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22555e96
编写于
11月 24, 2022
作者:
Z
zhangyikun02
提交者:
GitHub
11月 24, 2022
浏览文件
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电子邮件补丁
差异文件
add pad3d and pad3d_grad op for xpu, test=kunlun (#48306)
上级
ac8a4b16
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
999 addition
and
1 deletion
+999
-1
cmake/external/xpu.cmake
cmake/external/xpu.cmake
+1
-1
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+2
-0
paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
+108
-0
paddle/phi/kernels/xpu/pad3d_kernel.cc
paddle/phi/kernels/xpu/pad3d_kernel.cc
+187
-0
python/paddle/fluid/tests/unittests/xpu/test_pad3d_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_pad3d_op_xpu.py
+701
-0
未找到文件。
cmake/external/xpu.cmake
浏览文件 @
22555e96
...
@@ -10,7 +10,7 @@ set(XPU_RT_LIB_NAME "libxpurt.so")
...
@@ -10,7 +10,7 @@ set(XPU_RT_LIB_NAME "libxpurt.so")
if
(
NOT DEFINED XPU_BASE_URL
)
if
(
NOT DEFINED XPU_BASE_URL
)
set
(
XPU_BASE_URL_WITHOUT_DATE
set
(
XPU_BASE_URL_WITHOUT_DATE
"https://baidu-kunlun-product.su.bcebos.com/KL-SDK/klsdk-dev"
)
"https://baidu-kunlun-product.su.bcebos.com/KL-SDK/klsdk-dev"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/2022112
0
"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/2022112
4
"
)
else
()
else
()
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL
}
"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL
}
"
)
endif
()
endif
()
...
...
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
22555e96
...
@@ -433,6 +433,8 @@ XPUOpMap& get_kl2_ops() {
...
@@ -433,6 +433,8 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
())})},
{
"p_norm"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"p_norm"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"p_norm_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"p_norm_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"pad3d_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"pad3d"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"pool2d_grad"
,
{
"pool2d_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
...
...
paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
0 → 100644
浏览文件 @
22555e96
// Copyright (c) 2022 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 "paddle/phi/kernels/pad3d_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
Pad3dGradKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
out_grad
,
const
IntArray
&
paddings
,
const
std
::
string
&
mode
,
float
pad_value
,
const
std
::
string
&
data_format
,
DenseTensor
*
x_grad
)
{
T
value
=
static_cast
<
T
>
(
pad_value
);
std
::
vector
<
int64_t
>
pads
=
paddings
.
GetData
();
auto
*
d_out
=
&
out_grad
;
auto
*
d_in
=
x_grad
;
auto
d_in_dims
=
d_in
->
dims
();
const
T
*
d_out_data
=
d_out
->
data
<
T
>
();
T
*
d_in_data
=
dev_ctx
.
template
Alloc
<
T
>(
d_in
);
bool
is_ncdhw
=
true
;
if
(
data_format
==
"NDHWC"
)
{
is_ncdhw
=
false
;
}
const
int
num
=
d_in_dims
[
0
];
// n
int
channels
=
d_in_dims
[
1
];
// c
int
in_depth
=
d_in_dims
[
2
];
// xd
int
in_height
=
d_in_dims
[
3
];
// xh
int
in_width
=
d_in_dims
[
4
];
// xw
if
(
data_format
==
"NDHWC"
)
{
channels
=
d_in_dims
[
4
];
in_depth
=
d_in_dims
[
1
];
in_height
=
d_in_dims
[
2
];
in_width
=
d_in_dims
[
3
];
}
std
::
vector
<
int
>
pads_xpu
(
6
);
pads_xpu
[
0
]
=
pads
[
4
];
// pf
pads_xpu
[
1
]
=
pads
[
5
];
// pb
pads_xpu
[
2
]
=
pads
[
2
];
// pt
pads_xpu
[
3
]
=
pads
[
3
];
// pd
pads_xpu
[
4
]
=
pads
[
0
];
// pl
pads_xpu
[
5
]
=
pads
[
1
];
// pr
if
(
mode
==
"reflect"
)
{
int
r
=
xpu
::
reflection_pad3d_grad
(
dev_ctx
.
