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17953b3a
编写于
1月 03, 2017
作者:
X
xutianbing
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add TestUtil.h and TestUtil.cpp, moving from gserver/tests/ to testing/
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paddle/testing/TestUtil.cpp
paddle/testing/TestUtil.cpp
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paddle/testing/TestUtil.h
paddle/testing/TestUtil.h
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paddle/testing/TestUtil.cpp
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/* 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 "TestUtil.h"
#include <gflags/gflags.h>
#include "paddle/math/SparseMatrix.h"
DEFINE_int32
(
fixed_seq_length
,
0
,
"Produce some sequence of fixed length"
);
namespace
paddle
{
std
::
string
randStr
(
const
int
len
)
{
std
::
string
str
=
"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
;
std
::
string
s
=
""
;
for
(
int
i
=
0
;
i
<
len
;
++
i
)
s
+=
str
[(
rand
()
%
62
)];
// NOLINT
return
s
;
}
MatrixPtr
makeRandomSparseMatrix
(
size_t
height
,
size_t
width
,
bool
withValue
,
bool
useGpu
,
bool
equalNnzPerSample
)
{
std
::
vector
<
int64_t
>
ids
(
height
);
std
::
vector
<
int64_t
>
indices
(
height
+
1
);
indices
[
0
]
=
0
;
std
::
function
<
size_t
()
>
randomer
=
[]
{
return
uniformRandom
(
10
);
};
if
(
equalNnzPerSample
)
{
size_t
n
=
0
;
do
{
n
=
uniformRandom
(
10
);
}
while
(
!
n
);
randomer
=
[
=
]
{
return
n
;
};
}
for
(
size_t
i
=
0
;
i
<
height
;
++
i
)
{
indices
[
i
+
1
]
=
indices
[
i
]
+
std
::
min
(
randomer
(),
width
);
ids
[
i
]
=
i
;
}
if
(
!
withValue
)
{
std
::
vector
<
sparse_non_value_t
>
data
;
data
.
resize
(
indices
[
height
]
-
indices
[
0
]);
for
(
size_t
i
=
0
;
i
<
data
.
size
();
++
i
)
{
data
[
i
].
col
=
uniformRandom
(
width
);
}
auto
mat
=
Matrix
::
createSparseMatrix
(
height
,
width
,
data
.
size
(),
NO_VALUE
,
SPARSE_CSR
,
false
,
useGpu
);
if
(
useGpu
)
{
std
::
dynamic_pointer_cast
<
GpuSparseMatrix
>
(
mat
)
->
copyFrom
(
ids
.
data
(),
indices
.
data
(),
data
.
data
(),
HPPL_STREAM_DEFAULT
);
}
else
{
std
::
dynamic_pointer_cast
<
CpuSparseMatrix
>
(
mat
)
->
copyFrom
(
ids
.
data
(),
indices
.
data
(),
data
.
data
());
}
return
mat
;
}
else
{
std
::
vector
<
sparse_float_value_t
>
data
;
data
.
resize
(
indices
[
height
]
-
indices
[
0
]);
for
(
size_t
i
=
0
;
i
<
data
.
size
();
++
i
)
{
data
[
i
].
col
=
uniformRandom
(
width
);
data
[
i
].
value
=
rand
()
/
static_cast
<
float
>
(
RAND_MAX
);
// NOLINT
}
auto
mat
=
Matrix
::
createSparseMatrix
(
height
,
width
,
data
.
size
(),
FLOAT_VALUE
,
SPARSE_CSR
,
false
,
useGpu
);
if
(
useGpu
)
{
std
::
dynamic_pointer_cast
<
GpuSparseMatrix
>
(
mat
)
->
copyFrom
(
ids
.
data
(),
indices
.
data
(),
data
.
data
(),
HPPL_STREAM_DEFAULT
);
}
else
{
std
::
dynamic_pointer_cast
<
CpuSparseMatrix
>
(
mat
)
->
copyFrom
(
ids
.
data
(),
indices
.
data
(),
data
.
