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体验新版 GitCode,发现更多精彩内容 >>
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f276006f
编写于
8月 15, 2018
作者:
F
fengjiayi
提交者:
GitHub
8月 15, 2018
浏览文件
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差异文件
Merge pull request #12694 from JiayiFeng/dev_op_tensor_support
Make cross_entropy_op supporting tensor
上级
a197737c
d6b5302b
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
184 addition
and
58 deletion
+184
-58
paddle/fluid/framework/tensor.cc
paddle/fluid/framework/tensor.cc
+1
-0
paddle/fluid/framework/tensor_impl.h
paddle/fluid/framework/tensor_impl.h
+8
-0
paddle/fluid/operators/cross_entropy_op.cc
paddle/fluid/operators/cross_entropy_op.cc
+52
-37
paddle/fluid/operators/cross_entropy_op.h
paddle/fluid/operators/cross_entropy_op.h
+11
-3
paddle/fluid/operators/softmax_op.h
paddle/fluid/operators/softmax_op.h
+10
-18
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
+102
-0
未找到文件。
paddle/fluid/framework/tensor.cc
浏览文件 @
f276006f
...
...
@@ -112,5 +112,6 @@ Tensor& Tensor::Resize(const DDim& dims) {
const
DDim
&
Tensor
::
dims
()
const
{
return
dims_
;
}
int64_t
Tensor
::
numel
()
const
{
return
product
(
dims_
);
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/tensor_impl.h
浏览文件 @
f276006f
...
...
@@ -59,6 +59,14 @@ inline T* Tensor::mutable_data(platform::Place place) {
}
inline
Tensor
ReshapeToMatrix
(
const
Tensor
&
src
,
int
num_col_dims
)
{
int
rank
=
src
.
dims
().
size
();
PADDLE_ENFORCE_GE
(
rank
,
2
,
"'ReshapeToMatrix()' is only used for flatten high rank "
"tensors to matrixs. Can not be used in reshaping vectors."
);
if
(
rank
==
2
)
{
return
src
;
}
Tensor
res
;
res
.
ShareDataWith
(
src
);
res
.
Resize
(
flatten_to_2d
(
src
.
dims
(),
num_col_dims
));
...
...
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @
f276006f
...
...
@@ -28,23 +28,26 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
2UL
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
label_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
int
rank
=
x_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
"Input(X) and Input(Label) shall have the same rank."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
label_dims
[
1
],
"If Attr(soft_label) == true, the
2nd
dimension of "
PADDLE_ENFORCE_EQ
(
x_dims
[
rank
-
1
],
label_dims
[
rank
-
1
],
"If Attr(soft_label) == true, the
last
dimension of "
"Input(X) and Input(Label) should be equal."
);
}
else
{
PADDLE_ENFORCE_EQ
(
label_dims
[
1
],
1UL
,
"If Attr(softLabel) == false, the
2nd
dimension of "
PADDLE_ENFORCE_EQ
(
label_dims
[
rank
-
1
],
1UL
,
"If Attr(softLabel) == false, the
last
dimension of "
"Input(Label) should be 1."
);
}
ctx
->
SetOutputDim
(
"Y"
,
{
x_dims
[
0
],
1
});
auto
y_dims
=
x_dims
;
y_dims
[
rank
-
1
]
=
1
;
ctx
->
SetOutputDim
(
"Y"
,
y_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Y"
);
}
...
...
@@ -74,24 +77,28 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
dy_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
2
,
"Input(Y@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
2
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
label_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dy_dims
[
0
],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
dy_dims
[
1
],
1
,
"The 2nd dimension of Input(Y@Grad) should be 1."
);
int
rank
=
x_dims
.
size
();
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
rank
,
"Input(Y@Grad) and Input(X) should have the same rank."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
rank
,
"Input(Label) and Input(X) should have the same rank."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"The Input(X) and Input(Label) should have the same "
"shape except the last dimension."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
dy_dims
,
0
,
rank
-
1
),
"The Input(X) and Input(Y@Grad) should have the same "
"shape except the last dimension."
);
PADDLE_ENFORCE_EQ
(
dy_dims
[
rank
-
1
],
1
,
"The last dimension of Input(Y@Grad) should be 1."
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
label_dims
[
1
],
"When Attr(soft_label) == true, the
2nd
dimension of "
PADDLE_ENFORCE_EQ
(
x_dims
[
rank
-
1
],
label_dims
[
rank
-
1
],
"When Attr(soft_label) == true, the
last
dimension of "
"Input(X) and Input(Label) should be equal."
