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c300b1ba
编写于
3月 27, 2019
作者:
W
wopeizl
提交者:
GitHub
3月 27, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Tensor index (#16223)
* extend the slice function for python test=develop
上级
0d9d25d4
变更
5
显示空白变更内容
内联
并排
Showing
5 changed file
with
588 addition
and
1 deletion
+588
-1
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+9
-1
paddle/fluid/pybind/tensor_py.h
paddle/fluid/pybind/tensor_py.h
+253
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+177
-0
python/paddle/fluid/tests/unittests/test_tensor.py
python/paddle/fluid/tests/unittests/test_tensor.py
+53
-0
python/paddle/fluid/tests/unittests/test_variable.py
python/paddle/fluid/tests/unittests/test_variable.py
+96
-0
未找到文件。
paddle/fluid/pybind/pybind.cc
浏览文件 @
c300b1ba
...
@@ -347,7 +347,8 @@ PYBIND11_MODULE(core, m) {
...
@@ -347,7 +347,8 @@ PYBIND11_MODULE(core, m) {
.
def
(
"_set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"_set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"_get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"_get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"_place"
,
[](
Tensor
&
self
)
{
return
self
.
place
();
})
.
def
(
"_place"
,
[](
Tensor
&
self
)
{
return
self
.
place
();
})
.
def
(
"_dtype"
,
[](
Tensor
&
self
)
{
return
self
.
type
();
});
.
def
(
"_dtype"
,
[](
Tensor
&
self
)
{
return
self
.
type
();
})
.
def
(
"__getitem__"
,
PySliceTensor
,
py
::
return_value_policy
::
reference
);
py
::
class_
<
LoDTensor
,
Tensor
>
(
m
,
"LoDTensor"
,
R"DOC(
py
::
class_
<
LoDTensor
,
Tensor
>
(
m
,
"LoDTensor"
,
R"DOC(
LoDTensor is a Tensor with optional LoD information.
LoDTensor is a Tensor with optional LoD information.
...
@@ -499,6 +500,13 @@ PYBIND11_MODULE(core, m) {
...
@@ -499,6 +500,13 @@ PYBIND11_MODULE(core, m) {
Returns:
Returns:
out (bool): whether the lod is valid.
out (bool): whether the lod is valid.
)DOC"
)
.
def
(
"__getitem__"
,
PySliceTensor
,
py
::
return_value_policy
::
reference
,
R"DOC(
Slice the original Tensor, and remove the LoD information.
Returns:
out (Tensor): new Tensor(NOT LoDTensor).
)DOC"
);
)DOC"
);
py
::
class_
<
SelectedRows
>
(
m
,
"SelectedRows"
)
py
::
class_
<
SelectedRows
>
(
m
,
"SelectedRows"
)
...
...
paddle/fluid/pybind/tensor_py.h
浏览文件 @
c300b1ba
...
@@ -14,16 +14,22 @@ limitations under the License. */
...
@@ -14,16 +14,22 @@ limitations under the License. */
#pragma once
#pragma once
#include <Python.h>
#include <Python.h>
#include <algorithm>
#include <memory>
#include <string>
#include <string>
#include <tuple>
#include <tuple>
#include <vector>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/float16.h"
#include "pybind11/numpy.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/pybind11.h"
namespace
py
=
pybind11
;
namespace
paddle
{
namespace
paddle
{
namespace
pybind
{
namespace
pybind
{
namespace
details
{
namespace
details
{
...
@@ -191,6 +197,253 @@ inline void PyCPUTensorSetFromArray(
...
