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体验新版 GitCode,发现更多精彩内容 >>
提交
fafd3e0f
编写于
11月 20, 2017
作者:
C
caoying03
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into softsign
上级
dffa8fab
134eaf21
变更
21
隐藏空白更改
内联
并排
Showing
21 changed file
with
745 addition
and
190 deletion
+745
-190
paddle/operators/conv_cudnn_op.cc
paddle/operators/conv_cudnn_op.cc
+4
-3
paddle/operators/conv_cudnn_op.cu.cc
paddle/operators/conv_cudnn_op.cu.cc
+4
-2
paddle/operators/conv_transpose_cudnn_op.cc
paddle/operators/conv_transpose_cudnn_op.cc
+8
-4
paddle/operators/conv_transpose_cudnn_op.cu.cc
paddle/operators/conv_transpose_cudnn_op.cu.cc
+8
-4
paddle/operators/pool_cudnn_op.cc
paddle/operators/pool_cudnn_op.cc
+14
-2
paddle/operators/pool_cudnn_op.cu.cc
paddle/operators/pool_cudnn_op.cu.cc
+23
-4
paddle/operators/pool_op.cc
paddle/operators/pool_op.cc
+8
-4
paddle/operators/pool_op.cu.cc
paddle/operators/pool_op.cu.cc
+8
-4
paddle/operators/pool_with_index_op.cc
paddle/operators/pool_with_index_op.cc
+8
-4
paddle/operators/pool_with_index_op.cu.cc
paddle/operators/pool_with_index_op.cu.cc
+8
-4
paddle/operators/sequence_slice_op.cc
paddle/operators/sequence_slice_op.cc
+132
-0
paddle/operators/sequence_slice_op.cu
paddle/operators/sequence_slice_op.cu
+23
-0
paddle/operators/sequence_slice_op.h
paddle/operators/sequence_slice_op.h
+173
-0
paddle/platform/cudnn_helper.h
paddle/platform/cudnn_helper.h
+4
-2
paddle/platform/cudnn_helper_test.cc
paddle/platform/cudnn_helper_test.cc
+34
-0
python/paddle/trainer_config_helpers/activations.py
python/paddle/trainer_config_helpers/activations.py
+1
-1
python/paddle/trainer_config_helpers/networks.py
python/paddle/trainer_config_helpers/networks.py
+134
-8
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+0
-1
python/paddle/v2/fluid/tests/test_pool2d_op.py
python/paddle/v2/fluid/tests/test_pool2d_op.py
+26
-108
python/paddle/v2/fluid/tests/test_pool3d_op.py
python/paddle/v2/fluid/tests/test_pool3d_op.py
+78
-35
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
+47
-0
未找到文件。
paddle/operators/conv_cudnn_op.cc
浏览文件 @
fafd3e0f
...
...
@@ -40,7 +40,8 @@ REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops
::
ConvOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv_cudnn
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
conv_cudnn_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
conv_cudnn_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @
fafd3e0f
...
...
@@ -259,6 +259,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
}
// namespace operators
}
// namespace paddle
REGISTER_OP_GPU_KERNEL
(
conv_cudnn
,
paddle
::
operators
::
CudnnConvOpKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv_cudnn
,
paddle
::
operators
::
CudnnConvOpKernel
<
float
>
,
paddle
::
operators
::
CudnnConvOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
conv_cudnn_grad
,
paddle
::
operators
::
CudnnConvGradOpKernel
<
float
>
);
paddle
::
operators
::
CudnnConvGradOpKernel
<
float
>
,
paddle
::
operators
::
CudnnConvGradOpKernel
<
double
>
);
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @
fafd3e0f
...
...
@@ -61,10 +61,12 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL
(
conv2d_transpose_cudnn
,
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
conv2d_transpose_cudnn_grad
,
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP
(
conv3d_transpose_cudnn
,
ops
::
ConvTransposeOp
,
ops
::
CudnnConv3DTransposeOpMaker
,
conv3d_transpose_cudnn_grad
,
...
...
@@ -72,7 +74,9 @@ REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL
(
conv3d_transpose_cudnn
,
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
conv3d_transpose_cudnn_grad
,
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
GemmConvTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @
fafd3e0f
...
...
