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体验新版 GitCode,发现更多精彩内容 >>
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05c9642d
编写于
5月 14, 2020
作者:
S
suytingwan
提交者:
GitHub
5月 14, 2020
浏览文件
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浏览文件
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差异文件
Update paddle enforce message (#24498)
* test=develop error message update
上级
9f83f0fe
变更
19
隐藏空白更改
内联
并排
Showing
19 changed file
with
534 addition
and
281 deletion
+534
-281
paddle/fluid/operators/bilinear_tensor_product_op.cc
paddle/fluid/operators/bilinear_tensor_product_op.cc
+62
-31
paddle/fluid/operators/detection/anchor_generator_op.cc
paddle/fluid/operators/detection/anchor_generator_op.cc
+27
-14
paddle/fluid/operators/detection/bipartite_match_op.cc
paddle/fluid/operators/detection/bipartite_match_op.cc
+30
-14
paddle/fluid/operators/detection/generate_mask_labels_op.cc
paddle/fluid/operators/detection/generate_mask_labels_op.cc
+73
-35
paddle/fluid/operators/detection/target_assign_op.cc
paddle/fluid/operators/detection/target_assign_op.cc
+26
-14
paddle/fluid/operators/detection/target_assign_op.h
paddle/fluid/operators/detection/target_assign_op.h
+7
-2
paddle/fluid/operators/filter_by_instag_op.cc
paddle/fluid/operators/filter_by_instag_op.cc
+25
-14
paddle/fluid/operators/one_hot_v2_op.h
paddle/fluid/operators/one_hot_v2_op.h
+11
-3
paddle/fluid/operators/pool_with_index_op.cc
paddle/fluid/operators/pool_with_index_op.cc
+27
-19
paddle/fluid/operators/psroi_pool_op.cc
paddle/fluid/operators/psroi_pool_op.cc
+43
-27
paddle/fluid/operators/psroi_pool_op.h
paddle/fluid/operators/psroi_pool_op.h
+8
-4
paddle/fluid/operators/roi_pool_op.cc
paddle/fluid/operators/roi_pool_op.cc
+4
-1
paddle/fluid/operators/roi_pool_op.h
paddle/fluid/operators/roi_pool_op.h
+8
-4
paddle/fluid/operators/softmax_op.cc
paddle/fluid/operators/softmax_op.cc
+34
-18
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
+70
-39
paddle/fluid/operators/softmax_with_cross_entropy_op.h
paddle/fluid/operators/softmax_with_cross_entropy_op.h
+3
-2
paddle/fluid/operators/spp_op.cc
paddle/fluid/operators/spp_op.cc
+15
-10
paddle/fluid/operators/unsqueeze_op.cc
paddle/fluid/operators/unsqueeze_op.cc
+51
-27
paddle/fluid/operators/unsqueeze_op.h
paddle/fluid/operators/unsqueeze_op.h
+10
-3
未找到文件。
paddle/fluid/operators/bilinear_tensor_product_op.cc
浏览文件 @
05c9642d
...
...
@@ -28,39 +28,61 @@ class BilinearTensorProductOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(Weight) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Y"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Y) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Weight"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Weight) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) should not be null."
));
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
y_dims
=
ctx
->
GetInputDim
(
"Y"
);
auto
weight_dims
=
ctx
->
GetInputDim
(
"Weight"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
"The input(X) must be a 2D Tensor."
);
PADDLE_ENFORCE_EQ
(
y_dims
.
size
(),
2UL
,
"The input(Y) must be a 2D Tensor."
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
platform
::
errors
::
InvalidArgument
(
"The input(X) must be a 2D Tensor."
));
PADDLE_ENFORCE_EQ
(
y_dims
.
size
(),
2UL
,
platform
::
errors
::
InvalidArgument
(
"The input(Y) must be a 2D Tensor."
));
PADDLE_ENFORCE_EQ
(
weight_dims
.
size
(),
3UL
,
"The input(Weight) must be a 3D tensor."
);
platform
::
errors
::
InvalidArgument
(
"The input(Weight) must be a 3D tensor."
));
if
(
ctx
->
IsRuntime
()
||
(
x_dims
[
0
]
>
0
&&
y_dims
[
0
]
>
0
))
{
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
y_dims
[
0
],
"The first dimension(batch_size) of input(X) must be "
"equal to the first dimension of the input(Y)."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
y_dims
[
0
],
platform
::
errors
::
InvalidArgument
(
"The first dimension(batch_size) of input(X) must be "
"equal to the first dimension of the input(Y)."
));
}
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
weight_dims
[
1
],
"The second dimension of input(X) must be equal to "
"the second dimension of the input(Weight)."
);
platform
::
errors
::
InvalidArgument
(
"The second dimension of input(X) must be equal to "
"the second dimension of the input(Weight)."
));
PADDLE_ENFORCE_EQ
(
y_dims
[
1
],
weight_dims
[
2
],
"The second dimension of input(Y) must be equal to "
"the third dimension of the input(Weight)."
);
platform
::
errors
::
InvalidArgument
(
"The second dimension of input(Y) must be equal to "
"the third dimension of the input(Weight)."
));
if
(
ctx
->
HasInput
(
"Bias"
))
{
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
PADDLE_ENFORCE
(
bias_dims
.
size
()
==
2UL
&&
bias_dims
[
0
]
==
1UL
,
"The Input(Bias) must be a 2-D tensor with "
"the 2nd dimension fixed to 1 (a row vector)."
);
PADDLE_ENFORCE_EQ
(
bias_dims
.
size
(),
2UL
,
platform
::
errors
::
InvalidArgument
(
"The Input(Bias) must be a 2-D tensor with "
"the 2nd dimension fixed to 1 (a row vector)."
));
PADDLE_ENFORCE_EQ
(
bias_dims
[
0
],
1UL
,
platform
::
errors
::
InvalidArgument
(
"The Input(Bias) must be a 2-D tensor with "
"the 2nd dimension fixed to 1 (a row vector)."
));
PADDLE_ENFORCE_EQ
(
bias_dims
[
1
],
weight_dims
[
0
],
"The second dimension of input(Bias) must be equal "
"to the first dimension of the input(Weight)."
);
platform
::
errors
::
InvalidArgument
(
"The second dimension of input(Bias) must be equal "
"to the first dimension of the input(Weight)."
));
}
ctx
->
SetOutputDim
(
"Out"
,
{
x_dims
[
0
],
weight_dims
[
0
]});
...
...
@@ -104,27 +126,36 @@ class BilinearTensorProductOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(Weight) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Y"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Y) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Weight"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Weight) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Out@GRAD) should not be null."
));
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
y_dims
=
ctx
->
GetInputDim
(
"Y"
);
auto
weight_dims
=
ctx
->
GetInputDim
(
"Weight"
);
auto
out_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
out_dims
.
size
(),
2UL
,
"The input(Out@GRAD) must be a 2D Tensor."