x_context
(),
d_out_data
,
d_in_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reflection_pad3d_grad"
);
}
else
if
(
mode
==
"replicate"
)
{
int
r
=
xpu
::
replication_pad3d_grad
(
dev_ctx
.
x_context
(),
d_out_data
,
d_in_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"replication_pad3d_grad"
);
}
else
if
(
mode
==
"constant"
)
{
int
r
=
xpu
::
constant_pad3d_grad
(
dev_ctx
.
x_context
(),
d_out_data
,
d_in_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
value
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"constant_pad3d_grad"
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
pad3d_grad
,
XPU
,
ALL_LAYOUT
,
phi
::
Pad3dGradKernel
,
float
)
{}
paddle/phi/kernels/xpu/pad3d_kernel.cc
0 → 100644
浏览文件 @
22555e96
// Copyright (c) 2022 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 "paddle/phi/kernels/pad3d_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
Pad3dKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
IntArray
&
paddings
,
const
std
::
string
&
mode
,
float
pad_value
,
const
std
::
string
&
data_format
,
DenseTensor
*
out
)
{
T
value
=
static_cast
<
T
>
(
pad_value
);
std
::
vector
<
int64_t
>
pads
=
paddings
.
GetData
();
auto
in_dims
=
x
.
dims
();
const
T
*
in_data
=
x
.
data
<
T
>
();
bool
is_ncdhw
=
true
;
if
(
data_format
==
"NCDHW"
)
{
out
->
Resize
({
in_dims
[
0
],
in_dims
[
1
],
in_dims
[
2
]
+
pads
[
4
]
+
pads
[
5
],
in_dims
[
3
]
+
pads
[
2
]
+
pads
[
3
],
in_dims
[
4
]
+
pads
[
0
]
+
pads
[
1
]});
}
else
{
is_ncdhw
=
false
;
out
->
Resize
({
in_dims
[
0
],
in_dims
[
1
]
+
pads
[
4
]
+
pads
[
5
],
in_dims
[
2
]
+
pads
[
2
]
+
pads
[
3
],
in_dims
[
3
]
+
pads
[
0
]
+
pads
[
1
],
in_dims
[
4
]});
}
T
*
out_data
=
dev_ctx
.
template
Alloc
<
T
>(
out
);
const
int
num
=
in_dims
[
0
];
// n
int
channels
=
in_dims
[
1
];
// c
int
in_depth
=
in_dims
[
2
];
// xd
int
in_height
=
in_dims
[
3
];
// xh
int
in_width
=
in_dims
[
4
];
// xw
if
(
data_format
==
"NDHWC"
)
{
channels
=
in_dims
[
4
];
in_depth
=
in_dims
[
1
];
in_height
=
in_dims
[
2
];
in_width
=
in_dims
[
3
];
}
if
(
mode
==
"circular"
)
{
PADDLE_THROW
(
phi
::
errors
::
External
(
"XPU is not support circular padding mode in pad3d"
));
}
if
(
mode
==
"reflect"
)
{
PADDLE_ENFORCE_GT
(
in_depth
,
pads
[
4
],
errors
::
InvalidArgument
(
"The depth of Input(X)'s dimension should be "
"greater than pad_front"
" in reflect mode"
", but received depth(%d) and pad_front(%d)."
,
in_depth
,
pads
[
4
]));
PADDLE_ENFORCE_GT
(
in_depth
,
pads
[
5
],
errors
::
InvalidArgument
(
"The depth of Input(X)'s dimension should be "
"greater than pad_back"
" in reflect mode"
", but received depth(%d) and pad_back(%d)."
,
in_depth
,
pads
[
5
]));
PADDLE_ENFORCE_GT
(
in_height
,
pads
[
2
],
errors
::
InvalidArgument
(
"The height of Input(X)'s dimension should be "
"greater than pad_top"
" in reflect mode"
", but received depth(%d) and pad_top(%d)."