@@ -191,6 +197,253 @@ inline void PyCPUTensorSetFromArray(
std
::
memcpy
(
dst
,
array
.
data
(),
sizeof
(
uint16_t
)
*
array
.
size
());
std
::
memcpy
(
dst
,
array
.
data
(),
sizeof
(
uint16_t
)
*
array
.
size
());
}
}
template
<
typename
T
,
size_t
D
>
void
_sliceCompute
(
const
framework
::
Tensor
*
in
,
framework
::
Tensor
*
out
,
const
platform
::
CPUDeviceContext
&
ctx
,
const
std
::
vector
<
int
>
&
axes
,
const
std
::
vector
<
int
>
&
starts
)
{
auto
&
eigen_place
=
*
ctx
.
eigen_device
();
auto
place
=
in
->
place
();
auto
out_dims
=
out
->
dims
();
auto
in_dims
=
in
->
dims
();
auto
offsets
=
Eigen
::
array
<
int
,
D
>
();
auto
extents
=
Eigen
::
array
<
int
,
D
>
();
for
(
size_t
i
=
0
;
i
<
D
;
++
i
)
{
offsets
[
i
]
=
0
;
extents
[
i
]
=
out_dims
[
i
];
}
int
start
;
for
(
size_t
i
=
0
;
i
<
axes
.
size
();
++
i
)
{
start
=
starts
[
i
];
if
(
start
<
0
)
{
start
=
(
start
+
in_dims
[
axes
[
i
]]);
}
start
=
std
::
max
(
start
,
0
);
offsets
[
axes
[
i
]]
=
start
;
}
auto
in_t
=
framework
::
EigenTensor
<
T
,
D
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
*
in
);
auto
out_t
=
framework
::
EigenTensor
<
T
,
D
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
*
out
);
out_t
.
device
(
eigen_place
)
=
in_t
.
slice
(
offsets
,
extents
);
}
template
<
typename
T
>
void
_concatCompute
(
const
std
::
vector
<
paddle
::
framework
::
Tensor
>
&
ins
,
paddle
::
framework
::
Tensor
*
out
,
const
platform
::
CPUDeviceContext
&
ctx
,
int64_t
axis
)
{
if
(
axis
==
0
&&
ins
.
size
()
<
10
)
{
size_t
output_offset
=
0
;
for
(
auto
&
in
:
ins
)
{
auto
in_stride
=
framework
::
stride_numel
(
in
.
dims
());
auto
out_stride
=
framework
::
stride_numel
(
out
->
dims
());
paddle
::
operators
::
StridedNumelCopyWithAxis
<
T
>
(
ctx
,
axis
,
out
->
data
<
T
>
()
+
output_offset
,
out_stride
,
in
.
data
<
T
>
(),
in_stride
,
in_stride
[
axis
]);
output_offset
+=
in_stride
[
axis
];
}
}
else
{
paddle
::
operators
::
math
::
ConcatFunctor
<
platform
::
CPUDeviceContext
,
T
>
concat_functor
;
concat_functor
(
ctx
,
ins
,
static_cast
<
int
>
(
axis
),
out
);
}
}
void
_getSliceinfo
(
const
framework
::
Tensor
&
self
,
py
::
object
obj
,
const
int64_t
dim
,
int64_t
*
pstart
,
int64_t
*
pstop
,
int64_t
*
pstep
,
int64_t
*
pslicelength
)
{
auto
&
start
=
*
pstart
;
auto
&
stop
=
*
pstop
;
auto
&
step
=
*
pstep
;
auto
&
slicelength
=
*
pslicelength
;
const
framework
::
DDim
&
srcDDim
=
self
.
dims
();
if
(
dim
<
0
||
dim
>=
srcDDim
.
size
())
{
throw
py
::
index_error
();
}
if
(
py
::
isinstance
<
py
::
slice
>
(
obj
))
{
size_t
lstart
,
lstop
,
lstep
,
lslicelength
;
py
::
slice
s
=
static_cast
<
py
::
slice
>
(
obj
);
if
(
!
s
.
