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bdc2c2db
编写于
7月 12, 2020
作者:
W
wangchaochaohu
提交者:
GitHub
7月 12, 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
full Op:remove device, out and stop_gradient parameter for API 2.0 test=develop (#25257)
上级
548cdbc5
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
174 addition
and
212 deletion
+174
-212
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+18
-15
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
+7
-102
python/paddle/fluid/tests/unittests/test_full_op.py
python/paddle/fluid/tests/unittests/test_full_op.py
+6
-37
python/paddle/fluid/tests/unittests/test_ones_op.py
python/paddle/fluid/tests/unittests/test_ones_op.py
+83
-0
python/paddle/fluid/tests/unittests/test_zeros_op.py
python/paddle/fluid/tests/unittests/test_zeros_op.py
+43
-0
python/paddle/tensor/creation.py
python/paddle/tensor/creation.py
+17
-58
未找到文件。
python/paddle/fluid/layers/tensor.py
浏览文件 @
bdc2c2db
...
...
@@ -620,7 +620,7 @@ def assign(input, output=None):
return
output
def
fill_constant
(
shape
,
dtype
,
value
,
force_cpu
=
False
,
out
=
None
):
def
fill_constant
(
shape
,
dtype
,
value
,
force_cpu
=
False
,
out
=
None
,
name
=
None
):
"""
:alias_main: paddle.fill_constant
:alias: paddle.fill_constant,paddle.tensor.fill_constant,paddle.tensor.creation.fill_constant
...
...
@@ -638,12 +638,14 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
If ``shape`` is an Variable, it should be an 1-D Tensor .
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
be float16, float32, float64, int32, int64.
value(
float16|float32|float64|int32|int64
|Variable): The constant value used to initialize
value(
bool|float|int
|Variable): The constant value used to initialize
the Tensor to be created. If value is an Variable, it should be an 1-D Tensor.
force_cpu(bool): data should be on CPU if it's true, default value is False.
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.
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: Tensor which is created according to shape and dtype.
...
...
@@ -666,19 +668,16 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
data3 = fluid.layers.fill_constant(shape=[1, positive_2], dtype='float32', value=1.5) # data3=[1.5, 1.5]
# attr shape is an Variable Tensor.
shape = fluid.layers.fill_constant([
1,
2], "int32", 2) # shape=[2,2]
shape = fluid.layers.fill_constant([2], "int32", 2) # shape=[2,2]
data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
# attr value is an Variable Tensor.
val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
"""
inputs
=
{}
attrs
=
{
'force_cpu'
:
force_cpu
}
if
isinstance
(
value
,
Variable
):
inputs
[
'ValueTensor'
]
=
value
else
:
attrs
[
'value'
]
=
float
(
value
)
if
not
isinstance
(
value
,
Variable
):
if
convert_dtype
(
dtype
)
in
[
'int64'
,
'int32'
]:
attrs
[
'str_value'
]
=
str
(
int
(
value
))
else
:
...
...
@@ -702,13 +701,19 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
out
.
stop_gradient
=
True
return
out
helper
=
LayerHelper
(
"fill_constant"
,
**
locals
())
inputs
=
{}
if
isinstance
(
value
,
Variable
):
inputs
[
'ValueTensor'
]
=
value
check_dtype
(
dtype
,
'dtype'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'fill_constant'
)
check_type
(
shape
,
'shape'
,
(
Variable
,
list
,
tuple
),
'fill_constant'
)
if
isinstance
(
shape
,
Variable
):
check_
variable_and_dtype
(
shape
,
'shape'
,
[
'int32'
,
'int64'
],
'fill_constant'
)
check_
dtype
(
shape
.
dtype
,
'shape'
,
[
'int32'
,
'int64'
],
'fill_constant'
)
if
out
is
not
None
:
check_variable_and_dtype
(
out
,
'out'
,
[
convert_dtype
(
dtype
)],
'fill_constant'
)
...
...
