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体验新版 GitCode,发现更多精彩内容 >>
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提交
54fcafb5
编写于
12月 07, 2018
作者:
T
Tao Luo
提交者:
GitHub
12月 07, 2018
浏览文件
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差异文件
Merge pull request #14707 from yihuaxu/develop_
4f71a6ee
_conv3d_mkldnn_opt
Implement conv3d with mkldnn library
上级
b82a44ea
155328a4
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
207 addition
and
76 deletion
+207
-76
paddle/fluid/operators/conv_mkldnn_op.cc
paddle/fluid/operators/conv_mkldnn_op.cc
+94
-29
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+19
-3
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+12
-0
python/paddle/fluid/tests/unittests/test_conv3d_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_conv3d_mkldnn_op.py
+59
-0
python/paddle/fluid/tests/unittests/test_conv3d_op.py
python/paddle/fluid/tests/unittests/test_conv3d_op.py
+23
-44
未找到文件。
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @
54fcafb5
...
...
@@ -28,6 +28,46 @@ using mkldnn::stream;
using
platform
::
to_void_cast
;
using
platform
::
GetMKLDNNFormat
;
inline
void
GetWeightsTz
(
std
::
vector
<
int
>&
weights_tz
,
int
groups
,
// NOLINT
bool
is_conv3d
)
{
if
(
groups
>
1
)
{
if
(
is_conv3d
)
{
int
output
=
weights_tz
[
0
];
int
input
=
weights_tz
[
1
];
int
dimension
=
weights_tz
[
2
];
int
height
=
weights_tz
[
3
];
int
width
=
weights_tz
[
4
];
weights_tz
.
resize
(
6
);
weights_tz
[
0
]
=
groups
;
weights_tz
[
1
]
=
output
/
groups
;
weights_tz
[
2
]
=
input
;
weights_tz
[
3
]
=
dimension
;
weights_tz
[
4
]
=
height
;
weights_tz
[
5
]
=
width
;
}
else
{
int
output
=
weights_tz
[
0
];
int
input
=
weights_tz
[
1
];
int
height
=
weights_tz
[
2
];
int
width
=
weights_tz
[
3
];
weights_tz
.
resize
(
5
);
weights_tz
[
0
]
=
groups
;
weights_tz
[
1
]
=
output
/
groups
;
weights_tz
[
2
]
=
input
;
weights_tz
[
3
]
=
height
;
weights_tz
[
4
]
=
width
;
}
}
}
inline
mkldnn
::
memory
::
format
GetWeightsFormat
(
mkldnn
::
memory
::
format
format
,
int
groups
,
bool
is_conv3d
)
{
if
(
is_conv3d
)
{
return
(
groups
==
1
)
?
format
:
mkldnn
::
memory
::
format
::
goidhw
;
}
else
{
return
(
groups
==
1
)
?
format
:
mkldnn
::
memory
::
format
::
goihw
;
}
}
template
<
typename
T
>
class
ConvMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -52,10 +92,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
PADDLE_ENFORCE
(
filter
->
layout
()
==
DataLayout
::
kMKLDNN
&&
filter
->
format
()
!=
memory
::
format
::
format_undef
,
"Wrong layout/format set for Filter tensor"
);
PADDLE_ENFORCE
(
input
->
dims
().
size
()
==
4
,
"Input must be with 4
dimensions, i.e. NC
HW"
);
PADDLE_ENFORCE
(
filter
->
dims
().
size
()
==
4
,
"Filter must be with 4
dimensions, i.e. OI
HW"
);
PADDLE_ENFORCE
(
input
->
dims
().
size
()
==
4
||
input
->
dims
().
size
()
==
5
,
"Input must be with 4
or 5 dimensions, i.e. NCHW or NCD
HW"
);
PADDLE_ENFORCE
(
filter
->
dims
().
size
()
==
4
||
filter
->
dims
().
size
()
==
5
,
"Filter must be with 4
or 5 dimensions, i.e. OIHW or OID
HW"
);
if
(
bias
)
{
PADDLE_ENFORCE
(
bias
->
layout
()
==
DataLayout
::
kMKLDNN
&&
bias
->
format
()
!=
memory
::
format
::
format_undef
,
...
...
@@ -71,9 +111,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bool
fuse_residual_conn
=
ctx
.
