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体验新版 GitCode,发现更多精彩内容 >>
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f8eccb0b
编写于
7月 14, 2020
作者:
Z
zhupengyang
提交者:
GitHub
7月 14, 2020
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
zeros_like API: remove device; input -> x (#25413)
上级
f795a1bf
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
115 addition
and
60 deletion
+115
-60
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+10
-7
python/paddle/fluid/tests/unittests/test_zeros_like_op.py
python/paddle/fluid/tests/unittests/test_zeros_like_op.py
+82
-0
python/paddle/tensor/creation.py
python/paddle/tensor/creation.py
+23
-53
未找到文件。
python/paddle/fluid/layers/tensor.py
浏览文件 @
f8eccb0b
...
...
@@ -1454,14 +1454,17 @@ def zeros_like(x, out=None):
with `x`.
Args:
x(Variable): The input tensor which specifies shape and dtype, the input data dtype could be bool, float32, float64, int32, int64.
out(Variable, optional): If is :attr:`None` , the op will create the variable as output, the data type and shape of
\
this variable will be same as input :attr:`x`. If is a tensor, the data type and shape need to be same as input :attr:`x`.
The default value is :attr:`None` .
x(Variable): The input tensor which specifies shape and dtype, the
input data dtype could be bool, float32, float64, int32, int64.
out(Variable, optional): If is :attr:`None` , the op will create the
variable as output, the data type and shape of this variable will
be same as input :attr:`x`. If is a tensor, the data type and shape
need to be same as input :attr:`x`. The default value is :attr:`None` .
Returns:
Variable: The N-D tensor, the element in tensor is related to input data type, if the input data type is bool,
\
the output value is False, otherwise is zero. The output shape is the same as the input.
Variable: The N-D tensor, the element in tensor is related to input
data type, if the input data type is bool, the output value is
False, otherwise is zero. The output shape is the same as the input.
Examples:
.. code-block:: python
...
...
@@ -1480,7 +1483,7 @@ def zeros_like(x, out=None):
else
:
check_variable_and_dtype
(
out
,
"out"
,
[
'bool'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'
one
s_like'
)
'
zero
s_like'
)
helper
.
append_op
(
type
=
'fill_zeros_like'
,
inputs
=
{
'X'
:
[
x
]},
outputs
=
{
'Out'
:
[
out
]})
...
...
python/paddle/fluid/tests/unittests/test_zeros_like_op.py
0 → 100644
浏览文件 @
f8eccb0b
# Copyright (c) 2018 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
import
paddle
import
paddle.fluid
as
fluid
from
paddle
import
zeros_like
from
paddle.fluid
import
core
,
Program
,
program_guard
class
TestZerosLikeAPIError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
x
=
paddle
.
data
(
'x'
,
[
3
,
4
])
self
.
assertRaises
(
TypeError
,
zeros_like
,
x
,
'int8'
)
class
TestZerosLikeAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
shape
=
[
3
,
4
]
startup_program
=
Program
()
train_program
=
Program
()
with
program_guard
(
train_program
,
startup_program
):
x
=
paddle
.
data
(
'X'
,
shape
)
# 'bool', 'float32', 'float64', 'int32', 'int64'
out1
=
zeros_like
(
x
)
out2
=
zeros_like
(
x
,
np
.
bool
)
out3
=
zeros_like
(
x
,
'float64'
)
out4
=
zeros_like
(
x
,
'int32'
)
out5
=
zeros_like
(
x
,
'int64'
)
place
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
outs
=
exe
.
run
(
train_program
,
feed
=
{
'X'
:
np
.
ones
(
shape
).
astype
(
'float32'
)},
fetch_list
=
[
out1
,
out2
,
out3
,
out4
,
out5
])
for
i
,
dtype
in
enumerate
(
[
np
.
float32
,
np
.
bool
,
np
.
float64
,
np
.
int32
,
np
.
int64
]):
self
.
assertEqual
(
outs
[
i
].
dtype
,
dtype
)
self
.
assertEqual
((
outs
[
i
]
==
np
.
zeros
(
shape
,
dtype
)).
all
(),
True
)
class
TestZerosLikeImpeartive
(
unittest
.