,
in_height
,
pads
[
2
]));
PADDLE_ENFORCE_GT
(
in_height
,
pads
[
3
],
errors
::
InvalidArgument
(
"The height of Input(X)'s dimension should be "
"greater than pad_bottom"
" in reflect mode"
", but received depth(%d) and pad_bottom(%d)."
,
in_height
,
pads
[
3
]));
PADDLE_ENFORCE_GT
(
in_width
,
pads
[
0
],
errors
::
InvalidArgument
(
"The width of Input(X)'s dimension should be "
"greater than pad_left"
" in reflect mode"
", but received depth(%d) and pad_left(%d)."
,
in_width
,
pads
[
0
]));
PADDLE_ENFORCE_GT
(
in_width
,
pads
[
1
],
errors
::
InvalidArgument
(
"The width of Input(X)'s dimension should be "
"greater than pad_right"
" in reflect mode"
", but received depth(%d) and pad_right(%d)."
,
in_width
,
pads
[
1
]));
}
else
if
(
mode
==
"replicate"
)
{
PADDLE_ENFORCE_NE
(
in_depth
*
in_height
*
in_width
,
0
,
errors
::
InvalidArgument
(
"The input tensor size can not be 0 for circular "
"or replicate padding mode."
));
}
std
::
vector
<
int
>
pads_xpu
(
6
);
pads_xpu
[
0
]
=
pads
[
4
];
// pf
pads_xpu
[
1
]
=
pads
[
5
];
// pb
pads_xpu
[
2
]
=
pads
[
2
];
// pt
pads_xpu
[
3
]
=
pads
[
3
];
// pd
pads_xpu
[
4
]
=
pads
[
0
];
// pl
pads_xpu
[
5
]
=
pads
[
1
];
// pr
if
(
mode
==
"reflect"
)
{
int
r
=
xpu
::
reflection_pad3d
(
dev_ctx
.
x_context
(),
in_data
,
out_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"reflection_pad3d"
);
}
else
if
(
mode
==
"replicate"
)
{
int
r
=
xpu
::
replication_pad3d
(
dev_ctx
.
x_context
(),
in_data
,
out_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"replication_pad3d"
);
}
else
if
(
mode
==
"constant"
)
{
int
r
=
xpu
::
constant_pad3d
(
dev_ctx
.
x_context
(),
in_data
,
out_data
,
num
,
channels
,
in_depth
,
in_height
,
in_width
,
pads_xpu
,
value
,
is_ncdhw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"constant_pad3d"
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
pad3d
,
XPU
,
ALL_LAYOUT
,
phi
::
Pad3dKernel
,
float
)
{}
python/paddle/fluid/tests/unittests/xpu/test_pad3d_op_xpu.py
0 → 100644
浏览文件 @
22555e96
# Copyright (c) 2022 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.
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
import
paddle
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
(
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
,
)
from
paddle.fluid
import
Program
,
program_guard
,
Executor
,
default_main_program
paddle
.
enable_static
()
class
XPUTestPad3dOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'pad3d'
class
TestPad3dOp
(
XPUOpTest
):
def
setUp
(
self
):
paddle
.
enable_static
()
self
.
op_type
=
"pad3d"
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
value
=
0.0
self
.
initTestCase
()
self
.
python_api
=
paddle
.
nn
.
functional
.
pad
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)}
self
.
attrs
=
{}
if
self
.
variable_paddings
:
self
.
attrs
[
'paddings'
]
=
[]
self
.
inputs
[
'Paddings'
]
=
(
np
.
array
(
self
.
paddings
).
flatten
().
astype
(
"int32"
)
)
else
:
self
.
attrs
[
'paddings'
]
=
(
np
.
array
(
self
.
paddings
).
flatten
().
astype
(
"int32"
)
)
self
.
attrs
[
'value'
]
=
self
.
value
self
.
attrs
[
'mode'
]
=
self
.
mode
self
.
attrs
[
'data_format'
]
=
self
.
data_format
if
self
.
data_format
==
"NCDHW"
:
paddings
=
[
(
0
,
0
),
(
0
,
0
),
(
self
.
paddings
[
4
],
self
.
paddings
[
5
]),
(
self
.
paddings
[
2
],
self
.
