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009c049e
编写于
4月 07, 2020
作者:
S
silingtong123
提交者:
GitHub
4月 07, 2020
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电子邮件补丁
差异文件
Add randint op API (#23337)
* add randint op
上级
ea6a251c
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
585 addition
and
4 deletion
+585
-4
paddle/fluid/operators/randint_op.cc
paddle/fluid/operators/randint_op.cc
+170
-0
paddle/fluid/operators/randint_op.cu
paddle/fluid/operators/randint_op.cu
+76
-0
python/paddle/__init__.py
python/paddle/__init__.py
+1
-1
python/paddle/fluid/tests/unittests/test_randint_op.py
python/paddle/fluid/tests/unittests/test_randint_op.py
+173
-0
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+1
-1
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+164
-2
未找到文件。
paddle/fluid/operators/randint_op.cc
0 → 100644
浏览文件 @
009c049e
// 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.
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/uniform_random_op.h"
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
CPURandintKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
std
::
vector
<
int64_t
>
new_shape
;
auto
list_new_shape_tensor
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"ShapeTensorList"
);
if
(
list_new_shape_tensor
.
size
()
>
0
||
ctx
.
HasInput
(
"ShapeTensor"
))
{
if
(
ctx
.
HasInput
(
"ShapeTensor"
))
{
auto
*
shape_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"ShapeTensor"
);
new_shape
=
GetNewDataFromShapeTensor
(
shape_tensor
);
}
else
if
(
list_new_shape_tensor
.
size
()
>
0
)
{
new_shape
=
GetNewDataFromShapeTensorList
(
list_new_shape_tensor
);
}
}
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
if
(
!
new_shape
.
empty
())
out
->
Resize
(
framework
::
make_ddim
(
new_shape
));
T
*
data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int64_t
size
=
out
->
numel
();
std
::
random_device
rd
;
std
::
mt19937
gen
(
rd
());
std
::
uniform_int_distribution
<>
dist
(
ctx
.
Attr
<
int
>
(
"low"
),
ctx
.
Attr
<
int
>
(
"high"
)
-
1
);
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
data
[
i
]
=
dist
(
gen
);
}
};
class
RandintOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
InvalidArgument
(
"Output(Out) of RandintOp is null."
));
PADDLE_ENFORCE_LT
(
ctx
->
Attrs
().
Get
<
int
>
(
"low"
),
ctx
->
Attrs
().
Get
<
int
>
(
"high"
),
platform
::
errors
::
InvalidArgument
(
"randint's low must less then high, "
"but received: low = %d, high = %d."
,
ctx
->
Attrs
().
Get
<
int
>
(
"low"
),
ctx
->
Attrs
().
Get
<
int
>
(
"high"
)));
if
(
ctx
->
HasInputs
(
"ShapeTensorList"
))
{
// top prority shape
auto
inputs_name
=
ctx
->
Inputs
(
"ShapeTensorList"
);
PADDLE_ENFORCE_GT
(
inputs_name
.
size
(),
0
,
platform
::
errors
::
InvalidArgument
(
"Input(ShapeTensorList)'size of Op(randint) can't be zero."
"Please check the Attr(shape)'s size of"
"Op(fluid.layers.randint).)"
));
auto
out_dims
=
std
::
vector
<
int
>
(
inputs_name
.
size
(),
-
1
);
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
out_dims
));
return
;
}
auto
&
shape
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int64_t
>>
(
"shape"
);
if
(
ctx
->
HasInput
(
"ShapeTensor"
)
&&
shape
.
empty
())
{
auto
shape_dims
=
ctx
->
GetInputDim
(
"ShapeTensor"
);
PADDLE_ENFORCE_EQ
(
shape_dims
.
size
(),
1
,
platform
::
errors
::
InvalidArgument
(
"ShapeError: Input(ShapeTensor)' dimension size of "
"Op(randint) must be 1."
"But received ShapeTensor's dimensions = %d."
,
shape_dims
.
size
()));
int
num_ele
=
1
;
for
(
int
i
=
0
;
i
<
shape_dims
.
size
();
++
i
)
{
num_ele
*=
shape_dims
[
i
];
}
auto
vec_dims
=
std
::
vector
<
int64_t
>
(
num_ele
,
-
1
);
auto
out_dims
=
framework
::
make_ddim
(
vec_dims
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
return
;
}
PADDLE_ENFORCE_EQ
(
shape
.
empty
(),
false
,
platform
::
errors
::
InvalidArgument
(
"if there is no Input(ShapeTensorList) and no "
"Input(ShapeTensor),the "
"attr(shape) information must "
"be set by Attr(shape)."