@@ -235,11 +235,15 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2d_transpose_cudnn
,
ops
::
CudnnConvTransposeOpKernel
<
float
>
);
ops
::
CudnnConvTransposeOpKernel
<
float
>
,
ops
::
CudnnConvTransposeOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
conv2d_transpose_cudnn_grad
,
ops
::
CudnnConvTransposeGradOpKernel
<
float
>
);
ops
::
CudnnConvTransposeGradOpKernel
<
float
>
,
ops
::
CudnnConvTransposeGradOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
conv3d_transpose_cudnn
,
ops
::
CudnnConvTransposeOpKernel
<
float
>
);
ops
::
CudnnConvTransposeOpKernel
<
float
>
,
ops
::
CudnnConvTransposeOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
conv3d_transpose_cudnn_grad
,
ops
::
CudnnConvTransposeGradOpKernel
<
float
>
);
ops
::
CudnnConvTransposeGradOpKernel
<
float
>
,
ops
::
CudnnConvTransposeGradOpKernel
<
double
>
);
paddle/operators/pool_cudnn_op.cc
浏览文件 @
fafd3e0f
...
...
@@ -20,6 +20,18 @@ REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad,
ops
::
PoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pool2d_cudnn
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
pool2d_cudnn_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
)
REGISTER_OP
(
pool3d_cudnn
,
ops
::
PoolOp
,
ops
::
Pool3dOpMaker
,
pool3d_cudnn_grad
,
ops
::
PoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pool3d_cudnn
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
pool3d_cudnn_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
)
paddle/operators/pool_cudnn_op.cu.cc
浏览文件 @
fafd3e0f
...
...
@@ -52,7 +52,13 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedPoolingDescriptor
pool_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
DataLayout
layout
;
if
(
strides
.
size
()
==
2U
)
{
layout
=
DataLayout
::
kNCHW
;
}
else
{
layout
=
DataLayout
::
kNCDHW
;
}
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
...
...
@@ -112,7 +118,13 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedPoolingDescriptor
pool_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
DataLayout
layout
;
if
(
strides
.
size
()
==
2U
)
{
layout
=
DataLayout
::
kNCHW
;
}
else
{
layout
=
DataLayout
::
kNCDHW
;
}
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
...
...
@@ -150,5 +162,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn
,
ops
::
PoolCudnnOpKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn_grad
,
ops
::
PoolCudnnGradOpKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn
,
ops
::
PoolCudnnOpKernel
<
float
>
,
ops
::
PoolCudnnOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn_grad
,
ops
::
PoolCudnnGradOpKernel
<
float
>
,
ops
::
PoolCudnnGradOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool3d_cudnn
,
ops
::
PoolCudnnOpKernel
<
float
>
,
ops
::
PoolCudnnOpKernel
<
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool3d_cudnn_grad
,
ops
::
PoolCudnnGradOpKernel
<
float
>
,
ops
::
PoolCudnnGradOpKernel
<
double
>
);
paddle/operators/pool_op.cc
浏览文件 @
fafd3e0f
...
...
@@ -217,14 +217,18 @@ REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
ops
::
PoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pool2d
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
pool2d_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
)
REGISTER_OP
(
pool3d
,
ops
::
PoolOp
,
ops
::
Pool3dOpMaker
,
pool3d_grad
,
ops
::
PoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pool3d
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
pool3d_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/pool_op.cu.cc
浏览文件 @
fafd3e0f
...
...
@@ -17,11 +17,15 @@ limitations under the License. */
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
pool2d
,
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool2d_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool3d
,
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
PoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
pool3d_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
paddle/operators/pool_with_index_op.cc
浏览文件 @
fafd3e0f
...
...
@@ -250,10 +250,12 @@ REGISTER_OP(max_pool2d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL
(
max_pool2d_with_index
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
max_pool2d_with_index_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
)
REGISTER_OP
(
max_pool3d_with_index
,
ops
::
MaxPoolWithIndexOp
,
ops
::
MaxPool3dWithIndexOpMaker
,
max_pool3d_with_index_grad
,
...
...
@@ -261,7 +263,9 @@ REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL
(
max_pool3d_with_index
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
max_pool3d_with_index_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
)
paddle/operators/pool_with_index_op.cu.cc
浏览文件 @
fafd3e0f
...
...
