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a7de0e66
编写于
12月 17, 2021
作者:
K
kuizhiqing
提交者:
GitHub
12月 17, 2021
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差异文件
add op/api repeat/interleave (#37981)
上级
885767e3
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8
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8 changed file
with
957 addition
and
1 deletion
+957
-1
paddle/fluid/operators/repeat_interleave_op.cc
paddle/fluid/operators/repeat_interleave_op.cc
+174
-0
paddle/fluid/operators/repeat_interleave_op.cu
paddle/fluid/operators/repeat_interleave_op.cu
+307
-0
paddle/fluid/operators/repeat_interleave_op.h
paddle/fluid/operators/repeat_interleave_op.h
+196
-0
paddle/fluid/pybind/op_function_generator.h
paddle/fluid/pybind/op_function_generator.h
+1
-0
python/paddle/__init__.py
python/paddle/__init__.py
+2
-0
python/paddle/fluid/tests/unittests/test_repeat_interleave_op.py
...paddle/fluid/tests/unittests/test_repeat_interleave_op.py
+212
-0
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+3
-1
python/paddle/tensor/manipulation.py
python/paddle/tensor/manipulation.py
+62
-0
未找到文件。
paddle/fluid/operators/repeat_interleave_op.cc
0 → 100644
浏览文件 @
a7de0e66
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/repeat_interleave_op.h"
#include <memory>
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
RepeatInterleaveOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"X"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) of RepeatInterleaveOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of RepeatInterleaveOp should not be null."
));
auto
input_dim
=
ctx
->
GetInputDim
(
"X"
);
auto
dim
=
ctx
->
Attrs
().
Get
<
int
>
(
"dim"
);
auto
output_dim
=
framework
::
vectorize
(
input_dim
);
PADDLE_ENFORCE_EQ
(
dim
<
input_dim
.
size
()
&&
dim
>=
(
0
-
input_dim
.
size
()),
true
,
platform
::
errors
::
OutOfRange
(
"Attr(dim) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dim) = %d."
,
input_dim
.
size
(),
input_dim
.
size
()
-
1
,
dim
));
auto
repeats
=
ctx
->
Attrs
().
Get
<
int
>
(
"Repeats"
);
if
(
ctx
->
HasInput
(
"RepeatsTensor"
))
{
auto
repeats_dim
=
ctx
->
GetInputDim
(
"RepeatsTensor"
);
PADDLE_ENFORCE_EQ
(
repeats_dim
.
size
()
==
1
||
(
repeats_dim
.
size
()
==
2
&&
repeats_dim
[
1
]
==
1
),
true
,
platform
::
errors
::
InvalidArgument
(
"The 'shape' of Input(RepeatsTensor) must be 1-D tensor. "
"But received: the 'shape' of Input(Index) is [%s], "
"the dimension of Input(Index) is [%d]."
,
repeats_dim
,
repeats_dim
.
size
()));
PADDLE_ENFORCE_EQ
(
repeats_dim
[
0
]
!=
0
,
true
,
platform
::
errors
::
InvalidArgument
(
"The length of Input(RepeatsTensor) can't be 0."
));
if
(
dim
<
0
)
{
dim
+=
input_dim
.
size
();
}
output_dim
[
dim
]
=
-
1
;
}
else
if
(
repeats
>
0
)
{
output_dim
[
dim
]
=
input_dim
[
dim
]
*
repeats
;
}
VLOG
(
3
)
<<
"infershap out "
<<
output_dim
[
dim
];
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_dim
));
auto
type
=
ctx
->
GetInputsVarType
(
"X"
)[
0
];
if
(
type
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
data_type
=
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
);
return
framework
::
OpKernelType
(
data_type
,
ctx
.
device_context
());
}
};
class
RepeatInterleaveGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Out@GRAD) should be not null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(X@GRAD) should be not null."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
framework
::
GradVarName
(
"Out"
)),
ctx
.
device_context
());
}
};
class
RepeatInterleaveOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) the input tensor."
);
AddInput
(
"RepeatsTensor"
,
"the 1-D tensor containing the repeats alongsize the axis."
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"the output tensor."
);
AddAttr
<
int
>
(
"Repeats"
,
"the number of repetitions for each element."
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"dim"
,
"the dimension in which we repeat."
).
SetDefault
(
0
);
AddComment
(
R"DOC(
Returns a new tensor which repeats the input tensor
along dimension dim using the entries in repeats which
is a Tensor or int.
The returned tensor has the same number of dimensions
as the original tensor (input), except along the given axis.
