpu_place
,
const
Tensor
*
x
,
Tensor
*
z
,
const
std
::
string
&
uniq_name
)
...
...
@@ -94,19 +94,32 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
out_dims
=
out_size_data
;
}
}
else
{
float
scale
;
std
::
vector
<
float
>
scale
;
scale
.
reserve
(
3
);
auto
scale_tensor
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
if
(
scale_tensor
!=
nullptr
)
{
auto
scale_data
=
get_new_data_from_tensor
<
float
>
(
scale_tensor
);
scale
=
scale_data
[
0
];
scale
.
resize
(
3
,
scale_data
[
0
]);
std
::
copy
(
scale_data
.
begin
(),
scale_data
.
end
(),
scale
.
begin
());
}
else
{
scale
=
ctx
.
Attr
<
float
>
(
"scale"
);
std
::
string
op_type
=
ctx
.
Type
();
if
(
op_type
.
find
(
"v2"
)
==
std
::
string
::
npos
)
{
// v1
scale
.
push_back
(
ctx
.
Attr
<
float
>
(
"scale"
));
scale
.
push_back
(
scale
[
0
]);
scale
.
push_back
(
scale
[
0
]);
}
else
{
// v2
std
::
vector
<
float
>
scale_attr
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"scale"
);
scale
.
resize
(
3
,
scale_attr
[
0
]);
std
::
copy
(
scale_attr
.
begin
(),
scale_attr
.
end
(),
scale
.
begin
());
}
}
if
(
scale
>
0
)
{
if
(
scale
[
0
]
>
0.0
f
&&
scale
[
1
]
>
0.0
f
&&
scale
[
2
]
>
0.0
f
)
{
int
j
=
0
;
std
::
vector
<
int64_t
>
in_dhw_vec
=
framework
::
vectorize
(
in_dhw_dims
);
std
::
transform
(
in_dhw_vec
.
begin
(),
in_dhw_vec
.
end
(),
out_dims
.
begin
(),
[
&
](
int64_t
i
)
->
int
{
return
static_cast
<
int
>
(
i
*
scale
);
});
[
&
](
int64_t
i
)
->
int
{
return
static_cast
<
int
>
(
i
*
scale
[
j
++
]
);
});
}
}
...
...
@@ -172,3 +185,8 @@ REGISTER_OP_KERNEL(nearest_interp, MKLDNN, ::paddle::platform::CPUPlace,
ops
::
InterpolateMKLDNNKernel
<
float
>
);
REGISTER_OP_KERNEL
(
bilinear_interp
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
InterpolateMKLDNNKernel
<
float
>
);
REGISTER_OP_KERNEL
(
nearest_interp_v2
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
InterpolateMKLDNNKernel
<
float
>
);
REGISTER_OP_KERNEL
(
bilinear_interp_v2
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
InterpolateMKLDNNKernel
<
float
>
);
python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py
浏览文件 @
5d19f8d8
...
...
@@ -198,4 +198,6 @@ class TestBilinearNeighborInterpSame(TestBilinearInterpMKLDNNOp):
if
__name__
==
"__main__"
:
from
paddle
import
enable_static
enable_static
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_v2_mkldnn_op.py
0 → 100644
浏览文件 @
5d19f8d8
# Copyright (c) 2021 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
math
import
paddle
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
from
paddle.fluid.tests.unittests.op_test
import
OpTest
from
paddle.fluid.tests.unittests.op_test
import
skip_check_grad_ci
def
bilinear_interp_mkldnn_np
(
input
,
out_h
,
out_w
,
out_size
=
None
,
actual_shape
=
None
,
data_layout
=
'NCHW'
):
"""bilinear interpolation implement in shape [N, C, H, W]"""
if
data_layout
==
"NHWC"
:
input
=
np
.
transpose
(
input
,
(
0
,
3
,
1
,
2
))
# NHWC => NCHW
if
out_size
is
not
None
:
out_h
=
out_size
[
0
]
out_w
=
out_size
[
1
]
if
actual_shape
is
not
None
:
out_h
=
actual_shape
[
0
]
out_w
=
actual_shape
[
1
]
batch_size
,
channel
,
in_h
,
in_w
=
input
.