);
}
else
{
PADDLE_ENFORCE_EQ
(
label_dims
[
1
],
1
,
"When Attr(soft_label) == false, the
2nd
dimension of "
PADDLE_ENFORCE_EQ
(
label_dims
[
rank
-
1
],
1
,
"When Attr(soft_label) == false, the
last
dimension of "
"Input(Label) should be 1."
);
}
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
...
...
@@ -113,18 +120,20 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x D],"
" where N is the batch size and D is the number of classes. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator."
);
AddInput
(
"Label"
,
"(Tensor), the ground truth which is a 2-D tensor. When "
"soft_label is set to false, Label is a Tensor<int64> with shape "
"[N x 1]. When soft_label is set to true, Label is a "
"Tensor<float/double> with shape [N x D]."
);
"(Tensor, default Tensor<float>), a tensor whose last dimension "
"size is equal to the number of classes. This input is a "
"probability computed by the previous operator, which is almost "
"always the result of a softmax operator."
);
AddInput
(
"Label"
,
"(Tensor), the tensor which represents the ground truth. It has the "
"same shape with 'X' except the last dimension. When soft_label is set "
"to false, the last dimension size is 1; when soft_label is set to "
"true, the last dimension size is equal to the number of classes."
);
AddOutput
(
"Y"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape "
"[N x 1]. The cross entropy loss."
);
"(Tensor, default Tensor<float>), a tensor whose shape is same "
"with 'X' except that the last dimension size is 1. It "
"represents the cross entropy loss."
);
AddAttr
<
bool
>
(
"soft_label"
,
"(bool, default false), a flag indicating whether to "
"interpretate the given labels as soft labels."
)
...
...
@@ -132,6 +141,12 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
CrossEntropy Operator.
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
of flattened matrixs.
It supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
...
...
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @
f276006f
...
...
@@ -33,8 +33,13 @@ class CrossEntropyOpKernel : public framework::OpKernel<T> {
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
rank
=
x
->
dims
().
size
();
Tensor
x_2d
=
framework
::
ReshapeToMatrix
(
*
x
,
rank
-
1
);
Tensor
labels_2d
=
framework
::
ReshapeToMatrix
(
*
labels
,
rank
-
1
);
Tensor
y_2d
=
framework
::
ReshapeToMatrix
(
*
y
,
rank
-
1
);
math
::
CrossEntropyFunctor
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
y
,
x
,
labels
,
ctx
.
template
device_context
<
DeviceContext
>(),
&
y_2d
,
&
x_2d
,
&
labels_2d
,
ctx
.
Attr
<
bool
>
(
"soft_label"
));
}
};
...
...
@@ -98,9 +103,12 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
auto
*
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dx_data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
dx_data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int64_t
class_num
=
x
->
dims
()[
1
];
// Following computation only depends on the last dimension size. So it's
// unnecessary to convert tensors to 2-D views.
int
rank
=
x
->
dims
().
size
();
int64_t
class_num
=
x
->
dims
()[
rank
-
1
];
if
(
ctx
.
Attr
<
bool
>
(
"soft_label"
))
{
XeSoftlabelGradFunctor
<
T
>
functor
(
dx_data
,
dy
->
data
<
T
>
(),
x
->
data
<
T
>
(),
label
->
data
<
T
>
(),
...
...
paddle/fluid/operators/softmax_op.h
浏览文件 @
f276006f
...
...
@@ -31,16 +31,12 @@ class SoftmaxKernel : public framework::OpKernel<T> {
// allocate memory on device.
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
X
->
dims
();
auto
flattened_dims
=
framework
::
flatten_to_2d
(
dims
,
dims
.
size
()
-
1
);
framework
::
LoDTensor
flattened_x
;
framework
::
LoDTensor
flattened_out
;
flattened_x
.
ShareDataWith
(
*
X
).
Resize
(
flattened_dims
);
flattened_out
.
ShareDataWith
(
*
Out
).
Resize
(
flattened_dims
);
int
rank
=
X
->
dims
().
size
();
Tensor
X_2d
=
framework
::
ReshapeToMatrix
(
*
X
,
rank
-
1
);
Tensor
Out_2d
=
framework
::
ReshapeToMatrix
(
*
Out
,
rank
-
1
);
math
::
SoftmaxFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
flattened_x
,
&
flattened_out
);
context
.
template
device_context
<
DeviceContext
>(),
&
X_2d
,
&
Out_2d
);
}
};
...
...
@@ -55,18 +51,14 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
// allocate memory on device.
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
Out
->
dims
();
auto
flattened_dims
=
framework
::
flatten_to_2d
(
dims
,
dims
.
size
()
-
1
);
framework
::
LoDTensor
flattened_out
;
framework
::
LoDTensor
flattened_d_out
;
framework
::
LoDTensor
flattened_d_x
;
flattened_out
.
ShareDataWith
(
*
Out
).