Attr
<
bool
>
(
"fuse_residual_connection"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
bool
is_conv3d
=
strides
.
size
()
==
3U
;
// TODO(tpatejko): add support for dilation
PADDLE_ENFORCE
(
dilations
.
size
()
==
2
&&
dilations
[
0
]
==
1
&&
dilations
[
1
]
==
1
,
is_conv3d
?
dilations
.
size
()
==
3
&&
dilations
[
0
]
==
1
&&
dilations
[
1
]
==
1
&&
dilations
[
2
]
==
1
:
dilations
.
size
()
==
2
&&
dilations
[
0
]
==
1
&&
dilations
[
1
]
==
1
,
"dilation in convolution is not implemented yet"
);
const
T
*
input_data
=
input
->
data
<
T
>
();
...
...
@@ -83,18 +127,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
weights_tz
=
paddle
::
framework
::
vectorize2int
(
filter
->
dims
());
int
g
=
std
::
max
(
groups
,
1
);
if
(
g
>
1
)
{
int
o
=
weights_tz
[
0
];
int
i
=
weights_tz
[
1
];
int
h
=
weights_tz
[
2
];
int
w
=
weights_tz
[
3
];
weights_tz
.
resize
(
5
);
weights_tz
[
0
]
=
g
;
weights_tz
[
1
]
=
o
/
g
;
weights_tz
[
2
]
=
i
;
weights_tz
[
3
]
=
h
;
weights_tz
[
4
]
=
w
;
}
GetWeightsTz
(
weights_tz
,
g
,
is_conv3d
);
std
::
vector
<
int
>
dst_tz
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
// Get unique name for storing MKLDNN primitives
...
...
@@ -105,11 +138,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
primitive
>
pipeline
;
auto
src_format
=
input
->
format
();
mkldnn
::
memory
::
format
weights_format
=
GetWeightsFormat
(
filter
->
format
(),
g
,
is_conv3d
);
auto
user_src_md
=
platform
::
MKLDNNMemDesc
(
{
src_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
input
->
format
()
);
{
src_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
src_format
);
auto
user_weights_md
=
platform
::
MKLDNNMemDesc
(
{
weights_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
(
g
==
1
)
?
filter
->
format
()
:
mkldnn
::
memory
::
format
::
goihw
);
{
weights_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
weights_format
);
/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
...
...
@@ -119,10 +155,16 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto
chosen_memory_format
=
platform
::
data_format_to_memory_format
(
data_format
);
if
(
is_conv3d
)
{
chosen_memory_format
=
platform
::
MKLDNNFormatForSize
(
src_tz
.
size
(),
chosen_memory_format
);
}
weights_format
=
GetWeightsFormat
(
chosen_memory_format
,
g
,
is_conv3d
);
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory_format
);
auto
weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory
_format
);
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
weights
_format
);
std
::
vector
<
int
>
bias_tz
;
// TODO(mgallus): avoid empty vector creation.
// Currently used whenever bias is != nullptr.
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
...
...
@@ -263,8 +305,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const
mkldnn
::
engine
&
engine
,
const
bool
fuse_relu
,
const
bool
fuse_residual_conn
,
mkldnn
::
prop_kind
fwd_prop_kind
)
const
{
memory
::
dims
stride_dims
=
{
strides
[
0
],
strides
[
1
]}
;
memory
::
dims
padding_dims
=
{
paddings
[
0
],
paddings
[
1
]}
;
memory
::
dims
stride_dims
=
strides
;
memory
::
dims
padding_dims
=
paddings
;
auto
conv_desc
=
mkldnn
::
convolution_forward
::
desc
(
fwd_prop_kind
,
mkldnn
::
convolution_direct
,
src
,
weights
,
dst
,
...
...
@@ -288,8 +330,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const
mkldnn
::
engine
&
engine
,
const
bool
fuse_relu
,
const
bool
fuse_residual_conn
,
mkldnn
::
prop_kind
fwd_prop_kind
)
const
{
memory
::
dims
stride_dims
=
{
strides
[
0
],
strides
[
1
]}
;
memory
::
dims
padding_dims
=
{
paddings
[
0
],
paddings
[
1
]}
;
memory
::
dims
stride_dims
=
strides
;
memory
::
dims
padding_dims
=
paddings
;
auto
conv_desc
=
mkldnn
::
convolution_forward
::
desc
(
fwd_prop_kind
,
mkldnn
::
convolution_direct
,
src
,
weights
,
bias
,
dst
,
...
...
@@ -349,6 +391,7 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
bool
is_conv3d
=
strides
.
size
()
==
3U
;
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
filter_data
=
filter
->
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
...