compute
(
srcDDim
[
dim
],
&
lstart
,
&
lstop
,
&
lstep
,
&
lslicelength
))
{
throw
py
::
index_error
();
}
start
=
static_cast
<
int64_t
>
(
lstart
);
stop
=
static_cast
<
int64_t
>
(
lstop
);
step
=
static_cast
<
int64_t
>
(
lstep
);
slicelength
=
static_cast
<
int64_t
>
(
lslicelength
);
}
else
if
(
py
::
isinstance
<
py
::
int_
>
(
obj
))
{
start
=
static_cast
<
int64_t
>
(
static_cast
<
py
::
int_
>
(
obj
));
if
(
std
::
abs
(
start
)
>=
srcDDim
[
dim
])
{
throw
py
::
index_error
();
}
start
=
(
start
>=
0
)
?
start
:
srcDDim
[
dim
]
-
start
;
stop
=
start
+
1
;
step
=
1
;
slicelength
=
1
;
}
else
{
throw
py
::
index_error
();
}
}
inline
framework
::
Tensor
*
_getTensor
(
const
framework
::
Tensor
&
self
,
const
framework
::
DDim
&
ddim
)
{
framework
::
Tensor
*
output
=
new
framework
::
Tensor
();
output
->
Resize
(
ddim
);
auto
place
=
self
.
place
();
if
(
platform
::
is_cpu_place
(
place
))
{
output
->
mutable_data
(
boost
::
get
<
platform
::
CPUPlace
>
(
place
),
self
.
type
());
#ifdef PADDLE_WITH_CUDA
}
else
{
if
(
platform
::
is_cuda_pinned_place
(
place
))
{
output
->
mutable_data
(
boost
::
get
<
platform
::
CUDAPinnedPlace
>
(
place
),
self
.
type
());
}
else
if
((
platform
::
is_gpu_place
(
place
)))
{
output
->
mutable_data
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
),
self
.
type
());
}
#endif
}
return
output
;
}
template
<
typename
T
>
void
_sliceDapper
(
const
framework
::
Tensor
*
in
,
framework
::
Tensor
*
out
,
const
platform
::
CPUDeviceContext
&
ctx
,
const
std
::
vector
<
int
>
&
axes
,
const
std
::
vector
<
int
>
&
starts
,
int
size
)
{
switch
(
size
)
{
case
1
:
_sliceCompute
<
T
,
1
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
2
:
_sliceCompute
<
T
,
2
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
3
:
_sliceCompute
<
T
,
3
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
4
:
_sliceCompute
<
T
,
4
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
5
:
_sliceCompute
<
T
,
5
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
6
:
_sliceCompute
<
T
,
6
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
7
:
_sliceCompute
<
T
,
7
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
8
:
_sliceCompute
<
T
,
8
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
case
9
:
_sliceCompute
<
T
,
9
>
(
in
,
out
,
ctx
,
axes
,
starts
);
break
;
default:
PADDLE_THROW
(
"dim size not exepected, current is %d"
,
size
);
break
;
}
}
template
<
typename
T
>
inline
framework
::
Tensor
*
_sliceWrapper
(
const
framework
::
Tensor
&
self
,
const
platform
::
CPUDeviceContext
&
ctx
,
py
::
object
obj
,
int
dim
,
int64_t
start
,
int64_t
slicelength
)
{
framework
::
DDim
dstDDim
=
self
.
dims
();
dstDDim
[
dim
]
=
static_cast
<
int64_t
>
(
slicelength
);
std
::
vector
<
int
>
axes
({
dim
});
std
::
vector
<
int
>
starts
({
static_cast
<
int
>
(
start
)});
framework
::
Tensor
*
output
=
_getTensor
(
self
,
dstDDim
);
_sliceDapper
<
T
>
(
&
self
,
output
,
ctx
,
axes
,
starts
,
dstDDim
.
size
());
return
output
;
}
template
<
typename
T
>
inline
framework
::
Tensor
*
_sliceAndConcat
(
const
framework
::
Tensor
&
self
,
py
::
object
obj
,
int
dim
)
{
platform
::
CPUDeviceContext
ctx
;
int64_t
start
,
stop
,
step
,
slicelength
;
_getSliceinfo
(
self
,
obj
,
dim
,
&
start
,
&
stop
,
&
step
,
&
slicelength
);
if
(
step
==
1
||
slicelength
==
1
)
{
return
_sliceWrapper
<
T
>
(
self
,
ctx
,
obj
,
dim
,
start
,
slicelength
);
}
else
{
std
::
vector
<
framework
::
Tensor
>
ins
;
for
(
auto
i
=
0
;
i
<
slicelength
;
++
i
,
start
+=
step
)
{
ins
.