));
std
::
vector
<
int64_t
>
tensor_shape
;
tensor_shape
.
reserve
(
shape
.
size
());
for
(
auto
dim
:
shape
)
{
tensor_shape
.
push_back
(
static_cast
<
int64_t
>
(
dim
));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
tensor_shape
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
ctx
.
Attr
<
int
>
(
"dtype"
)),
ctx
.
GetPlace
());
}
};
class
RandintOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"ShapeTensor"
,
"(Tensor<int64_t> or Tensor<int32_t>, optional) . If provided, "
"randint"
"according to "
"this given shape. It means that it has a higher priority than "
"Attr(shape) but a lower priority than Input(ShapeTensor)."
)
.
AsDispensable
();
AddInput
(
"ShapeTensorList"
,
"(vector<Tensor<int64_t>> or vector<Tensor<int32_t>>, optional). "
"If provided, randint use this. The shape of the tensor "
"must be [1], it has the highest priority comparing with "
"Input(ShapeTensor) and attr(shape)."
)
.
AsDuplicable
()
.
AsDispensable
();
AddOutput
(
"Out"
,
"The output tensor of randint op"
);
AddComment
(
R"DOC(
This operator initializes a tensor with random integers sampled from a
uniform distribution. The random result is in set [low, high).
)DOC"
);
AddAttr
<
std
::
vector
<
int64_t
>>
(
"shape"
,
"The shape of the output tensor."
)
.
SetDefault
({});
AddAttr
<
int
>
(
"low"
,
"The lower bound on the range of random values to generate."
);
AddAttr
<
int
>
(
"high"
,
"The upper bound on the range of random values to generate."
);
AddAttr
<
int
>
(
"dtype"
,
"Output tensor data type. [Default INT64]."
)
.
SetDefault
(
framework
::
proto
::
VarType
::
INT64
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
randint
,
ops
::
RandintOp
,
ops
::
RandintOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
imperative
::
OpBase
>
)
REGISTER_OP_CPU_KERNEL
(
randint
,
ops
::
CPURandintKernel
<
int
>
,
ops
::
CPURandintKernel
<
int64_t
>
)
paddle/fluid/operators/randint_op.cu
0 → 100644
浏览文件 @
009c049e
// 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.
#include <thrust/random.h>
#include <thrust/transform.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/uniform_random_op.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
struct
UniformIntGenerator
{
T
low_
,
high_
;
__host__
__device__
UniformIntGenerator
(
T
low
,
T
high
)
:
low_
(
low
),
high_
(
high
)
{}
__host__
__device__
T
operator
()(
const
unsigned
int
n
)
const
{
thrust
::
minstd_rand
rng
;
rng
.
seed
(
0
);
thrust
::
uniform_int_distribution
<
T
>
dist
(
low_
,
high_
);
rng
.
discard
(
n
);
T
out
=
dist
(
rng
);
return
out
;
}
};
// Use std::uniform_int_distribution and thrust::uniform_int_distribution(thrust
// is a std library in CUDA) to
// implement randint.
template
<
typename
T
>
class
GPURandintKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
std
::
vector
<
int64_t
>
new_shape
;
auto
list_new_shape_tensor
=
context
.
MultiInput
<
framework
::
Tensor
>
(
"ShapeTensorList"
);
if
(
list_new_shape_tensor
.
size
()
>
0
||
context
.
HasInput
(
"ShapeTensor"
))
{
if
(
context
.
HasInput
(
"ShapeTensor"
))
{
auto
*
shape_tensor
=
context
.
Input
<
framework
::
Tensor
>
(
"ShapeTensor"
);
new_shape
=
GetNewDataFromShapeTensor
(
shape_tensor
);
}
else
if
(
list_new_shape_tensor
.
size
()
>
0
)
{
new_shape
=
GetNewDataFromShapeTensorList
(
list_new_shape_tensor
);
}
}
auto
*
out
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
if
(
!
new_shape
.
empty
())
out
->
Resize
(
framework
::
make_ddim
(
new_shape
));
T
*
data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
low
=
static_cast
<
T
>
(
context
.