)DOC"
);
}
};
template
<
typename
T
>
class
RepeatInterleaveGradMaker
:
public
framework
::
SingleGradOpMaker
<
T
>
{
public:
using
framework
::
SingleGradOpMaker
<
T
>::
SingleGradOpMaker
;
protected:
void
Apply
(
GradOpPtr
<
T
>
op
)
const
override
{
op
->
SetType
(
"repeat_interleave_grad"
);
op
->
SetInput
(
"X"
,
this
->
Input
(
"X"
));
op
->
SetInput
(
"RepeatsTensor"
,
this
->
Input
(
"RepeatsTensor"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
this
->
OutputGrad
(
"Out"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
this
->
InputGrad
(
"X"
));
op
->
SetAttrMap
(
this
->
Attrs
());
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERER
(
RepeatInterleaveGradNoNeedBufferVarsInferer
,
"X"
);
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
repeat_interleave
,
ops
::
RepeatInterleaveOp
,
ops
::
RepeatInterleaveOpMaker
,
ops
::
RepeatInterleaveGradMaker
<
paddle
::
framework
::
OpDesc
>
,
ops
::
RepeatInterleaveGradMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OPERATOR
(
repeat_interleave_grad
,
ops
::
RepeatInterleaveGradOp
,
ops
::
RepeatInterleaveGradNoNeedBufferVarsInferer
);
REGISTER_OP_CPU_KERNEL
(
repeat_interleave
,
ops
::
RepeatInterleaveKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
RepeatInterleaveKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
RepeatInterleaveKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
RepeatInterleaveKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
repeat_interleave_grad
,
ops
::
RepeatInterleaveGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
RepeatInterleaveGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
RepeatInterleaveGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
RepeatInterleaveGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/repeat_interleave_op.cu
0 → 100644
浏览文件 @
a7de0e66
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/repeat_interleave_op.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
namespace
paddle
{
namespace
operators
{
using
platform
::
PADDLE_CUDA_NUM_THREADS
;
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
// function borrowed from repeat_interleave_op
template
<
typename
T
,
typename
IndexT
>
__global__
void
index_select_cuda_kernel
(
const
T
*
input
,
T
*
output
,
const
IndexT
*
index
,
int64_t
N
,
int64_t
stride
,
int64_t
size
,
int64_t
delta
)
{
int64_t
idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
idx
>=
N
)
{
return
;
}
int64_t
pre_idx
=
idx
/
(
stride
*
size
);
int64_t
dim_idx
=
idx
%
(
stride
*
size
)
/
stride
;
IndexT
src_dim_idx
=
index
[
dim_idx
];
int64_t
input_idx
=
idx
+
(
delta
*
pre_idx
+
src_dim_idx
-
dim_idx
)
*
stride
;
output
[
idx
]
=
input
[
input_idx
];
}
template
<
typename
T
,
typename
IndexT
>
__global__
void
index_select_grad_cuda_kernel
(
const
T
*
output_grad
,
T
*
input_grad
,
const
IndexT
*
index
,
int64_t
nums
,
int64_t
N
,
int64_t
stride
,
int64_t
size
,
int64_t
delta
)
{
int64_t
idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
idx
>=
N
)
{
return
;
}
int64_t
pre_idx
=
idx
/
(
stride
*
size
);
int64_t
dim_idx
=
idx
%
(
stride
*
size
)
/
stride
;
IndexT
src_dim_idx
=
index
[
dim_idx
];
int64_t
input_idx
=
idx
+
(
delta
*
pre_idx
+
src_dim_idx
-
dim_idx
)
*
stride
;
paddle
::
platform
::
CudaAtomicAdd
(
&
input_grad
[
input_idx
],
output_grad
[
idx
]);
}
template
<
typename
T
>
__global__
void
index_select_grad_init
(
T
*
input_grad
,
int64_t
N
)
{
int64_t
idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
idx
>=
N
)
{
return
;
}
input_grad
[
idx
]
=
0.0
;
}
template
<
typename
DeviceContext
,
typename
T
>
class
RepeatInterleaveCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
// auto* index = context.Input<LoDTensor>("RepeatsTensor");
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
int
dim
=
context
.
Attr
<
int
>
(
"dim"
);
auto
input_dim
=
in
->
dims
();
dim
=
dim
>=
0
?
dim
:
dim
+
input_dim
.
size
();
auto
stride_dim
=
framework
::
stride
(
input_dim
);
int64_t
stride
=
stride_dim
[
dim
];
auto
stream
=
context
.
template
device_context
<
platform
::
CUDADeviceContext
>().
stream
();
int
repeats
=
context
.
Attr
<
int
>
(
"Repeats"
);
framework
::
LoDTensor
index
;
auto
*
in_data
=
in
->
data
<
T
>
();
if
(
context
.
HasInput
(
"RepeatsTensor"
))
{
auto
repeats_tensor
=
context
.
Input
<
framework
::
LoDTensor
>
(
"RepeatsTensor"
);
PADDLE_ENFORCE_EQ
(
repeats_tensor
->
dims
()[
0
]
==
in
->
dims
()[
dim
],
true
,
platform
::
errors
::
InvalidArgument
(
"The length of Input(RepeatsTensor) must be the "
"same as length of Input(X) in axis. "
"But received: [%s], required: [%d]."