emplace_back
(
*
_sliceWrapper
<
T
>
(
self
,
ctx
,
obj
,
dim
,
start
,
1
));
}
// do the concat operation
framework
::
DDim
dstDDim
=
self
.
dims
();
dstDDim
[
dim
]
=
static_cast
<
int64_t
>
(
slicelength
);
framework
::
Tensor
*
output1
=
_getTensor
(
self
,
dstDDim
);
_concatCompute
<
T
>
(
ins
,
output1
,
ctx
,
dim
);
return
output1
;
}
}
inline
framework
::
Tensor
*
_sliceTensor
(
const
framework
::
Tensor
&
self
,
py
::
object
obj
,
int
dim
)
{
auto
src_type
=
self
.
type
();
switch
(
src_type
)
{
case
framework
::
proto
::
VarType
::
FP16
:
return
_sliceAndConcat
<
paddle
::
platform
::
float16
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
FP32
:
return
_sliceAndConcat
<
float
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
FP64
:
return
_sliceAndConcat
<
double
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
INT32
:
return
_sliceAndConcat
<
int
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
INT64
:
return
_sliceAndConcat
<
int64_t
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
BOOL
:
return
_sliceAndConcat
<
bool
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
INT16
:
return
_sliceAndConcat
<
bool
>
(
self
,
obj
,
dim
);
case
framework
::
proto
::
VarType
::
UINT8
:
return
_sliceAndConcat
<
bool
>
(
self
,
obj
,
dim
);
default:
PADDLE_THROW
(
"Not support type %d"
,
src_type
);
}
}
inline
framework
::
Tensor
*
_pySliceTensor
(
const
framework
::
Tensor
&
self
,
py
::
object
obj
)
{
if
(
py
::
isinstance
<
py
::
tuple
>
(
obj
))
{
py
::
list
l
=
static_cast
<
py
::
list
>
(
obj
);
std
::
unique_ptr
<
framework
::
Tensor
>
target
;
framework
::
Tensor
*
src
=
const_cast
<
framework
::
Tensor
*>
(
&
self
);
for
(
auto
i
=
0
;
i
<
static_cast
<
int
>
(
l
.
size
());
++
i
)
{
src
=
_sliceTensor
(
*
src
,
l
[
i
],
i
);
if
(
i
+
1
==
static_cast
<
int
>
(
l
.
size
()))
{
return
src
;
}
else
{
target
.
reset
(
src
);
}
}
return
nullptr
;
}
else
{
return
_sliceTensor
(
self
,
obj
,
0
);
}
}
inline
framework
::
Tensor
*
PySliceTensor
(
const
framework
::
Tensor
&
self
,
py
::
object
obj
)
{
if
(
platform
::
is_gpu_place
(
self
.
place
()))
{
std
::
unique_ptr
<
framework
::
Tensor
>
holder
;
framework
::
Tensor
src
;
framework
::
TensorCopySync
(
self
,
platform
::
CPUPlace
(),
&
src
);
framework
::
Tensor
*
output
=
_pySliceTensor
(
src
,
obj
);
holder
.
reset
(
output
);
framework
::
Tensor
*
dst
=
_getTensor
(
*
output
,
output
->
dims
());
framework
::
TensorCopySync
(
*
output
,
self
.
place
(),
dst
);
return
dst
;
}
else
{
return
_pySliceTensor
(
self
,
obj
);
}
}
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_CUDA
template
<
typename
T
>
template
<
typename
T
>
void
PyCUDATensorSetFromArray
(
void
PyCUDATensorSetFromArray
(
...
...
python/paddle/fluid/framework.py
浏览文件 @
c300b1ba
...