);
platform
::
errors
::
InvalidArgument
(
"The input(Out@GRAD) must be a 2D Tensor."
));
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
out_dims
[
0
],
"The first dimension(batch_size) of input(Out@GRAD) must be "
"equal to the first dimension of the Input(X)."
);
platform
::
errors
::
InvalidArgument
(
"The first dimension(batch_size) of input(Out@GRAD) must be "
"equal to the first dimension of the Input(X)."
));
PADDLE_ENFORCE_EQ
(
weight_dims
[
0
],
out_dims
[
1
],
"The second dimension of input(Out@GRAD) must be equal to "
"the third dimension of the Input(Weight)."
);
platform
::
errors
::
InvalidArgument
(
"The second dimension of input(Out@GRAD) must be equal to "
"the third dimension of the Input(Weight)."
));
auto
bias_grad_name
=
framework
::
GradVarName
(
"Bias"
);
if
(
ctx
->
HasOutput
(
bias_grad_name
))
{
...
...
paddle/fluid/operators/detection/anchor_generator_op.cc
浏览文件 @
05c9642d
...
...
@@ -22,16 +22,23 @@ class AnchorGeneratorOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Input"
),
"Input(Input) of AnchorGeneratorOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Anchors"
),
"Output(Anchors) of AnchorGeneratorOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Variances"
),
"Output(Variances) of AnchorGeneratorOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Input"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Input) of AnchorGeneratorOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Anchors"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Anchors) of AnchorGeneratorOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Variances"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Variances) of AnchorGeneratorOp should not be null."
));
auto
input_dims
=
ctx
->
GetInputDim
(
"Input"
);
PADDLE_ENFORCE
(
input_dims
.
size
()
==
4
,
"The layout of input is NCHW."
);
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
"The layout of input is NCHW."
));
auto
anchor_sizes
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
float
>>
(
"anchor_sizes"
);
auto
aspect_ratios
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
float
>>
(
"aspect_ratios"
);
...
...
@@ -87,10 +94,12 @@ class AnchorGeneratorOpMaker : public framework::OpProtoAndCheckerMaker {
"equals to 64**2."
)
.
AddCustomChecker
([](
const
std
::
vector
<
float
>&
anchor_sizes
)
{
PADDLE_ENFORCE_GT
(
anchor_sizes
.
size
(),
0UL
,
"Size of anchor_sizes must be at least 1."
);
platform
::
errors
::
InvalidArgument
(
"Size of anchor_sizes must be at least 1."
));
for
(
size_t
i
=
0
;
i
<
anchor_sizes
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
anchor_sizes
[
i
],
0.0
,
"anchor_sizes[%d] must be positive."
,
i
);
platform
::
errors
::
InvalidArgument
(
"anchor_sizes[%d] must be positive."
,
i
));
}
});
AddAttr
<
std
::
vector
<
float
>>
(
...
...
@@ -105,10 +114,12 @@ class AnchorGeneratorOpMaker : public framework::OpProtoAndCheckerMaker {
"in box regression deltas"
)
.
AddCustomChecker
([](
const
std
::
vector
<
float
>&
variances
)
{
PADDLE_ENFORCE_EQ
(
variances
.
size
(),
4UL
,
"Must and only provide 4 variance."
);
platform
::
errors
::
InvalidArgument
(
"Must provide 4 variance only."
));
for
(
size_t
i
=
0
;
i
<
variances
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
variances
[
i
],
0.0
,
"variance[%d] must be greater than 0."
,
i
);
platform
::
errors
::
InvalidArgument
(
"variance[%d] must be greater than 0."
,
i
));
}
});
...
...
@@ -119,10 +130,12 @@ class AnchorGeneratorOpMaker : public framework::OpProtoAndCheckerMaker {
.
AddCustomChecker
([](
const
std
::
vector
<
float
>&
stride
)
{
PADDLE_ENFORCE_EQ
(
stride
.
size
(),
2UL
,
"Must and only provide 2 stride for width and height."
);
platform
::
errors
::
InvalidArgument
(
"Must provide 2 stride for width and height only."
));
for
(
size_t
i
=
0
;
i
<
stride
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
stride
[
i
],
0.0
,
"stride[%d] should be larger than 0."
,
i
);
platform
::
errors
::
InvalidArgument
(
"stride[%d] should be larger than 0."
,
i
));
}
});
...
...
paddle/fluid/operators/detection/bipartite_match_op.cc
浏览文件 @
05c9642d
...
...
@@ -26,17 +26,23 @@ class BipartiteMatchOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"DistMat"
),
"Input(DistMat) of BipartiteMatch should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ColToRowMatchIndices"
),
"Output(ColToRowMatchIndices) of BipartiteMatch should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ColToRowMatchDist"
),
"Output(ColToRowMatchDist) of BipartiteMatch should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"DistMat"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(DistMat) of BipartiteMatch should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"ColToRowMatchIndices"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(ColToRowMatchIndices) of BipartiteMatch "
"should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"ColToRowMatchDist"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(ColToRowMatchDist) of BipartiteMatch should not be null."
));
auto
dims
=
ctx
->
GetInputDim
(
"DistMat"
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"The rank of Input(DistMat) must be 2."
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"The rank of Input(DistMat) must be 2."
));
ctx
->
SetOutputDim
(
"ColToRowMatchIndices"
,
dims
);
ctx
->
SetOutputDim
(
"ColToRowMatchDist"
,
dims
);
...
...
@@ -64,7 +70,9 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
// The match_dist must be initialized to 0 at first.
void
BipartiteMatch
(
const
Tensor
&
dist
,
int
*
match_indices
,
T
*
match_dist
)
const
{
PADDLE_ENFORCE_EQ
(
dist
.
dims
().
size
(),
2
,
"The rank of dist must be 2."
);
PADDLE_ENFORCE_EQ
(
dist
.
dims
().
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"The rank of dist must be 2."
));
int64_t
row
=
dist
.
dims
()[
0
];
int64_t
col
=
dist
.
dims
()[
1
];
auto
*
dist_data
=
dist
.
data
<
T
>
();
...
...
@@ -127,7 +135,11 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
// Cannot find good match.
break
;
}
else
{
PADDLE_ENFORCE_EQ
(
match_indices
[
max_idx
],
-
1
);
PADDLE_ENFORCE_EQ
(
match_indices
[
max_idx
],
-
1
,
platform
::
errors
::
InvalidArgument
(
"The match_indices must be initialized to -1 at [%d]."
,
max_idx
));
match_indices
[
max_idx
]
=
max_row_idx
;
match_dist
[
max_idx
]
=
max_dist
;
// Erase the row index.
...
...
@@ -163,7 +175,10 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
}
}
if
(
max_row_idx
!=
-
1
)
{
PADDLE_ENFORCE_EQ
(
match_indices
[
j
],
-
1
);
PADDLE_ENFORCE_EQ
(
match_indices
[
j
],
-
1
,
platform
::
errors
::
InvalidArgument
(
"The match_indices must be initialized to -1 at [%d]."
