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体验新版 GitCode,发现更多精彩内容 >>
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ad349e77
编写于
11月 21, 2018
作者:
Z
Zhaolong Xing
提交者:
GitHub
11月 21, 2018
浏览文件
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差异文件
Merge pull request #14452 from NHZlX/fix_avg_pool_trt_bug
fix avg pool trt bug
上级
1d9b2a45
e62872df
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
324 addition
and
61 deletion
+324
-61
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+1
-1
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
+89
-53
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
+9
-7
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
+1
-0
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
+64
-0
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
+111
-0
paddle/fluid/operators/math/pooling.cu
paddle/fluid/operators/math/pooling.cu
+36
-0
paddle/fluid/operators/math/pooling.h
paddle/fluid/operators/math/pooling.h
+13
-0
未找到文件。
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
ad349e77
...
...
@@ -18,7 +18,7 @@ nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
nv_test
(
test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine conv_op conv_transpose_op SERIAL
)
nv_test
(
test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine pool_op SERIAL
)
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine pool_op
tensorrt_plugin
SERIAL
)
nv_test
(
test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine tensorrt_plugin
elementwise_add_op elementwise_mul_op SERIAL
)
...
...
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @
ad349e77
...
...
@@ -13,25 +13,57 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
void
DealCeilMode
(
const
nvinfer1
::
Dims
&
input_shape
,
std
::
vector
<
int
>
ksize
,
std
::
vector
<
int
>
strides
,
std
::
vector
<
int
>
paddings
,
nvinfer1
::
DimsHW
*
pre_pad
,
nvinfer1
::
DimsHW
*
post_pad
,
int
input_dims
)
{
int
input_height
=
input_shape
.
d
[
input_dims
-
2
];
int
input_width
=
input_shape
.
d
[
input_dims
-
1
];
int
floor_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
;
int
ceil_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
;
int
floor_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
;
int
ceil_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
;
if
(
floor_h_output_size
!=
ceil_h_output_size
)
{
post_pad
->
h
()
=
strides
[
0
]
-
1
;
}
if
(
floor_w_output_size
!=
ceil_w_output_size
)
{
post_pad
->
w
()
=
strides
[
1
]
-
1
;
}
}
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class
Pool2dOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
3
)
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
40
)
<<
"convert a fluid pool2d op to tensorrt pool2d layer without bias"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"X"
).
size
(),
1
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Output
(
"Out"
).
size
(),
1
);
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
nvinfer1
::
Dims
input_shape
=
input1
->
getDimensions
();
int
input_dims
=
input_shape
.
nbDims
;
PADDLE_ENFORCE_EQ
(
input_dims
,
3UL
);
bool
global_pooling
=
boost
::
get
<
bool
>
(
op_desc
.
GetAttr
(
"global_pooling"
));
std
::
string
pool_type
=
...
...
@@ -44,23 +76,6 @@ class Pool2dOpConverter : public OpConverter {
boost
::
get
<
std
::
vector
<
int
>>
(
op_desc
.
GetAttr
(
"paddings"
));
bool
ceil_mode
=
boost
::
get
<
bool
>
(
op_desc
.
GetAttr
(
"ceil_mode"
));
nvinfer1
::
Dims
input_shape
=
input1
->
getDimensions
();
int
nbDims
=
input_shape
.
nbDims
;
nvinfer1
::
DimsHW
nv_ksize
(
ksize
[
0
],
ksize
[
1
]);
nvinfer1
::
DimsHW
nv_strides
(
strides
[
0
],
strides
[
1
]);
nvinfer1
::
DimsHW
nv_paddings
(
paddings
[
0
],
paddings
[
1
]);
if
(
global_pooling
==
true
)
{
nv_ksize
.
d
[
0
]
=
input_shape
.
d
[
nbDims
-
2
];
nv_ksize
.
d
[
1
]
=
input_shape
.
d
[
nbDims
-
1
];
nv_strides
.
h
()
=
1
;
nv_strides
.
w
()
=
1
;
nv_paddings
.
h
()
=
0
;
nv_paddings
.
w
()
=
0
;
}
PADDLE_ENFORCE_EQ
(
input1
->
getDimensions
().
nbDims
,
3UL
);
nvinfer1
::
PoolingType
nv_pool_type
=
nvinfer1
::
PoolingType
::
kMAX
;
if
(
pool_type
==
"max"
)
{
nv_pool_type
=
nvinfer1
::
PoolingType
::
kMAX
;
...