data
());
}
return
mat
;
}
}
void
generateSequenceStartPositions
(
size_t
batchSize
,
IVectorPtr
&
sequenceStartPositions
)
{
ICpuGpuVectorPtr
gpuCpuVec
;
generateSequenceStartPositions
(
batchSize
,
gpuCpuVec
);
sequenceStartPositions
=
gpuCpuVec
->
getMutableVector
(
false
);
}
void
generateSequenceStartPositions
(
size_t
batchSize
,
ICpuGpuVectorPtr
&
sequenceStartPositions
)
{
int
numSeqs
;
if
(
FLAGS_fixed_seq_length
!=
0
)
{
numSeqs
=
std
::
ceil
((
float
)
batchSize
/
(
float
)
FLAGS_fixed_seq_length
);
}
else
{
numSeqs
=
batchSize
/
10
+
1
;
}
sequenceStartPositions
=
ICpuGpuVector
::
create
(
numSeqs
+
1
,
/* useGpu= */
false
);
int
*
buf
=
sequenceStartPositions
->
getMutableData
(
false
);
int64_t
pos
=
0
;
int
len
=
FLAGS_fixed_seq_length
;
int
maxLen
=
2
*
batchSize
/
numSeqs
;
for
(
int
i
=
0
;
i
<
numSeqs
;
++
i
)
{
if
(
FLAGS_fixed_seq_length
==
0
)
{
len
=
uniformRandom
(
std
::
min
<
int64_t
>
(
maxLen
,
batchSize
-
pos
-
numSeqs
+
i
))
+
1
;
}
buf
[
i
]
=
pos
;
pos
+=
len
;
VLOG
(
1
)
<<
" len="
<<
len
;
}
buf
[
numSeqs
]
=
batchSize
;
}
void
generateSubSequenceStartPositions
(
const
ICpuGpuVectorPtr
&
sequenceStartPositions
,
ICpuGpuVectorPtr
&
subSequenceStartPositions
)
{
int
numSeqs
=
sequenceStartPositions
->
getSize
()
-
1
;
const
int
*
buf
=
sequenceStartPositions
->
getData
(
false
);
int
numOnes
=
0
;
for
(
int
i
=
0
;
i
<
numSeqs
;
++
i
)
{
if
(
buf
[
i
+
1
]
-
buf
[
i
]
==
1
)
{
++
numOnes
;
}
}
// each seq has two sub-seq except length 1
int
numSubSeqs
=
numSeqs
*
2
-
numOnes
;
subSequenceStartPositions
=
ICpuGpuVector
::
create
(
numSubSeqs
+
1
,
/* useGpu= */
false
);
int
*
subBuf
=
subSequenceStartPositions
->
getMutableData
(
false
);
int
j
=
0
;
for
(
int
i
=
0
;
i
<
numSeqs
;
++
i
)
{
if
(
buf
[
i
+
1
]
-
buf
[
i
]
==
1
)
{
subBuf
[
j
++
]
=
buf
[
i
];
}
else
{
int
len
=
uniformRandom
(
buf
[
i
+
1
]
-
buf
[
i
]
-
1
)
+
1
;
subBuf
[
j
++
]
=
buf
[
i
];
subBuf
[
j
++
]
=
buf
[
i
]
+
len
;
}
}
subBuf
[
j
]
=
buf
[
numSeqs
];
}
void
generateMDimSequenceData
(
const
IVectorPtr
&
sequenceStartPositions
,
IVectorPtr
&
cpuSequenceDims
)
{
/* generate sequences with 2 dims */
int
numSeqs
=
sequenceStartPositions
->
getSize
()
-
1
;
int
numDims
=
2
;
cpuSequenceDims
=
IVector
::
create
(
numSeqs
*
numDims
,
/* useGpu= */
false
);
int
*
bufStarts
=
sequenceStartPositions
->
getData
();
int
*
bufDims
=
cpuSequenceDims
->
getData
();
for
(
int
i
=
0
;
i
<
numSeqs
;
i
++
)
{
int
len
=
bufStarts
[
i
+
1
]
-
bufStarts
[
i
];
/* get width and height randomly */
std
::
vector
<
int
>
dimVec
;
for
(
int
j
=
0
;
j
<
len
;
j
++
)
{
if
(
len
%
(
j
+
1
)
==
0
)
{
dimVec
.
push_back
(
1
);
}
}
int
idx
=
rand
()
%
dimVec
.
size
();
// NOLINT use rand_r
bufDims
[
i
*
numDims
]
=
dimVec
[
idx
];
bufDims
[
i
*
numDims
+
1
]
=
len
/
dimVec
[
idx
];
}
}
void
generateMDimSequenceData
(
const
ICpuGpuVectorPtr
&
sequenceStartPositions
,
IVectorPtr
&
cpuSequenceDims
)
{
/* generate sequences with 2 dims */
int
numSeqs
=
sequenceStartPositions
->
getSize
()
-
1
;
int
numDims
=
2
;
cpuSequenceDims
=
IVector
::
create
(
numSeqs
*
numDims
,
/* useGpu= */
false
);
const
int
*
bufStarts
=
sequenceStartPositions
->
getData
(
false
);
int
*
bufDims
=
cpuSequenceDims
->
getData
();
for
(
int
i
=
0
;
i
<
numSeqs
;
i
++
)
{
int
len
=
bufStarts
[
i
+
1
]
-
bufStarts
[
i
];
/* get width and height randomly */
std
::
vector
<
int
>
dimVec
;
for
(
int
j
=
0
;
j
<
len
;
j
++
)
{
if
(
len
%
(
j
+
1
)
==
0
)
{
dimVec
.