@@ -18,14 +18,18 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL
(
max_pool2d_with_index
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
max_pool2d_with_index_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
)
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
)
REGISTER_OP_GPU_KERNEL
(
max_pool3d_with_index
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
max_pool3d_with_index_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
)
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
)
paddle/operators/sequence_slice_op.cc
0 → 100755
浏览文件 @
fafd3e0f
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/sequence_slice_op.h"
namespace
paddle
{
namespace
operators
{
class
SequenceSliceOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Offset"
),
"Input(Offset) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Length"
),
"Input(Length) of SequenceSliceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceSliceOp should not be null."
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
offset_dim
=
ctx
->
GetInputDim
(
"Offset"
);
auto
length_dim
=
ctx
->
GetInputDim
(
"Length"
);
PADDLE_ENFORCE_EQ
(
offset_dim
.
size
(),
2UL
,
"Only support one level sequence now, The rank of offset must be 2."
);
PADDLE_ENFORCE_EQ
(
length_dim
.
size
(),
2UL
,
"Only support one level sequence now, The rank of Length must be 2."
);
// Initialize the output's dims to maximum,
// and re-set to real dims by the value of Offset and Length at kernel
ctx
->
SetOutputDim
(
"Out"
,
input_dims
);
}
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
SequenceSliceGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"The gradient of Out should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
"X"
)),
"The gradient of X should not be null."
);
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputsDim
(
"X"
));
}
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
SequenceSliceOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceSliceOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor), "
"the input of SequenceSliceOp."
);
AddInput
(
"Offset"
,
"(Tensor), "
"a vector<int> to describe the offset of every input sequence for "
"sub sequence item."
);
AddInput
(
"Length"
,
"(Tensor), "
"a vector<int> to describe the length of every input sequence for "
"sub sequence item."
);
AddOutput
(
"Out"
,
"(LoDTensor), the output of SequenceSliceOp."
);
AddComment
(
R"DOC(
Sequence slice operator
The operator crops a subsequence from given sequence with given start offset and subsequence length.
It only supports sequence (LoD Tensor with level number is 1).
- Case:
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2;
e1, e2]]
LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
Offset = [[0], [1]]; Length = [[2], [1]]
Out = [[a1, a2;
b1, b2]
[e1, e2]]
LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sequence_slice
,
ops
::
SequenceSliceOp
,
ops
::
SequenceSliceOpMaker
,
sequence_slice_grad
,
ops
::
SequenceSliceGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_slice
,
ops
::
SequenceSliceOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_slice_grad
,
ops
::
SequenceSliceGradOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sequence_slice_op.cu
0 → 100755
浏览文件 @
fafd3e0f
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/sequence_slice_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sequence_slice
,
ops
::
SequenceSliceOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sequence_slice_grad
,
ops
::
SequenceSliceGradOpKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sequence_slice_op.h
0 → 100755
浏览文件 @
fafd3e0f
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/strided_memcpy.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
using
LoD
=
framework
::
LoD
;
template
<
typename
T
>
inline
LoD
SequenceSliceLoD
(
const
T
&
in
,
const
int64_t
*
offset_data
,
const
int64_t
*
length_data
)
{
auto
out_lod
=
in
.
lod
();
size_t
lod_offset
=
0
;
auto
n
=
in
.
lod
()[
0
].
size
()
-
1
;
out_lod
[
0
][
0
]
=
0
;
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
lod_offset
+=
length_data
[
i
];
out_lod
[
0
][
i
+
1
]
=
lod_offset
;
}
return
out_lod
;
}
template
<
typename
Place
,
typename
T
>
class
SequenceSliceOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
offset
=
ctx
.
Input
<
Tensor
>
(
"Offset"
);
auto
*
length
=
ctx
.
Input
<
Tensor
>
(
"Length"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
lod
=
in
->
lod
();
auto
n
=
lod
[
0
].
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
PADDLE_ENFORCE_EQ
(
n
,
static_cast
<
size_t
>
(
length
->
dims
()[
0
]),
"The size of input-sequence and length-array should be the same"
)
PADDLE_ENFORCE_EQ
(
n
,
static_cast
<
size_t
>
(
offset
->
dims
()[
0
]),
"The size of input-sequence and offset-array should be the same"
)
const
int64_t
*
offset_data
=
offset
->
data
<
int64_t
>
();
const
int64_t
*
length_data
=
length
->
data
<
int64_t
>
();
framework
::
Tensor
offset_cpu
;
framework
::
Tensor
length_cpu
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
offset_cpu
.
mutable_data
<
T
>
(
offset
->
dims
(),
platform
::
CPUPlace
());
offset_cpu
.