paddings
[
3
]),
(
self
.
paddings
[
0
],
self
.
paddings
[
1
]),
]
else
:
paddings
=
[
(
0
,
0
),
(
self
.
paddings
[
4
],
self
.
paddings
[
5
]),
(
self
.
paddings
[
2
],
self
.
paddings
[
3
]),
(
self
.
paddings
[
0
],
self
.
paddings
[
1
]),
(
0
,
0
),
]
if
self
.
mode
==
"constant"
:
out
=
np
.
pad
(
self
.
inputs
[
'X'
],
paddings
,
mode
=
self
.
mode
,
constant_values
=
self
.
value
,
)
elif
self
.
mode
==
"reflect"
:
out
=
np
.
pad
(
self
.
inputs
[
'X'
],
paddings
,
mode
=
self
.
mode
)
elif
self
.
mode
==
"replicate"
:
out
=
np
.
pad
(
self
.
inputs
[
'X'
],
paddings
,
mode
=
"edge"
)
elif
self
.
mode
==
"circular"
:
out
=
np
.
pad
(
self
.
inputs
[
'X'
],
paddings
,
mode
=
"wrap"
)
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
(
check_eager
=
True
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
check_eager
=
True
)
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
0
,
0
,
0
,
0
,
0
]
self
.
mode
=
"constant"
self
.
data_format
=
"NCDHW"
self
.
pad_value
=
0.0
self
.
variable_paddings
=
False
class
TestCase1
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
1
,
2
,
3
,
4
,
5
]
self
.
mode
=
"constant"
self
.
data_format
=
"NCDHW"
self
.
value
=
1.0
self
.
variable_paddings
=
False
class
TestCase2
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
1
,
1
,
1
,
1
,
1
,
1
]
self
.
mode
=
"constant"
self
.
data_format
=
"NDHWC"
self
.
value
=
1.0
self
.
variable_paddings
=
False
class
TestCase3
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
1
,
1
,
0
,
2
,
3
]
self
.
mode
=
"reflect"
self
.
data_format
=
"NCDHW"
self
.
variable_paddings
=
False
class
TestCase4
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
4
,
4
,
4
,
4
,
4
)
self
.
paddings
=
[
0
,
1
,
2
,
1
,
2
,
3
]
self
.
mode
=
"reflect"
self
.
data_format
=
"NDHWC"
self
.
variable_paddings
=
False
class
TestCase5
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
1
,
2
,
3
,
2
,
1
]
self
.
mode
=
"replicate"
self
.
data_format
=
"NCDHW"
self
.
variable_paddings
=
False
class
TestCase6
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
4
,
4
,
4
,
4
,
4
)
self
.
paddings
=
[
5
,
4
,
2
,
1
,
2
,
3
]
self
.
mode
=
"replicate"
self
.
data_format
=
"NDHWC"
self
.
variable_paddings
=
False
class
TestCase7
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
1
,
2
,
3
,
4
,
5
]
self
.
mode
=
"constant"
self
.
data_format
=
"NCDHW"
self
.
value
=
1.0
self
.
variable_paddings
=
True
class
TestCase8
(
TestPad3dOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
2
,
3
,
4
,
5
,
6
)
self
.
paddings
=
[
0
,
1
,
2
,
3
,
4
,
5
]
self
.
mode
=
"constant"
self
.
data_format
=
"NDHWC"
self
.
value
=
1.0
self
.
variable_paddings
=
True
class
TestPadAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
places
=
[
paddle
.