,
repeats_tensor
->
dims
()[
0
],
in
->
dims
()[
dim
]));
const
auto
&
index_type
=
repeats_tensor
->
type
();
bool
index_type_match
=
index_type
==
framework
::
proto
::
VarType
::
INT64
||
index_type
==
framework
::
proto
::
VarType
::
INT32
;
PADDLE_ENFORCE_EQ
(
index_type_match
,
true
,
platform
::
errors
::
InvalidArgument
(
"Input(RepeatsTensor) holds the wrong type, it holds %s, but "
"desires to be %s or %s"
,
paddle
::
framework
::
DataTypeToString
(
index_type
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT32
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT64
)));
if
(
index_type
==
framework
::
proto
::
VarType
::
INT64
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int64_t
>
(
*
repeats_tensor
,
&
index
);
const
int64_t
*
index_data
=
index
.
data
<
int64_t
>
();
auto
output_dim
=
framework
::
vectorize
(
in
->
dims
());
output_dim
[
dim
]
=
index
.
dims
()[
0
];
out
->
Resize
(
framework
::
make_ddim
(
output_dim
));
auto
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
numel
=
out
->
numel
();
int64_t
size
=
output_dim
[
dim
];
int64_t
delta
=
input_dim
[
dim
]
-
size
;
index_select_cuda_kernel
<
T
,
int64_t
><<<
(
numel
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_data
,
out_data
,
index_data
,
numel
,
stride
,
size
,
delta
);
}
else
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int
>
(
*
repeats_tensor
,
&
index
);
const
int
*
index_data
=
index
.
data
<
int
>
();
auto
output_dim
=
framework
::
vectorize
(
in
->
dims
());
output_dim
[
dim
]
=
index
.
dims
()[
0
];
out
->
Resize
(
framework
::
make_ddim
(
output_dim
));
auto
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
numel
=
out
->
numel
();
int64_t
size
=
output_dim
[
dim
];
int64_t
delta
=
input_dim
[
dim
]
-
size
;
index_select_cuda_kernel
<
T
,
int
><<<
(
numel
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_data
,
out_data
,
index_data
,
numel
,
stride
,
size
,
delta
);
}
}
else
if
(
repeats
>
0
)
{
int64_t
index_size
=
in
->
dims
()[
dim
]
*
repeats
;
std
::
vector
<
int
>
index_vec
(
index_size
);
for
(
int
i
=
0
;
i
<
in
->
dims
()[
dim
];
i
++
)
{
std
::
fill_n
(
index_vec
.
begin
()
+
i
*
repeats
,
repeats
,
i
);
}
index
.
Resize
(
framework
::
make_ddim
({
index_size
}));
auto
ctx
=
paddle
::
platform
::
DeviceContextPool
::
Instance
().
Get
(
context
.
GetPlace
());
paddle
::
framework
::
TensorFromVector
<
int
>
(
index_vec
,
*
ctx
,
&
index
);
auto
output_dim
=
framework
::
vectorize
(
in
->
dims
());
output_dim
[
dim
]
=
index_size
;
out
->
Resize
(
framework
::
make_ddim
(
output_dim
));
auto
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
numel
=
out
->
numel
();
int64_t
size
=
output_dim
[
dim
];
int64_t
delta
=
input_dim
[
dim
]
-
size
;
const
int
*
index_data
=
index
.
data
<
int
>
();
index_select_cuda_kernel
<
T
,
int
><<<
(
numel
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_data
,
out_data
,
index_data
,
numel
,
stride
,
size
,
delta
);
platform
::
GpuStreamSync
(
stream
);
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"repeats must given with RepeatsTensor (tensor) or repeats (int)"
));
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
RepeatInterleaveGradCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
output_grad
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_grad
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_grad_data
=
output_grad
->
data
<
T
>
();
auto
*
in_grad_data
=
in_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
dim
=
context
.
Attr
<
int
>
(
"dim"
);
auto
input_dim
=
in_grad
->
dims
();
auto
output_dim
=
output_grad
->
dims
();
dim
=
dim
>=
0
?
dim
:
dim
+
input_dim
.
size
();
auto
stride_dim
=
framework
::
stride
(
input_dim
);
int64_t
stride
=
stride_dim
[
dim
];
int64_t
size
=
output_dim
[
dim
];
int64_t
delta
=
input_dim
[
dim
]
-
size
;
int64_t
numel
=
in_grad
->
numel
();
int64_t
out_nums
=
output_grad
->
numel
();
auto
stream
=
context
.
template
device_context
<
platform
::
CUDADeviceContext
>().
stream
();
index_select_grad_init
<
T
><<<
(
numel
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_grad_data
,
numel
);
int
repeats
=
context
.
Attr
<
int
>
(
"Repeats"
);
framework
::
LoDTensor
index
;
if
(
context
.
HasInput
(
"RepeatsTensor"
))
{
auto
repeats_tensor
=
context
.