@@ -627,6 +627,183 @@ class Variable(object):
...
@@ -627,6 +627,183 @@ class Variable(object):
"""
"""
self
.
error_clip
=
error_clip
self
.
error_clip
=
error_clip
def
_slice_indices
(
self
,
slice
,
length
):
"""
Reference implementation for the slice.indices method.
"""
# Compute step and length as integers.
step
=
1
if
slice
.
step
is
None
else
slice
.
step
# Raise ValueError for negative length or zero step.
if
length
<
0
:
raise
ValueError
(
"length should not be negative"
)
if
step
==
0
:
raise
ValueError
(
"slice step cannot be zero"
)
# Find lower and upper bounds for start and stop.
lower
=
-
1
if
step
<
0
else
0
upper
=
length
-
1
if
step
<
0
else
length
# Compute start.
if
slice
.
start
is
None
:
start
=
upper
if
step
<
0
else
lower
else
:
start
=
slice
.
start
start
=
max
(
start
+
length
,
lower
)
if
start
<
0
else
min
(
start
,
upper
)
# Compute stop.
if
slice
.
stop
is
None
:
stop
=
lower
if
step
<
0
else
upper
else
:
stop
=
slice
.
stop
stop
=
max
(
stop
+
length
,
lower
)
if
stop
<
0
else
min
(
stop
,
upper
)
return
start
,
stop
,
step
def
_detectEllipsis
(
self
,
item
):
has_ellipsis
=
False
start
=
0
end
=
len
(
self
.
shape
)
for
index
,
o
in
enumerate
(
item
):
if
o
is
Ellipsis
:
if
has_ellipsis
:
raise
ValueError
(
"Index can have one ellipsis only."
)
has_ellipsis
=
True
start
=
index
else
:
if
has_ellipsis
:
end
=
index
return
has_ellipsis
,
start
,
end
def
_reconstructSliceinfo
(
self
,
item
):
has_ellipsis
,
start
,
end
=
self
.
_detectEllipsis
(
item
)
if
has_ellipsis
:
newitem
=
[]
for
i
in
range
(
start
):
newitem
.
append
(
item
[
i
])
for
i
in
range
(
start
,
end
):
newitem
.
append
(
slice
(
None
,
None
,
None
))
for
i
in
range
(
end
,
len
(
item
)):
newitem
.
append
(
item
[
i
])
return
newitem
else
:
return
None
def
_detectContinuesSlice
(
self
,
item
):
starts
=
[]
ends
=
[]
for
index
,
o
in
enumerate
(
item
):
if
isinstance
(
o
,
int
):
start
=
int
(
o
)
if
(
index
>
0
and
index
>=
self
.
shape
[
index
])
\
or
(
index
<
0
and
(
index
+
self
.
shape
[
index
])
<
0
):
raise
IndexError
(
"invalid index"
)
start
=
max
(
start
+
self
.
shape
[
index
],
0
)
if
start
<
0
else
min
(
start
,
self
.
shape
[
index
])
starts
.
append
(
start
)
ends
.
append
(
start
+
1
)
elif
isinstance
(
o
,
slice
):
start
,
stop
,
step
=
self
.
_slice_indices
(
o
,
self
.
shape
[
index
])
if
step
==
1
or
step
==
-
1
:
starts
.
append
(
start
)
ends
.
append
(
stop
)
else
:
return
False
,
None
else
:
raise
IndexError
(
"Valid index accept int or slice or ellipsis"
)
return
True
,
[
starts
,
ends
]
def
_cloneVar
(
self
,
copy
=
False
):
if
not
copy
:
return
self
.
block
.
create_var
(
name
=
unique_name
.
generate
(
"."
.
join
(
self
.
name
)),
dtype
=
self
.
dtype
,
persistable
=
self
.
persistable
,
stop_gradient
=
self
.
_stop_gradient
,
)
else
:
return
self
def
_sliceVar
(
self
,
axes
,
starts
,
ends
):
new_var
=
self
.