Attr
<
int
>
(
"low"
));
T
high
=
static_cast
<
T
>
(
context
.
Attr
<
int
>
(
"high"
))
-
1
;
thrust
::
counting_iterator
<
unsigned
int
>
index_sequence_begin
(
0
);
int64_t
size
=
out
->
numel
();
thrust
::
transform
(
index_sequence_begin
,
index_sequence_begin
+
size
,
thrust
::
device_ptr
<
T
>
(
data
),
UniformIntGenerator
<
T
>
(
low
,
high
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
randint
,
ops
::
GPURandintKernel
<
int
>
,
ops
::
GPURandintKernel
<
int64_t
>
)
python/paddle/__init__.py
浏览文件 @
009c049e
...
...
@@ -90,7 +90,7 @@ from .tensor.logic import elementwise_equal #DEFINE_ALIAS
# from .tensor.random import randn #DEFINE_ALIAS
from
.tensor.random
import
randperm
# from .tensor.random import rand #DEFINE_ALIAS
# from .tensor.random import randint
#DEFINE_ALIAS
from
.tensor.random
import
randint
#DEFINE_ALIAS
# from .tensor.math import abs #DEFINE_ALIAS
# from .tensor.math import acos #DEFINE_ALIAS
# from .tensor.math import asin #DEFINE_ALIAS
...
...
python/paddle/fluid/tests/unittests/test_randint_op.py
0 → 100644
浏览文件 @
009c049e
# 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
numpy
as
np
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
import
paddle
def
output_hist
(
out
):
hist
,
_
=
np
.
histogram
(
out
,
range
=
(
-
5
,
10
))
hist
=
hist
.
astype
(
"float32"
)
hist
/=
float
(
out
.
size
)
prob
=
0.1
*
np
.
ones
((
10
))
return
hist
,
prob
class
TestRandintOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"randint"
self
.
inputs
=
{}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
10000
,
784
)).
astype
(
"float32"
)}
def
init_attrs
(
self
):
self
.
attrs
=
{
"shape"
:
[
10000
,
784
],
"low"
:
-
5
,
"high"
:
10
}
self
.
output_hist
=
output_hist
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
verify_output
(
self
,
outs
):
hist
,
prob
=
self
.
output_hist
(
np
.
array
(
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.1
),
"hist: "
+
str
(
hist
))
class
TestRandintOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
main_prog
=
Program
()
start_prog
=
Program
()
with
program_guard
(
main_prog
,
start_prog
):
def
test_shape
():
shape
=
np
.
array
([
2
,
3
])
paddle
.
randint
(
5
,
shape
=
shape
,
dtype
=
'int32'
)
self
.
assertRaises
(
TypeError
,
test_shape
)
def
test_dtype
():
paddle
.
randint
(
5
,
shape
=
[
32
,
32
],
dtype
=
'float32'
)
self
.
assertRaises
(
TypeError
,
test_dtype
)
def
test_low_high
():
paddle
.
randint
(
low
=
5
,
high
=
5
,
shape
=
[
32
,
32
],
dtype
=
'int32'
)
self
.
assertRaises
(
ValueError
,
test_low_high
)
class
TestRandintOp_attr_tensorlist
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"randint"
self
.
new_shape
=
(
10000
,
784
)
shape_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
new_shape
):
shape_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
"int64"
)
*
ele
))
self
.
inputs
=
{
'ShapeTensorList'
:
shape_tensor
}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
10000
,
784
)).
astype
(
"int32"
)}
def
init_attrs
(
self
):
self
.
attrs
=
{
"low"
:
-
5
,
"high"
:
10
}
self
.
output_hist
=
output_hist
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
verify_output
(
self
,
outs
):
hist
,
prob
=
self
.
output_hist
(
np
.
array
(
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.1
),
"hist: "
+
str
(
hist
))
class
TestRandint_attr_tensor
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"randint"
self
.
inputs
=
{
"ShapeTensor"
:
np
.
array
([
10000
,
784
]).
astype
(
"int64"
)}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
10000
,
784
)).
astype
(
"int64"
)}
def
init_attrs
(
self
):
self
.
attrs
=
{
"low"
:
-
5
,
"high"
:
10
}
self
.
output_hist
=
output_hist
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
verify_output
(
self
,
outs
):
hist
,
prob
=
self
.
output_hist
(
np
.
array
(
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.1
),
"hist: "
+
str
(
hist
))
# Test python API
class
TestRandintAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
startup_program
=
fluid
.