XPUPlace
(
0
)]
self
.
dtype
=
self
.
in_type
def
check_static_result_1
(
self
,
place
):
paddle
.
enable_static
()
with
program_guard
(
Program
(),
Program
()):
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
pad
=
[
1
,
2
,
1
,
1
,
3
,
4
]
mode
=
"constant"
value
=
100
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
fluid
.
data
(
name
=
"x"
,
shape
=
input_shape
)
result
=
F
.
pad
(
x
=
x
,
pad
=
pad
,
value
=
value
,
mode
=
mode
,
data_format
=
"NCDHW"
)
exe
=
Executor
(
place
)
fetches
=
exe
.
run
(
default_main_program
(),
feed
=
{
"x"
:
input_data
},
fetch_list
=
[
result
],
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
)
np
.
testing
.
assert_allclose
(
fetches
[
0
],
np_out
,
rtol
=
1e-05
)
def
check_static_result_2
(
self
,
place
):
paddle
.
enable_static
()
with
program_guard
(
Program
(),
Program
()):
input_shape
=
(
2
,
3
,
4
,
5
,
6
)
pad
=
[
1
,
2
,
1
,
1
,
1
,
2
]
mode
=
"reflect"
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
fluid
.
data
(
name
=
"x"
,
shape
=
input_shape
)
result1
=
F
.
pad
(
x
=
x
,
pad
=
pad
,
mode
=
mode
,
data_format
=
"NCDHW"
)
result2
=
F
.
pad
(
x
=
x
,
pad
=
pad
,
mode
=
mode
,
data_format
=
"NDHWC"
)
exe
=
Executor
(
place
)
fetches
=
exe
.
run
(
default_main_program
(),
feed
=
{
"x"
:
input_data
},
fetch_list
=
[
result1
,
result2
],
)
np_out1
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
data_format
=
"NCDHW"
)
np_out2
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
data_format
=
"NDHWC"
)
np
.
testing
.
assert_allclose
(
fetches
[
0
],
np_out1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
fetches
[
1
],
np_out2
,
rtol
=
1e-05
)
def
check_static_result_3
(
self
,
place
):
paddle
.
enable_static
()
with
program_guard
(
Program
(),
Program
()):
input_shape
=
(
2
,
3
,
4
,
5
,
6
)
pad
=
[
1
,
2
,
1
,
1
,
3
,
4
]
mode
=
"replicate"
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
fluid
.
data
(
name
=
"x"
,
shape
=
input_shape
)
result1
=
F
.
pad
(
x
=
x
,
pad
=
pad
,
mode
=
mode
,
data_format
=
"NCDHW"
)
result2
=
F
.
pad
(
x
=
x
,
pad
=
pad
,
mode
=
mode
,
data_format
=
"NDHWC"
)
exe
=
Executor
(
place
)
fetches
=
exe
.
run
(
default_main_program
(),
feed
=
{
"x"
:
input_data
},
fetch_list
=
[
result1
,
result2
],
)
np_out1
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
data_format
=
"NCDHW"
)
np_out2
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
data_format
=
"NDHWC"
)
np
.
testing
.
assert_allclose
(
fetches
[
0
],
np_out1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
fetches
[
1
],
np_out2
,
rtol
=
1e-05
)
def
_get_numpy_out
(
self
,
input_data
,
pad
,
mode
,
value
=
0
,
data_format
=
"NCDHW"
):
if
mode
==
"constant"
and
len
(
pad
)
==
len
(
input_data
.
shape
)
*
2
:
pad
=
np
.
reshape
(
pad
,
(
-
1
,
2
)).
tolist
()
elif
data_format
==
"NCDHW"
:
pad
=
[
(
0
,
0
),
(
0
,
0
),
(
pad
[
4
],
pad
[
5
]),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
]
elif
data_format
==
"NDHWC"
:
pad
=
[
(
0
,
0
),
(
pad
[
4
],
pad
[
5
]),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
(
0
,
0
),
]
elif
data_format
==
"NCHW"
:
pad
=
[
(
0
,
0
),
(
0
,
0
),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
]
elif
data_format
==
"NHWC"
:
pad
=
[
(
0
,
0
),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
(
0
,
0
),
]
elif
data_format
==
"NCL"
:
pad
=
[
(
0
,
0
),
(
0
,
0
),
(
pad
[
0
],
pad
[
1
]),
]
elif
data_format
==
"NLC"
:
pad
=
[
(
0
,
0
),
(
pad
[
0
],
pad
[
1
]),
(
0
,
0
),
]
if
mode
==
"constant"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
mode
,
constant_values
=
value
)
elif
mode
==
"reflect"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
mode
)
elif
mode
==
"replicate"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
"edge"
)
elif
mode
==
"circular"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
"wrap"
)
return
out
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result_1
(
place
=
place
)
self
.