,
j
));
match_indices
[
j
]
=
max_row_idx
;
match_dist
[
j
]
=
max_dist
;
}
...
...
@@ -183,8 +198,9 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
?
1
:
static_cast
<
int64_t
>
(
dist_mat
->
lod
().
back
().
size
()
-
1
);
if
(
dist_mat
->
lod
().
size
())
{
PADDLE_ENFORCE_EQ
(
dist_mat
->
lod
().
size
(),
1UL
,
"Only support 1 level of LoD."
);
PADDLE_ENFORCE_EQ
(
dist_mat
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"Only support 1 level of LoD."
));
}
match_indices
->
mutable_data
<
int
>
({
n
,
col
},
context
.
GetPlace
());
match_dist
->
mutable_data
<
T
>
({
n
,
col
},
context
.
GetPlace
());
...
...
paddle/fluid/operators/detection/generate_mask_labels_op.cc
浏览文件 @
05c9642d
...
...
@@ -40,35 +40,49 @@ class GenerateMaskLabelsOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"ImInfo"
),
"Input(ImInfo) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GtClasses"
),
"Input(GtClasses) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"IsCrowd"
),
"Input(IsCrowd) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GtSegms"
),
"Input(GtSegms) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Rois"
),
"Input(Rois) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"LabelsInt32"
),
"Input(LabelsInt32) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"MaskRois"
),
"Output(MaskRois) of GenerateMaskLabelsOp should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"RoiHasMaskInt32"
),
"Output(RoiHasMaskInt32) of GenerateMaskLabelsOp should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"MaskInt32"
),
"Output(MaskInt32) of GenerateMaskLabelsOp should not be null"
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"ImInfo"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(ImInfo) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"GtClasses"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(GtClasses) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"IsCrowd"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(IsCrowd) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"GtSegms"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(GtSegms) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Rois"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Rois) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"LabelsInt32"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(LabelsInt32) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"MaskRois"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(MaskRois) of GenerateMaskLabelsOp should not be null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"RoiHasMaskInt32"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(RoiHasMaskInt32) of GenerateMaskLabelsOp "
"should not be null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"MaskInt32"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(MaskInt32) of GenerateMaskLabelsOp should not be null"
));
auto
im_info_dims
=
ctx
->
GetInputDim
(
"ImInfo"
);
auto
gt_segms_dims
=
ctx
->
GetInputDim
(
"GtSegms"
);
PADDLE_ENFORCE_EQ
(
im_info_dims
.
size
(),
2
,
"The rank of Input(ImInfo) must be 2."
);
platform
::
errors
::
InvalidArgument
(
"The rank of Input(ImInfo) must be 2."
));
PADDLE_ENFORCE_EQ
(
gt_segms_dims
.
size
(),
2
,
"The rank of Input(GtSegms) must be 2."
);
platform
::
errors
::
InvalidArgument
(
"The rank of Input(GtSegms) must be 2."
));
PADDLE_ENFORCE_EQ
(
gt_segms_dims
[
1
],
2
,
"The second dim of Input(GtSegms) must be 2."
);
platform
::
errors
::
InvalidArgument
(
"The second dim of Input(GtSegms) must be 2."
));
int
num_classes
=
ctx
->
Attrs
().
Get
<
int
>
(
"num_classes"
);
int
resolution
=
ctx
->
Attrs
().
Get
<
int
>
(
"resolution"
);
...
...
@@ -134,7 +148,11 @@ std::vector<Tensor> SampleMaskForOneImage(
const
int
*
gt_classes_data
=
gt_classes
.
data
<
int
>
();
const
int
*
is_crowd_data
=
is_crowd
.
data
<
int
>
();
const
int
*
label_int32_data
=
label_int32
.
data
<
int
>
();
PADDLE_ENFORCE_EQ
(
roi_size
,
label_int32
.
dims
()[
0
]);
PADDLE_ENFORCE_EQ
(
roi_size
,
label_int32
.
dims
()[
0
],
platform
::
errors
::
InvalidArgument
(
"The first dim of label [%d] is the different from "
"roi_size [%d], they should be same."
,
label_int32
.
dims
()[
0
],
roi_size
));
std
::
vector
<
int
>
mask_gt_inds
,
fg_inds
;
std
::
vector
<
std
::
vector
<
std
::
vector
<
T
>>>
gt_polys
;
...
...
@@ -155,7 +173,12 @@ std::vector<Tensor> SampleMaskForOneImage(
for
(
int
j
=
0
;
j
<
poly_num
;
++
j
)
{
int
s
=
lod2
[
s_idx
+
j
];
int
e
=
lod2
[
s_idx
+
j
+
1
];
PADDLE_ENFORCE_NE
(
s
,
e
);
PADDLE_ENFORCE_NE
(
s
,
e
,
platform
::
errors
::
InvalidArgument
(
"The start point and the end point in the poly "
"segment [%d] should not be same, but received "
"the start point [%d] and the end point [%d]."
,
i
,
s
,
e
));
std
::
vector
<
T
>
plts
(
polys_data
+
s
*
2
,
polys_data
+
e
*
2
);
polys
.
push_back
(
plts
);
}
...
...
@@ -295,19 +318,34 @@ class GenerateMaskLabelsKernel : public framework::OpKernel<T> {
int
num_classes
=
ctx
.
Attr
<
int
>
(
"num_classes"
);
int
resolution
=
ctx
.
Attr
<
int
>
(
"resolution"
);
PADDLE_ENFORCE_EQ
(
gt_classes
->
lod
().
size
(),
1UL
,
"GenerateMaskLabelsOp gt_classes needs 1 level of LoD"
);
PADDLE_ENFORCE_EQ
(
is_crowd
->
lod
().
size
(),
1UL
,
"GenerateMaskLabelsOp is_crowd needs 1 level of LoD"
);
PADDLE_ENFORCE_EQ
(
gt_classes
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"GenerateMaskLabelsOp gt_classes needs 1 level of LoD"
));
PADDLE_ENFORCE_EQ
(
is_crowd
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"GenerateMaskLabelsOp is_crowd needs 1 level of LoD"
));
PADDLE_ENFORCE_EQ
(
rois
->
lod
().
size
(),
1UL
,
"GenerateMaskLabelsOp rois needs 1 level of LoD"
);
PADDLE_ENFORCE_EQ
(
label_int32
->
lod
().
size
(),
1UL
,
"GenerateMaskLabelsOp label_int32 needs 1 level of LoD"
);
PADDLE_ENFORCE_EQ
(
gt_segms
->
lod
().
size
(),
3UL
);
platform
::
errors
::
InvalidArgument
(
"GenerateMaskLabelsOp rois needs 1 level of LoD"
));
PADDLE_ENFORCE_EQ
(
label_int32
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"GenerateMaskLabelsOp label_int32 needs 1 level of LoD"
));
PADDLE_ENFORCE_EQ
(
gt_segms
->
lod
().
size
(),
3UL
,
platform
::
errors
::
InvalidArgument
(
"GenerateMaskLabelsOp gt_segms needs 3 level of LoD"
));
int64_t
n
=
static_cast
<
int64_t
>
(
gt_classes
->
lod
().
back
().
size
()
-
1
);
PADDLE_ENFORCE_EQ
(
gt_segms
->
lod
()[
0
].
size
()
-
1
,
n
);
PADDLE_ENFORCE_EQ
(
gt_segms
->
lod
()[
0
].
size
()
-
1
,
n
,
platform
::
errors
::
InvalidArgument
(
"Batchsize of Input(gt_segms) and Input(gt_classes) should be "
"same, but received gt_segms[%d], gt_classes[%d]."