Input
<
framework
::
LoDTensor
>
(
"RepeatsTensor"
);
const
auto
&
index_type
=
repeats_tensor
->
type
();
bool
index_type_match
=
index_type
==
framework
::
proto
::
VarType
::
INT64
||
index_type
==
framework
::
proto
::
VarType
::
INT32
;
PADDLE_ENFORCE_EQ
(
index_type_match
,
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Index) holds the wrong type, it holds %s, but "
"desires to be %s or %s"
,
paddle
::
framework
::
DataTypeToString
(
index_type
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT32
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT64
)));
if
(
index_type
==
framework
::
proto
::
VarType
::
INT64
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int64_t
>
(
*
repeats_tensor
,
&
index
);
int64_t
index_nums
=
index
.
numel
();
const
int64_t
*
index_data
=
index
.
data
<
int64_t
>
();
index_select_grad_cuda_kernel
<
T
,
int64_t
><<<
(
out_nums
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
output_grad_data
,
in_grad_data
,
index_data
,
index_nums
,
out_nums
,
stride
,
size
,
delta
);
platform
::
GpuStreamSync
(
stream
);
}
else
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int
>
(
*
repeats_tensor
,
&
index
);
int64_t
index_nums
=
index
.
numel
();
const
int
*
index_data
=
index
.
data
<
int
>
();
index_select_grad_cuda_kernel
<
T
,
int
><<<
(
out_nums
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
output_grad_data
,
in_grad_data
,
index_data
,
index_nums
,
out_nums
,
stride
,
size
,
delta
);
platform
::
GpuStreamSync
(
stream
);
}
}
else
if
(
repeats
>
0
)
{
int64_t
index_size
=
in_grad
->
dims
()[
dim
]
*
repeats
;
std
::
vector
<
int
>
index_vec
(
index_size
);
for
(
int
i
=
0
;
i
<
in_grad
->
dims
()[
dim
];
i
++
)
{
std
::
fill_n
(
index_vec
.
begin
()
+
i
*
repeats
,
repeats
,
i
);
}
index
.
Resize
(
framework
::
make_ddim
({
index_size
}));
auto
ctx
=
paddle
::
platform
::
DeviceContextPool
::
Instance
().
Get
(
context
.
GetPlace
());
paddle
::
framework
::
TensorFromVector
<
int
>
(
index_vec
,
*
ctx
,
&
index
);
const
int
*
index_data
=
index
.
data
<
int
>
();
int64_t
index_nums
=
index
.
numel
();
index_select_grad_cuda_kernel
<
T
,
int
><<<
(
out_nums
+
PADDLE_CUDA_NUM_THREADS
-
1
)
/
PADDLE_CUDA_NUM_THREADS
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
output_grad_data
,
in_grad_data
,
index_data
,
index_nums
,
out_nums
,
stride
,
size
,
delta
);
platform
::
GpuStreamSync
(
stream
);
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"repeats must given with RepeatsTensor (tensor) or repeats (int)"
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
repeat_interleave
,
ops
::
RepeatInterleaveCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
RepeatInterleaveCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
RepeatInterleaveCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
,
ops
::
RepeatInterleaveCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
RepeatInterleaveCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
repeat_interleave_grad
,
ops
::
RepeatInterleaveGradCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
RepeatInterleaveGradCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
RepeatInterleaveGradCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
,
ops
::
RepeatInterleaveGradCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
RepeatInterleaveGradCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/repeat_interleave_op.h
0 → 100644
浏览文件 @
a7de0e66
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/index_select_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
using
DDim
=
framework
::
DDim
;
template
<
typename
DeviceContext
,
typename
RepeatsT
=
int
>
void
RepeatsTensor2IndexTensor
(
const
LoDTensor
&
repeats
,
LoDTensor
*
index
)
{
LoDTensor
repeats_cpu_copy
;
if
(
!
platform
::
is_cpu_place
(
repeats
.
place
()))
{
framework
::
TensorCopySync
(
repeats
,
platform
::
CPUPlace
(),
&
repeats_cpu_copy
);
}
const
RepeatsT
*
repeats_data
=
platform
::
is_cpu_place
(
repeats
.
place
())
?
repeats
.
data
<
RepeatsT
>
()
:
repeats_cpu_copy
.
data
<
RepeatsT
>
();
int64_t
index_size
=
0
;
for
(
int
i
=
0
;
i
<
repeats
.
dims
()[
0
];
i
++
)
{
index_size
+=
repeats_data
[
i
];
}
std
::
vector
<
RepeatsT
>
index_vec
(
index_size
);
int
offset
=
0
;
for
(
int
i
=
0
;
i
<
repeats
.
dims
()[
0
];
i
++
)
{
std
::
fill_n
(
index_vec
.
begin
()
+
offset
,
repeats_data
[
i
],
i
);
offset
+=
repeats_data
[
i
];
}
index
->
Resize
(
framework
::
make_ddim
({
index_size
}));
auto
ctx
=
paddle
::
platform
::
DeviceContextPool
::
Instance
().
