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体验新版 GitCode,发现更多精彩内容 >>
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dcb3da59
编写于
10月 26, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine code
上级
05239b6f
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
56 addition
and
117 deletion
+56
-117
paddle/operators/math/sequence_project.h
paddle/operators/math/sequence_project.h
+2
-2
paddle/operators/sequence_conv_op.cc
paddle/operators/sequence_conv_op.cc
+7
-7
paddle/operators/sequence_conv_op.h
paddle/operators/sequence_conv_op.h
+13
-14
python/paddle/v2/framework/tests/test_seq_conv.py
python/paddle/v2/framework/tests/test_seq_conv.py
+34
-94
未找到文件。
paddle/operators/math/sequence_project.h
浏览文件 @
dcb3da59
...
@@ -90,8 +90,8 @@ template <typename Place, typename T>
...
@@ -90,8 +90,8 @@ template <typename Place, typename T>
class
SequenceProjectFunctor
{
class
SequenceProjectFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
LoDTensor
&
in
,
framework
::
LoD
Tensor
&
padding_data
,
framework
::
LoDTensor
&
in
,
framework
::
Tensor
&
padding_data
,
framework
::
LoD
Tensor
&
col
,
bool
padding_trainable
,
framework
::
Tensor
&
col
,
bool
padding_trainable
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
up_pad
,
int
down_pad
,
bool
gradient
,
bool
input_grad
,
int
up_pad
,
int
down_pad
,
bool
gradient
,
bool
input_grad
,
bool
pad_grad
)
{
bool
pad_grad
)
{
...
...
paddle/operators/sequence_conv_op.cc
浏览文件 @
dcb3da59
...
@@ -29,10 +29,6 @@ class SequenceConvOp : public framework::OperatorWithKernel {
...
@@ -29,10 +29,6 @@ class SequenceConvOp : public framework::OperatorWithKernel {
"Input(Filter) of SequenceConvOp should not be null."
);
"Input(Filter) of SequenceConvOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceConvOp should not be null."
);
"Output(Out) of SequenceConvOp should not be null."
);
// PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() >
// 0 failed, 0 <= 0)
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PaddingData"
),
"Input(PaddingData) of SequenceConvOp should not be null."
);
int
context_length
=
ctx
->
Attrs
().
Get
<
int
>
(
"context_length"
);
int
context_length
=
ctx
->
Attrs
().
Get
<
int
>
(
"context_length"
);
bool
padding_trainable
=
ctx
->
Attrs
().
Get
<
bool
>
(
"padding_trainable"
);
bool
padding_trainable
=
ctx
->
Attrs
().
Get
<
bool
>
(
"padding_trainable"
);
...
@@ -48,6 +44,9 @@ class SequenceConvOp : public framework::OperatorWithKernel {
...
@@ -48,6 +44,9 @@ class SequenceConvOp : public framework::OperatorWithKernel {
"number_of_input_features)."
);
"number_of_input_features)."
);
if
(
padding_trainable
)
{
if
(
padding_trainable
)
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PaddingData"
),
"Input(PaddingData) of SequenceConvOp should not be null."
);
framework
::
DDim
padding_dim
=
ctx
->
GetInputDim
(
"PaddingData"
);
framework
::
DDim
padding_dim
=
ctx
->
GetInputDim
(
"PaddingData"
);
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
...
@@ -106,11 +105,12 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -106,11 +105,12 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
"(A float LoDTensor) the input of SequenceConvOp, a vector of "
"(A float LoDTensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (minibatch, number_of_input_features)."
);
"2-D matrix of size (minibatch, number_of_input_features)."
);
AddInput
(
"PaddingData"
,
AddInput
(
"PaddingData"
,
"(
A float LoD
Tensor) the input of SequenceConvOp, a vector of "
"(Tensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (up_pad + down_pad, "
"2-D matrix of size (up_pad + down_pad, "
"number_of_input_features). "
);
"number_of_input_features). "
)
.
AsDispensable
();
AddInput
(
"Filter"
,
AddInput
(
"Filter"
,
"(
A float LoD
Tensor) the input of SequenceConvOp, a vector of "
"(Tensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (context_length x number_of_input_features)."
);
"2-D matrix of size (context_length x number_of_input_features)."
);
AddOutput
(
"Out"
,
AddOutput
(
"Out"
,
"(A float LoDTensor) the output of SequenceConvOp, a vector "
"(A float LoDTensor) the output of SequenceConvOp, a vector "
...
...
paddle/operators/sequence_conv_op.h
浏览文件 @
dcb3da59
...
@@ -36,7 +36,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
...
@@ -36,7 +36,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
filter
=
*
context
.
Input
<
LoD
Tensor
>
(
"Filter"
);
auto
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// out->set_lod(in->lod());
// out->set_lod(in->lod());
...