_cloneVar
()
self
.
block
.
append_op
(
type
=
"slice"
,
inputs
=
{
'Input'
:
[
self
]},
outputs
=
{
'Out'
:
[
new_var
]},
attrs
=
{
'axes'
:
axes
,
'starts'
:
starts
,
'ends'
:
ends
})
return
new_var
def
_concatVar
(
self
,
inputs
,
axis
):
new_var
=
self
.
_cloneVar
()
self
.
block
.
append_op
(
type
=
"concat"
,
inputs
=
{
'X'
:
inputs
},
outputs
=
{
'Out'
:
[
new_var
]},
attrs
=
{
'axis'
:
axis
,
})
return
new_var
def
_sliceAndConcatVar
(
self
,
item
,
axis
):
if
isinstance
(
item
,
slice
):
if
self
.
shape
[
axis
]
<
0
:
return
self
.
_cloneVar
(
True
)
start
,
stop
,
step
=
self
.
_slice_indices
(
item
,
self
.
shape
[
axis
])
if
step
==
1
:
return
self
.
_sliceVar
([
axis
],
[
start
],
[
stop
])
else
:
vars
=
[]
if
step
>
0
:
while
start
<
stop
:
vars
.
append
(
self
.
_sliceVar
([
axis
],
[
start
],
[
start
+
1
]))
start
+=
step
else
:
while
start
>
stop
:
vars
.
append
(
self
.
_sliceVar
([
axis
],
[
start
],
[
start
+
1
]))
start
+=
step
return
self
.
_concatVar
(
vars
,
axis
)
elif
isinstance
(
item
,
int
):
if
self
.
shape
[
axis
]
<
0
:
return
self
.
_cloneVar
(
True
)
index
=
int
(
item
)
if
(
index
>
0
and
index
>=
self
.
shape
[
axis
])
\
or
(
index
<
0
and
(
index
+
self
.
shape
[
axis
])
<
0
):
raise
IndexError
(
"invalid index"
)
return
self
.
_sliceVar
([
axis
],
[
index
],
[
index
+
1
])
else
:
raise
IndexError
(
"Valid index accept int or slice or tuple"
)
def
__getitem__
(
self
,
item
):
"""
Slice the variable.
Args:
item(int/slice/tuple) : the index.
Returns:
Sliced variable
"""
new_var
=
None
if
isinstance
(
item
,
tuple
):
if
len
(
item
)
>
len
(
self
.
shape
):
raise
IndexError
(
"Too many indexes"
)
newitem
=
self
.
_reconstructSliceinfo
(
item
)
or
item
check
,
info
=
self
.
_detectContinuesSlice
(
newitem
)
if
check
:
starts
=
info
[
0
]
ends
=
info
[
1
]
axes
=
[
i
for
i
in
range
(
len
(
starts
))]
return
self
.
_sliceVar
(
axes
,
starts
,
ends
)
else
:
new_var
=
self
for
index
,
o
in
enumerate
(
newitem
):
new_var
=
new_var
.
_sliceAndConcatVar
(
o
,
index
)
else
:
new_var
=
self
.
_sliceAndConcatVar
(
item
,
0
)
return
new_var
def
get_all_op_protos
():
def
get_all_op_protos
():
"""
"""
...
...
python/paddle/fluid/tests/unittests/test_tensor.py
浏览文件 @
c300b1ba
...
@@ -14,6 +14,7 @@
...
@@ -14,6 +14,7 @@
from
__future__
import
print_function
from
__future__
import
print_function
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
import
unittest
import
unittest
import
numpy
import
numpy
...
@@ -183,6 +184,58 @@ class TestTensor(unittest.TestCase):
...
@@ -183,6 +184,58 @@ class TestTensor(unittest.TestCase):
tensor_array
=
numpy
.
array
(
tensor
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
def
run_sliece_tensor
(
self
,
place
):
tensor
=
fluid
.
Tensor
()
shape
=
[
3
,
3
,
3
]
tensor
.