Get
(
repeats
.
place
());
paddle
::
framework
::
TensorFromVector
<
RepeatsT
>
(
index_vec
,
*
ctx
,
index
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
RepeatInterleaveKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
inputs
=
*
context
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
*
output
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
int
dim
=
context
.
Attr
<
int
>
(
"dim"
);
if
(
dim
<
0
)
{
dim
+=
inputs
.
dims
().
size
();
}
int
repeats
=
context
.
Attr
<
int
>
(
"Repeats"
);
framework
::
LoDTensor
index
;
if
(
context
.
HasInput
(
"RepeatsTensor"
))
{
auto
repeats_tensor
=
context
.
Input
<
framework
::
LoDTensor
>
(
"RepeatsTensor"
);
PADDLE_ENFORCE_EQ
(
repeats_tensor
->
dims
()[
0
]
==
inputs
.
dims
()[
dim
],
true
,
platform
::
errors
::
InvalidArgument
(
"The length of Input(RepeatsTensor) must be the "
"same as length of Input(X) in axis. "
"But received: [%s], required: [%d]."
,
repeats_tensor
->
dims
()[
0
],
inputs
.
dims
()[
dim
]));
const
auto
&
index_type
=
repeats_tensor
->
type
();
bool
index_type_match
=
index_type
==
framework
::
proto
::
VarType
::
INT32
||
index_type
==
framework
::
proto
::
VarType
::
INT64
;
PADDLE_ENFORCE_EQ
(
index_type_match
,
true
,
platform
::
errors
::
InvalidArgument
(
"Input(RepeatsTensor) holds the wrong type, it holds %s, but "
"desires to be %s or %s"
,
paddle
::
framework
::
DataTypeToString
(
index_type
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT32
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT64
)));
if
(
index_type
==
framework
::
proto
::
VarType
::
INT32
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int
>
(
*
repeats_tensor
,
&
index
);
auto
output_dim
=
framework
::
vectorize
(
inputs
.
dims
());
output_dim
[
dim
]
=
index
.
dims
()[
0
];
output
->
Resize
(
framework
::
make_ddim
(
output_dim
));
IndexSelectInner
<
DeviceContext
,
T
,
int
>
(
context
,
&
inputs
,
index
,
output
,
dim
);
}
else
if
(
index_type
==
framework
::
proto
::
VarType
::
INT64
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int64_t
>
(
*
repeats_tensor
,
&
index
);
auto
output_dim
=
framework
::
vectorize
(
inputs
.
dims
());
output_dim
[
dim
]
=
index
.
dims
()[
0
];
output
->
Resize
(
framework
::
make_ddim
(
output_dim
));
IndexSelectInner
<
DeviceContext
,
T
,
int64_t
>
(
context
,
&
inputs
,
index
,
output
,
dim
);
}
}
else
if
(
repeats
>
0
)
{
int64_t
index_size
=
inputs
.
dims
()[
dim
]
*
repeats
;
std
::
vector
<
int
>
index_vec
(
index_size
);
for
(
int
i
=
0
;
i
<
inputs
.
dims
()[
dim
];
i
++
)
{
std
::
fill_n
(
index_vec
.
begin
()
+
i
*
repeats
,
repeats
,
i
);
}
index
.
Resize
(
framework
::
make_ddim
({
index_size
}));
paddle
::
framework
::
TensorFromVector
<
int
>
(
index_vec
,
&
index
);
auto
output_dim
=
framework
::
vectorize
(
inputs
.
dims
());
output_dim
[
dim
]
=
index_size
;
output
->
Resize
(
framework
::
make_ddim
(
output_dim
));
IndexSelectInner
<
DeviceContext
,
T
,
int
>
(
context
,
&
inputs
,
index
,
output
,
dim
);
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"repeats must given with RepeatsTensor (tensor) or repeats (int)"
));
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
RepeatInterleaveGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x_grad
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
out_grad
=
context
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
int
dim
=
context
.
Attr
<
int
>
(
"dim"
);
if
(
dim
<
0
)
{
dim
+=
out_grad
->
dims
().
size
();
}
int
repeats
=
context
.
Attr
<
int
>
(
"Repeats"
);
framework
::
LoDTensor
index
;
if
(
context
.
HasInput
(
"RepeatsTensor"
))
{
auto
repeats_tensor
=
context
.