...
@@ -358,8 +401,14 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
src_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
std
::
vector
<
int
>
weights_tz
=
paddle
::
framework
::
vectorize2int
(
filter
->
dims
());
int
g
=
std
::
max
(
groups
,
1
);
GetWeightsTz
(
weights_tz
,
g
,
is_conv3d
);
std
::
vector
<
int
>
dst_tz
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
auto
src_format
=
input
->
format
();
mkldnn
::
memory
::
format
weights_format
=
GetWeightsFormat
(
filter
->
format
(),
g
,
is_conv3d
);
// Get an unique name from "argument" name of "Output" variable
// as well as attributes of primitive to be created
// This name will be used as key when saving info into device context
...
...
@@ -372,9 +421,9 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
// Create user memory descriptors
auto
user_src_md
=
platform
::
MKLDNNMemDesc
(
{
src_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
input
->
format
()
);
{
src_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
src_format
);
auto
user_weights_md
=
platform
::
MKLDNNMemDesc
(
{
weights_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
filter
->
format
()
);
{
weights_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
weights_format
);
auto
user_diff_dst_md
=
platform
::
MKLDNNMemDesc
(
{
dst_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
output_grad
->
format
());
...
...
@@ -386,14 +435,20 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto
chosen_memory_format
=
platform
::
data_format_to_memory_format
(
data_format
);
if
(
is_conv3d
)
{
chosen_memory_format
=
platform
::
MKLDNNFormatForSize
(
src_tz
.
size
(),
chosen_memory_format
);
}
weights_format
=
GetWeightsFormat
(
chosen_memory_format
,
g
,
is_conv3d
);
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory_format
);
auto
diff_src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory_format
);
auto
weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory
_format
);
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
weights
_format
);
auto
diff_weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory
_format
);
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
weights
_format
);
auto
diff_dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory_format
);
...
...
@@ -500,3 +555,13 @@ REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN,
::
paddle
::
platform
::
CPUPlace
,
FP32
,
ops
::
kConvMKLDNNFP32
,
ops
::
ConvMKLDNNGradOpKernel
<
float
>
);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE
(
conv3d
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
FP32
,
ops
::
kConvMKLDNNFP32
,
ops
::
ConvMKLDNNOpKernel
<
float
>
);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE
(
conv3d_grad
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
FP32
,
ops
::
kConvMKLDNNFP32
,
ops
::
ConvMKLDNNGradOpKernel
<
float
>
);
paddle/fluid/operators/conv_op.cc
浏览文件 @
54fcafb5
...
...
@@ -134,14 +134,14 @@ void Conv2DOpMaker::Make() {
"The format of output tensor is X (one-dimensional) of size equal"
"to the number of output channels. Only used with MKL-DNN."
)
.
AsDispensable
();
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW."
);
AddInput
(
"ResidualData"
,
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
"Used with fuse_residual_connection fusion."
)
.
AsDispensable
();
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
...
...
@@ -232,6 +232,10 @@ $$
}
void
Conv3DOpMaker
::
Make
()
{
AddAttr
<
bool
>
(
"is_test"
,
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true."
)
.
SetDefault
(
false
);
AddInput
(
"Input"
,
"(Tensor) The input tensor of convolution operator. "
...
...
@@ -247,6 +251,11 @@ void Conv3DOpMaker::Make() {
"is the width of the filter."
"If the groups attribute is greater than 1, C equals the number of "
"input image channels divided by the groups."
);
AddInput
(
"ResidualData"
,
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
"Used with fuse_residual_connection fusion."
)
.
AsDispensable
();
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW."
);
...
...
@@ -280,6 +289,13 @@ void Conv3DOpMaker::Make() {
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"fuse_relu"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"fuse_residual_connection"
,
"(bool, default false) Only used in mkldnn kernel. Used "
"whenever convolution output is as an input to residual "
"connection."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"data_format"
,
"(string, default NCHW) Only used in "
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
54fcafb5
...
...