_set_dims
(
shape
)
tensor_array
=
numpy
.
array
([[[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]],
[[
10
,
11
,
12
],
[
13
,
14
,
15
],
[
16
,
17
,
18
]],
[[
19
,
20
,
21
],
[
22
,
23
,
24
],
[
25
,
26
,
27
]]])
tensor
.
set
(
tensor_array
,
place
)
n1
=
tensor
[
1
]
t1
=
tensor_array
[
1
]
self
.
assertTrue
((
numpy
.
array
(
n1
)
==
numpy
.
array
(
t1
)).
all
())
n2
=
tensor
[
1
:]
t2
=
tensor_array
[
1
:]
self
.
assertTrue
((
numpy
.
array
(
n2
)
==
numpy
.
array
(
t2
)).
all
())
n3
=
tensor
[
0
:
2
:]
t3
=
tensor_array
[
0
:
2
:]
self
.
assertTrue
((
numpy
.
array
(
n3
)
==
numpy
.
array
(
t3
)).
all
())
n4
=
tensor
[
2
::
-
2
]
t4
=
tensor_array
[
2
::
-
2
]
self
.
assertTrue
((
numpy
.
array
(
n4
)
==
numpy
.
array
(
t4
)).
all
())
n5
=
tensor
[
2
::
-
2
][
0
]
t5
=
tensor_array
[
2
::
-
2
][
0
]
self
.
assertTrue
((
numpy
.
array
(
n5
)
==
numpy
.
array
(
t5
)).
all
())
n6
=
tensor
[
2
:
-
1
:
-
1
]
t6
=
tensor_array
[
2
:
-
1
:
-
1
]
self
.
assertTrue
((
numpy
.
array
(
n6
)
==
numpy
.
array
(
t6
)).
all
())
n7
=
tensor
[
0
:,
0
:]
t7
=
tensor_array
[
0
:,
0
:]
self
.
assertTrue
((
numpy
.
array
(
n7
)
==
numpy
.
array
(
t7
)).
all
())
n8
=
tensor
[
0
::
1
,
0
::
-
1
,
2
:]
t8
=
tensor_array
[
0
::
1
,
0
::
-
1
,
2
:]
self
.
assertTrue
((
numpy
.
array
(
n8
)
==
numpy
.
array
(
t8
)).
all
())
def
test_sliece_tensor
(
self
):
# run cpu first
place
=
core
.
CPUPlace
()
self
.
run_sliece_tensor
(
place
)
if
core
.
is_compiled_with_cuda
():
place
=
core
.
CUDAPlace
(
0
)
self
.
run_sliece_tensor
(
place
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_variable.py
浏览文件 @
c300b1ba
...
@@ -16,8 +16,10 @@ from __future__ import print_function
...
@@ -16,8 +16,10 @@ from __future__ import print_function
import
unittest
import
unittest
from
paddle.fluid.framework
import
default_main_program
,
Program
,
convert_np_dtype_to_dtype_
from
paddle.fluid.framework
import
default_main_program
,
Program
,
convert_np_dtype_to_dtype_
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
import
numpy
as
np
import
numpy
as
np
from
test_imperative_base
import
new_program_scope
class
TestVariable
(
unittest
.
TestCase
):
class
TestVariable
(
unittest
.
TestCase
):
...
@@ -60,6 +62,100 @@ class TestVariable(unittest.TestCase):
...
@@ -60,6 +62,100 @@ class TestVariable(unittest.TestCase):
name
=
'step_scopes'
,
type
=
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
name
=
'step_scopes'
,
type
=
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
self
.
assertEqual
(
core
.
VarDesc
.
VarType
.
STEP_SCOPES
,
var
.
type
)
self
.
assertEqual
(
core
.
VarDesc
.
VarType
.