,
gt_segms
->
lod
()[
0
].
size
()
-
1
,
n
));
int
mask_dim
=
num_classes
*
resolution
*
resolution
;
int
roi_num
=
rois
->
lod
().
back
()[
n
];
...
...
paddle/fluid/operators/detection/target_assign_op.cc
浏览文件 @
05c9642d
...
...
@@ -22,29 +22,41 @@ class TargetAssignOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of TargetAssignOp should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"MatchIndices"
),
"Input(MatchIndices) of TargetAssignOp should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of TargetAssignOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"OutWeight"
),
"Output(OutWeight) of TargetAssignOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) of TargetAssignOp should not be null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"MatchIndices"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(MatchIndices) of TargetAssignOp should not be null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of TargetAssignOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"OutWeight"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(OutWeight) of TargetAssignOp should not be null."
));
auto
in_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
mi_dims
=
ctx
->
GetInputDim
(
"MatchIndices"
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
3
,
"The rank of Input(X) must be 3."
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
3
,
platform
::
errors
::
InvalidArgument
(
"The rank of Input(X) must be 3."
));
PADDLE_ENFORCE_EQ
(
mi_dims
.
size
(),
2
,
"The rank of Input(MatchIndices) must be 2."
);
platform
::
errors
::
InvalidArgument
(
"The rank of Input(MatchIndices) must be 2."
));
if
(
ctx
->
HasInput
(
"NegIndices"
))
{
auto
neg_dims
=
ctx
->
GetInputDim
(
"NegIndices"
);
PADDLE_ENFORCE_EQ
(
neg_dims
.
size
(),
2
,
"The rank of Input(NegIndices) must be 2."
);
PADDLE_ENFORCE_EQ
(
neg_dims
[
1
],
1
,
"The last dimension of Out(NegIndices) must be 1."
);
platform
::
errors
::
InvalidArgument
(
"The rank of Input(NegIndices) must be 2."
));
PADDLE_ENFORCE_EQ
(
neg_dims
[
1
],
1
,
platform
::
errors
::
InvalidArgument
(
"The last dimension of Out(NegIndices) must be 1."
));
}
auto
n
=
mi_dims
[
0
];
...
...
paddle/fluid/operators/detection/target_assign_op.h
浏览文件 @
05c9642d
...
...
@@ -90,7 +90,9 @@ class TargetAssignKernel : public framework::OpKernel<T> {
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out_wt
=
ctx
.
Output
<
framework
::
Tensor
>
(
"OutWeight"
);
PADDLE_ENFORCE_EQ
(
x
->
lod
().
size
(),
1UL
);
PADDLE_ENFORCE_EQ
(
x
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"TargetAssignOp input(X) needs 1 level of LoD"
));
int
mismatch_value
=
ctx
.
Attr
<
int
>
(
"mismatch_value"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
...
...
@@ -121,7 +123,10 @@ class TargetAssignKernel : public framework::OpKernel<T> {
auto
*
neg_indices
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"NegIndices"
);
if
(
neg_indices
)
{
PADDLE_ENFORCE_EQ
(
neg_indices
->
lod
().
size
(),
1UL
);
PADDLE_ENFORCE_EQ
(
neg_indices
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"TargetAssignOp input(NegIndices) needs 1 level of LoD"
));
const
int
*
neg_idx_data
=
neg_indices
->
data
<
int
>
();
auto
neg_lod
=
neg_indices
->
lod
().
back
();
#if defined(PADDLE_WITH_CUDA)
...
...
paddle/fluid/operators/filter_by_instag_op.cc
浏览文件 @
05c9642d
...
...
@@ -24,19 +24,25 @@ class FilterByInstagOp : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Ins"
),
true
,
"Input(Ins) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Ins"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Ins) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Ins_tag"
),
true
,
"Input(Ins_tag) should be not null."
);
platform
::
errors
::
InvalidArgument
(
"Input(Ins_tag) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Filter_tag"
),
true
,
"Input(Filter_tag) should be not null."
);
platform
::
errors
::
InvalidArgument
(
"Input(Filter_tag) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
"Output(Out) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"LossWeight"
),
true
,
"Output(LossWeight) shoudl not be null."
);
platform
::
errors
::
InvalidArgument
(
"Output(LossWeight) shoudl not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"IndexMap"
),
true
,
"Output(IndexMap) should be not null."
);
platform
::
errors
::
InvalidArgument
(
"Output(IndexMap) should be not null."
));
auto
x1_dims
=
ctx
->
GetInputDim
(
"Ins"
);
// batch_size * vec
...
...
@@ -85,15 +91,20 @@ class FilterByInstagOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"IndexMap"
),
true
,
"Input(IndexMap) should be not null"
);
platform
::
errors
::
InvalidArgument
(
"Input(IndexMap) should be not null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
"Grad Input(Out) should be not null"
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Ins"
),
true
,
"Input(Ins) should be not null"
);
platform
::
errors
::
InvalidArgument
(
"Grad Input(Out) should be not null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Ins"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Ins) should be not null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"LossWeight"
),
true
,
"Input(LossWeight) should be not null"
);
platform
::
errors
::
InvalidArgument
(
"Input(LossWeight) should be not null"
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Ins"
)),
true
,
"Grad Output(Ins) should be not null"
);
platform
::
errors
::
InvalidArgument
(
"Grad Output(Ins) should be not null"
));
auto
grad_out_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
auto
x1_dims
=
ctx
->
GetInputDim
(
"Ins"
);
...
...
paddle/fluid/operators/one_hot_v2_op.h
浏览文件 @
05c9642d
...
...
@@ -51,11 +51,19 @@ struct OneHotV2OpFunctor {
}
}
else
{
for
(
int
i
=
0
;
i
<
numel
;
++
i
)
{
PADDLE_ENFORCE_GE
(
p_in_data
[
i
],
0
,
"Illegal index value, should be at least 0."