@@ -113,6 +113,18 @@ inline mkldnn::memory::format MKLDNNFormatForSize(
return
mkldnn
::
memory
::
format
::
x
;
}
else
if
(
dims_size
==
2
)
{
return
mkldnn
::
memory
::
format
::
nc
;
}
else
if
(
dims_size
==
3
)
{
if
(
data_format
==
mkldnn
::
memory
::
format
::
nchw
)
{
return
mkldnn
::
memory
::
format
::
ncw
;
}
else
if
(
data_format
==
mkldnn
::
memory
::
format
::
nhwc
)
{
return
mkldnn
::
memory
::
format
::
nwc
;
}
}
else
if
(
dims_size
==
5
)
{
if
(
data_format
==
mkldnn
::
memory
::
format
::
nchw
)
{
return
mkldnn
::
memory
::
format
::
ncdhw
;
}
else
if
(
data_format
==
mkldnn
::
memory
::
format
::
nhwc
)
{
return
mkldnn
::
memory
::
format
::
ndhwc
;
}
}
return
data_format
;
}
...
...
python/paddle/fluid/tests/unittests/test_conv3d_mkldnn_op.py
0 → 100644
浏览文件 @
54fcafb5
# Copyright (c) 2018 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
from
test_conv3d_op
import
TestConv3dOp
,
TestCase1
,
TestWithGroup1
,
TestWithGroup2
,
TestWith1x1
,
TestWithInput1x1Filter1x1
class
TestMKLDNN
(
TestConv3dOp
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
class
TestMKLDNNCase1
(
TestCase1
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
class
TestMKLDNNGroup1
(
TestWithGroup1
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
class
TestMKLDNNGroup2
(
TestWithGroup2
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
class
TestMKLDNNWith1x1
(
TestWith1x1
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
class
TestMKLDNNWithInput1x1Filter1x1
(
TestWithInput1x1Filter1x1
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
data_format
=
"NCHW"
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_conv3d_op.py
浏览文件 @
54fcafb5
...
...
@@ -74,6 +74,8 @@ class TestConv3dOp(OpTest):
def
setUp
(
self
):
self
.
op_type
=
"conv3d"
self
.
use_cudnn
=
False
self
.
use_mkldnn
=
False
self
.
data_format
=
"AnyLayout"
self
.
dtype
=
np
.
float32
self
.
init_kernel_type
()
self
.
init_group
()
...
...
@@ -83,8 +85,7 @@ class TestConv3dOp(OpTest):
conv3d_param
=
{
'stride'
:
self
.
stride
,
'pad'
:
self
.
pad
,
'dilations'
:
self
.
dilations
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
'dilations'
:
self
.
dilations
}
input
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
self
.
dtype
)
...
...
@@ -101,7 +102,9 @@ class TestConv3dOp(OpTest):
'paddings'
:
self
.
pad
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
'use_cudnn'
:
self
.
use_cudnn
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'data_format'
:
self
.
data_format
}
self
.
outputs
=
{
'Output'
:
output
}
...
...
@@ -109,59 +112,35 @@ class TestConv3dOp(OpTest):
return
core
.
is_compiled_with_cuda
()
and
self
.
use_cudnn
def
test_check_output
(
self
):
if
self
.
testcudnn
():
place
=
core
.
CUDAPlace
(
0
)
self
.
check_output_with_place
(
place
,
atol
=
1e-5
)
else
:
self
.
check_output
()
place
=
core
.
CUDAPlace
(
0
)
if
self
.
testcudnn
()
else
core
.
CPUPlace
()
self
.
check_output_with_place
(
place
,
atol
=
1e-5
)
def
test_check_grad
(
self
):
if
self
.
dtype
==
np
.
float16
:
return
if
self
.
testcudnn
():
place
=
core
.
CUDAPlace
(
0
)
self
.
check_grad_with_place
(
place
,
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.03
)
else
:
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.03
)
place
=
core
.
CUDAPlace
(
0
)
if
self
.
testcudnn
()
else
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
{
'Input'
,
'Filter'
},
'Output'
,
max_relative_error
=
0.03
)
def
test_check_grad_no_filter
(
self
):
if
self
.
dtype
==
np
.
float16
:
return
if
self
.
testcudnn
():
place
=
core
.
CUDAPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
'Input'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Filter'
]))
else
:
self
.
check_grad
(
[
'Input'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Filter'
]))
place
=
core
.
CUDAPlace
(
0
)
if
self
.
testcudnn
()
else
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
[
'Input'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_no_input
(
self
):
if
self
.
dtype
==
np
.
float16
:
return
if
self
.
testcudnn
():
place
=
core
.
CUDAPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
'Filter'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Input'
]))
else
:
self
.
check_grad
(
[
'Filter'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Input'
]))
place
=
core
.
CUDAPlace
(
0
)
if
self
.
testcudnn
()
else
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
[
'Input'
],
'Output'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Input'
]))
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
,
0
]
...
...
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