shape
out
=
np
.
zeros
((
batch_size
,
channel
,
out_h
,
out_w
))
for
oh
in
range
(
out_h
):
h0
=
int
(
math
.
floor
((
oh
+
0.5
)
*
in_h
/
out_h
-
0.5
))
h1
=
int
(
math
.
ceil
((
oh
+
0.5
)
*
in_h
/
out_h
-
0.5
))
h0
=
max
(
h0
,
0
)
h1
=
min
(
h1
,
in_h
-
1
)
Wh
=
(
oh
+
0.5
)
*
in_h
/
out_h
-
0.5
-
h0
for
ow
in
range
(
out_w
):
w0
=
int
(
math
.
floor
((
ow
+
0.5
)
*
in_w
/
out_w
-
0.5
))
w1
=
int
(
math
.
ceil
((
ow
+
0.5
)
*
in_w
/
out_w
-
0.5
))
w0
=
max
(
w0
,
0
)
w1
=
min
(
w1
,
in_w
-
1
)
Ww
=
(
ow
+
0.5
)
*
in_w
/
out_w
-
0.5
-
w0
input_h0_w0
=
input
[:,
:,
h0
,
w0
]
input_h1_w0
=
input
[:,
:,
h1
,
w0
]
input_h0_w1
=
input
[:,
:,
h0
,
w1
]
input_h1_w1
=
input
[:,
:,
h1
,
w1
]
out
[:,
:,
oh
,
ow
]
=
input_h0_w0
*
(
1
-
Wh
)
*
(
1
-
Ww
)
+
input_h1_w0
*
Wh
*
(
1
-
Ww
)
+
input_h0_w1
*
(
1
-
Wh
)
*
Ww
+
input_h1_w1
*
Wh
*
Ww
if
data_layout
==
"NHWC"
:
out
=
np
.
transpose
(
out
,
(
0
,
2
,
3
,
1
))
# NCHW => NHWC
return
out
.
astype
(
input
.
dtype
)
@
skip_check_grad_ci
(
reason
=
"Haven not implement interpolate grad kernel."
)
class
TestBilinearInterpMKLDNNOp
(
OpTest
):
def
init_test_case
(
self
):
pass
def
setUp
(
self
):
self
.
op_type
=
"bilinear_interp_v2"
self
.
interp_method
=
'bilinear'
self
.
_cpu_only
=
True
self
.
use_mkldnn
=
True
self
.
input_shape
=
[
1
,
1
,
2
,
2
]
self
.
data_layout
=
'NCHW'
# priority: actual_shape > out_size > scale > out_h & out_w
self
.
out_h
=
1
self
.
out_w
=
1
self
.
scale
=
2.0
self
.
out_size
=
None
self
.
actual_shape
=
None
self
.
init_test_case
()
input_np
=
np
.
random
.
random
(
self
.
input_shape
).
astype
(
"float32"
)
if
self
.
data_layout
==
"NCHW"
:
in_h
=
self
.
input_shape
[
2
]
in_w
=
self
.
input_shape
[
3
]
else
:
in_h
=
self
.
input_shape
[
1
]
in_w
=
self
.
input_shape
[
2
]
scale_h
=
0
scale_w
=
0
if
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
scale_h
=
float
(
self
.
scale
)
scale_w
=
float
(
self
.
scale
)
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
scale_w
=
self
.
scale
[
0
]
scale_h
=
self
.
scale
[
0
]
elif
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
>
1
:
scale_w
=
self
.
scale
[
1
]
scale_h
=
self
.
scale
[
0
]
if
scale_h
>
0
and
scale_w
>
0
:
out_h
=
int
(
in_h
*
scale_h
)
out_w
=
int
(
in_w
*
scale_w
)
else
:
out_h
=
self
.
out_h
out_w
=
self
.
out_w
output_np
=
bilinear_interp_mkldnn_np
(
input_np
,
out_h
,
out_w
,
self
.
out_size
,
self
.
actual_shape
,
self
.
data_layout
)
if
isinstance
(
self
.
scale
,
float
):
self
.
scale
=
[
self
.
scale
,
self
.
scale
]
self
.