check_static_result_2
(
place
=
place
)
self
.
check_static_result_3
(
place
=
place
)
def
test_dygraph_1
(
self
):
paddle
.
disable_static
()
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
pad
=
[
1
,
2
,
1
,
1
,
3
,
4
]
pad_3
=
[
1
,
2
,
1
,
1
,
3
,
4
,
5
,
6
,
7
,
8
]
mode
=
"constant"
value
=
100
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
np_out1
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NCDHW"
)
np_out2
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NDHWC"
)
np_out3
=
self
.
_get_numpy_out
(
input_data
,
pad_3
,
mode
,
value
,
data_format
=
"NCDHW"
)
tensor_data
=
paddle
.
to_tensor
(
input_data
)
y1
=
F
.
pad
(
tensor_data
,
pad
=
pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCDHW"
,
)
y2
=
F
.
pad
(
tensor_data
,
pad
=
pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NDHWC"
,
)
y3
=
F
.
pad
(
tensor_data
,
pad
=
pad_3
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCDHW"
,
)
np
.
testing
.
assert_allclose
(
y1
.
numpy
(),
np_out1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y2
.
numpy
(),
np_out2
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y3
.
numpy
(),
np_out3
,
rtol
=
1e-05
)
def
test_dygraph_2
(
self
):
paddle
.
disable_static
()
input_shape
=
(
2
,
3
,
4
,
5
)
pad
=
[
1
,
1
,
3
,
4
]
pad_3
=
[
1
,
2
,
1
,
1
,
3
,
4
,
5
,
6
]
mode
=
"constant"
value
=
100
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
np_out1
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NCHW"
)
np_out2
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NHWC"
)
np_out3
=
self
.
_get_numpy_out
(
input_data
,
pad_3
,
mode
,
value
,
data_format
=
"NCHW"
)
tensor_data
=
paddle
.
to_tensor
(
input_data
)
tensor_pad
=
paddle
.
to_tensor
(
pad
,
dtype
=
"int32"
)
y1
=
F
.
pad
(
tensor_data
,
pad
=
tensor_pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCHW"
,
)
y2
=
F
.
pad
(
tensor_data
,
pad
=
tensor_pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NHWC"
,
)
y3
=
F
.
pad
(
tensor_data
,
pad
=
pad_3
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCHW"
,
)
np
.
testing
.
assert_allclose
(
y1
.
numpy
(),
np_out1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y2
.
numpy
(),
np_out2
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y3
.
numpy
(),
np_out3
,
rtol
=
1e-05
)
def
test_dygraph_3
(
self
):
paddle
.
disable_static
()
input_shape
=
(
3
,
4
,
5
)
pad
=
[
3
,
4
]
pad_3
=
[
3
,
4
,
5
,
6
,
7
,
8
]
mode
=
"constant"
value
=
100
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
np_out1
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NCL"
)
np_out2
=
self
.
_get_numpy_out
(
input_data
,
pad
,
mode
,
value
,
data_format
=
"NLC"
)
np_out3
=
self
.
_get_numpy_out
(
input_data
,
pad_3
,
mode
,
value
,
data_format
=
"NCL"
)
tensor_data
=
paddle
.
to_tensor
(
input_data
)
tensor_pad
=
paddle
.
to_tensor
(
pad
,
dtype
=
"int32"
)
y1
=
F
.
pad
(
tensor_data
,
pad
=
tensor_pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCL"
,
)
y2
=
F
.
pad
(
tensor_data
,
pad
=
tensor_pad
,
mode
=
mode
,
value
=
value
,
data_format
=
"NLC"
,
)
y3
=
F
.
pad
(
tensor_data
,
pad
=
pad_3
,
mode
=
mode
,
value
=
value
,
data_format
=
"NCL"
,
)
np
.
testing
.
assert_allclose
(
y1
.
numpy
(),
np_out1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y2
.
numpy
(),
np_out2
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
y3
.
numpy
(),
np_out3
,
rtol
=
1e-05
)
class
TestPad3dAPI
(
unittest
.