@@ -1048,7 +1053,7 @@ def ones(shape, dtype, force_cpu=False):
return
fill_constant
(
value
=
1.0
,
**
locals
())
def
zeros
(
shape
,
dtype
,
force_cpu
=
False
):
def
zeros
(
shape
,
dtype
,
force_cpu
=
False
,
name
=
None
):
"""
The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0.
Its :attr:`stop_gradient` will be set to True to stop gradient computation.
...
...
@@ -1060,6 +1065,8 @@ def zeros(shape, dtype, force_cpu=False):
force_cpu (bool, optional): Whether force to store the output tensor in CPU memory.
If :attr:`force_cpu` is False, the output tensor will be stored in running device memory.
Default: 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 data type :attr:`dtype` with shape :attr:`shape` and all elements set to 0.
...
...
@@ -1070,10 +1077,6 @@ def zeros(shape, dtype, force_cpu=False):
import paddle.fluid as fluid
data = fluid.layers.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
"""
check_type
(
shape
,
'shape'
,
(
list
,
tuple
),
'zeros'
)
check_dtype
(
dtype
,
'create data type'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'zeros'
)
return
fill_constant
(
value
=
0.0
,
**
locals
())
...
...
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
浏览文件 @
bdc2c2db
...
...
@@ -22,8 +22,8 @@ import paddle
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
import
numpy
as
np
from
paddle.fluid
import
compiler
,
Program
,
program_guard
# Situation 1: Attr(shape) is a list(without tensor)
...
...
@@ -85,16 +85,14 @@ class TestFillConstantOp4(OpTest):
class
TestFillConstantOp5
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
data
=
fluid
.
data
(
name
=
"X"
,
shape
=
[
1
],
dtype
=
"float32"
)
out
=
paddle
.
zeros
(
shape
=
[
1
],
out
=
data
,
dtype
=
"float32"
)
with
program_guard
(
Program
()):
out_np
=
np
.
zeros
(
shape
=
(
1
),
dtype
=
'float32'
)
out
=
paddle
.
zeros
(
shape
=
[
1
],
dtype
=
"float32"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
=
exe
.
run
(
feed
=
{
"X"
:
np
.
array
(
[
0.1
],
dtype
=
"float32"
)},
fetch_list
=
[
data
,
out
])
self
.
assertEqual
(
result
[
0
],
result
[
1
])
with
fluid
.
program_guard
(
fluid
.
Program
()):
result
=
exe
.
run
(
fetch_list
=
[
out
])
self
.
assertEqual
((
result
==
out_np
).
all
(),
True
)
with
program_guard
(
Program
()):
data
=
fluid
.
data
(
name
=
"X"
,
shape
=
[
1
],
dtype
=
"float32"
)
out
=
paddle
.
ones
(
shape
=
[
1
],
out
=
data
,
dtype
=
"float32"
)
place
=
fluid
.
CPUPlace
()
...
...
@@ -389,98 +387,5 @@ class TestFillConstantOpError(unittest.TestCase):
self
.
assertRaises
(
TypeError
,
test_shape_tensor_list_dtype
)
class
ApiZerosTest
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"float64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"float64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"int64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"int64"
,
device
=
"cpu"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
class
ApiOnesTest
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"float64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"float64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"int64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"int64"
,
device
=
"cpu"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
class
ApiOnesZerosError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
def
test_error1
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
10
,
dtype
=
"int64"
,
device
=
"opu"
)
self
.
assertRaises
(
ValueError
,
test_error1
)
def
test_error2
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
10
,
dtype
=
"int64"
,
device
=
"opu"
)
self
.
assertRaises
(
ValueError
,
test_error2
)
def
test_error3
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
ones
(
shape
=
10
,
dtype
=
"int64"
)
self
.
assertRaises
(
TypeError
,
test_error3
)
def
test_error4
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
ones
(
shape
=
[
10
],
dtype
=
"int8"
)
self
.
assertRaises
(
TypeError
,
test_error4
)
def
test_error5
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
zeros
(
shape
=
10
,
dtype
=
"int64"
)
self
.
assertRaises
(
TypeError
,
test_error5
)
def
test_error6
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
zeros
(
shape
=
[
10
],
dtype
=
"int8"
)
self
.
assertRaises
(
TypeError
,
test_error6
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_full_op.py
浏览文件 @
bdc2c2db
...