inputs
=
{
'X'
:
input_np
}
if
self
.
out_size
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
out_size
if
self
.
actual_shape
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
actual_shape
self
.
attrs
=
{
'interp_method'
:
self
.
interp_method
,
'out_h'
:
self
.
out_h
,
'out_w'
:
self
.
out_w
,
'scale'
:
self
.
scale
,
'data_layout'
:
self
.
data_layout
,
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
outputs
=
{
'Out'
:
output_np
}
def
test_check_output
(
self
):
self
.
check_output
(
check_dygraph
=
False
)
class
TestBilinearInterpOpMKLDNNNHWC
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
3
,
2
,
32
,
16
]
self
.
out_h
=
27
self
.
out_w
=
49
self
.
scale
=
[
2.0
,
3.0
]
self
.
data_layout
=
'NHWC'
class
TestBilinearNeighborInterpMKLDNNCase2
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
3
,
3
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
class
TestBilinearNeighborInterpCase3
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
128
self
.
scale
=
[
0.1
,
0.05
]
class
TestBilinearNeighborInterpCase4
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
[
13.0
,
15.0
]
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
class
TestBilinearNeighborInterpCase5
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
self
.
out_size
=
np
.
array
([
13
,
13
]).
astype
(
"int32"
)
class
TestBilinearNeighborInterpCase6
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
1.0
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
class
TestBilinearNeighborInterpSame
(
TestBilinearInterpMKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
2
,
3
,
32
,
64
]
self
.
out_h
=
32
self
.
out_w
=
64
self
.
scale
=
2.0
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
if
__name__
==
"__main__"
:
from
paddle
import
enable_static
enable_static
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py
浏览文件 @
5d19f8d8
...
...
@@ -163,4 +163,6 @@ class TestNearestNeighborInterpSame(TestNearestInterpMKLDNNOp):
if
__name__
==
"__main__"
:
from
paddle
import
enable_static
enable_static
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.py
0 → 100644
浏览文件 @
5d19f8d8
# Copyright (c) 2021 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
from
paddle.fluid.tests.unittests.op_test
import
OpTest
from
paddle.fluid.tests.unittests.op_test
import
skip_check_grad_ci
def
nearest_neighbor_interp_mkldnn_np
(
X
,
out_h
,
out_w
,
out_size
=
None
,
actual_shape
=
None
,
data_layout
=
'NCHW'
):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if
data_layout
==
"NHWC"
:
X
=
np
.
transpose
(
X
,
(
0
,
3
,
1
,
2
))
# NHWC => NCHW
if
out_size
is
not
None
:
out_h
=
out_size
[
0
]
out_w
=
out_size
[
1
]
if
actual_shape
is
not
None
:
out_h
=
actual_shape
[
0
]
out_w
=
actual_shape
[
1
]
n
,
c
,
in_h
,
in_w
=
X
.
shape
fh
=
fw
=
0.0
if
(
out_h
>
1
):
fh
=
out_h
*
1.0
/
in_h
if
(
out_w
>
1
):
fw
=
out_w
*
1.0
/
in_w
out
=
np
.
zeros
((
n
,
c
,
out_h
,
out_w
))
for
oh
in
range
(
out_h
):
ih
=
int
(
round
((
oh
+
0.5
)
/
fh
-
0.5
))
for
ow
in
range
(
out_w
):
iw
=
int
(
round
((
ow
+
0.5
)
/
fw
-
0.5
))
out
[:,
:,
oh
,
ow
]
=
X
[:,
:,
ih
,
iw
]
if
data_layout
==
"NHWC"
:
out
=
np
.
transpose
(
out
,
(
0
,
2
,
3
,
1
))
# NCHW => NHWC
return
out
.
astype
(
X
.
dtype
)
@
skip_check_grad_ci
(
reason
=
"Haven not implement interpolate grad kernel."
)
class
TestNearestInterpV2MKLDNNOp
(
OpTest
):
def
init_test_case
(
self
):
pass
def
setUp
(
self
):
self
.
op_type
=
"nearest_interp_v2"
self
.
interp_method
=
'nearest'
self
.