@@ -50,9 +50,9 @@ class SequenceConvKernel : public framework::OpKernel<T> {
...
@@ -50,9 +50,9 @@ class SequenceConvKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
"Only support one level sequence now."
);
"Only support one level sequence now."
);
const
LoD
Tensor
*
padding_data
=
nullptr
;
const
Tensor
*
padding_data
=
nullptr
;
if
(
padding_trainable
)
{
if
(
padding_trainable
)
{
padding_data
=
context
.
Input
<
LoD
Tensor
>
(
"PaddingData"
);
padding_data
=
context
.
Input
<
Tensor
>
(
"PaddingData"
);
}
}
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
...
@@ -63,7 +63,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
...
@@ -63,7 +63,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
// use col_shape in the im2col calculation
// use col_shape in the im2col calculation
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
sequence_width
*
context_length
};
sequence_width
*
context_length
};
LoD
Tensor
col
;
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// Because if padding_trainable is false, padding data should be zeros.
// Because if padding_trainable is false, padding data should be zeros.
auto
temp
=
framework
::
EigenVector
<
T
>::
Flatten
(
col
);
auto
temp
=
framework
::
EigenVector
<
T
>::
Flatten
(
col
);
...
@@ -73,7 +73,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
...
@@ -73,7 +73,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
paddle
::
operators
::
math
::
SequenceProjectFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
SequenceProjectFunctor
<
Place
,
T
>
seq_project_functor
;
seq_project_functor
;
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
LoDTensor
*
pad_data
=
const_cast
<
LoD
Tensor
*>
(
padding_data
);
Tensor
*
pad_data
=
const_cast
<
Tensor
*>
(
padding_data
);
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
padding_trainable
,
context_start
,
context_length
,
padding_trainable
,
context_start
,
context_length
,
...
@@ -91,12 +91,11 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
...
@@ -91,12 +91,11 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
filter_g
=
auto
*
filter_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"Filter"
));
auto
*
padding_data_g
=
auto
*
padding_data_g
=
context
.
Output
<
LoD
Tensor
>
(
framework
::
GradVarName
(
"PaddingData"
));
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"PaddingData"
));
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
filter
=
context
.
Input
<
LoD
Tensor
>
(
"Filter"
);
auto
*
filter
=
context
.
Input
<
Tensor
>
(
"Filter"
);
int
context_start
=
context
.
Attr
<
int
>
(
"context_start"
);
int
context_start
=
context
.
Attr
<
int
>
(
"context_start"
);
int
context_length
=
context
.
Attr
<
int
>
(
"context_length"
);
int
context_length
=
context
.
Attr
<
int
>
(
"context_length"
);
...
@@ -115,7 +114,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
...
@@ -115,7 +114,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
// use col_shape in the im2col calculation
// use col_shape in the im2col calculation
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
sequence_width
*
context_length
};
sequence_width
*
context_length
};
LoD
Tensor
col
;
Tensor
col
;
if
(
in_g
||
filter_g
||
(
padding_trainable
&&
padding_data_g
))
{
if
(
in_g
||
filter_g
||
(
padding_trainable
&&
padding_data_g
))
{
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
...
@@ -161,17 +160,17 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
...
@@ -161,17 +160,17 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
functor
(
context
.
device_context
(),
filter_g
,
0
);
functor
(
context
.
device_context
(),
filter_g
,
0
);
Tensor
filter_grad_
=
*
filter_g
;
Tensor
filter_grad_
=
*
filter_g
;
Tensor
out_grad_
=
*
out_g
;
LoD
Tensor
out_grad_
=
*
out_g
;
const
LoD
Tensor
*
padding_data
=
nullptr
;
const
Tensor
*
padding_data
=
nullptr
;
if
(
padding_trainable
)
{
if
(
padding_trainable
)
{
padding_data
=
context
.
Input
<
LoD
Tensor
>
(
"PaddingData"
);
padding_data
=
context
.
Input
<
Tensor
>
(
"PaddingData"
);
}
}
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
LoDTensor
*
pad_data
=
const_cast
<
LoD
Tensor
*>
(
padding_data
);
Tensor
*
pad_data
=
const_cast
<
Tensor
*>
(
padding_data
);
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
padding_trainable
,
context_start
,
context_length
,
padding_trainable
,
context_start
,
context_length
,
...
...
python/paddle/v2/framework/tests/test_seq_conv.py
浏览文件 @
dcb3da59
...
@@ -20,24 +20,29 @@ class TestSeqProject(OpTest):
...