...
@@ -37,33 +37,19 @@ class TestFullAPI(unittest.TestCase):
shape_tensor_int64
=
fluid
.
data
(
name
=
"shape_tensor_int64"
,
shape
=
[
2
],
dtype
=
"int64"
)
out_1
=
paddle
.
full
(
shape
=
[
1
,
2
],
dtype
=
"float32"
,
fill_value
=
1.1
,
device
=
'gpu'
)
out_1
=
paddle
.
full
(
shape
=
[
1
,
2
],
dtype
=
"float32"
,
fill_value
=
1.1
)
out_2
=
paddle
.
full
(
shape
=
[
1
,
positive_2_int32
],
dtype
=
"float32"
,
fill_value
=
1.1
,
device
=
'cpu'
)
shape
=
[
1
,
positive_2_int32
],
dtype
=
"float32"
,
fill_value
=
1.1
)
out_3
=
paddle
.
full
(
shape
=
[
1
,
positive_2_int64
],
dtype
=
"float32"
,
fill_value
=
1.1
,
device
=
'gpu'
)
shape
=
[
1
,
positive_2_int64
],
dtype
=
"float32"
,
fill_value
=
1.1
)
out_4
=
paddle
.
full
(
shape
=
shape_tensor_int32
,
dtype
=
"float32"
,
fill_value
=
1.2
,
out
=
out_3
)
shape
=
shape_tensor_int32
,
dtype
=
"float32"
,
fill_value
=
1.2
)
out_5
=
paddle
.
full
(
shape
=
shape_tensor_int64
,
dtype
=
"float32"
,
fill_value
=
1.1
,
device
=
'gpu'
,
stop_gradient
=
False
)
shape
=
shape_tensor_int64
,
dtype
=
"float32"
,
fill_value
=
1.1
)
out_6
=
paddle
.
full
(
shape
=
shape_tensor_int64
,
dtype
=
np
.
float32
,
fill_value
=
1.1
)
...
...
@@ -83,7 +69,7 @@ class TestFullAPI(unittest.TestCase):
assert
np
.
array_equal
(
res_1
,
np
.
full
([
1
,
2
],
1.1
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_2
,
np
.
full
([
1
,
2
],
1.1
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_3
,
np
.
full
([
1
,
2
],
1.
2
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_3
,
np
.
full
([
1
,
2
],
1.
1
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_4
,
np
.
full
([
1
,
2
],
1.2
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_5
,
np
.
full
([
1
,
2
],
1.1
,
dtype
=
"float32"
))
assert
np
.
array_equal
(
res_6
,
np
.
full
([
1
,
2
],
1.1
,
dtype
=
"float32"
))
...
...
@@ -94,28 +80,11 @@ class TestFullOpError(unittest.TestCase):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
#for ci coverage
x1
=
fluid
.
layers
.
data
(
name
=
'x1'
,
shape
=
[
1
],
dtype
=
"int16"
)
x2
=
np
.
random
.
randn
(
1
,
2
).
astype
(
'int32'
)
self
.
assertRaises
(
ValueError
,
paddle
.
full
,
shape
=
[
1
],
fill_value
=
5
,
dtype
=
'uint4'
)
self
.
assertRaises
(
TypeError
,
paddle
.
full
,
shape
=
[
1
],
fill_value
=
5
,
dtype
=
'int32'
,
out
=
x2
)
self
.
assertRaises
(
TypeError
,
paddle
.
full
,
shape
=
[
1
],
fill_value
=
5
,
dtype
=
'int16'
,
out
=
x1
)
# The argument dtype of full must be one of bool, float16,
#float32, float64, int32 or int64
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
1
],
dtype
=
"int32"
)
self
.
assertRaises
(
TypeError
,
paddle
.
full
,
shape
=
[
1
],
fill_value
=
5
,
dtype
=
'uint8'
)
...