Input
<
framework
::
LoDTensor
>
(
"RepeatsTensor"
);
const
auto
&
index_type
=
repeats_tensor
->
type
();
bool
index_type_match
=
index_type
==
framework
::
proto
::
VarType
::
INT32
||
index_type
==
framework
::
proto
::
VarType
::
INT64
;
PADDLE_ENFORCE_EQ
(
index_type_match
,
true
,
platform
::
errors
::
InvalidArgument
(
"Input(Repeats) holds the wrong type, it holds %s, but "
"desires to be %s or %s"
,
paddle
::
framework
::
DataTypeToString
(
index_type
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT32
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT64
)));
if
(
index_type
==
framework
::
proto
::
VarType
::
INT32
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int
>
(
*
repeats_tensor
,
&
index
);
IndexSelectGradInner
<
DeviceContext
,
T
,
int
>
(
context
,
*
out_grad
,
index
,
x_grad
,
dim
);
}
else
if
(
index_type
==
framework
::
proto
::
VarType
::
INT64
)
{
RepeatsTensor2IndexTensor
<
DeviceContext
,
int64_t
>
(
*
repeats_tensor
,
&
index
);
IndexSelectGradInner
<
DeviceContext
,
T
,
int64_t
>
(
context
,
*
out_grad
,
index
,
x_grad
,
dim
);
}
}
else
if
(
repeats
>
0
)
{
int64_t
index_size
=
x_grad
->
dims
()[
dim
]
*
repeats
;
std
::
vector
<
int
>
index_vec
(
index_size
);
for
(
int
i
=
0
;
i
<
x_grad
->
dims
()[
dim
];
i
++
)
{
std
::
fill_n
(
index_vec
.
begin
()
+
i
*
repeats
,
repeats
,
i
);
}
index
.
Resize
(
framework
::
make_ddim
({
index_size
}));
paddle
::
framework
::
TensorFromVector
<
int
>
(
index_vec
,
&
index
);
IndexSelectGradInner
<
DeviceContext
,
T
,
int
>
(
context
,
*
out_grad
,
index
,
x_grad
,
dim
);
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"repeats must given with RepeatsTensor (tensor) or repeats (int)"
));
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/pybind/op_function_generator.h
浏览文件 @
a7de0e66
...
...
@@ -44,6 +44,7 @@ std::map<std::string, std::set<std::string>> op_ins_map = {
{
"nll_loss"
,
{
"X"
,
"Label"
,
"Weight"
}},
{
"bilinear_tensor_product"
,
{
"X"
,
"Y"
,
"Weight"
,
"Bias"
}},
{
"gather"
,
{
"X"
,
"Index"
,
"Axis"
}},
{
"repeat_interleave"
,
{
"X"
,
"RepeatsTensor"
}},
{
"roi_pool"
,
{
"X"
,
"ROIs"
,
"RoisNum"
}},
{
"roi_align"
,
{
"X"
,
"ROIs"
,
"RoisNum"
}},
{
"psroi_pool"
,
{
"X"
,
"ROIs"
,
"RoisNum"
}},
...
...
python/paddle/__init__.py
浏览文件 @
a7de0e66
...
...
@@ -159,6 +159,7 @@ from .tensor.manipulation import tensordot # noqa: F401
from
.tensor.manipulation
import
as_complex
# noqa: F401
from
.tensor.manipulation
import
as_real
# noqa: F401
from
.tensor.manipulation
import
moveaxis
# noqa: F401
from
.tensor.manipulation
import
repeat_interleave
# noqa: F401
from
.tensor.math
import
abs
# noqa: F401
from
.tensor.math
import
acos
# noqa: F401
from
.tensor.math
import
asin
# noqa: F401
...
...
@@ -579,4 +580,5 @@ __all__ = [ # noqa
'fmax'
,
'fmin'
,
'moveaxis'
,
'repeat_interleave'
,
]
python/paddle/fluid/tests/unittests/test_repeat_interleave_op.py
0 → 100644
浏览文件 @
a7de0e66
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
paddle
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
class
TestRepeatInterleaveOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"repeat_interleave"
self
.
init_dtype_type
()
index_np
=
np
.
random
.
randint
(
low
=
0
,
high
=
3
,
size
=
self
.
index_size
).
astype
(
self
.
index_type
)
x_np
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
x_type
)
self
.
inputs
=
{
'X'
:
x_np
,
'RepeatsTensor'
:
index_np
}
self
.
attrs
=
{
'dim'
:
self
.
dim
}
outer_loop
=
np
.
prod
(
self
.
x_shape
[:
self
.
dim
])
x_reshape
=
[
outer_loop
]
+
list
(
self
.
x_shape
[
self
.
dim
:])
x_np_reshape
=
np
.
reshape
(
x_np
,
tuple
(
x_reshape
))
out_list
=
[]
for
i
in
range
(
outer_loop
):
for
j
in
range
(
self
.
index_size
):
for
k
in
range
(
index_np
[
j
]):
out_list
.
append
(
x_np_reshape
[
i
,
j
])
self
.
out_shape
=
list
(
self
.
x_shape
)
self
.
out_shape
[
self
.
dim
]
=
np
.
sum
(
index_np
)
self
.
out_shape
=
tuple
(
self
.
out_shape
)
out
=
np
.
reshape
(
out_list
,
self
.
out_shape
)
self
.
outputs
=
{
'Out'
:
out
}
def
init_dtype_type
(
self
):
self
.