);
PADDLE_ENFORCE_GE
(
p_in_data
[
i
],
0
,
platform
::
errors
::
InvalidArgument
(
"Illegal index value, Input(input) value should be at least 0, "
"but received input (%d) less than 0"
,
p_in_data
[
i
]));
PADDLE_ENFORCE_LT
(
p_in_data
[
i
],
depth_
,
"Illegal index value, should be less than depth (%d)."
,
depth_
);
platform
::
errors
::
InvalidArgument
(
"Illegal index value, Input(input) value should be less than "
"Input(depth), "
"but received input (%d) not less than depth (%d)"
,
p_in_data
[
i
],
depth_
));
*
(
p_out_data
+
i
*
depth_
+
p_in_data
[
i
])
=
1.0
;
}
}
...
...
paddle/fluid/operators/pool_with_index_op.cc
浏览文件 @
05c9642d
...
...
@@ -29,12 +29,15 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Mask"
),
"Output(Mask) of Pooling should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) of Pooling should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of Pooling should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Mask"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Mask) of Pooling should not be null."
));
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
...
...
@@ -54,12 +57,16 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
}
}
PADDLE_ENFORCE
(
in_x_dims
.
size
()
-
ksize
.
size
()
==
2U
,
"Input size and pooling size should be consistent."
);
PADDLE_ENFORCE_EQ
(
in_x_dims
.
size
()
-
ksize
.
size
(),
2U
,
platform
::
errors
::
InvalidArgument
(
"Input size and pooling size should be consistent."
));
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
strides
.
size
(),
"Strides size and pooling size should be the same."
);
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
paddings
.
size
(),
"Paddings size and pooling size should be the same."
);
platform
::
errors
::
InvalidArgument
(
"Strides size and pooling size should be the same."
));
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
paddings
.
size
(),
platform
::
errors
::
InvalidArgument
(
"Paddings size and pooling size should be the same."
));
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
in_x_dims
[
1
]});
if
(
adaptive
)
{
...
...
@@ -90,15 +97,16 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Mask"
),
true
,
platform
::
errors
::
NotFound
(
"Input(Mask) must not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
NotFound
(
"Input(X) must not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
NotFound
(
"Input(Out@GRAD) should not be null."
));
platform
::
errors
::
InvalidArgument
(
"Input(Mask) must not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
true
,
platform
::
errors
::
NotFound
(
"Output(X@GRAD) should not be null."
));
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) must not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Out@GRAD) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(X@GRAD) should not be null."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
...
...
paddle/fluid/operators/psroi_pool_op.cc
浏览文件 @
05c9642d
...
...
@@ -81,43 +81,57 @@ class PSROIPoolOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of PSROIPoolOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"ROIs"
),
"Input(ROIs) of PSROIPoolOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of PSROIPoolOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) of PSROIPoolOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"ROIs"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(ROIs) of PSROIPoolOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of PSROIPoolOp should not be null."
));
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
rois_dims
=
ctx
->
GetInputDim
(
"ROIs"
);
PADDLE_ENFORCE
(
input_dims
.
size
()
==
4
,
"The format of input tensor is NCHW"
);
PADDLE_ENFORCE
(
rois_dims
.
size
()
==
2
,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"
);
PADDLE_ENFORCE
(
rois_dims
[
1
]
==
4
,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"
);
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
"The format of input tensor is NCHW"
));
PADDLE_ENFORCE_EQ
(
rois_dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"
));
PADDLE_ENFORCE_EQ
(
rois_dims
[
1
],
4
,
platform
::
errors
::
InvalidArgument
(
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"
));
int
pooled_height
=
ctx
->
Attrs
().
Get
<
int
>
(
"pooled_height"
);
int
pooled_width
=
ctx
->
Attrs
().
Get
<
int
>
(
"pooled_width"
);
int
output_channels
=
ctx
->
Attrs
().
Get
<
int
>
(
"output_channels"
);
float
spatial_scale
=
ctx
->
Attrs
().
Get
<
float
>
(
"spatial_scale"
);
PADDLE_ENFORCE
(
input_dims
[
1
]
==
output_channels
*
pooled_height
*
pooled_width
,
"the channel of X(%d) should be equal to the product of "
"output_channels(%d), pooled_height(%d) and pooled_width(%d)"
,
input_dims
[
1
],
output_channels
,
pooled_height
,
pooled_width
);
PADDLE_ENFORCE_EQ
(
input_dims
[
1
],
output_channels
*
pooled_height
*
pooled_width
,
platform
::
errors
::
InvalidArgument
(
"the channel of X(%d) "
"should be equal to the product of "
"output_channels(%d), pooled_height(%d) and pooled_width(%d)"
,
input_dims
[
1
],
output_channels
,
pooled_height
,
pooled_width
));
PADDLE_ENFORCE_GT
(
pooled_height
,
0
,
"The pooled output height must be greater than 0"
);
platform
::
errors
::
InvalidArgument
(
"The pooled output height must be greater than 0"
));
PADDLE_ENFORCE_GT
(
pooled_width
,
0
,
"The pooled output width must be greater than 0"
);
platform
::
errors
::
InvalidArgument
(
"The pooled output width must be greater than 0"
));
PADDLE_ENFORCE_GT
(
output_channels
,
1
,
"The pooled output channels must greater than 1"
);
platform
::
errors
::
InvalidArgument
(
"The pooled output channels must greater than 1"
));
PADDLE_ENFORCE_GT
(
spatial_scale
,
0.0
f
,
"The spatial scale must greater than 0."
);
platform
::
errors
::
InvalidArgument
(
"The spatial scale must greater than 0."
));
auto
out_dims
=
input_dims
;
out_dims
[
0
]
=
rois_dims
[
0
];
...
...
@@ -142,10 +156,12 @@ class PSROIPoolGradOp : public framework::OperatorWithKernel {
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
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"The gradient of X should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"The gradient of Out should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"The gradient of X should not be null."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
...
...
paddle/fluid/operators/psroi_pool_op.h
浏览文件 @
05c9642d
...
...
@@ -54,15 +54,19 @@ class CPUPSROIPoolOpKernel : public framework::OpKernel<T> {
int
rois_batch_size
=
rois_lod
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
rois_batch_size
,
batch_size
,
"the rois_batch_size and input(X) batch_size should be the same."
);
platform
::
errors
::
InvalidArgument
(
"the rois_batch_size and input(X) "
"batch_size should be the same."
));
int
rois_num_with_lod
=
rois_lod
[
rois_batch_size
];
PADDLE_ENFORCE_EQ
(
rois_num_with_lod
,
rois_num
,
"the rois_num from input and lod must be the same"
);
platform
::
errors
::
InvalidArgument
(
"the rois_num from input and lod must be the same"
));
PADDLE_ENFORCE_EQ
(
input_channels
,
output_channels
*
pooled_height
*
pooled_width
,
"the channels of input X should equal the product of "
"output_channels x pooled_height x pooled_width"
);
platform
::
errors
::
InvalidArgument
(
"the channels of input "
"X should equal the product of "
"output_channels x pooled_height x pooled_width"
));
// calculate batch id index for each roi according to LoD
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
...