STEP_SCOPES
,
var
.
type
)
def
_test_slice
(
self
):
b
=
default_main_program
().
current_block
()
w
=
b
.
create_var
(
dtype
=
"float64"
,
shape
=
[
784
,
100
,
100
],
lod_level
=
0
)
for
i
in
range
(
3
):
nw
=
w
[
i
]
self
.
assertEqual
((
1
,
100
,
100
),
nw
.
shape
)
nw
=
w
[:]
self
.
assertEqual
((
784
,
100
,
100
),
nw
.
shape
)
nw
=
w
[:,
:,
...]
self
.
assertEqual
((
784
,
100
,
100
),
nw
.
shape
)
nw
=
w
[::
2
,
::
2
,
:]
self
.
assertEqual
((
392
,
50
,
100
),
nw
.
shape
)
nw
=
w
[::
-
2
,
::
-
2
,
:]
self
.
assertEqual
((
392
,
50
,
100
),
nw
.
shape
)
self
.
assertEqual
(
0
,
nw
.
lod_level
)
place
=
fluid
.
CPUPlace
()
main
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
):
exe
=
fluid
.
Executor
(
place
)
tensor_array
=
np
.
array
(
[[[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]],
[[
10
,
11
,
12
],
[
13
,
14
,
15
],
[
16
,
17
,
18
]],
[[
19
,
20
,
21
],
[
22
,
23
,
24
],
[
25
,
26
,
27
]]]).
astype
(
'float32'
)
var
=
fluid
.
layers
.
assign
(
tensor_array
)
var1
=
var
[
0
,
1
,
1
]
var2
=
var
[
1
:]
var3
=
var
[
0
:
1
]
var4
=
var
[...,
]
var5
=
var
[
2
::
-
2
]
var6
=
var
[
1
,
1
:,
1
:]
var7
=
var
[
1
,
...,
1
:]
var8
=
var
[
1
,
...]
local_out
=
exe
.
run
(
main
,
fetch_list
=
[
var
,
var1
,
var2
,
var3
,
var4
,
var5
,
var6
,
var7
,
var8
])
self
.
assertTrue
((
np
.
array
(
local_out
[
1
])
==
np
.
array
(
tensor_array
[
0
,
1
,
1
])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
2
])
==
np
.
array
(
tensor_array
[
1
:])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
3
])
==
np
.
array
(
tensor_array
[
0
:
1
])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
4
])
==
np
.
array
(
tensor_array
[...,
])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
5
])
==
np
.
array
(
tensor_array
[
2
::
-
2
])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
6
])
==
np
.
array
(
tensor_array
[
1
,
1
:,
1
:])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
7
])
==
np
.
array
(
tensor_array
[
1
,
...,
1
:])).
all
())
self
.
assertTrue
((
np
.
array
(
local_out
[
8
])
==
np
.
array
(
tensor_array
[
1
,
...])).
all
())
def
test_slice
(
self
):
self
.
_test_slice
()
class
TestVariableImperative
(
unittest
.
TestCase
):
def
_test_slice
(
self
):
b
=
default_main_program
().
current_block
()
w
=
b
.
create_var
(
dtype
=
"float64"
,
shape
=
[
784
,
100
,
100
],
lod_level
=
0
)
for
i
in
range
(
3
):
nw
=
w
[
i
]
self
.
assertEqual
([
1
,
100
,
100
],
nw
.
shape
)
nw
=
w
[:]
self
.
assertEqual
([
784
,
100
,
100
],
nw
.
shape
)
nw
=
w
[:,
:,
:]
self
.
assertEqual
([
784
,
100
,
100
],
nw
.
shape
)
nw
=
w
[::
2
,
::
2
,
:]
self
.
assertEqual
([
392
,
50
,
100
],
nw
.
shape
)
nw
=
w
[::
-
2
,
::
-
2
,
:]
self
.
assertEqual
([
392
,
50
,
100
],
nw
.
shape
)
nw
=
w
[
0
::
-
2
,
0
::
-
2
,
:]
self
.
assertEqual
([
1
,
1
,
100
],
nw
.
shape
)
def
test_slice
(
self
):
with
fluid
.
imperative
.
guard
():
self
.
_test_slice
()
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
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