...
python/paddle/fluid/tests/unittests/test_ones_op.py
0 → 100644
浏览文件 @
bdc2c2db
# 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
from
op_test
import
OpTest
import
paddle
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
import
numpy
as
np
class
ApiOnesTest
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"float64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"float64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"int64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
[
10
],
dtype
=
"int64"
,
device
=
"cpu"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
ones
])
expected_result
=
np
.
ones
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
class
ApiOnesZerosError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
def
test_error1
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
10
,
dtype
=
"int64"
,
device
=
"opu"
)
self
.
assertRaises
(
ValueError
,
test_error1
)
def
test_error2
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
paddle
.
ones
(
shape
=
10
,
dtype
=
"int64"
,
device
=
"opu"
)
self
.
assertRaises
(
ValueError
,
test_error2
)
def
test_error3
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
ones
(
shape
=
10
,
dtype
=
"int64"
)
self
.
assertRaises
(
TypeError
,
test_error3
)
def
test_error4
():
with
fluid
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
ones
(
shape
=
[
10
],
dtype
=
"int8"
)
self
.
assertRaises
(
TypeError
,
test_error4
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_zeros_op.py
浏览文件 @
bdc2c2db
...
...
@@ -18,6 +18,7 @@ import unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
...
...
@@ -33,5 +34,47 @@ class TestZerosOpError(unittest.TestCase):
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
zeros
,
shape
,
dtype
)
class
ApiZerosTest
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
paddle
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"float64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"float64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
paddle
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"int64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
with
paddle
.
program_guard
(
fluid
.
Program
()):
zeros
=
paddle
.
zeros
(
shape
=
[
10
],
dtype
=
"int64"
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
result
,
=
exe
.
run
(
fetch_list
=
[
zeros
])
expected_result
=
np
.
zeros
(
10
,
dtype
=
"int64"
)
self
.
assertEqual
((
result
==
expected_result
).
all
(),
True
)
class
ApiZerosError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
def
test_error1
():
with
paddle
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
zeros
(
shape
=
10
,
dtype
=
"int64"
)
self
.
assertRaises
(
TypeError
,
test_error1
)
def
test_error2
():
with
paddle
.
program_guard
(
fluid
.
Program
()):
ones
=
fluid
.
layers
.
zeros
(
shape
=
[
10
],
dtype
=
"int8"
)
self
.
assertRaises
(
TypeError
,
test_error2
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/tensor/creation.py
浏览文件 @
bdc2c2db
...
...
@@ -223,7 +223,7 @@ def ones_like(input, dtype=None, device=None, name=None):
return
out
def
zeros
(
shape
,
dtype
,
out
=
None
,
devic
e
=
None
):
def
zeros
(
shape
,
dtype
=
None
,
nam
e
=
None
):
"""
:alias_main: paddle.zeros
:alias: paddle.zeros,paddle.tensor.zeros,paddle.tensor.creation.zeros
...
...
@@ -232,14 +232,10 @@ def zeros(shape, dtype, out=None, device=None):
Args:
shape(tuple|list): Shape of output tensor.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor, it supports
bool, float16, float32, float64, int32 and int64.
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.
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 False.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of output tensor, it supports
bool, float16, float32, float64, int32 and int64. Default: if None, the date type is float32.
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 data type :attr:`dtype` with shape :attr:`shape` and all elements set to 0.
...
...
@@ -248,21 +244,14 @@ def zeros(shape, dtype, out=None, device=None):
.. code-block:: python
import paddle
paddle.enable_imperative() # Now we are in imperative mode
data = paddle.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
data = paddle.zeros(shape=[2, 2], dtype='
float32', device='cpu') # [[0., 0.], [0., 0.