@@ -20,24 +20,29 @@ class TestSeqProject(OpTest):
# one level, batch size
# one level, batch size
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
input_size
[
0
],
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
input_size
[
0
],
self
.
input_size
[
1
]]).
astype
(
'float32'
)
self
.
input_size
[
1
]]).
astype
(
'float32'
)
self
.
begin_pad
=
np
.
max
([
0
,
-
self
.
context_start
])
self
.
end_pad
=
np
.
max
([
0
,
self
.
context_start
+
self
.
context_length
-
1
])
self
.
total_pad
=
self
.
begin_pad
+
self
.
end_pad
if
self
.
total_pad
==
0
:
self
.
total_pad
=
1
# PaddingData mast be not empty.
# Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
padding_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
total_pad
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
w
=
np
.
random
.
uniform
(
w
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
context_length
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
0.1
,
1
,
[
self
.
context_length
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
begin_pad
=
np
.
max
([
0
,
-
self
.
context_start
])
end_pad
=
np
.
max
([
0
,
self
.
context_start
+
self
.
context_length
-
1
])
total_pad
=
begin_pad
+
end_pad
padding_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
total_pad
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
self
.
pad_data
=
padding_data
self
.
inputs
=
{
self
.
inputs
=
{
'X'
:
(
x
,
self
.
lod
),
'X'
:
(
x
,
self
.
lod
),
'PaddingData'
:
(
padding_data
,
[[
0
,
self
.
total_pad
]]),
'Filter'
:
w
,
'Filter'
:
(
w
,
[[
0
,
self
.
context_length
]])
}
}
self
.
inputs_val
=
[
'X'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'Filter'
]
self
.
inputs_val_no_f
=
[
'X'
]
if
total_pad
!=
0
:
self
.
inputs
[
'PaddingData'
]
=
padding_data
self
.
inputs_val
=
[
'X'
,
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_f
=
[
'PaddingData'
,
'X'
]
self
.
attrs
=
{
self
.
attrs
=
{
'context_start'
:
self
.
context_start
,
'context_start'
:
self
.
context_start
,
'context_length'
:
self
.
context_length
,
'context_length'
:
self
.
context_length
,
...
@@ -51,7 +56,7 @@ class TestSeqProject(OpTest):
...
@@ -51,7 +56,7 @@ class TestSeqProject(OpTest):
def
compute
(
self
):
def
compute
(
self
):
x
,
lod
=
self
.
inputs
[
'X'
]
x
,
lod
=
self
.
inputs
[
'X'
]
filter
=
self
.
inputs
[
'Filter'
]
filter
=
self
.
inputs
[
'Filter'
]
pading_data
,
_
=
self
.
inputs
[
'PaddingData'
]
pading_data
=
self
.
pad_data
out
=
np
.
zeros
((
self
.
input_size
[
0
],
self
.
context_length
*
out
=
np
.
zeros
((
self
.
input_size
[
0
],
self
.
context_length
*
self
.
input_size
[
1
])).
astype
(
'float32'
)
self
.
input_size
[
1
])).
astype
(
'float32'
)
lod
=
lod
[
0
]
lod
=
lod
[
0
]
...
@@ -90,12 +95,12 @@ class TestSeqProject(OpTest):
...
@@ -90,12 +95,12 @@ class TestSeqProject(OpTest):
out
[
out_begin
:
out_end
,
j
*
self
.
input_size
[
1
]:(
j
+
1
)
*
out
[
out_begin
:
out_end
,
j
*
self
.
input_size
[
1
]:(
j
+
1
)
*
self
.
input_size
[
1
]]
+=
in_sub
self
.
input_size
[
1
]]
+=
in_sub
filter_dim
=
filter
[
0
]
.
shape
filter_dim
=
filter
.
shape
output_dim
=
self
.
outputs
[
'Out'
].
shape
output_dim
=
self
.
outputs
[
'Out'
].
shape
filter
[
0
]
.
shape
=
filter_dim
[
0
]
*
filter_dim
[
1
]
filter
.
shape
=
filter_dim
[
0
]
*
filter_dim
[
1
]
self
.
outputs
[
'Out'
].
shape
=
(
output_dim
[
0
],
)
self
.
outputs
[
'Out'
].
shape
=
(
output_dim
[
0
],
)
np
.
dot
(
out
,
filter
[
0
]
,
out
=
self
.
outputs
[
'Out'
])
np
.
dot
(
out
,
filter
,
out
=
self
.
outputs
[
'Out'
])
filter
[
0
]
.
shape
=
filter_dim
filter
.
shape
=
filter_dim
self
.
outputs
[
'Out'
].
shape
=
output_dim
self
.
outputs
[
'Out'
].
shape
=
output_dim
def
test_check_output
(
self
):
def
test_check_output
(
self
):
...
@@ -104,16 +109,14 @@ class TestSeqProject(OpTest):
...