...
@@ -70,42 +85,63 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW
(
"TensorRT unsupported pooling type!"
);
}
if
(
ceil_mode
)
{
nvinfer1
::
DimsHW
pre_pad
(
0
,
0
);
nvinfer1
::
DimsHW
post_pad
(
0
,
0
);
int
input_height
=
input_shape
.
d
[
nbDims
-
2
];
int
input_width
=
input_shape
.
d
[
nbDims
-
1
];
int
floor_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
;
int
ceil_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
;
int
floor_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
;
int
ceil_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
;
if
(
floor_h_output_size
!=
ceil_h_output_size
)
{
post_pad
.
h
()
=
strides
[
0
]
-
1
;
nvinfer1
::
DimsHW
nv_ksize
(
ksize
[
0
],
ksize
[
1
]);
nvinfer1
::
DimsHW
nv_strides
(
strides
[
0
],
strides
[
1
]);
nvinfer1
::
DimsHW
nv_paddings
(
paddings
[
0
],
paddings
[
1
]);
nvinfer1
::
ILayer
*
layer
=
nullptr
;
if
(
global_pooling
==
true
)
{
nv_ksize
.
d
[
0
]
=
input_shape
.
d
[
input_dims
-
2
];
nv_ksize
.
d
[
1
]
=
input_shape
.
d
[
input_dims
-
1
];
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
layer
,
"pool layer could not be created."
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
layer
->
setName
((
"pool2d (Output: "
+
output_name
+
")"
).
c_str
());
layer
->
getOutput
(
0
)
->
setName
(
output_name
.
c_str
());
engine_
->
SetITensor
(
output_name
,
layer
->
getOutput
(
0
));
if
(
test_mode
)
{
engine_
->
DeclareOutput
(
output_name
);
}
return
;
}
if
(
floor_w_output_size
!=
ceil_w_output_size
)
{
post_pad
.
w
()
=
strides
[
1
]
-
1
;
if
(
pool_type
==
"max"
)
{
nvinfer1
::
DimsHW
pre_pad
(
paddings
[
0
],
paddings
[
1
]);
nvinfer1
::
DimsHW
post_pad
(
paddings
[
0
],
paddings
[
1
]);
if
(
ceil_mode
)
{
// If ceil mode is true, we will pad the appropriate size to the input.
DealCeilMode
(
input_shape
,
ksize
,
strides
,
paddings
,
&
pre_pad
,
&
post_pad
,
input_dims
);
auto
*
pad_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Padding
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
pre_pad
,
post_pad
);
PADDLE_ENFORCE_NOT_NULL
(
pad_layer
,
"pad layer in poolOp converter could not be created."
);
input1
=
pad_layer
->
getOutput
(
0
);
}
auto
*
pool_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
pool_layer
,
"pool layer could not be created."
);
pool_layer
->
setStride
(
nv_strides
);
pool_layer
->
setPadding
(
nv_paddings
);
layer
=
pool_layer
;
}
else
{
// Average pooling needs to exclude the padding pixels from the average
// mean.
// It is not supported well by TRT, we use a plugin here.
std
::
vector
<
int
>
input_shape_v
;
for
(
int
i
=
0
;
i
<
input_dims
;
i
++
)
{
input_shape_v
.
push_back
(
input_shape
.
d
[
i
]);
}
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Padding
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
pre_pad
,
post_pad
);
input1
=
layer
->
getOutput
(
0
)
;
plugin
::
AvgPoolPlugin
*
plugin
=
new
plugin
::
AvgPoolPlugin
(
ceil_mode
,
ksize
,
strides
,
paddings
,
input_shape_v
);
auto
*
avg_pool_layer
=
engine_
->
AddPlugin
(
&
input1
,
1
,
plugin
);
layer
=
avg_pool_layer
;
}
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
layer
,
"pool layer could not be created."