CopyFrom
(
*
offset
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
offset_data
=
offset_cpu
.
data
<
int64_t
>
();
length_cpu
.
mutable_data
<
T
>
(
length
->
dims
(),
platform
::
CPUPlace
());
length_cpu
.
CopyFrom
(
*
length
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
length_data
=
length_cpu
.
data
<
int64_t
>
();
}
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
PADDLE_ENFORCE_LT
(
0
,
offset_data
[
i
],
"The offset[%d] must greater than zero."
,
i
)
PADDLE_ENFORCE_LT
(
0
,
length_data
[
i
],
"The length[%d] must greater than zero."
,
i
)
PADDLE_ENFORCE_LT
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
],
lod
[
0
][
i
+
1
],
"The target tensor's length overflow."
)
}
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_lod
=
SequenceSliceLoD
(
*
in
,
offset_data
,
length_data
);
auto
out_dims
=
in
->
dims
();
out_dims
[
0
]
=
out_lod
[
0
][
out_lod
[
0
].
size
()
-
1
];
out
->
Resize
(
out_dims
);
out
->
set_lod
(
out_lod
);
auto
in_stride
=
framework
::
stride
(
in
->
dims
());
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
size_t
out_offset
=
0
;
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
Tensor
in_t
=
in
->
Slice
(
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]),
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
]));
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
in_t
.
data
<
T
>
(),
in_stride
,
in_t
.
dims
(),
out_stride
,
out
->
data
<
T
>
()
+
out_offset
);
out_offset
+=
length_data
[
i
]
*
in_stride
[
0
];
}
}
};
template
<
typename
Place
,
typename
T
>
class
SequenceSliceGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
offset
=
ctx
.
Input
<
Tensor
>
(
"Offset"
);
auto
*
length
=
ctx
.
Input
<
Tensor
>
(
"Length"
);
auto
*
out_grad
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
const
int64_t
*
offset_data
=
offset
->
data
<
int64_t
>
();
const
int64_t
*
length_data
=
length
->
data
<
int64_t
>
();
framework
::
Tensor
offset_cpu
;
framework
::
Tensor
length_cpu
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
offset_cpu
.
mutable_data
<
T
>
(
offset
->
dims
(),
platform
::
CPUPlace
());
offset_cpu
.
CopyFrom
(
*
offset
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
offset_data
=
offset_cpu
.
data
<
int64_t
>
();
length_cpu
.
mutable_data
<
T
>
(
length
->
dims
(),
platform
::
CPUPlace
());
length_cpu
.
CopyFrom
(
*
length
,
platform
::
CPUPlace
(),
ctx
.
device_context
());
length_data
=
length_cpu
.
data
<
int64_t
>
();
}
auto
lod
=
in
->
lod
();
auto
out_lod
=
out_grad
->
lod
();
if
(
x_grad
)
{
x_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
x_grad
->
set_lod
(
in
->
lod
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
ctx
.
device_context
(),
x_grad
,
static_cast
<
T
>
(
0
));
auto
out_grad_stride
=
framework
::
stride
(
out_grad
->
dims
());
for
(
size_t
i
=
0
;
i
<
out_lod
[
0
].
size
()
-
1
;
++
i
)
{
Tensor
out_grad_t
=
out_grad
->
Slice
(
static_cast
<
int
>
(
out_lod
[
0
][
i
]),
static_cast
<
int
>
(
out_lod
[
0
][
i
+
1
]));
auto
out_grad_stride
=
framework
::
stride
(
out_grad_t
.
dims
());
auto
x_grad_stride
=
framework
::
stride
(
x_grad
->
dims
());
Tensor
x_grad_t
=
x_grad
->
Slice
(
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]),
static_cast
<
int
>
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
]));
StridedMemcpy
<
T
>
(
ctx
.
device_context
(),
out_grad_t
.
data
<
T
>
(),
out_grad_stride
,
out_grad_t
.
dims
(),
x_grad_stride
,
x_grad_t
.
data
<
T
>
());
}
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/platform/cudnn_helper.h
浏览文件 @
fafd3e0f
...
...