dim
=
1
self
.
x_type
=
np
.
float64
self
.
index_type
=
np
.
int64
self
.
x_shape
=
(
8
,
4
,
5
)
self
.
index_size
=
self
.
x_shape
[
self
.
dim
]
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestRepeatInterleaveOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"repeat_interleave"
self
.
init_dtype_type
()
index_np
=
2
x_np
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
x_type
)
self
.
inputs
=
{
'X'
:
x_np
}
#, 'RepeatsTensor': None}
self
.
attrs
=
{
'dim'
:
self
.
dim
,
'Repeats'
:
index_np
}
outer_loop
=
np
.
prod
(
self
.
x_shape
[:
self
.
dim
])
x_reshape
=
[
outer_loop
]
+
list
(
self
.
x_shape
[
self
.
dim
:])
x_np_reshape
=
np
.
reshape
(
x_np
,
tuple
(
x_reshape
))
out_list
=
[]
for
i
in
range
(
outer_loop
):
for
j
in
range
(
self
.
index_size
):
for
k
in
range
(
index_np
):
out_list
.
append
(
x_np_reshape
[
i
,
j
])
self
.
out_shape
=
list
(
self
.
x_shape
)
self
.
out_shape
[
self
.
dim
]
=
index_np
*
self
.
index_size
self
.
out_shape
=
tuple
(
self
.
out_shape
)
out
=
np
.
reshape
(
out_list
,
self
.
out_shape
)
self
.
outputs
=
{
'Out'
:
out
}
def
init_dtype_type
(
self
):
self
.
dim
=
1
self
.
x_type
=
np
.
float64
self
.
x_shape
=
(
8
,
4
,
5
)
self
.
index_size
=
self
.
x_shape
[
self
.
dim
]
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestIndexSelectAPI
(
unittest
.
TestCase
):
def
input_data
(
self
):
self
.
data_x
=
np
.
array
([[
1.0
,
2.0
,
3.0
,
4.0
],
[
5.0
,
6.0
,
7.0
,
8.0
],
[
9.0
,
10.0
,
11.0
,
12.0
]])
self
.
data_index
=
np
.
array
([
0
,
1
,
2
,
1
]).
astype
(
'int32'
)
def
test_repeat_interleave_api
(
self
):
paddle
.
enable_static
()
self
.
input_data
()
# case 1:
with
program_guard
(
Program
(),
Program
()):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
-
1
,
4
])
index
=
fluid
.
layers
.
data
(
name
=
'repeats'
,
shape
=
[
4
],
dtype
=
'int32'
,
append_batch_size
=
False
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
axis
=
1
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
res
,
=
exe
.
run
(
feed
=
{
'x'
:
self
.
data_x
,
'repeats'
:
self
.
data_index
},
fetch_list
=
[
z
.
name
],
return_numpy
=
False
)
expect_out
=
np
.
repeat
(
self
.
data_x
,
self
.
data_index
,
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np
.
array
(
res
)))
# case 2:
repeats
=
np
.
array
([
1
,
2
,
1
]).
astype
(
'int32'
)
with
program_guard
(
Program
(),
Program
()):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
-
1
,
4
])
index
=
fluid
.
layers
.
data
(
name
=
'repeats'
,
shape
=
[
3
],
dtype
=
'int32'
,
append_batch_size
=
False
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
axis
=
0
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
res
,
=
exe
.
run
(
feed
=
{
'x'
:
self
.
data_x
,
'repeats'
:
repeats
,
},
fetch_list
=
[
z
.
name
],
return_numpy
=
False
)
expect_out
=
np
.
repeat
(
self
.
data_x
,
repeats
,
axis
=
0
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np
.
array
(
res
)))
repeats
=
2
with
program_guard
(
Program
(),
Program
()):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
-
1
,
4
])
z
=
paddle
.
repeat_interleave
(
x
,
repeats
,
axis
=
0
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
res
,
=
exe
.
run
(
feed
=
{
'x'
:
self
.
data_x
},
fetch_list
=
[
z
.
name
],
return_numpy
=
False
)
expect_out
=
np
.
repeat
(
self
.
data_x
,
repeats
,
axis
=
0
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np
.
array
(
res
)))
def
test_dygraph_api
(
self
):
self
.
input_data
()
# case axis none
input_x
=
np
.
array
([[
1
,
2
,
1
],
[
1
,
2
,
3
]]).
astype
(
'int32'
)
index_x
=
np
.
array
([
1
,
1
,
2
,
1
,
2
,
2
]).
astype
(
'int32'
)
with
fluid
.
dygraph
.
guard
():
x
=
fluid
.
dygraph
.
to_variable
(
input_x
)
index
=
fluid
.
dygraph
.
to_variable
(
index_x
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
None
)
np_z
=
z
.
numpy
()
expect_out
=
np
.
repeat
(
input_x
,
index_x
,
axis
=
None
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np_z
))
# case repeats int
with
fluid
.