TestCase
):
def
test_out
(
self
):
shape
=
[
3
,
4
]
place
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
with
paddle
.
imperative
.
guard
(
place
):
x
=
paddle
.
imperative
.
to_variable
(
np
.
ones
(
shape
))
for
dtype
in
[
np
.
bool
,
np
.
float32
,
np
.
float64
,
np
.
int32
,
np
.
int64
]:
out
=
zeros_like
(
x
,
dtype
)
self
.
assertEqual
((
out
.
numpy
()
==
np
.
zeros
(
shape
,
dtype
)).
all
(),
True
)
out
=
paddle
.
tensor
.
zeros_like
(
x
)
self
.
assertEqual
((
out
.
numpy
()
==
np
.
zeros
(
shape
,
dtype
)).
all
(),
True
)
out
=
paddle
.
tensor
.
creation
.
zeros_like
(
x
)
self
.
assertEqual
((
out
.
numpy
()
==
np
.
zeros
(
shape
,
dtype
)).
all
(),
True
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/tensor/creation.py
浏览文件 @
f8eccb0b
...
...
@@ -98,7 +98,7 @@ def full_like(x, fill_value, dtype=None, name=None):
helper
=
LayerHelper
(
"full_like"
,
**
locals
())
check_dtype
(
dtype
,
'dtype'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'full_like'
)
'full_like
/zeros_like
'
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
helper
.
append_op
(
...
...
@@ -107,7 +107,7 @@ def full_like(x, fill_value, dtype=None, name=None):
attrs
=
{
'value'
:
fill_value
,
"dtype"
:
dtype
},
outputs
=
{
'Out'
:
[
out
]})
out
.
stop_gradient
=
True
return
out
...
...
@@ -254,74 +254,44 @@ def zeros(shape, dtype=None, name=None):
return
fill_constant
(
value
=
0.0
,
shape
=
shape
,
dtype
=
dtype
,
name
=
name
)
def
zeros_like
(
input
,
dtype
=
None
,
devic
e
=
None
,
name
=
None
):
def
zeros_like
(
x
,
dtyp
e
=
None
,
name
=
None
):
"""
:alias_main: paddle.zeros_like
:alias: paddle.zeros_like,
paddle.tensor.zeros_like,
paddle.tensor.creation.zeros_like
:alias: paddle.zeros_like,
paddle.tensor.zeros_like,
paddle.tensor.creation.zeros_like
This function creates a zeros tensor which has identical shape and dtype
with `input`.
Args:
input(Variable): The input tensor which specifies shape and dtype.The dtype of input can be
bool, float32, float64, int32, int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set bool, float32, float64, int32, int64.
The default value is None, the dtype is the same as input.
device(str, optional): Which device to run the operator. The :attr:`device` must be
None, 'cpu', 'gpu'. If :attr:`device` is None, it will be choose the device that the user set in
the paddle program. Default value is None.
name(str, optional): The name of output variable, normally there is no need for user to set this this property.
Default value is None, the framework set the name of output variable.
x(Variable): The input tensor which specifies shape and dtype. The
dtype of input can be bool, float16, float32, float64, int32, int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can
be set bool, float16, float32, float64, int32, int64. The default
value is None, the dtype is the same as input.
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:
out(Variable): The tensor variable storing the output.
Raise:
TypeError: If dtype is not bool, float16, float32, float64, int32 or int64.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
x = fluid.data(name='x', dtype='float32', shape=[3])
data = paddle.ones_like(x) # data=[1.0, 1.0, 1.0]
data1 = paddle.ones_like(input=x, device="gpu") #data1=[1.0, 1.0. 1.0]
import paddle
import numpy as np
"""
paddle.enable_imperative()
helper
=
LayerHelper
(
"zeros_like"
,
**
locals
())
x = paddle.imperative.to_variable(np.array([1,2,3], dtype='float32'))
out1 = paddle.zeros_like(x) # [1.0, 1.0, 1.0]
out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
attrs
=
{
"value"
:
0.0
}
var_dtype
=
None
if
dtype
is
not
None
:
check_dtype
(
dtype
,
'create data type'
,
[
'bool'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'zeros_like'
)
var_dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
attrs
[
"dtype"
]
=
var_dtype
else
:
var_dtype
=
input
.
dtype
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
var_dtype
)
if
device
is
not
None
:
if
device
not
in
[
'cpu'
,
'gpu'
]:
raise
ValueError
(
"The value of 'device' in zeros_op must be cpu or gpu, but received %s."
%
(
device
))
with
fluid
.
device_guard
(
device
):
helper
.
append_op
(
type
=
'fill_any_like'
,
inputs
=
{
'X'
:
[
input
]},
attrs
=
attrs
,
outputs
=
{
'Out'
:
[
out
]})
return
out
helper
.
append_op
(
type
=
'fill_any_like'
,
inputs
=
{
'X'
:
[
input
]},
attrs
=
attrs
,
outputs
=
{
'Out'
:
[
out
]})
out
.
stop_gradient
=
True
return
out
"""
return
full_like
(
x
=
x
,
fill_value
=
0
,
dtype
=
dtype
,
name
=
name
)
def
eye
(
num_rows
,
...
...
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