push_back
(
1
);
}
}
int
idx
=
rand
()
%
dimVec
.
size
();
// NOLINT use rand_r
bufDims
[
i
*
numDims
]
=
dimVec
[
idx
];
bufDims
[
i
*
numDims
+
1
]
=
len
/
dimVec
[
idx
];
}
}
void
checkMatrixEqual
(
const
MatrixPtr
&
a
,
const
MatrixPtr
&
b
)
{
EXPECT_EQ
(
a
->
getWidth
(),
b
->
getWidth
());
EXPECT_EQ
(
a
->
getHeight
(),
b
->
getHeight
());
EXPECT_EQ
(
a
->
isTransposed
(),
b
->
isTransposed
());
for
(
size_t
r
=
0
;
r
<
a
->
getHeight
();
++
r
)
{
for
(
size_t
c
=
0
;
c
<
a
->
getWidth
();
++
c
)
{
EXPECT_FLOAT_EQ
(
a
->
getElement
(
r
,
c
),
b
->
getElement
(
r
,
c
));
}
}
}
void
checkVectorEqual
(
const
IVectorPtr
&
a
,
const
IVectorPtr
&
b
)
{
EXPECT_EQ
(
a
->
getSize
(),
b
->
getSize
());
for
(
size_t
r
=
0
;
r
<
a
->
getSize
();
++
r
)
{
EXPECT_FLOAT_EQ
(
a
->
get
(
r
),
b
->
get
(
r
));
}
}
}
// namespace paddle
paddle/testing/TestUtil.h
0 → 100644
浏览文件 @
17953b3a
/* 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 <gtest/gtest.h>
#include "paddle/math/Matrix.h"
namespace
paddle
{
std
::
string
randStr
(
const
int
len
);
inline
int
uniformRandom
(
int
n
)
{
return
n
==
0
?
0
:
rand
()
%
n
;
}
inline
bool
approximatelyEqual
(
float
a
,
float
b
,
float
epsilon
)
{
return
fabs
(
a
-
b
)
<=
((
fabs
(
a
)
<
fabs
(
b
)
?
fabs
(
b
)
:
fabs
(
a
))
*
epsilon
);
}
MatrixPtr
makeRandomSparseMatrix
(
size_t
height
,
size_t
width
,
bool
withValue
,
bool
useGpu
,
bool
equalNnzPerSample
=
false
);
/**
* @brief generate sequenceStartPositions for INPUT_SEQUENCE_DATA,
* INPUT_HASSUB_SEQUENCE_DATA and INPUT_SEQUENCE_LABEL
*
* @param batchSize batchSize
* sequenceStartPositions[out] generation output
*/
void
generateSequenceStartPositions
(
size_t
batchSize
,
IVectorPtr
&
sequenceStartPositions
);
void
generateSequenceStartPositions
(
size_t
batchSize
,
ICpuGpuVectorPtr
&
sequenceStartPositions
);
/**
* @brief generate subSequenceStartPositions for INPUT_HASSUB_SEQUENCE_DATA
* according to sequenceStartPositions
*
* @param sequenceStartPositions[in] input
* subSequenceStartPositions[out] generation output
*/
void
generateSubSequenceStartPositions
(
const
IVectorPtr
&
sequenceStartPositions
,
IVectorPtr
&
subSequenceStartPositions
);
void
generateSubSequenceStartPositions
(
const
ICpuGpuVectorPtr
&
sequenceStartPositions
,
ICpuGpuVectorPtr
&
subSequenceStartPositions
);
/**
* @brief generate cpuSequenceDims for INPUT_SEQUENCE_MDIM_DATA according to
* sequenceStartPositions
*
* @param sequenceStartPositions[in] input
* cpuSequenceDims[out] generation output
*/
void
generateMDimSequenceData
(
const
IVectorPtr
&
sequenceStartPositions
,
IVectorPtr
&
cpuSequenceDims
);
void
generateMDimSequenceData
(
const
ICpuGpuVectorPtr
&
sequenceStartPositions
,
IVectorPtr
&
cpuSequenceDims
);
void
checkMatrixEqual
(
const
MatrixPtr
&
a
,
const
MatrixPtr
&
b
);
void
checkVectorEqual
(
const
IVectorPtr
&
a
,
const
IVectorPtr
&
b
);
}
// namespace paddle
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