...
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @
05c9642d
...
...
@@ -36,7 +36,10 @@ class ROIPoolOp : public framework::OperatorWithKernel {
if
(
ctx
->
HasInput
(
"RoisLod"
))
{
auto
rois_lod_dims
=
ctx
->
GetInputDim
(
"RoisLod"
);
PADDLE_ENFORCE
(
rois_lod_dims
.
size
()
==
1
,
""
);
PADDLE_ENFORCE_EQ
(
rois_lod_dims
.
size
(),
1
,
platform
::
errors
::
InvalidArgument
(
"The lod information tensor of ROIs should "
"be one-dimensional"
));
}
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
...
...
paddle/fluid/operators/roi_pool_op.h
浏览文件 @
05c9642d
...
...
@@ -63,7 +63,8 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
rois_batch_size
=
rois_lod_t
->
numel
();
PADDLE_ENFORCE_EQ
(
rois_batch_size
-
1
,
batch_size
,
"The rois_batch_size and imgs batch_size must be the same."
);
platform
::
errors
::
InvalidArgument
(
"The rois_batch_size and imgs "
"batch_size must be the same."
));
auto
*
rois_lod
=
rois_lod_t
->
data
<
int64_t
>
();
for
(
int
n
=
0
;
n
<
rois_batch_size
-
1
;
++
n
)
{
for
(
int
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
...
...
@@ -75,10 +76,13 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
rois_batch_size
=
rois_lod
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
rois_batch_size
,
batch_size
,
"The rois_batch_size and imgs batch_size must be the same."
);
platform
::
errors
::
InvalidArgument
(
"The rois_batch_size and imgs "
"batch_size must be the same."
));
int
rois_num_with_lod
=
rois_lod
[
rois_batch_size
];
PADDLE_ENFORCE_EQ
(
rois_num
,
rois_num_with_lod
,
"The rois_num from input and lod must be the same."
);
PADDLE_ENFORCE_EQ
(
rois_num
,
rois_num_with_lod
,
platform
::
errors
::
InvalidArgument
(
"The rois_num from input "
"and lod must be the same."
));
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
for
(
size_t
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
roi_batch_id_data
[
i
]
=
n
;
...
...
paddle/fluid/operators/softmax_op.cc
浏览文件 @
05c9642d
...
...
@@ -34,21 +34,30 @@ class SoftmaxOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SoftmaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SoftmaxOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
NotFound
(
"Input(X) of SoftmaxOp is not found."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
NotFound
(
"Output(Out) of SoftmaxOp is not found."
));
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
rank_x
=
dim_x
.
size
();
auto
axis
=
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
);
PADDLE_ENFORCE
(
axis
>=
-
rank_x
&&
axis
<
rank_x
,
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(X)."
);
PADDLE_ENFORCE_GE
(
axis
,
-
rank_x
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(X)."
));
PADDLE_ENFORCE_LT
(
axis
,
rank_x
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(X)."
));
auto
use_cudnn
=
ctx
->
Attrs
().
Get
<
bool
>
(
"use_cudnn"
);
if
(
axis
!=
rank_x
-
1
&&
axis
!=
-
1
)
{
PADDLE_ENFORCE
(
!
use_cudnn
,
"CUDNN kernel only support axis as -1."
);
PADDLE_ENFORCE_EQ
(
use_cudnn
,
false
,
platform
::
errors
::
InvalidArgument
(
"CUDNN kernel only support axis as -1."
));
}
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
...
...
@@ -78,8 +87,9 @@ class SoftmaxOp : public framework::OperatorWithKernel {
auto
input_data_type
=
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
);
if
(
input_data_type
==
framework
::
proto
::
VarType
::
FP16
)
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"float16 can only be used on GPU place"
);
PADDLE_ENFORCE_EQ
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
true
,
platform
::
errors
::
InvalidArgument
(
"float16 can only be used on GPU place"
));
}
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
(),
layout_
,
...
...
@@ -157,12 +167,17 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Out"
),
"Input(Out) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"Out"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out) and its gradients should have a same shape."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Out) is not found."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Out@GRAD) is not found."
));
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"Out"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)),
platform
::
errors
::
InvalidArgument
(
"Input(Out) and its gradients "
"should have a same shape."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)));
...
...
@@ -191,8 +206,9 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
auto
input_data_type
=
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
framework
::
GradVarName
(
"Out"
));
if
(
input_data_type
==
framework
::
proto
::
VarType
::
FP16
)
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"float16 can only be used on GPU place"
);
PADDLE_ENFORCE_EQ
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
true
,
platform
::
errors
::
InvalidArgument
(
"float16 can only be used on GPU place"
));
}
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
(),
layout_
,
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
浏览文件 @
05c9642d
...
...
@@ -108,39 +108,51 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Logits"
),
"Input(Logits) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Softmax"
),
"Output(Softmax) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Loss"
),
"Output(Loss) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Logits"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Logits) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Label"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Label) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Softmax"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Softmax) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Loss"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Loss) should be not null."
));
auto
axis
=
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
);
auto
logits_dims
=
ctx
->
GetInputDim
(
"Logits"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
logits_rank
=
logits_dims
.
size
();
PADDLE_ENFORCE
(
axis
>=
-
logits_rank
&&
axis
<
logits_rank
,
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
);
PADDLE_ENFORCE_GE
(
axis
,
-
logits_rank
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
));
PADDLE_ENFORCE_LT
(
axis
,
logits_rank
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
));
axis
=
CanonicalAxis
(
axis
,
logits_rank
);
for
(
int
i
=
0
;
i
<
logits_rank
;
i
++
)
{
if
(
i
!=
axis
)
{
if
(
ctx
->
IsRuntime
()
||
(
logits_dims
[
i
]
>
0
&&
labels_dims
[
i
]
>
0
))
{
PADDLE_ENFORCE_EQ
(
logits_dims
[
i
],
labels_dims
[
i
],
"Input(Logits) and Input(Label) should in same shape
in "
"dimensions except axis."
);
PADDLE_ENFORCE_EQ
(
logits_dims
[
i
],
labels_dims
[
i
],
platform
::
errors
::
InvalidArgument
(
"Input(Logits) and Input(Label) should
in "
"same shape in dimensions except axis."
)
);
}
}
}
auto
numeric_stable_mode
=
ctx
->
Attrs
().
Get
<
bool
>
(
"numeric_stable_mode"
);
if
(
axis
!=
logits_rank
-
1
)
{
PADDLE_ENFORCE
(
numeric_stable_mode
,
"Attr(axis) can only be -1 when not in numeric_stable_mode."
);
PADDLE_ENFORCE_EQ
(
numeric_stable_mode
,
true
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) can only be -1 "
"when not in numeric_stable_mode."
));
}
bool
soft_label
=
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
);
...
...