@@ -224,13 +224,15 @@ class ScopedConvolutionDescriptor {
PADDLE_ENFORCE_EQ
(
pads
.
size
(),
strides
.
size
());
PADDLE_ENFORCE_EQ
(
pads
.
size
(),
dilations
.
size
());
#if
CUDNN_VERSION < 6000
#if
!CUDNN_VERSION_MIN(6, 0, 0)
// cudnn v5 does not support dilation conv, the argument is called upscale
// instead of dilations and it is must be one.
for
(
size_t
i
=
0
;
i
<
dilations
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
dilations
[
i
],
1
,
"Dilations conv is not supported in this cuDNN version"
);
"Dilations conv is not supported in this cuDNN version(%d.%d.%d)."
,
CUDNN_VERSION
/
1000
,
CUDNN_VERSION
%
1000
/
100
,
CUDNN_VERSION
%
100
);
}
#endif
...
...
paddle/platform/cudnn_helper_test.cc
浏览文件 @
fafd3e0f
...
...
@@ -38,6 +38,26 @@ TEST(CudnnHelper, ScopedTensorDescriptor) {
EXPECT_EQ
(
strides
[
2
],
6
);
EXPECT_EQ
(
strides
[
1
],
36
);
EXPECT_EQ
(
strides
[
0
],
144
);
// test tensor5d: ScopedTensorDescriptor
ScopedTensorDescriptor
tensor5d_desc
;
std
::
vector
<
int
>
shape_5d
=
{
2
,
4
,
6
,
6
,
6
};
auto
desc_5d
=
tensor5d_desc
.
descriptor
<
float
>
(
DataLayout
::
kNCDHW
,
shape_5d
);
std
::
vector
<
int
>
dims_5d
(
5
);
std
::
vector
<
int
>
strides_5d
(
5
);
paddle
::
platform
::
dynload
::
cudnnGetTensorNdDescriptor
(
desc_5d
,
5
,
&
type
,
&
nd
,
dims_5d
.
data
(),
strides_5d
.
data
());
EXPECT_EQ
(
nd
,
5
);
for
(
size_t
i
=
0
;
i
<
dims_5d
.
size
();
++
i
)
{
EXPECT_EQ
(
dims_5d
[
i
],
shape_5d
[
i
]);
}
EXPECT_EQ
(
strides_5d
[
4
],
1
);
EXPECT_EQ
(
strides_5d
[
3
],
6
);
EXPECT_EQ
(
strides_5d
[
2
],
36
);
EXPECT_EQ
(
strides_5d
[
1
],
216
);
EXPECT_EQ
(
strides_5d
[
0
],
864
);
}
TEST
(
CudnnHelper
,
ScopedFilterDescriptor
)
{
...
...
@@ -60,6 +80,20 @@ TEST(CudnnHelper, ScopedFilterDescriptor) {
for
(
size_t
i
=
0
;
i
<
shape
.
size
();
++
i
)
{
EXPECT_EQ
(
kernel
[
i
],
shape
[
i
]);
}
ScopedFilterDescriptor
filter_desc_4d
;
std
::
vector
<
int
>
shape_4d
=
{
2
,
3
,
3
,
3
};
auto
desc_4d
=
filter_desc
.
descriptor
<
float
>
(
DataLayout
::
kNCDHW
,
shape_4d
);
std
::
vector
<
int
>
kernel_4d
(
4
);
paddle
::
platform
::
dynload
::
cudnnGetFilterNdDescriptor
(
desc_4d
,
4
,
&
type
,
&
format
,
&
nd
,
kernel_4d
.
data
());
EXPECT_EQ
(
GetCudnnTensorFormat
(
DataLayout
::
kNCHW
),
format
);
EXPECT_EQ
(
nd
,
4
);
for
(
size_t
i
=
0
;
i
<
shape_4d
.
size
();
++
i
)
{
EXPECT_EQ
(
kernel_4d
[
i
],
shape_4d
[
i
]);
}
}
TEST
(
CudnnHelper
,
ScopedConvolutionDescriptor
)
{
...
...
python/paddle/trainer_config_helpers/activations.py
浏览文件 @
fafd3e0f
...
...
@@ -256,7 +256,7 @@ class SoftSignActivation(BaseActivation):
SoftSign Activation.