);
layer
->
setStride
(
nv_strides
);
layer
->
setPadding
(
nv_paddings
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
layer
->
setName
((
"pool2d (Output: "
+
output_name
+
")"
).
c_str
());
...
...
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @
ad349e77
...
...
@@ -20,20 +20,21 @@ namespace paddle {
namespace
inference
{
namespace
tensorrt
{
void
test_pool2d
(
bool
global_pooling
,
bool
ceil_mode
)
{
void
test_pool2d
(
bool
global_pooling
,
bool
ceil_mode
,
std
::
string
pool_type
=
"max"
)
{
framework
::
Scope
scope
;
std
::
unordered_set
<
std
::
string
>
parameters
;
TRTConvertValidation
validator
(
5
,
parameters
,
scope
,
1
<<
15
);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator
.
DeclInputVar
(
"pool2d-X"
,
nvinfer1
::
Dims3
(
3
,
13
,
14
));
validator
.
DeclInputVar
(
"pool2d-X"
,
nvinfer1
::
Dims3
(
3
,
6
,
7
));
if
(
global_pooling
)
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
1
,
1
));
else
if
(
ceil_mode
)
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
6
,
7
));
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
3
,
4
));
else
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
6
,
6
));
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
3
,
3
));
// Prepare Op description
framework
::
OpDesc
desc
;
...
...
@@ -41,10 +42,10 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
desc
.
SetInput
(
"X"
,
{
"pool2d-X"
});
desc
.
SetOutput
(
"Out"
,
{
"pool2d-Out"
});
std
::
vector
<
int
>
ksize
({
3
,
3
});
std
::
vector
<
int
>
ksize
({
2
,
2
});
std
::
vector
<
int
>
strides
({
2
,
2
});
std
::
vector
<
int
>
paddings
({
0
,
0
});
std
::
string
pooling_t
=
"max"
;
std
::
string
pooling_t
=
pool_type
;
desc
.
SetAttr
(
"pooling_type"
,
pooling_t
);
desc
.
SetAttr
(
"ksize"
,
ksize
);
...
...
@@ -63,7 +64,8 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
TEST
(
Pool2dOpConverter
,
normal
)
{
test_pool2d
(
false
,
false
);
}
TEST
(
Pool2dOpConverter
,
test_global_pooling
)
{
test_pool2d
(
true
,
false
);
}
TEST
(
Pool2dOpConverter
,
test_ceil_mode
)
{
test_pool2d
(
false
,
true
);
}
TEST
(
Pool2dOpConverter
,
max_ceil_test
)
{
test_pool2d
(
false
,
true
);
}
TEST
(
Pool2dOpConverter
,
avg_ceil_test
)
{
test_pool2d
(
false
,
true
,
"avg"
);
}
}
// namespace tensorrt
}
// namespace inference
...
...
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
浏览文件 @
ad349e77
nv_library
(
tensorrt_plugin
SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
avg_pool_op_plugin.cu
DEPS enforce tensorrt_engine
)
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
0 → 100644
浏览文件 @
ad349e77
// 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.
#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
#include "paddle/fluid/operators/math/pooling.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
namespace
plugin
{
nvinfer1
::
Dims
AvgPoolPlugin
::
getOutputDimensions
(
int
index
,
const
nvinfer1
::
Dims
*
inputDims
,
int
nbInputs
)
{
assert
(
nbInputs
==
1
);
assert
(
index
==
0
);
assert
(
inputDims
[
0
].
nbDims
==
3
);
nvinfer1
::
Dims
const
&
input_dims
=
inputDims
[
0
];
nvinfer1
::
Dims
output_dims
=
input_dims
;
output_dims
.