@@ -148,14 +160,18 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
if
(
ctx
->
IsRuntime
()
||
(
logits_dims
[
axis
]
>
0
&&
labels_dims
[
axis
]
>
0
))
{
PADDLE_ENFORCE_EQ
(
logits_dims
[
axis
],
labels_dims
[
axis
],
"If Attr(soft_label) == true, the axis dimension of "
"Input(X) and Input(Label) should be equal."
);
platform
::
errors
::
InvalidArgument
(
"If Attr(soft_label) == true, "
"the axis dimension of "
"Input(X) and Input(Label) should be equal."
));
}
}
else
{
if
(
ctx
->
IsRuntime
()
||
labels_dims
[
axis
]
>
0
)
{
PADDLE_ENFORCE_EQ
(
labels_dims
[
axis
],
1UL
,
"If Attr(soft_label) == false, the axis dimension of "
"Input(Label) should be 1."
);
PADDLE_ENFORCE_EQ
(
labels_dims
[
axis
],
1UL
,
platform
::
errors
::
InvalidArgument
(
"If Attr(soft_label) == false, "
"the axis dimension of "
"Input(Label) should be 1."
));
}
}
...
...
@@ -182,21 +198,31 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Loss"
)),
"Input(Loss@Grad) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Softmax"
),
"Input(Softmax) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Logits"
)),
"Output(Logits@Grad) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Loss"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Loss@Grad) should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Softmax"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Softmax) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Label"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Label) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Logits"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Logits@Grad) should be not null."
));
auto
axis
=
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
);
auto
softmax_dims
=
ctx
->
GetInputDim
(
"Softmax"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
softmax_rank
=
softmax_dims
.
size
();
PADDLE_ENFORCE
(
axis
>=
-
softmax_rank
&&
axis
<
softmax_rank
,
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
);
PADDLE_ENFORCE_GE
(
axis
,
-
softmax_rank
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
));
PADDLE_ENFORCE_LT
(
axis
,
softmax_rank
,
platform
::
errors
::
InvalidArgument
(
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(Logits)."
));
axis
=
CanonicalAxis
(
axis
,
softmax_rank
);
for
(
int
i
=
0
;
i
<
softmax_rank
;
i
++
)
{
...
...
@@ -204,8 +230,9 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
if
(
ctx
->
IsRuntime
()
||
(
softmax_dims
[
i
]
>
0
&&
labels_dims
[
i
]
>
0
))
{
PADDLE_ENFORCE_EQ
(
softmax_dims
[
i
],
labels_dims
[
i
],
"Input(Logits) and Input(Label) should in same shape in "
"dimensions except axis."
);
platform
::
errors
::
InvalidArgument
(
"Input(Logits) and Input(Label) should in same shape in "
"dimensions except axis."
));
}
}
}
...
...
@@ -215,14 +242,18 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
if
(
ctx
->
IsRuntime
()
||
(
softmax_dims
[
axis
]
>
0
&&
labels_dims
[
axis
]
>
0
))
{
PADDLE_ENFORCE_EQ
(
softmax_dims
[
axis
],
labels_dims
[
axis
],
"If Attr(soft_label) == true, the axis dimension of "
"Input(X) and Input(Label) should be equal."
);
platform
::
errors
::
InvalidArgument
(
"If Attr(soft_label) == true, "
"the axis dimension of "
"Input(X) and Input(Label) should be equal."
));
}
}
else
{
if
(
ctx
->
IsRuntime
()
||
labels_dims
[
axis
]
>
0
)
{
PADDLE_ENFORCE_EQ
(
labels_dims
[
axis
],
1UL
,
"If Attr(soft_label) == false, the axis dimension of "
"Input(Label) should be 1."
);
PADDLE_ENFORCE_EQ
(
labels_dims
[
axis
],
1UL
,
platform
::
errors
::
InvalidArgument
(
"If Attr(soft_label) == false, "
"the axis dimension of "
"Input(Label) should be 1."
));
}
}
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.h
浏览文件 @
05c9642d
...
...
@@ -31,8 +31,9 @@ template <typename T>
class
SoftmaxWithCrossEntropyKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
context
.
GetPlace
()),
"This kernel only runs on CPU."
);
PADDLE_ENFORCE_EQ
(
platform
::
is_cpu_place
(
context
.
GetPlace
()),
true
,
platform
::
errors
::
Unimplemented
(
"This kernel only runs on CPU."
));
const
Tensor
*
logits
=
context
.
Input
<
Tensor
>
(
"Logits"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
...
...
paddle/fluid/operators/spp_op.cc
浏览文件 @
05c9642d
...
...
@@ -62,15 +62,17 @@ class SppOp : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SppOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SppOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) of SppOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of SppOp should not be null."
));
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
int
pyramid_height
=
ctx
->
Attrs
().
Get
<
int
>
(
"pyramid_height"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
,
"Spping intput must be of 4-dimensional."
);
PADDLE_ENFORCE_EQ
(
in_x_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
"Spping intput must be of 4-dimensional."
));
int
outlen
=
((
std
::
pow
(
4
,
pyramid_height
)
-
1
)
/
(
4
-
1
))
*
in_x_dims
[
1
];
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
outlen
});
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
...
...
@@ -81,9 +83,12 @@ class SppOpGrad : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) must not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X@GRAD) should not be null."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
...
...
paddle/fluid/operators/unsqueeze_op.cc
浏览文件 @
05c9642d
...
...
@@ -27,16 +27,22 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
"Input(X) of Unsqueeze operator should not be null."
);
platform
::
errors
::
InvalidArgument
(
"Input(X) of "
"Unsqueeze operator should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
"Output(Out) of Unsqueeze operator should not be null."
);
platform
::
errors
::
InvalidArgument
(
"Output(Out) of "
"Unsqueeze operator should not be null."
));
const
auto
&
axes
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"axes"
);
const
auto
&
x_dims
=
ctx
->
GetInputDim
(
"X"
);
// Validity Check: input tensor dims (<6).
PADDLE_ENFORCE_LE
(
x_dims
.
size
(),
6
,
"Invalid dimensions, the rank of Input(X) "
"should be in the range of [1, 6] (Eigen limit)"
);
platform
::
errors
::
InvalidArgument
(
"Invalid "
"dimensions, the rank of Input(X) "
"should be in the range of [1, 6] (Eigen limit)"
));
if
(
!
axes
.
empty
())
{
auto
out_dims
=
GetOutputShape
(
axes
,
x_dims
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
...
...
@@ -49,24 +55,29 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
auto
AxesTensorList
=
ctx
->
Inputs
(
"AxesTensorList"
);
int
output_size
=
x_dims
.
size
()
+
static_cast
<
int
>
(
AxesTensorList
.
size
());
PADDLE_ENFORCE_LE
(
output_size
,
6
,
"The output tensor's rank should be less than 6."
);
platform
::
errors
::
InvalidArgument
(
"The output tensor's rank should be less than 6."