Resize
(
flattened_dims
);
flattened_d_out
.
ShareDataWith
(
*
dOut
).
Resize
(
flattened_dims
);
flattened_d_x
.
ShareDataWith
(
*
dX
).
Resize
(
flattened_dims
);
int
rank
=
Out
->
dims
().
size
();
Tensor
Out_2d
=
framework
::
ReshapeToMatrix
(
*
Out
,
rank
-
1
);
Tensor
dOut_2d
=
framework
::
ReshapeToMatrix
(
*
dOut
,
rank
-
1
);
Tensor
dX_2d
=
framework
::
ReshapeToMatrix
(
*
dX
,
rank
-
1
);
math
::
SoftmaxGradFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
flattened_out
,
&
flattened_d_out
,
&
flattened_d_x
);
context
.
template
device_context
<
DeviceContext
>(),
&
Out_2d
,
&
dOut_2d
,
&
dX_2d
);
}
};
...
...
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
浏览文件 @
f276006f
...
...
@@ -105,5 +105,107 @@ class TestCrossEntropyOp3(OpTest):
[
"X"
],
"Y"
,
max_relative_error
=
0.05
,
numeric_grad_delta
=
0.001
)
class
TestCrossEntropyOp4
(
OpTest
):
"""Test high rank tensor cross-entropy with discrete one-hot labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
shape
=
[
10
,
2
,
4
]
ins_num
=
np
.
prod
(
np
.
array
(
shape
))
class_num
=
10
X_2d
=
randomize_probability
(
ins_num
,
class_num
,
dtype
=
'float64'
)
label_2d
=
np
.
random
.
randint
(
0
,
class_num
,
(
ins_num
,
1
),
dtype
=
"int64"
)
cross_entropy_2d
=
np
.
asmatrix
(
[[
-
np
.
log
(
X_2d
[
i
][
label_2d
[
i
][
0
]])]
for
i
in
range
(
X_2d
.
shape
[
0
])],
dtype
=
"float64"
)
X
=
X_2d
.
reshape
(
shape
+
[
class_num
])
label
=
label_2d
.
reshape
(
shape
+
[
1
])
cross_entropy
=
np
.
array
(
cross_entropy_2d
).
reshape
(
shape
+
[
1
])
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
False
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Y"
,
numeric_grad_delta
=
0.001
)
class
TestCrossEntropyOp5
(
OpTest
):
"""Test high rank tensor cross-entropy with vectorized soft labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
shape
=
[
4
,
3
]
ins_num
=
np
.
prod
(
np
.
array
(
shape
))
class_num
=
37
X_2d
=
randomize_probability
(
ins_num
,
class_num
)
label_2d
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
ins_num
,
class_num
]).
astype
(
"float32"
)
label_2d
/=
label_2d
.
sum
(
axis
=
1
,
keepdims
=
True
)
cross_entropy_2d
=
(
-
label_2d
*
np
.
log
(
X_2d
)).
sum
(
axis
=
1
,
keepdims
=
True
).
astype
(
"float32"
)
X
=
X_2d
.
reshape
(
shape
+
[
class_num
])
label
=
label_2d
.
reshape
(
shape
+
[
class_num
])
cross_entropy
=
np
.
array
(
cross_entropy_2d
).
reshape
(
shape
+
[
1
])
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
(
[
"X"
],
"Y"
,
max_relative_error
=
0.05
,
numeric_grad_delta
=
0.001
)
class
TestCrossEntropyOp6
(
OpTest
):
"""Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
shape
=
[
4
,
3
,
2
]
ins_num
=
np
.
prod
(
np
.
array
(
shape
))
class_num
=
17
X_2d
=
randomize_probability
(
ins_num
,
class_num
)
label_index_2d
=
np
.
random
.
randint
(
0
,
class_num
,
(
ins_num
),
dtype
=
"int32"
)
label_2d
=
np
.
zeros
(
X_2d
.
shape
)
label_2d
[
np
.
arange
(
ins_num
),
label_index_2d
]
=
1
cross_entropy_2d
=
np
.
asmatrix
(
[[
-
np
.
log
(
X_2d
[
i
][
label_index_2d
[
i
]])]
for
i
in
range
(
X_2d
.
shape
[
0
])],
dtype
=
"float32"
)
X
=
X_2d
.
reshape
(
shape
+
[
class_num
])
label
=
label_2d
.
reshape
(
shape
+
[
class_num
])
cross_entropy
=
np
.
array
(
cross_entropy_2d
).
reshape
(
shape
+
[
1
])
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
.
astype
(
np
.
float32
)}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
(
[
"X"
],
"Y"
,
max_relative_error
=
0.05
,
numeric_grad_delta
=
0.001
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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