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体验新版 GitCode,发现更多精彩内容 >>
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bcf86e5c
编写于
12月 24, 2021
作者:
zhouweiwei2014
提交者:
GitHub
12月 24, 2021
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电子邮件补丁
差异文件
add new API/OP: paddle.poisson (#38117)
* add new API/OP:paddle.poisson * fix comment
上级
7339a124
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
506 addition
and
18 deletion
+506
-18
paddle/fluid/operators/poisson_op.cc
paddle/fluid/operators/poisson_op.cc
+132
-0
paddle/fluid/operators/poisson_op.cu
paddle/fluid/operators/poisson_op.cu
+92
-0
paddle/fluid/operators/poisson_op.h
paddle/fluid/operators/poisson_op.h
+41
-0
paddle/fluid/operators/uniform_random_op.cc
paddle/fluid/operators/uniform_random_op.cc
+2
-3
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+1
-1
python/paddle/__init__.py
python/paddle/__init__.py
+3
-2
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+2
-2
python/paddle/fluid/tests/unittests/test_bernoulli_op.py
python/paddle/fluid/tests/unittests/test_bernoulli_op.py
+2
-6
python/paddle/fluid/tests/unittests/test_poisson_op.py
python/paddle/fluid/tests/unittests/test_poisson_op.py
+181
-0
python/paddle/nn/initializer/dirac.py
python/paddle/nn/initializer/dirac.py
+6
-4
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+1
-0
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+43
-0
未找到文件。
paddle/fluid/operators/poisson_op.cc
0 → 100644
浏览文件 @
bcf86e5c
/* 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 <string>
#include "paddle/fluid/operators/poisson_op.h"
namespace
paddle
{
namespace
operators
{
class
PoissonOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"PoissonOp"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"Out"
),
"Output"
,
"Out"
,
"PoissonOp"
);
auto
dim
=
ctx
->
GetInputDim
(
"X"
);
ctx
->
SetOutputDim
(
"Out"
,
dim
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
),
ctx
.
GetPlace
());
}
};
class
PoissonOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) The input tensor of poisson op"
);
AddOutput
(
"Out"
,
"The output tensor of poisson op, it has the same shape and "
"dtype with input. Each element corresponds to input tensor"
);
AddComment
(
R"DOC(
This operator generate random value that obey poisson distribution.
)DOC"
);
}
};
class
PoissonOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
&
GetInputOutputWithSameType
()
const
override
{
static
std
::
unordered_map
<
std
::
string
,
std
::
string
>
m
{{
"X"
,
/*->*/
"Out"
}};
return
m
;
}
};
template
<
typename
T
>
class
PoissonKernel
<
platform
::
CPUDeviceContext
,
T
>
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
T
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int64_t
size
=
x
->
numel
();
auto
gen
=
framework
::
DefaultCPUGenerator
();
auto
engine
=
gen
->
GetCPUEngine
();
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
std
::
poisson_distribution
<>
dist
(
x_data
[
i
]);
out_data
[
i
]
=
static_cast
<
T
>
(
dist
(
*
engine
));
}
}
};
class
PoissonGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input"
,
"Out_Grad"
,
"PoissonGradOp"
);
auto
dout_dim
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
dout_dim
);
}
};
template
<
typename
T
>
class
PoissonGradOpMaker
:
public
framework
::
SingleGradOpMaker
<
T
>
{
public:
using
framework
::
SingleGradOpMaker
<
T
>::
SingleGradOpMaker
;
protected:
void
Apply
(
GradOpPtr
<
T
>
retv
)
const
override
{
retv
->
SetType
(
"poisson_grad"
);
retv
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
this
->
OutputGrad
(
"Out"
));
retv
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
this
->
InputGrad
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OPERATOR
(
poisson
,
ops
::
PoissonOp
,
ops
::
PoissonOpMaker
,
ops
::
PoissonOpInferVarType
,
ops
::
PoissonGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
ops
::
PoissonGradOpMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OPERATOR
(
poisson_grad
,
ops
::
PoissonGradOp
);