.. math::
f(z)=
\\
frac{
1
}{1 + |z|}
f(z)=
\\
frac{
z
}{1 + |z|}
"""
def
__init__
(
self
):
...
...
python/paddle/trainer_config_helpers/networks.py
浏览文件 @
fafd3e0f
...
...
@@ -11,7 +11,7 @@
# 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
math
from
activations
import
LinearActivation
,
ReluActivation
,
SoftmaxActivation
,
\
IdentityActivation
,
TanhActivation
,
SequenceSoftmaxActivation
...
...
@@ -26,9 +26,9 @@ __all__ = [
'sequence_conv_pool'
,
'simple_lstm'
,
"simple_img_conv_pool"
,
"img_conv_bn_pool"
,
'lstmemory_group'
,
'lstmemory_unit'
,
'small_vgg'
,
'img_conv_group'
,
'vgg_16_network'
,
'gru_unit'
,
'gru_group'
,
'simple_gru'
,
'simple_attention'
,
'dot_product_attention'
,
'
simple_gru2
'
,
'
bidirectional_gru'
,
'text_conv_pool'
,
'bidirectional_lstm'
,
'inputs
'
,
'outputs'
'simple_attention'
,
'dot_product_attention'
,
'
multi_head_attention
'
,
'
simple_gru2'
,
'bidirectional_gru'
,
'text_conv_pool'
,
'bidirectional_lstm
'
,
'
inputs'
,
'
outputs'
]
######################################################
...
...
@@ -1476,10 +1476,8 @@ def dot_product_attention(encoded_sequence,
expand_as
=
encoded_sequence
,
name
=
'%s_expand'
%
name
)
m
=
linear_comb_layer
(
weights
=
expanded
,
vectors
=
encoded_sequence
,
name
=
'%s_dot-product'
%
name
)
m
=
dot_prod_layer
(
input1
=
expanded
,
input2
=
encoded_sequence
,
name
=
'%s_dot-product'
%
name
)
attention_weight
=
fc_layer
(
input
=
m
,
...
...
@@ -1498,6 +1496,134 @@ def dot_product_attention(encoded_sequence,
input
=
scaled
,
pooling_type
=
SumPooling
(),
name
=
"%s_pooling"
%
name
)
@
wrap_name_default
()
def
multi_head_attention
(
query
,
key
,
value
,
key_proj_size
,
value_proj_size
,
head_num
,
attention_type
,
softmax_param_attr
=
None
,
name
=
None
):
"""
Calculate and return a context vector with dot-product attention mechanism.
The dimension of the context vector equals to value_proj_size * head_num.
Please refer to **Attention Is All You Need** for more details. The link is
as follows:
https://arxiv.org/abs/1706.03762.
The example usage is:
.. code-block:: python
context = multi_head_attention(query=decoder_state,
key=enc_seq,
value=enc_seq,
key_proj_size=64,
value_pro_size=64,
head_num=8,
attention_type='dot-product attention')
:param name: A prefix attached to the name of each layer that defined inside
the multi_head_attention.
:type name: basestring
:param softmax_param_attr: The parameter attribute of sequence softmax
that is used to produce attention weight.
:type softmax_param_attr: ParameterAttribute
:param query: query is used to calculate attention weights over values at current step.
:type query: LayerOutput
:param key: key is used to calculate the attention weight of the corresponding value.
:type key: LayerOutput
:param value: value is the sequence to be attended.
:type value: LayerOutput
:param key_proj_size: The dimension of the linear projection performed on key and query.
:type key_proj_size: int
:param value_proj_size: The dimension of the linear projection performed on value.
:type value_proj_size: int
:param head_num: The number of attention heads.
:type head_num: int
:param attention_type: The type of the attention mechanism used in each attention
heads. Now, we only support scaled dot-product attention and
additive attention.
:type attention_type: basestring
:return: The context vector.