dygraph
.
guard
():
x
=
fluid
.
dygraph
.
to_variable
(
input_x
)
index
=
2
z
=
paddle
.
repeat_interleave
(
x
,
index
,
None
)
np_z
=
z
.
numpy
()
expect_out
=
np
.
repeat
(
input_x
,
index
,
axis
=
None
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np_z
))
# case 1:
with
fluid
.
dygraph
.
guard
():
x
=
fluid
.
dygraph
.
to_variable
(
self
.
data_x
)
index
=
fluid
.
dygraph
.
to_variable
(
self
.
data_index
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
-
1
)
np_z
=
z
.
numpy
()
expect_out
=
np
.
repeat
(
self
.
data_x
,
self
.
data_index
,
axis
=-
1
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np_z
))
with
fluid
.
dygraph
.
guard
():
x
=
fluid
.
dygraph
.
to_variable
(
self
.
data_x
)
index
=
fluid
.
dygraph
.
to_variable
(
self
.
data_index
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
1
)
np_z
=
z
.
numpy
()
expect_out
=
np
.
repeat
(
self
.
data_x
,
self
.
data_index
,
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np_z
))
# case 2:
index_x
=
np
.
array
([
1
,
2
,
1
]).
astype
(
'int32'
)
with
fluid
.
dygraph
.
guard
():
x
=
fluid
.
dygraph
.
to_variable
(
self
.
data_x
)
index
=
fluid
.
dygraph
.
to_variable
(
index_x
)
z
=
paddle
.
repeat_interleave
(
x
,
index
,
axis
=
0
)
np_z
=
z
.
numpy
()
expect_out
=
np
.
repeat
(
self
.
data_x
,
index
,
axis
=
0
)
self
.
assertTrue
(
np
.
allclose
(
expect_out
,
np_z
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/tensor/__init__.py
浏览文件 @
a7de0e66
...
...
@@ -114,6 +114,7 @@ from .manipulation import tensordot # noqa: F401
from
.manipulation
import
as_complex
# noqa: F401
from
.manipulation
import
as_real
# noqa: F401
from
.manipulation
import
moveaxis
# noqa: F401
from
.manipulation
import
repeat_interleave
# noqa: F401
from
.math
import
abs
# noqa: F401
from
.math
import
acos
# noqa: F401
from
.math
import
asin
# noqa: F401
...
...
@@ -436,7 +437,8 @@ tensor_method_func = [ #noqa
'lerp'
,
'lerp_'
,
'angle'
,
'moveaxis'
'moveaxis'
,
'repeat_interleave'
,
]
#this list used in math_op_patch.py for magic_method bind
...
...
python/paddle/tensor/manipulation.py
浏览文件 @
a7de0e66
...
...
@@ -2584,6 +2584,68 @@ def as_real(x, name=None):
return
out
def
repeat_interleave
(
x
,
repeats
,
axis
=
None
,
name
=
None
):
"""
Returns a new tensor which repeats the ``x`` tensor along dimension ``axis`` using
the entries in ``repeats`` which is a int or a Tensor.
Args:
x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float32, float64, int32, int64.
repeats (Tensor or int): The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.
axis (int, optional): The dimension in which we manipulate. Default: if None, the output tensor is flatten.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor with same data type as ``x``.
x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
repeats = paddle.to_tensor([3, 2, 1], dtype='int32')
paddle.repeat_interleave(x, repeats, 1)
# [[1, 1, 1, 2, 2, 3],
# [4, 4, 4, 5, 5, 6]]
paddle.repeat_interleave(x, 2, 0)
# [[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6]]
paddle.repeat_interleave(x, 2, None)
# [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]
"""
if
axis
is
None
:
x
=
paddle
.
flatten
(
x
)
axis
=
0
if
in_dygraph_mode
():
if
isinstance
(
repeats
,
int
):
return
_C_ops
.
repeat_interleave
(
x
,
None
,
'Repeats'
,
repeats
,
'dim'
,
axis
)
elif
isinstance
(
repeats
,
Variable
):
return
_C_ops
.
repeat_interleave
(
x
,
repeats
,
'dim'
,
axis
)
helper
=
LayerHelper
(
"repeat_interleave"
,
**
locals
())
check_variable_and_dtype
(
x
,
'x'
,
[
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'paddle.tensor.manipulation.repeat_interleave'
)
out
=
helper
.
create_variable_for_type_inference
(
x
.
dtype
)
helper
.
append_op
(
type
=
'repeat_interleave'
,
inputs
=
{
'X'
:
x
,
'RepeatsTensor'
:
repeats
if
isinstance
(
repeats
,
Variable
)
else
None
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'dim'
:
axis
,
'Repeats'
:
repeats
if
isinstance
(
repeats
,
int
)
else
0
})
return
out
def
moveaxis
(
x
,
source
,
destination
,
name
=
None
):
"""
Move the axis of tensor from ``source`` position to ``destination`` position.
...
...
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