REGISTER_OP_CPU_KERNEL
(
poisson
,
ops
::
PoissonKernel
<
plat
::
CPUDeviceContext
,
float
>
,
ops
::
PoissonKernel
<
plat
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
poisson_grad
,
ops
::
PoissonGradKernel
<
plat
::
CPUDeviceContext
,
float
>
,
ops
::
PoissonGradKernel
<
plat
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/poisson_op.cu
0 → 100644
浏览文件 @
bcf86e5c
/* 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. */
#ifdef __NVCC__
#include <curand_kernel.h>
#endif
#ifdef __HIPCC__
#include <hiprand_kernel.h>
#endif
#include "paddle/fluid/operators/poisson_op.h"
#include "paddle/fluid/platform/for_range.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
struct
PoissonCudaFunctor
{
public:
PoissonCudaFunctor
(
const
T
*
in
,
T
*
out
,
unsigned
int
seed
,
unsigned
int
offset
)
:
in_
(
in
),
out_
(
out
),
seed_
(
seed
),
offset_
(
offset
)
{}
__device__
void
operator
()(
int64_t
idx
)
{
#ifdef __NVCC__
curandStatePhilox4_32_10_t
state
;
curand_init
(
seed_
,
idx
,
offset_
,
&
state
);
out_
[
idx
]
=
static_cast
<
T
>
(
curand_poisson
(
&
state
,
in_
[
idx
]));
#elif __HIPCC__
hiprandStatePhilox4_32_10_t
state
;
hiprand_init
(
seed_
,
idx
,
offset_
,
&
state
);
out_
[
idx
]
=
static_cast
<
T
>
(
hiprand_poisson
(
&
state
,
in_
[
idx
]));
#endif
}
private:
const
T
*
in_
;
T
*
out_
;
const
unsigned
int
seed_
;
const
unsigned
int
offset_
;
};
template
<
typename
T
>
class
PoissonKernel
<
platform
::
CUDADeviceContext
,
T
>
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
T
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
size
=
x
->
numel
();
int64_t
device_id
=
BOOST_GET_CONST
(
platform
::
CUDAPlace
,
ctx
.
GetPlace
()).
GetDeviceId
();
auto
gen_cuda
=
framework
::
GetDefaultCUDAGenerator
(
device_id
);
auto
seed_offset
=
gen_cuda
->
IncrementOffset
(
20
);
uint64_t
seed
=
seed_offset
.
first
;
uint64_t
offset
=
seed_offset
.
second
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
platform
::
ForRange
<
platform
::
CUDADeviceContext
>
for_range
(
dev_ctx
,
size
);
PoissonCudaFunctor
<
T
>
functor
(
x_data
,
out_data
,
seed
,
offset
);
for_range
(
functor
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_CUDA_KERNEL
(
poisson
,
ops
::
PoissonKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
PoissonKernel
<
plat
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
poisson_grad
,
ops
::
PoissonGradKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
PoissonGradKernel
<
plat
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/poisson_op.h
0 → 100644
浏览文件 @
bcf86e5c
// 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/generator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
PoissonKernel
;
template
<
typename
DeviceContext
,
typename
T
>
class
PoissonGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SetConstant
<
DeviceContext
,
T
>
functor
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
functor
(
dev_ctx
,
dx
,
static_cast
<
T
>
(
0
));
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/uniform_random_op.cc
浏览文件 @
bcf86e5c
...
...
@@ -27,7 +27,7 @@ namespace {
template
<
typename
T
>
inline
void
UniformRealDistribution
(
T
*
data
,
const
int64_t
&
size
,
const
float
&
min
,
const
float
&
max
,
const
unsigned
int
&
seed
)
{
const
unsigned
int
seed
)
{
VLOG
(
4
)
<<
"[CPU] UniformRandomKernel<T>"
;
std
::
uniform_real_distribution
<
T
>
dist
(
static_cast
<
T
>
(
min
),
static_cast
<
T
>
(
max
));
...
...
@@ -41,8 +41,7 @@ inline void UniformRealDistribution(T *data, const int64_t &size,
template
<
>
inline
void
UniformRealDistribution
(
paddle
::
platform
::
bfloat16
*
data
,
const
int64_t
&
size
,
const
float
&
min
,
const
float
&
max
,
const
unsigned
int
&
seed
)
{
const
float
&
max
,
const
unsigned
int
seed
)
{
VLOG
(
4
)
<<
"[CPU] UniformRandomKernel<bfloat16>"
;
std
::
uniform_real_distribution
<
float
>
dist
(
min
,
max
);
auto
engine
=
paddle
::
framework
::
GetCPURandomEngine
(
seed
);
...
...
paddle/scripts/paddle_build.sh
浏览文件 @
bcf86e5c
...
...