:rtype: LayerOutput
"""
assert
attention_type
in
[
'dot-product attention'
,
'additive attention'
]
with
mixed_layer
(
size
=
key_proj_size
*
head_num
,
name
=
'%s_query_proj'
%
name
)
as
query_proj
:
query_proj
+=
full_matrix_projection
(
query
)
query_proj
=
expand_layer
(
input
=
query_proj
,
expand_as
=
key
)
with
mixed_layer
(
size
=
key_proj_size
*
head_num
,
name
=
'%s_key_proj'
%
name
)
as
key_proj
:
key_proj
+=
full_matrix_projection
(
key
)
with
mixed_layer
(
size
=
value_proj_size
*
head_num
,
name
=
'%s_value_proj'
%
name
)
as
value_proj
:
value_proj
+=
full_matrix_projection
(
value
)
head_list
=
[]
for
i
in
range
(
head_num
):
with
mixed_layer
(
size
=
key_proj_size
)
as
sub_query_proj
:
sub_query_proj
+=
identity_projection
(
query_proj
,
offset
=
key_proj_size
*
i
,
size
=
key_proj_size
)
with
mixed_layer
(
size
=
key_proj_size
)
as
sub_key_proj
:
sub_key_proj
+=
identity_projection
(
key_proj
,
offset
=
key_proj_size
*
i
,
size
=
key_proj_size
)
with
mixed_layer
(
size
=
value_proj_size
)
as
sub_value_proj
:
sub_value_proj
+=
identity_projection
(
value_proj
,
offset
=
value_proj_size
*
i
,
size
=
value_proj_size
)
if
attention_type
==
'dot-product attention'
:
m
=
dot_prod_layer
(
input1
=
sub_query_proj
,
input2
=
sub_key_proj
,
name
=
'%s_dot-product_%d'
%
(
name
,
i
))
m
=
slope_intercept_layer
(
input
=
m
,
slope
=
math
.
sqrt
(
1.0
/
key_proj_size
),
name
=
'%s_dot-product_scaling_%d'
%
(
name
,
i
))
else
:
with
mixed_layer
(
size
=
key_proj_size
,
act
=
TanhActivation
(),
name
=
'%s_combine_%d'
%
(
name
,
i
))
as
m
:
m
+=
identity_projection
(
sub_query_proj
)
m
+=
identity_projection
(
sub_key_proj
)
attention_weight
=
fc_layer
(
input
=
m
,
size
=
1
,
act
=
SequenceSoftmaxActivation
(),
param_attr
=
softmax_param_attr
,
name
=
"%s_softmax_%d"
%
(
name
,
i
),
bias_attr
=
False
)
scaled
=
scaling_layer
(
weight
=
attention_weight
,
input
=
sub_value_proj
,
name
=
'%s_scaling_%d'
%
(
name
,
i
))
head
=
pooling_layer
(
input
=
scaled
,
pooling_type
=
SumPooling
(),
name
=
"%s_pooling_%d"
%
(
name
,
i
))
head_list
.
append
(
head
)
attended
=
concat_layer
(
head_list
)
return
attended
def
inputs
(
layers
,
*
args
):
"""
Declare the inputs of network. The order of input should be as same as
...
...
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
fafd3e0f
...
...
@@ -11,7 +11,6 @@ test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_l
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/v2/fluid/tests/test_pool2d_op.py
浏览文件 @
fafd3e0f
...
...
@@ -3,8 +3,7 @@ import numpy as np
from
op_test
import
OpTest
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
...
...
@@ -23,8 +22,7 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
return
out
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
...
...
@@ -47,6 +45,7 @@ def avg_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class
TestPool2d_Op
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
init_global_pool
()
self
.
init_op_type
()
self
.
init_pool_type
()
if
self
.
global_pool
:
...
...
@@ -75,8 +74,6 @@ class TestPool2d_Op(OpTest):
self
.
check_grad
(
set
([
'X'
]),
'Out'
,
max_relative_error
=
0.07
)
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
...
...
@@ -87,12 +84,14 @@ class TestPool2d_Op(OpTest):
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
True
class
TestCase1
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
...
...
@@ -103,12 +102,14 @@ class TestCase1(TestPool2d_Op):
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase2
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
...
...
@@ -119,152 +120,69 @@ class TestCase2(TestPool2d_Op):
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase3
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase3
(
TestPool2d_Op
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCase4
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase4
(
TestCase1
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCase5
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
class
TestCase5
(
TestCase2
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
#--------------------test pool2d_cudnn--------------------
class
TestCaseCudnn1
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCudnnCase1
(
TestPool2d_Op
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn2
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCudnnCase2
(
TestCase1
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn3
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
class
TestCudnnCase3
(
TestCase2
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn4
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCudnnCase4
(
TestCase3
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCaseCudnn5
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCudnnCase5
(
TestCase4
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCaseCudnn6
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
class
TestCudnnCase6
(
TestCase5
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/fluid/tests/test_pool3d_op.py
浏览文件 @
fafd3e0f
...