_cpu_only
=
True
self
.
use_mkldnn
=
True
self
.
input_shape
=
[
1
,
1
,
2
,
2
]
self
.
data_layout
=
'NCHW'
# priority: actual_shape > out_size > scale > out_h & out_w
self
.
out_h
=
1
self
.
out_w
=
1
self
.
scale
=
[
2.0
,
3.0
]
self
.
out_size
=
None
self
.
actual_shape
=
None
self
.
init_test_case
()
input_np
=
np
.
random
.
random
(
self
.
input_shape
).
astype
(
"float32"
)
if
self
.
data_layout
==
"NCHW"
:
in_h
=
self
.
input_shape
[
2
]
in_w
=
self
.
input_shape
[
3
]
else
:
in_h
=
self
.
input_shape
[
1
]
in_w
=
self
.
input_shape
[
2
]
scale_h
=
0
scale_w
=
0
if
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
scale_h
=
float
(
self
.
scale
)
scale_w
=
float
(
self
.
scale
)
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
scale_w
=
self
.
scale
[
0
]
scale_h
=
self
.
scale
[
0
]
elif
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
>
1
:
scale_w
=
self
.
scale
[
1
]
scale_h
=
self
.
scale
[
0
]
if
scale_h
>
0
and
scale_w
>
0
:
out_h
=
int
(
in_h
*
scale_h
)
out_w
=
int
(
in_w
*
scale_w
)
else
:
out_h
=
self
.
out_h
out_w
=
self
.
out_w
output_np
=
nearest_neighbor_interp_mkldnn_np
(
input_np
,
out_h
,
out_w
,
self
.
out_size
,
self
.
actual_shape
,
self
.
data_layout
)
if
isinstance
(
self
.
scale
,
float
):
self
.
scale
=
[
self
.
scale
]
self
.
inputs
=
{
'X'
:
input_np
}
if
self
.
out_size
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
out_size
if
self
.
actual_shape
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
actual_shape
self
.
attrs
=
{
'interp_method'
:
self
.
interp_method
,
'out_h'
:
self
.
out_h
,
'out_w'
:
self
.
out_w
,
'scale'
:
self
.
scale
,
'data_layout'
:
self
.
data_layout
,
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
outputs
=
{
'Out'
:
output_np
}
def
test_check_output
(
self
):
self
.
check_output
(
check_dygraph
=
False
)
class
TestNearestInterpOpV2MKLDNNNHWC
(
TestNearestInterpV2MKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
3
,
2
,
32
,
16
]
self
.
out_h
=
27
self
.
out_w
=
49
self
.
scale
=
[
2.0
,
3.0
]
self
.
data_layout
=
'NHWC'
class
TestNearestNeighborInterpV2MKLDNNCase2
(
TestNearestInterpV2MKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
3
,
3
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
class
TestNearestNeighborInterpV2MKLDNNCase3
(
TestNearestInterpV2MKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
128
self
.
scale
=
[
0.1
,
0.05
]
class
TestNearestNeighborInterpV2MKLDNNCase4
(
TestNearestInterpV2MKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
[
13.0
,
15.0
]
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
class
TestNearestNeighborInterpV2MKLDNNSame
(
TestNearestInterpV2MKLDNNOp
):
def
init_test_case
(
self
):
self
.
input_shape
=
[
2
,
3
,
32
,
64
]
self
.
out_h
=
32
self
.
out_w
=
64
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
if
__name__
==
"__main__"
:
from
paddle
import
enable_static
enable_static
()
unittest
.
main
()
tools/static_mode_white_list.py
浏览文件 @
5d19f8d8
...
...
@@ -603,7 +603,9 @@ STATIC_MODE_TESTING_LIST = [
'test_fc_mkldnn_op'
,
'test_fc_bf16_mkldnn_op'
,
'test_nearest_interp_mkldnn_op'
,
'test_nearest_interp_v2_mkldnn_op'
,
'test_bilinear_interp_mkldnn_op'
,
'test_bilinear_interp_v2_mkldnn_op'
,
'test_fusion_gru_int8_mkldnn_op'
,
'test_fusion_gru_bf16_mkldnn_op'
,
'test_fusion_gru_mkldnn_op'
,
...
...
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