d
[
1
]
=
output_shape_
[
1
];
output_dims
.
d
[
2
]
=
output_shape_
[
2
];
return
output_dims
;
}
int
AvgPoolPlugin
::
enqueue
(
int
batchSize
,
const
void
*
const
*
inputs
,
void
**
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
{
auto
const
&
input_dims
=
this
->
getInputDims
(
0
);
int
input_size
=
0
;
float
const
*
idata
=
reinterpret_cast
<
float
const
*>
(
inputs
[
0
]);
float
**
odatas
=
reinterpret_cast
<
float
**>
(
outputs
);
paddle
::
operators
::
math
::
AvgPool
<
float
>
pool_process
;
paddle
::
operators
::
math
::
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
AvgPool
<
float
>
,
float
>
pool2d_forward
;
std
::
vector
<
int
>
input_shape
=
input_shape_
;
std
::
vector
<
int
>
output_shape
=
output_shape_
;
input_shape
.
insert
(
input_shape
.
begin
(),
batchSize
);
output_shape
.
insert
(
output_shape
.
begin
(),
batchSize
);
pool2d_forward
(
idata
,
input_shape
,
output_shape
,
ksize_
,
strides_
,
paddings_
,
pool_process
,
true
,
odatas
[
0
],
stream
);
return
cudaGetLastError
()
!=
cudaSuccess
;
}
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
0 → 100644
浏览文件 @
ad349e77
// 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.
#pragma once
#include <cassert>
#include <vector>
#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
namespace
plugin
{
class
AvgPoolPlugin
:
public
PluginTensorRT
{
private:
bool
ceil_mode_
;
std
::
vector
<
int
>
ksize_
;
std
::
vector
<
int
>
strides_
;
std
::
vector
<
int
>
paddings_
;
std
::
vector
<
int
>
input_shape_
;
std
::
vector
<
int
>
output_shape_
;
protected:
size_t
getSerializationSize
()
override
{
return
SerializedSize
(
ceil_mode_
)
+
SerializedSize
(
ksize_
)
+
SerializedSize
(
strides_
)
+
SerializedSize
(
paddings_
)
+
SerializedSize
(
input_shape_
)
+
getBaseSerializationSize
();
}
// TRT will call this func when we need to serialize the configuration of
// tensorrt.
// It should not be called by users.
void
serialize
(
void
*
buffer
)
override
{
serializeBase
(
buffer
);
SerializeValue
(
&
buffer
,
ceil_mode_
);
SerializeValue
(
&
buffer
,
ksize_
);
SerializeValue
(
&
buffer
,
strides_
);
SerializeValue
(
&
buffer
,
paddings_
);
SerializeValue
(
&
buffer
,
input_shape_
);
}
public:
AvgPoolPlugin
(
bool
ceil_mode
,
std
::
vector
<
int
>
ksize
,
std
::
vector
<
int
>
strides
,
std
::
vector
<
int
>
paddings
,
std
::
vector
<
int
>
input_shape
)
:
ceil_mode_
(
ceil_mode
),
ksize_
(
ksize
),
strides_
(
strides
),
paddings_
(
paddings
),
input_shape_
(
input_shape
)
{
int
output_h
,
output_w
;
output_shape_
=
input_shape_
;
if
(
!
ceil_mode_
)
{
output_h
=
(
input_shape
[
1
]
-
ksize_
[
0
]
+
2
*
paddings_
[
0
])
/
strides_
[
0
]
+
1
;
output_w
=
(
input_shape
[
2
]
-
ksize_
[
1
]
+
2
*
paddings_
[
1
])
/
strides_
[
1
]
+
1
;
}
else
{
output_h
=
(
input_shape
[
1
]
-
ksize_
[
0
]
+
2
*
paddings_
[
0
]
+
strides_
[
0
]
-
1
)
/
strides_
[
0
]
+
1
;
output_w
=
(
input_shape
[
2
]
-
ksize_
[
1
]
+
2
*
paddings_
[
1
]
+
strides_
[
1
]
-
1
)
/
strides_
[
1
]
+
1
;
}
output_shape_
[
1
]
=
output_h
;
output_shape_
[
2
]
=
output_w
;
}
// It was used for tensorrt deserialization.