...
@@ -3,8 +3,7 @@ import numpy as np
from
op_test
import
OpTest
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
...
...
@@ -27,8 +26,7 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
return
out
def
avg_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
def
avg_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
...
...
@@ -55,6 +53,10 @@ def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class
TestPool3d_Op
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
init_global_pool
()
self
.
init_op_type
()
self
.
init_pool_type
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
...
...
@@ -81,74 +83,115 @@ class TestPool3d_Op(OpTest):
self
.
check_grad
(
set
([
'X'
]),
'Out'
,
max_relative_error
=
0.07
)
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool3d"
self
.
pool_type
=
"avg"
self
.
pool3D_forward_naive
=
avg_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
,
5
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
0
,
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool3D_forward_naive
=
avg_pool3D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
True
class
TestCase1
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool3d"
self
.
pool_type
=
"avg"
self
.
pool3D_forward_naive
=
avg_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
,
7
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
0
,
0
,
0
]
class
TestCase2
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool3D_forward_naive
=
avg_pool3D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase2
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
,
7
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
1
,
1
,
1
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool3D_forward_naive
=
avg_pool3D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase3
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool3D_forward_naive
=
max_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
,
5
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
0
,
0
,
0
]
class
TestCase4
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
class
TestCase4
(
TestCase1
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool3D_forward_naive
=
max_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
,
7
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
0
,
0
,
0
]
class
TestCase5
(
TestPool3d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
class
TestCase5
(
TestCase2
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool3D_forward_naive
=
max_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
,
7
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
1
,
1
,
1
]
#--------------------test pool3d_cudnn--------------------
class
TestCudnnCase1
(
TestPool3d_Op
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
class
TestCudnnCase2
(
TestCase1
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
class
TestCudnnCase3
(
TestCase2
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
class
TestCudnnCase4
(
TestCase3
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
class
TestCudnnCase5
(
TestCase4
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
class
TestCudnnCase6
(
TestCase5
):
def
init_op_type
(
self
):
self
.
op_type
=
"pool3d_cudnn"
if
__name__
==
'__main__'
:
...
...
python/paddle/v2/fluid/tests/test_sequence_slice_op.py
0 → 100644
浏览文件 @
fafd3e0f
import
unittest
import
numpy
as
np
import
sys
from
op_test
import
OpTest
class
TestSequenceSliceOp
(
OpTest
):
def
set_data
(
self
):
self
.
init_test_case
()
# only supprot one level LoD
x
=
np
.
random
.
random
(
self
.
x_dim
).
astype
(
'float32'
)
lod
=
self
.
x_lod
offset
=
np
.
array
(
self
.
offset
).
astype
(
"int64"
)
length
=
np
.
array
(
self
.
length
).
astype
(
"int64"
)
self
.
inputs
=
{
'X'
:
(
x
,
lod
),
'Offset'
:
offset
,
'Length'
:
length
}
outs
=
[]
#np.zeros((100, 3, 2)).astype('float32')
out_lod
=
[[
0
]]
out_lod_offset
=
0
for
i
in
range
(
len
(
offset
)):
sub_x
=
x
[
lod
[
0
][
i
]
+
offset
[
i
,
0
]:
lod
[
0
][
i
]
+
offset
[
i
,
0
]
+
length
[
i
,
0
],
:]
out_lod_offset
=
out_lod_offset
+
len
(
sub_x
)
outs
.
append
(
sub_x
)
out_lod
[
0
].
append
(
out_lod_offset
)
outs
=
np
.
concatenate
(
outs
,
axis
=
0
)
self
.
outputs
=
{
'Out'
:
(
outs
,
out_lod
)}
def
init_test_case
(
self
):
self
.
x_dim
=
(
100
,
3
,
2
)
self
.
x_lod
=
[[
0
,
20
,
40
,
60
,
80
,
100
]]
self
.
offset
=
[[
1
],
[
2
],
[
3
],
[
4
],
[
5
]]
self
.
length
=
[[
10
],
[
8
],
[
6
],
[
4
],
[
2
]]
def
setUp
(
self
):
self
.
op_type
=
"sequence_slice"
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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