@@ -575,7 +575,7 @@ EOF
export
http_proxy
=
export
https_proxy
=
set
-x
set
+ex
if
[
"
$1
"
==
"cp36-cp36m"
]
;
then
pip3.6 uninstall
-y
paddlepaddle
...
...
python/paddle/__init__.py
浏览文件 @
bcf86e5c
...
...
@@ -64,8 +64,6 @@ import paddle.reader # noqa: F401
import
paddle.static
# noqa: F401
import
paddle.vision
# noqa: F401
from
.tensor.random
import
bernoulli
# noqa: F401
from
.tensor.attribute
import
is_complex
# noqa: F401
from
.tensor.attribute
import
is_integer
# noqa: F401
from
.tensor.attribute
import
rank
# noqa: F401
...
...
@@ -248,6 +246,8 @@ from .tensor.math import angle # noqa: F401
from
.tensor.math
import
fmax
# noqa: F401
from
.tensor.math
import
fmin
# noqa: F401
from
.tensor.random
import
bernoulli
# noqa: F401
from
.tensor.random
import
poisson
# noqa: F401
from
.tensor.random
import
multinomial
# noqa: F401
from
.tensor.random
import
standard_normal
# noqa: F401
from
.tensor.random
import
normal
# noqa: F401
...
...
@@ -488,6 +488,7 @@ __all__ = [ # noqa
'exp'
,
'expm1'
,
'bernoulli'
,
'poisson'
,
'sinh'
,
'round'
,
'DataParallel'
,
...
...
python/paddle/fluid/initializer.py
浏览文件 @
bcf86e5c
...
...
@@ -1152,12 +1152,12 @@ def calculate_gain(nonlinearity, param=None):
Args:
nonlinearity(str): name of nonlinearity activation function. If it is a linear function, which is one of
"linear/conv1d/conv2d/conv3d/conv1d_transpose/conv2d_transpose/conv3d_transpose" ,
will return 1.0
"linear/conv1d/conv2d/conv3d/conv1d_transpose/conv2d_transpose/conv3d_transpose" ,
1.0 will be returned.
param(bool|int|float, optional): optional parameter for somme nonlinearity function. Now, it only applies to
'leaky_relu'. Default: None, it will be calculated as 0.01 in the formula.
Returns:
The recommended gain value for
nonlinearity function.
A float value, which is the recommended gain for this
nonlinearity function.
Examples:
.. code-block:: python
...
...
python/paddle/fluid/tests/unittests/test_bernoulli_op.py
浏览文件 @
bcf86e5c
...
...
@@ -32,18 +32,14 @@ class TestBernoulliOp(OpTest):
def
setUp
(
self
):
self
.
op_type
=
"bernoulli"
self
.
inputs
=
{
"X"
:
np
.
random
.
uniform
(
size
=
(
1000
,
784
))}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
1000
,
784
)).
astype
(
"float32"
)}
def
init_attrs
(
self
):
self
.
attrs
=
{}
self
.
output
_hist
=
output_hist
self
.
output
s
=
{
"Out"
:
np
.
zeros
((
1000
,
784
)).
astype
(
"float32"
)}
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
]))
hist
,
prob
=
output_hist
(
np
.
array
(
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.01
),
"hist: "
+
str
(
hist
))
...
...
python/paddle/fluid/tests/unittests/test_poisson_op.py
0 → 100644
浏览文件 @
bcf86e5c
# 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.
import
unittest
import
paddle
import
numpy
as
np
from
op_test
import
OpTest
import
math
paddle
.
enable_static
()
def
output_hist
(
out
,
lam
,
a
,
b
):
prob
=
[]
bin
=
[]
for
i
in
range
(
a
,
b
+
1
):
prob
.
append
((
lam
**
i
)
*
math
.
exp
(
-
lam
)
/
math
.
factorial
(
i
))
bin
.
append
(
i
)
bin
.
append
(
b
+
0.1
)
hist
,
_
=
np
.
histogram
(
out
,
bin
)
hist
=
hist
.
astype
(
"float32"
)
hist
=
hist
/
float
(
out
.
size
)
return
hist
,
prob
class
TestPoissonOp1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"poisson"
self
.
config
()
self
.
attrs
=
{}
self
.
inputs
=
{
'X'
:
np
.
full
([
1024
,
1024
],
self
.
lam
,
dtype
=
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
np
.
ones
([
1024
,
1024
],
dtype
=
self
.
dtype
)}
def
config
(
self
):
self
.