Program
()
train_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_program
,
startup_program
):
# results are from [0, 5).
output1
=
paddle
.
randint
(
5
)
# shape is a list and dtype is 'int32'
output2
=
paddle
.
randint
(
low
=-
100
,
high
=
100
,
shape
=
[
64
,
64
],
dtype
=
'int32'
)
# shape is a tuple and dtype is 'int64'
output3
=
paddle
.
randint
(
low
=-
100
,
high
=
100
,
shape
=
(
32
,
32
,
3
),
dtype
=
'int64'
)
# shape is a tensorlist and dtype is 'float32'
dim_1
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
32
)
dim_2
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
50
)
output4
=
paddle
.
randint
(
low
=-
100
,
high
=
100
,
shape
=
[
dim_1
,
5
],
dtype
=
'int32'
)
# shape is a tensor and dtype is 'float64'
var_shape
=
fluid
.
data
(
name
=
'var_shape'
,
shape
=
[
2
],
dtype
=
"int64"
)
output5
=
paddle
.
randint
(
low
=
1
,
high
=
1000
,
shape
=
var_shape
,
dtype
=
'int64'
)
place
=
fluid
.
CPUPlace
()
if
fluid
.
core
.
is_compiled_with_cuda
():
place
=
fluid
.
CUDAPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_program
)
outs
=
exe
.
run
(
train_program
,
feed
=
{
'var_shape'
:
np
.
array
([
100
,
100
]).
astype
(
'int64'
)},
fetch_list
=
[
output1
,
output2
,
output3
,
output4
,
output5
])
class
TestRandintDygraphMode
(
unittest
.
TestCase
):
def
test_check_output
(
self
):
with
fluid
.
dygraph
.
guard
():
x
=
paddle
.
randint
(
10
,
shape
=
[
10
],
dtype
=
"int32"
)
x_np
=
x
.
numpy
()
for
i
in
range
(
10
):
self
.
assertTrue
((
x_np
[
i
]
>=
0
and
x_np
[
i
]
<
10
))
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/tensor/__init__.py
浏览文件 @
009c049e
...
...
@@ -64,7 +64,7 @@ from .logic import elementwise_equal #DEFINE_ALIAS
# from .random import shuffle #DEFINE_ALIAS
# from .random import randn #DEFINE_ALIAS
# from .random import rand #DEFINE_ALIAS
# from .random import randint
#DEFINE_ALIAS
from
.random
import
randint
#DEFINE_ALIAS
from
.random
import
randperm
# from .math import abs #DEFINE_ALIAS
# from .math import acos #DEFINE_ALIAS
...
...
python/paddle/tensor/random.py
浏览文件 @
009c049e
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
# TODO: define random functions
# __all__ = ['gaussin',
# 'uniform',
# 'shuffle',
...
...
@@ -21,12 +22,173 @@
# 'randint']
from
..fluid
import
core
from
..fluid.framework
import
device_guard
,
in_dygraph_mode
,
_varbase_creator
from
..fluid.framework
import
device_guard
,
in_dygraph_mode
,
_varbase_creator
,
Variable
from
..fluid.layers.layer_function_generator
import
templatedoc
from
..fluid.layer_helper
import
LayerHelper
from
..fluid.data_feeder
import
convert_dtype
,
check_variable_and_dtype
,
check_type
,
check_dtype
from
..fluid.layers
import
utils
from
..fluid.layers.tensor
import
fill_constant
__all__
=
[
'randperm'
,
'randint'
]
def
randint
(
low
,
high
=
None
,
shape
=
None
,
out
=
None
,
dtype
=
None
,
device
=
None
,
stop_gradient
=
False
,
name
=
None
):
"""
This function returns a Tensor filled with random integers from the "discrete uniform" distribution of the
specified data type in the interval [low, high). If high is None (the default), then results are from [0, low).
Args:
low (int): The lower bound on the range of random values to generate, the low is included in the range.
(unless high=None, in which case this parameter is one above the highest such integer).
high (int, optional): The upper bound on the range of random values to generate, the high is excluded
in the range. Default None(see above for behavior if high=None).
shape (list|tuple|Variable, optional): The shape of the output Tensor, if the shape is a list or tuple,
its elements can be an integer
or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64.