]]
data = paddle.zeros(shape=[2, 2], dtype='
int32', name='zeros') # [[0, 0], [0, 0
]]
"""
check_dtype
(
dtype
,
'create data type'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'zeros'
)
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
):
return
fill_constant
(
value
=
0.0
,
shape
=
shape
,
dtype
=
dtype
,
out
=
out
)
return
fill_constant
(
value
=
0.0
,
shape
=
shape
,
dtype
=
dtype
,
out
=
out
)
if
dtype
is
None
:
dtype
=
'float32'
return
fill_constant
(
value
=
0.0
,
shape
=
shape
,
dtype
=
dtype
,
name
=
name
)
def
zeros_like
(
input
,
dtype
=
None
,
device
=
None
,
name
=
None
):
...
...
@@ -398,13 +387,7 @@ def eye(num_rows,
return
out
def
full
(
shape
,
fill_value
,
out
=
None
,
dtype
=
None
,
device
=
None
,
stop_gradient
=
True
,
name
=
None
):
def
full
(
shape
,
fill_value
,
dtype
=
None
,
name
=
None
):
"""
:alias_main: paddle.full
:alias: paddle.full,paddle.tensor.full,paddle.tensor.creation.full
...
...
@@ -418,17 +401,9 @@ def full(shape,
If ``shape`` is an Variable, it should be an 1-D Tensor .
fill_value(bool|float16|float32|float64|int32|int64|Variable): The constant value
used to initialize the Tensor to be created. If fill_value is an Variable, it must be an 1-D Tensor.
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 float16, float32, float64, int32, int64, if dytpe is `None`, the data
type of created tensor is `float32`
device(str, optional): On which device to run this Op. The :attr:`device` must be
None, 'cpu' or 'gpu'. If :attr:`device` is None, the device that the user set in
the paddle program will be chosen. Default value is None.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is True.
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`.
...
...
@@ -437,28 +412,26 @@ def full(shape,
Raises:
TypeError: The `dtype` must be one of None, bool, float16, float32, float64, int32 and int64.
TypeError: The `out` must be a Variable.
TypeError: The `shape` must be one of Variable, list tuple.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_imperative() # Now we are in imperative mode
data1 = paddle.full(shape=[2,1], fill_value=0, dtype='int64') # data1=[[0],[0]]
data2 = paddle.full(shape=[2,1], fill_value=5, dtype='int64', device='gpu') # data2=[[5],[5]]
# attr shape is a list which contains Variable Tensor.
positive_2 =
fluid.layers
.fill_constant([1], "int32", 2)
positive_2 =
paddle
.fill_constant([1], "int32", 2)
data3 = paddle.full(shape=[1, positive_2], dtype='float32', fill_value=1.5) # data3=[1.5, 1.5]
# attr shape is an Variable Tensor.
shape =
fluid.layers.fill_constant([1,
2], "int32", 2) # shape=[2,2]
shape =
paddle.fill_constant([
2], "int32", 2) # shape=[2,2]
data4 = paddle.full(shape=shape, dtype='bool', fill_value=True) # data4=[[True,True],[True,True]]
# attr value is an Variable Tensor.
val =
fluid.layers
.fill_constant([1], "float32", 2.0) # val=[2.0]
val =
paddle
.fill_constant([1], "float32", 2.0) # val=[2.0]
data5 = paddle.full(shape=[2,1], fill_value=val, dtype='float32') #data5=[[2.0],[2.0]]
"""
...
...
@@ -467,21 +440,7 @@ def full(shape,
if
dtype
is
None
:
dtype
=
'float32'
check_dtype
(
dtype
,
'create data type'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'full'
)
check_type
(
shape
,
'shape'
,
(
Variable
,
list
,
tuple
),
'full'
)
if
out
is
not
None
:
check_type
(
out
,
'out'
,
(
Variable
),
'full'
)
if
out
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
out
.
stop_gradient
=
stop_gradient
with
device_guard
(
device
):
out
=
fill_constant
(
shape
=
shape
,
dtype
=
dtype
,
value
=
fill_value
,
out
=
out
)
return
out
return
fill_constant
(
shape
=
shape
,
dtype
=
dtype
,
value
=
fill_value
,
name
=
name
)
def
arange
(
start
,
end
,
step
=
1
,
dtype
=
None
,
name
=
None
):
...
...
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