lam
=
10
self
.
a
=
5
self
.
b
=
15
self
.
dtype
=
"float64"
def
verify_output
(
self
,
outs
):
hist
,
prob
=
output_hist
(
np
.
array
(
outs
[
0
]),
self
.
lam
,
self
.
a
,
self
.
b
)
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0.01
),
"actual: {}, expected: {}"
.
format
(
hist
,
prob
))
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
user_defined_grads
=
[
np
.
zeros
(
[
1024
,
1024
],
dtype
=
self
.
dtype
)],
user_defined_grad_outputs
=
[
np
.
random
.
rand
(
1024
,
1024
).
astype
(
self
.
dtype
)
])
class
TestPoissonOp2
(
TestPoissonOp1
):
def
config
(
self
):
self
.
lam
=
5
self
.
a
=
1
self
.
b
=
9
self
.
dtype
=
"float32"
class
TestPoissonAPI
(
unittest
.
TestCase
):
def
test_static
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
(),
paddle
.
static
.
Program
()):
x_np
=
np
.
random
.
rand
(
10
,
10
)
x
=
paddle
.
static
.
data
(
name
=
"x"
,
shape
=
[
10
,
10
],
dtype
=
'float64'
)
y
=
paddle
.
poisson
(
x
)
exe
=
paddle
.
static
.
Executor
()
y_np
=
exe
.
run
(
paddle
.
static
.
default_main_program
(),
feed
=
{
"x"
:
x_np
},
fetch_list
=
[
y
])
self
.
assertTrue
(
np
.
min
(
y_np
)
>=
0
)
def
test_dygraph
(
self
):
paddle
.
disable_static
()
x
=
paddle
.
randn
([
10
,
10
],
dtype
=
'float32'
)
y
=
paddle
.
poisson
(
x
)
self
.
assertTrue
(
np
.
min
(
y
.
numpy
())
>=
0
)
paddle
.
enable_static
()
# Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
def
test_fixed_random_number
(
self
):
if
not
paddle
.
is_compiled_with_cuda
():
return
paddle
.
disable_static
()
paddle
.
set_device
(
'gpu'
)
paddle
.
seed
(
2021
)
x
=
paddle
.
full
([
32
,
3
,
1024
,
768
],
10.
,
dtype
=
"float32"
)
y
=
paddle
.
poisson
(
x
)
y_np
=
y
.
numpy
()
expect
=
[
13.
,
13.
,
11.
,
8.
,
12.
,
6.
,
9.
,
15.
,
16.
,
6.
,
13.
,
12.
,
9.
,
15.
,
17.
,
8.
,
11.
,
16.
,
11.
,
10.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
0
,
0
,
0
,
0
:
20
],
expect
))
expect
=
[
15.
,
7.
,
12.
,
8.
,
14.
,
10.
,
10.
,
11.
,
11.
,
11.
,
21.
,
6.
,
9.
,
13.
,
13.
,
11.
,
6.
,
9.
,
12.
,
12.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
8
,
1
,
300
,
200
:
220
],
expect
))
expect
=
[
10.
,
15.
,
9.
,
6.
,
4.
,
13.
,
10.
,
10.
,
13.
,
12.
,
9.
,
7.
,
10.
,
14.
,
7.
,
10.
,
8.
,
5.
,
10.
,
14.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
16
,
1
,
600
,
400
:
420
],
expect
))
expect
=
[
10.
,
9.
,
14.
,
12.
,
8.
,
9.
,
7.
,
8.
,
11.
,
10.
,
13.
,
8.
,
12.
,
9.
,
7.
,
8.
,
11.
,
11.
,
12.
,
5.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
24
,
2
,
900
,
600
:
620
],
expect
))
expect
=
[
15.
,
5.
,
11.
,
13.
,
12.
,
12.
,
13.
,
16.
,
9.
,
9.
,
7.
,
9.
,
13.
,
11.
,
15.
,
6.
,
11.
,
9.
,
10.
,
10.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
31
,
2
,
1023
,
748
:
768
],
expect
))
x
=
paddle
.
full
([
16
,
1024
,
1024
],
5.
,
dtype
=
"float32"
)
y
=
paddle
.
poisson
(
x
)
y_np
=
y
.
numpy
()
expect
=
[
4.
,
5.
,
2.
,
9.
,
8.
,
7.
,
4.
,
7.
,
4.
,
7.
,
6.
,
3.
,
10.
,
7.
,
5.