TestCase
):
def
_get_numpy_out
(
self
,
input_data
,
pad
,
mode
,
value
=
0.0
,
data_format
=
"NCDHW"
):
if
data_format
==
"NCDHW"
:
pad
=
[
(
0
,
0
),
(
0
,
0
),
(
pad
[
4
],
pad
[
5
]),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
]
else
:
pad
=
[
(
0
,
0
),
(
pad
[
4
],
pad
[
5
]),
(
pad
[
2
],
pad
[
3
]),
(
pad
[
0
],
pad
[
1
]),
(
0
,
0
),
]
if
mode
==
"constant"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
mode
,
constant_values
=
value
)
elif
mode
==
"reflect"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
mode
)
elif
mode
==
"replicate"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
"edge"
)
elif
mode
==
"circular"
:
out
=
np
.
pad
(
input_data
,
pad
,
mode
=
"wrap"
)
return
out
def
setUp
(
self
):
self
.
places
=
[
paddle
.
XPUPlace
(
0
)]
self
.
dtype
=
self
.
in_type
def
test_class
(
self
):
paddle
.
disable_static
()
for
place
in
self
.
places
:
input_shape
=
(
3
,
4
,
5
,
6
,
7
)
pad
=
[
1
,
2
,
2
,
1
,
1
,
0
]
pad_int
=
1
value
=
100
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
pad_reflection
=
nn
.
Pad3D
(
padding
=
pad
,
mode
=
"reflect"
)
pad_replication
=
nn
.
Pad3D
(
padding
=
pad
,
mode
=
"replicate"
)
pad_constant
=
nn
.
Pad3D
(
padding
=
pad
,
mode
=
"constant"
,
value
=
value
)
pad_constant_int
=
nn
.
Pad3D
(
padding
=
pad_int
,
mode
=
"constant"
,
value
=
value
)
pad_circular
=
nn
.
Pad3D
(
padding
=
pad
,
mode
=
"circular"
)
data
=
paddle
.
to_tensor
(
input_data
)
output
=
pad_reflection
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
"reflect"
,
data_format
=
"NCDHW"
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
output
=
pad_replication
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
"replicate"
,
data_format
=
"NCDHW"
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
output
=
pad_constant
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
"constant"
,
value
=
value
,
data_format
=
"NCDHW"
,
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
output
=
pad_constant_int
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
[
pad_int
]
*
6
,
"constant"
,
value
=
value
,
data_format
=
"NCDHW"
,
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
def
test_pad_tensor
(
self
):
paddle
.
disable_static
()
for
place
in
self
.
places
:
input_shape
=
(
3
,
4
,
5
,
6
,
7
)
pad
=
[
1
,
2
,
2
,
1
,
1
,
0
]
pad_tensor
=
paddle
.
to_tensor
(
pad
)
input_data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
np
.
float32
)
pad_reflection_ncdhw
=
nn
.
Pad3D
(
padding
=
pad_tensor
,
mode
=
"reflect"
,
data_format
=
"NCDHW"
)
pad_reflection_ndhwc
=
nn
.
Pad3D
(
padding
=
pad_tensor
,
mode
=
"reflect"
,
data_format
=
"NDHWC"
)
data
=
paddle
.
to_tensor
(
input_data
)
output
=
pad_reflection_ncdhw
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
"reflect"
,
data_format
=
"NCDHW"
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
output
=
pad_reflection_ndhwc
(
data
)
np_out
=
self
.
_get_numpy_out
(
input_data
,
pad
,
"reflect"
,
data_format
=
"NDHWC"
)
np
.
testing
.
assert_allclose
(
output
.
numpy
(),
np_out
,
rtol
=
1e-05
)
class
TestPad3dOpError
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
places
=
[
paddle
.