// It should not be called by users.
AvgPoolPlugin
(
void
const
*
serialData
,
size_t
serialLength
)
{
deserializeBase
(
serialData
,
serialLength
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
ceil_mode_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
ksize_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
strides_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
paddings_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
input_shape_
);
}
AvgPoolPlugin
*
clone
()
const
override
{
return
new
AvgPoolPlugin
(
ceil_mode_
,
ksize_
,
strides_
,
paddings_
,
input_shape_
);
}
const
char
*
getPluginType
()
const
override
{
return
"avg_pool"
;
}
int
getNbOutputs
()
const
override
{
return
1
;
}
nvinfer1
::
Dims
getOutputDimensions
(
int
index
,
const
nvinfer1
::
Dims
*
inputs
,
int
nbInputDims
)
override
;
int
initialize
()
override
{
return
0
;
}
int
enqueue
(
int
batchSize
,
const
void
*
const
*
inputs
,
void
**
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
override
;
};
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
paddle/fluid/operators/math/pooling.cu
浏览文件 @
ad349e77
...
...
@@ -153,6 +153,37 @@ __global__ void KernelMaxPool2DGrad(
}
}
template
<
typename
PoolProcess
,
typename
T
>
void
Pool2dDirectCUDAFunctor
<
PoolProcess
,
T
>::
operator
()(
const
T
*
input
,
const
std
::
vector
<
int
>&
input_shape
,
const
std
::
vector
<
int
>&
output_shape
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
T
*
output
,
cudaStream_t
stream
)
{
const
int
batch_size
=
input_shape
[
0
];
const
int
input_channels
=
input_shape
[
1
];
const
int
input_height
=
input_shape
[
2
];
const
int
input_width
=
input_shape
[
3
];
const
int
output_channels
=
output_shape
[
1
];
const
int
output_height
=
output_shape
[
2
];
const
int
output_width
=
output_shape
[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_width
=
strides
[
1
];
const
int
padding_height
=
paddings
[
0
];
const
int
padding_width
=
paddings
[
1
];
int
nthreads
=
batch_size
*
output_channels
*
output_height
*
output_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelPool2D
<
PoolProcess
,
T
><<<
grid
,
threads
,
0
,
stream
>>>
(
nthreads
,
input
,
input_channels
,
input_height
,
input_width
,
output_height
,
output_width
,
ksize_height
,
ksize_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
pool_compute
,
exclusive
,
output
);
}
/*
* All tensors are in NCHW format.
* Ksize, strides, paddings are two elements. These two elements represent
...
...
@@ -291,6 +322,11 @@ class MaxPool2dGradFunctor<platform::CUDADeviceContext, T> {
}
};
template
class
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
MaxPool
<
float
>,
float
>
;
template
class
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
AvgPool
<
float
>,
float
>
;
template
class
MaxPool2dGradFunctor
<
platform
::
CUDADeviceContext
,
float
>;
template
class
MaxPool2dGradFunctor
<
platform
::
CUDADeviceContext
,
double
>;
...
...
paddle/fluid/operators/math/pooling.h
浏览文件 @
ad349e77
...
...
@@ -82,6 +82,19 @@ class AvgPoolGrad {
* This is different from average pooling. So we rewrite the max_pool_grad:
* MaxPool2dGradFunctor, MaxPool3dGradFunctor.
*/
#ifdef PADDLE_WITH_CUDA
template
<
typename
PoolProcess
,
typename
T
>
class
Pool2dDirectCUDAFunctor
{
public:
void
operator
()(
const
T
*
input
,
const
std
::
vector
<
int
>&
input_shape
,
const
std
::
vector
<
int
>&
output_shape
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
T
*
output
,
cudaStream_t
stream
);
};
#endif
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
class
Pool2dFunctor
{
public:
...
...
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