,
7.
,
2.
,
5.
,
5.
,
6.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
0
,
0
,
100
:
120
],
expect
))
expect
=
[
1.
,
4.
,
8.
,
11.
,
6.
,
5.
,
4.
,
4.
,
7.
,
4.
,
4.
,
7.
,
11.
,
6.
,
5.
,
3.
,
4.
,
6.
,
3.
,
3.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
4
,
300
,
300
:
320
],
expect
))
expect
=
[
7.
,
5.
,
4.
,
6.
,
8.
,
5.
,
6.
,
7.
,
7.
,
7.
,
3.
,
10.
,
5.
,
10.
,
4.
,
5.
,
8.
,
7.
,
5.
,
7.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
8
,
600
,
600
:
620
],
expect
))
expect
=
[
8.
,
6.
,
7.
,
4.
,
3.
,
0.
,
4.
,
6.
,
6.
,
4.
,
3.
,
10.
,
5.
,
1.
,
3.
,
8.
,
8.
,
2.
,
1.
,
4.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
12
,
900
,
900
:
920
],
expect
))
expect
=
[
2.
,
1.
,
14.
,
3.
,
6.
,
5.
,
2.
,
2.
,
6.
,
5.
,
7.
,
4.
,
8.
,
4.
,
8.
,
4.
,
5.
,
7.
,
1.
,
7.
]
self
.
assertTrue
(
np
.
array_equal
(
y_np
[
15
,
1023
,
1000
:
1020
],
expect
))
paddle
.
enable_static
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/nn/initializer/dirac.py
浏览文件 @
bcf86e5c
...
...
@@ -27,11 +27,13 @@ class Dirac(Initializer):
as many channels are reserved as possible.
In this initialize method, elements in the middle of convolution kernels will
be set to 1 . The formula can be described as
:
be set to 1 . The formula can be described as
follow.
$ Assuming: N=min(in\_channels, out\_channels)$
.. math::
$ X[d, d, shape[2]//2, shape[3]//2, ...]=1, \ d=0,1...N$
Assuming: N=min(in\_channels, out\_channels)
X[d, d, shape[2]//2, shape[3]//2, ...]=1, \ d=0,1...N
Args:
groups(int): 0-dimension of the Tensor will be divided by groups, each group has the same value.
...
...
@@ -46,7 +48,7 @@ class Dirac(Initializer):
import paddle
#1.For kernel_size is uneven number:
#1.
For kernel_size is uneven number:
attr = paddle.ParamAttr(initializer=paddle.nn.initializer.Dirac())
conv = paddle.nn.Conv1D(3, 2, 3, weight_attr=attr)
...
...
python/paddle/tensor/__init__.py
浏览文件 @
bcf86e5c
...
...
@@ -225,6 +225,7 @@ from .random import rand # noqa: F401
from
.random
import
randint
# noqa: F401
from
.random
import
randint_like
# noqa: F401
from
.random
import
randperm
# noqa: F401
from
.random
import
poisson
# noqa: F401
from
.search
import
argmax
# noqa: F401
from
.search
import
argmin
# noqa: F401
from
.search
import
argsort
# noqa: F401
...
...
python/paddle/tensor/random.py
浏览文件 @
bcf86e5c
...
...
@@ -79,6 +79,49 @@ def bernoulli(x, name=None):
return
out
def
poisson
(
x
,
name
=
None
):
"""
This OP returns a tensor filled with random number from a Poisson Distribution.
.. math::
out_i ~ Poisson (x_i)
Args:
x(Tensor): A tensor with rate parameter of poisson Distribution. The data type
should be float32, float64.
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 filled with random number with the same shape and dtype as ``x``.
Examples:
.. code-block:: python
import paddle
paddle.set_device('gpu')
paddle.seed(2021)
x = paddle.uniform([2,3], min=1.0, max=5.0)
out = paddle.poisson(x)
# [[0., 5., 1.],
# [4., 3., 0.]])
"""
if
in_dygraph_mode
():
return
_C_ops
.
poisson
(
x
)
check_variable_and_dtype
(
x
,
"x"
,
[
"float32"
,
"float64"
],
"poisson"
)
helper
=
LayerHelper
(
"poisson"
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'poisson'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{})
return
out
def
multinomial
(
x
,
num_samples
=
1
,
replacement
=
False
,
name
=
None
):
"""
This OP returns a Tensor filled with random values sampled from a Multinomical
...
...
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