If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be
int32 or int64. Default is None, in which case the shape is [1].
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output Tensor
which can be int32, int64, if dytpe is `None`, the data
type of created Tensor is `int64`
device(str, optional): This parameter specifies that the Tensor is created
on the GPU or CPU.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is False.
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:
Variable: A Tensor of the specified shape filled with random integers.
Raises:
TypeError: Randint's low must less then high.
Examples:
.. code-block:: python
import paddle
import paddle.tensor as tensor
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
result_1 = paddle.randint(low=-5, high=5, shape=[3, 4], dtype="int64")
# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
result_2 = paddle.randint(low=-5, high=5, shape=[dim_1, dim_2], dtype="int32")
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = padddle.randint(low=-5, high=5, shape=var_shape, dtype="int32")
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = paddle.randint(low=-5, high=5, shape=var_shape_int32, dtype="int64")
# example 4:
# Input only one parameter
# low=0, high=10, shape=[1], dtype='int64'
result_4 = paddle.randint(10)
"""
__all__
=
[
'randperm'
]
def
get_new_shape_tensor
(
list_shape
):
new_shape_tensor
=
[]
for
dim
in
list_shape
:
if
isinstance
(
dim
,
Variable
):
dim
.
stop_gradient
=
True
new_shape_tensor
.
append
(
dim
)
else
:
assert
isinstance
(
dim
,
int
)
or
isinstance
(
dim
,
long
)
temp_out
=
helper
.
create_variable_for_type_inference
(
'int64'
)
fill_constant
([
1
],
'int64'
,
dim
,
force_cpu
=
True
,
out
=
temp_out
)
new_shape_tensor
.
append
(
temp_out
)
return
new_shape_tensor
def
get_attr_shape
(
list_shape
):
unk_dim_idx
=
-
1
attrs_shape
=
[]
for
dim_idx
,
dim_size
in
enumerate
(
list_shape
):
if
isinstance
(
dim_size
,
Variable
):
attrs_shape
.
append
(
-
1
)
else
:
attrs_shape
.
append
(
dim_size
)
assert
dim_size
>
0
,
(
"Each dimension size given in shape must not be negative "
"except one unknown dimension."
)
return
attrs_shape
if
dtype
is
None
:
dtype
=
'int64'
check_dtype
(
dtype
,
'dtype'
,
[
'int32'
,
'int64'
],
'randint'
)
inputs
=
dict
()
attrs
=
dict
()
if
shape
is
None
:
shape
=
[
1
]
assert
len
(
shape
)
>
0
,
(
"The size of argument(shape) can't be zero."
)
helper
=
LayerHelper
(
"randint"
,
**
locals
())
if
in_dygraph_mode
():
attrs
[
'shape'
]
=
shape
else
:
if
isinstance
(
shape
,
Variable
):
shape
.
stop_gradient
=
True
inputs
[
"ShapeTensor"
]
=
shape
elif
isinstance
(
shape
,
(
list
,
tuple
)):
assert
len
(
shape
)
>
0
,
(
"The size of argument(shape) can't be zero."
)
if
utils
.
_contain_var
(
shape
):
inputs
[
'ShapeTensorList'
]
=
get_new_shape_tensor
(
shape
)
else
:
attrs
[
"shape"
]
=
get_attr_shape
(
shape
)
check_type
(
shape
,
'shape'
,
(
list
,
tuple
,
Variable
),
'randint'
)
if
high
is
None
:
high
=
low
low
=
0
attrs
[
'low'
]
=
low
attrs
[
'high'
]
=
high
if
(
low
>=
high
):
raise
ValueError
(
"randint's low must less then high, but received low = {0}, "
"high = {1}"
.
format
(
low
,
high
))
if
out
is
None
:
if
name
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
dtype
,
persistable
=
False
)
else
:
check_dtype
(
dtype
,
'dtype'
,
convert_dtype
(
out
.
dtype
),
'randint'
,
"(The dtype in randint must be the same with out's dtype.)"
)
attrs
[
'dtype'
]
=
out
.
dtype
out
.
stop_gradient
=
stop_gradient
if
device
is
None
:
helper
.
append_op
(
type
=
'randint'
,
inputs
=
inputs
,
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
else
:
with
device_guard
(
device
):
helper
.
append_op
(
type
=
'randint'
,
inputs
=
inputs
,
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
return
out
@
templatedoc
()
...
...
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