@@ -104,16 +109,14 @@ class TestSeqProject(OpTest):
def
test_check_grad
(
self
):
def
test_check_grad
(
self
):
if
self
.
padding_trainable
:
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
check_grad
(
set
([
'X'
,
'PaddingData'
,
'Filter'
]),
set
(
self
.
inputs_val
),
'Out'
,
max_relative_error
=
0.05
)
'Out'
,
max_relative_error
=
0.05
)
def
test_check_grad_input
(
self
):
def
test_check_grad_input
(
self
):
self
.
check_grad
(
self
.
check_grad
(
[
'X'
],
[
'X'
],
'Out'
,
'Out'
,
max_relative_error
=
0.05
,
max_relative_error
=
0.05
,
no_grad_set
=
set
(
[
'PaddingData'
,
'Filter'
]
))
no_grad_set
=
set
(
self
.
inputs_val_no_x
))
def
test_check_grad_padding_data
(
self
):
def
test_check_grad_padding_data
(
self
):
if
self
.
padding_trainable
:
if
self
.
padding_trainable
:
...
@@ -128,9 +131,10 @@ class TestSeqProject(OpTest):
...
@@ -128,9 +131,10 @@ class TestSeqProject(OpTest):
[
'Filter'
],
[
'Filter'
],
'Out'
,
'Out'
,
max_relative_error
=
0.05
,
max_relative_error
=
0.05
,
no_grad_set
=
set
(
[
'X'
,
'PaddingData'
]
))
no_grad_set
=
set
(
self
.
inputs_val_no_f
))
def
test_check_grad_input_filter
(
self
):
def
test_check_grad_input_filter
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
check_grad
(
[
'X'
,
'Filter'
],
[
'X'
,
'Filter'
],
'Out'
,
'Out'
,
...
@@ -140,7 +144,7 @@ class TestSeqProject(OpTest):
...
@@ -140,7 +144,7 @@ class TestSeqProject(OpTest):
def
test_check_grad_padding_input
(
self
):
def
test_check_grad_padding_input
(
self
):
if
self
.
padding_trainable
:
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
check_grad
(
[
'X'
,
'PaddingData'
]
,
self
.
inputs_val_no_f
,
'Out'
,
'Out'
,
max_relative_error
=
0.05
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Filter'
]))
no_grad_set
=
set
([
'Filter'
]))
...
@@ -148,7 +152,7 @@ class TestSeqProject(OpTest):
...
@@ -148,7 +152,7 @@ class TestSeqProject(OpTest):
def
test_check_grad_padding_filter
(
self
):
def
test_check_grad_padding_filter
(
self
):
if
self
.
padding_trainable
:
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
check_grad
(
[
'PaddingData'
,
'Filter'
]
,
self
.
inputs_val_no_x
,
'Out'
,
'Out'
,
max_relative_error
=
0.05
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'X'
]))
no_grad_set
=
set
([
'X'
]))
...
@@ -191,69 +195,5 @@ class TestSeqProjectCase2(TestSeqProject):
...
@@ -191,69 +195,5 @@ class TestSeqProjectCase2(TestSeqProject):
[
self
.
input_size
[
0
]]]
[
self
.
input_size
[
0
]]]
'''
class TestSeqProjectCases(TestSeqProject):
def setUp(self):
self.init_test_case()
self.op_type = 'sequence_project'
num = 0
for context_start in [-5, -3, -1, 0, 3]:
for context_length in [1, 2, 5, 7]:
for batch_size in [1, 2, 5, 7]:
for padding_trainable in [False, True]:
if context_length == 1 and context_start == 0 and padding_trainable:
continue
self.context_start = context_start
self.context_length = context_length
self.padding_trainable = padding_trainable
self.input_size = [batch_size, 23]
x = np.random.uniform(0.1, 1,
self.input_size).astype('float32')
self.lod = [[0, self.input_size[0]]]
if self.input_size[0] > 2:
idx = range(self.input_size[0])
del idx[0]
self.lod = [
[0] + np.sort(random.sample(idx, 2)).tolist() +
[self.input_size[0]]
]
self.begin_pad = np.max([0, -self.context_start])
self.end_pad = np.max([0, self.context_start + self.context_length - 1])
self.total_pad = self.begin_pad + self.end_pad
if self.total_pad == 0:
self.total_pad = 1
# PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
padding_data = np.random.uniform(
0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32')
self.inputs = {
'X': (x, self.lod),
'PaddingData': (padding_data, [[0, self.total_pad]])
}
self.attrs = {
'context_start': self.context_start,
'context_length': self.context_length,
'padding_trainable': self.padding_trainable,
'context_stride': self.context_stride
}
out = np.zeros((self.input_size[0], self.input_size[1] *
self.context_length)).astype('float32')
self.outputs = {'Out': out}
print num
print self.attrs
print batch_size
print padding_trainable
print "$$$$$$$$$$$$$"
self.compute()
self.test_check_output()
num += 1
'''
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
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