));
std
::
vector
<
int
>
vec_out_dims
(
output_size
,
-
1
);
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
vec_out_dims
));
}
else
if
(
ctx
->
HasInput
(
"AxesTensor"
))
{
auto
axes_dims
=
ctx
->
GetInputDim
(
"AxesTensor"
);
PADDLE_ENFORCE_EQ
(
axes_dims
.
size
(),
1
,
"Input(AxesTensor)'s dimension of Op(unsqueeze) must be 1. "
"But received AxesTensor's shape = [%s], "
"AxesTensor's dimension = %d."
,
axes_dims
,
axes_dims
.
size
());
PADDLE_ENFORCE_GE
(
axes_dims
[
0
],
0
,
"Input(AxesTensor)'s shape must be known. But received "
"AxesTensor's shape = [%s]"
,
axes_dims
);
PADDLE_ENFORCE_EQ
(
axes_dims
.
size
(),
1
,
platform
::
errors
::
InvalidArgument
(
"Input(AxesTensor)'s dimension of "
"Op(unsqueeze) must be 1. "
"But received AxesTensor's shape = [%s], "
"AxesTensor's dimension = %d."
,
axes_dims
,
axes_dims
.
size
()));
PADDLE_ENFORCE_GE
(
axes_dims
[
0
],
0
,
platform
::
errors
::
InvalidArgument
(
"Input(AxesTensor)'s shape must be known. But received "
"AxesTensor's shape = [%s]"
,
axes_dims
));
int
output_size
=
x_dims
.
size
()
+
static_cast
<
int
>
(
axes_dims
[
0
]);
PADDLE_ENFORCE_LE
(
output_size
,
6
,
"The output tensor's rank should be less than 6."
);
platform
::
errors
::
InvalidArgument
(
"The output tensor's rank should be less than 6."
));
std
::
vector
<
int
>
vec_out_dims
(
output_size
,
-
1
);
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
vec_out_dims
));
}
...
...
@@ -80,13 +91,19 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
// Validity Check: rank range.
PADDLE_ENFORCE_LE
(
output_size
,
6
,
"The output tensor's rank should be less than 6."
);
platform
::
errors
::
InvalidArgument
(
"The output tensor's rank should be less than 6."
));
for
(
int
axis
:
unsqz_dims
)
{
int
cur
=
axis
<
0
?
axis
+
cur_output_size
+
1
:
axis
;
// Vaildity Check: the axis bound
PADDLE_ENFORCE_GE
(
cur
,
0
);
PADDLE_ENFORCE_LE
(
cur
,
cur_output_size
);
PADDLE_ENFORCE_GE
(
cur
,
0
,
platform
::
errors
::
InvalidArgument
(
"The insert dimension value should "
"not be less than 0"
));
PADDLE_ENFORCE_LE
(
cur
,
cur_output_size
,
platform
::
errors
::
InvalidArgument
(
"The insert dimension value shoud not be larger "
"than the dimension size of input tensor"
));
// Move old axis, and insert new axis
for
(
int
i
=
cur_output_size
;
i
>=
cur
;
--
i
)
{
if
(
output_shape
[
i
]
==
1
)
{
...
...
@@ -151,13 +168,17 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
.
AddCustomChecker
([](
const
std
::
vector
<
int
>
&
axes
)
{
// Validity Check: axes dims (<6).
PADDLE_ENFORCE_LT
(
static_cast
<
int
>
(
axes
.
size
()),
6
,
"Invalid dimensions, dynamic dimensions should be "
"within [1, 6] dimensions (Eigen limit)."
);
platform
::
errors
::
InvalidArgument
(
"Invalid "
"dimensions, dynamic dimensions should be "
"within [1, 6] dimensions (Eigen limit)."
));
// Validity Check: the range of unsqueeze axis.
for
(
int
axis
:
axes
)
{
PADDLE_ENFORCE_LT
(
axis
,
6
,
"Invalid dimensions, input axis should be"
" within [1, 6] dimensions (Eigen limit)."
);
platform
::
errors
::
InvalidArgument
(
"Invalid "
"dimensions, input axis should be"
"within [1, 6] dimensions (Eigen limit)."
));
}
});
AddComment
(
R"DOC(
...
...
@@ -219,7 +240,8 @@ class Unsqueeze2Op : public UnsqueezeOp {
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"XShape"
),
true
,
"Output(XShape) of Unsqueeze operator should not be null."
);
platform
::
errors
::
InvalidArgument
(
"Output(XShape) of Unsqueeze "
"operator should not be null."
));
std
::
vector
<
int64_t
>
xshape_dims
(
x_dims
.
size
()
+
1
);
xshape_dims
[
0
]
=
0
;
for
(
int
i
=
0
;
i
<
x_dims
.
size
();
++
i
)
{
...
...
@@ -259,10 +281,12 @@ class Unsqueeze2GradOp : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE_EQ
(
context
->
HasInput
(
"XShape"
),
true
,
"Input(XShape) shouldn't be null."
);
PADDLE_ENFORCE_EQ
(
context
->
HasInput
(
"XShape"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(XShape) shouldn't be null."
));
PADDLE_ENFORCE_EQ
(
context
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
"Input(Out@GRAD) shouldn't be null."
);
platform
::
errors
::
InvalidArgument
(
"Input(Out@GRAD) shouldn't be null."
));
auto
xshape_dims
=
context
->
GetInputDim
(
"XShape"
);
auto
x_dims
=
framework
::
slice_ddim
(
xshape_dims
,
1
,
xshape_dims
.
size
());
context
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
...
...
paddle/fluid/operators/unsqueeze_op.h
浏览文件 @
05c9642d
...
...
@@ -66,13 +66,20 @@ class UnsqueezeKernel : public framework::OpKernel<T> {
// Validity Check: rank range.
PADDLE_ENFORCE_LE
(
output_size
,
6
,
"The output tensor's rank should be less than 6."
);
platform
::
errors
::
InvalidArgument
(
"The output "
"tensor's rank should be less than 6."
));
for
(
int
axis
:
unsqz_dims
)
{
int
cur
=
axis
<
0
?
axis
+
cur_output_size
+
1
:
axis
;
// Vaildity Check: the axis bound
PADDLE_ENFORCE_GE
(
cur
,
0
);
PADDLE_ENFORCE_LE
(
cur
,
cur_output_size
);
PADDLE_ENFORCE_GE
(
cur
,
0
,
platform
::
errors
::
InvalidArgument
(
"The insert dimension value should "
"not be less than 0"
));
PADDLE_ENFORCE_LE
(
cur
,
cur_output_size
,
platform
::
errors
::
InvalidArgument
(
"The insert dimension value shoule not be larger "
"than the dimension size of input tensor"
));
// Move old axis, and insert new axis
for
(
int
i
=
cur_output_size
;
i
>=
cur
;
--
i
)
{
if
(
output_shape
[
i
]
==
1
)
{
...
...
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