XPUPlace
(
0
)]
self
.
dtype
=
self
.
in_type
def
test_errors
(
self
):
def
test_variable
():
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
y
=
F
.
pad
(
x
=
data
,
pad
=
[
1
,
1
,
1
,
1
,
1
,
1
],
data_format
=
"NCDHW"
)
def
test_reflect_1
():
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
to_tensor
(
data
)
y
=
F
.
pad
(
x
,
pad
=
[
5
,
6
,
1
,
1
,
1
,
1
],
value
=
1
,
mode
=
'reflect'
,
data_format
=
"NCDHW"
,
)
def
test_reflect_2
():
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
to_tensor
(
data
)
y
=
F
.
pad
(
x
,
pad
=
[
1
,
1
,
4
,
3
,
1
,
1
],
value
=
1
,
mode
=
'reflect'
,
data_format
=
"NCDHW"
,
)
def
test_reflect_3
():
input_shape
=
(
1
,
2
,
3
,
4
,
5
)
data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
to_tensor
(
data
)
y
=
F
.
pad
(
x
,
pad
=
[
1
,
1
,
1
,
1
,
2
,
3
],
value
=
1
,
mode
=
'reflect'
,
data_format
=
"NCDHW"
,
)
def
test_replicate_1
():
input_shape
=
(
1
,
2
,
0
,
4
,
5
)
data
=
np
.
random
.
rand
(
*
input_shape
).
astype
(
self
.
dtype
)
x
=
paddle
.
to_tensor
(
data
)
y
=
F
.
pad
(
x
,
pad
=
[
1
,
1
,
1
,
1
,
2
,
3
],
mode
=
'replicate'
,
data_format
=
"NCDHW"
,
)
paddle
.
disable_static
()
for
place
in
self
.
places
:
self
.
assertRaises
(
ValueError
,
test_variable
)
self
.
assertRaises
(
Exception
,
test_reflect_1
)
self
.
assertRaises
(
Exception
,
test_reflect_2
)
self
.
assertRaises
(
Exception
,
test_reflect_3
)
self
.
assertRaises
(
Exception
,
test_replicate_1
)
paddle
.
enable_static
()
class
TestPadDataformatError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
def
test_ncl
():
input_shape
=
(
1
,
2
,
3
,
4
)
pad
=
paddle
.
to_tensor
(
np
.
array
([
2
,
1
,
2
,
1
]).
astype
(
'int32'
))
data
=
(
np
.
arange
(
np
.
prod
(
input_shape
),
dtype
=
np
.
float64
).
reshape
(
input_shape
)
+
1
)
my_pad
=
nn
.
Pad1D
(
padding
=
pad
,
mode
=
"replicate"
,
data_format
=
"NCL"
)
data
=
paddle
.
to_tensor
(
data
)
result
=
my_pad
(
data
)
def
test_nchw
():
input_shape
=
(
1
,
2
,
4
)
pad
=
paddle
.
to_tensor
(
np
.
array
([
2
,
1
,
2
,
1
]).
astype
(
'int32'
))
data
=
(
np
.
arange
(
np
.
prod
(
input_shape
),
dtype
=
np
.
float64
).
reshape
(
input_shape
)
+
1
)
my_pad
=
nn
.
Pad1D
(
padding
=
pad
,
mode
=
"replicate"
,
data_format
=
"NCHW"
)
data
=
paddle
.
to_tensor
(
data
)
result
=
my_pad
(
data
)
def
test_ncdhw
():
input_shape
=
(
1
,
2
,
3
,
4
)
pad
=
paddle
.
to_tensor
(
np
.
array
([
2
,
1
,
2
,
1
]).
astype
(
'int32'
))
data
=
(
np
.
arange
(
np
.
prod
(
input_shape
),
dtype
=
np
.
float64
).
reshape
(
input_shape
)
+
1
)
my_pad
=
nn
.
Pad1D
(
padding
=
pad
,
mode
=
"replicate"
,
data_format
=
"NCDHW"
)
data
=
paddle
.
to_tensor
(
data
)
result
=
my_pad
(
data
)
self
.
assertRaises
(
AssertionError
,
test_ncl
)
self
.
assertRaises
(
AssertionError
,
test_nchw
)
self
.
assertRaises
(
AssertionError
,
test_ncdhw
)
support_types
=
get_xpu_op_support_types
(
'pad3d'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestPad3dOp
,
stype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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