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c239f15a
编写于
4月 13, 2022
作者:
Z
zhiboniu
提交者:
GitHub
4月 13, 2022
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
tensor fluid code transfer part2 (#41096)
上级
1e56ca8a
变更
11
展开全部
隐藏空白更改
内联
并排
Showing
11 changed file
with
1756 addition
and
111 deletion
+1756
-111
python/paddle/fft.py
python/paddle/fft.py
+2
-1
python/paddle/fluid/tests/unittests/test_crop_tensor_op.py
python/paddle/fluid/tests/unittests/test_crop_tensor_op.py
+9
-9
python/paddle/fluid/tests/unittests/test_slice_op.py
python/paddle/fluid/tests/unittests/test_slice_op.py
+4
-4
python/paddle/fluid/tests/unittests/test_strided_slice_op.py
python/paddle/fluid/tests/unittests/test_strided_slice_op.py
+4
-4
python/paddle/tensor/attribute.py
python/paddle/tensor/attribute.py
+110
-19
python/paddle/tensor/creation.py
python/paddle/tensor/creation.py
+285
-18
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+28
-26
python/paddle/tensor/manipulation.py
python/paddle/tensor/manipulation.py
+1311
-27
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+1
-1
python/paddle/tensor/search.py
python/paddle/tensor/search.py
+1
-1
python/paddle/tensor/stat.py
python/paddle/tensor/stat.py
+1
-1
未找到文件。
python/paddle/fft.py
浏览文件 @
c239f15a
...
...
@@ -15,7 +15,8 @@
from
typing
import
Sequence
import
numpy
as
np
import
paddle
from
.tensor.attribute
import
is_complex
,
is_floating_point
,
is_integer
,
_real_to_complex_dtype
,
_complex_to_real_dtype
from
.tensor.attribute
import
is_complex
,
is_floating_point
,
is_integer
from
.tensor.creation
import
_real_to_complex_dtype
,
_complex_to_real_dtype
from
.fluid.framework
import
_non_static_mode
from
.
import
_C_ops
from
.fluid.data_feeder
import
check_variable_and_dtype
...
...
python/paddle/fluid/tests/unittests/test_crop_tensor_op.py
浏览文件 @
c239f15a
...
...
@@ -17,6 +17,7 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
...
...
@@ -225,31 +226,30 @@ class TestCropTensorException(unittest.TestCase):
offset
=
fluid
.
data
(
name
=
'offset'
,
shape
=
[
1
],
dtype
=
'int32'
)
def
attr_shape_type
():
out
=
fluid
.
layers
.
crop_tensor
(
input1
,
shape
=
3
)
out
=
paddle
.
crop
(
input1
,
shape
=
3
)
def
attr_shape_dtype
():
out
=
fluid
.
layers
.
crop_tensor
(
input1
,
shape
=
[
2
,
2.0
,
3
,
3
])
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
2.0
,
3
,
3
])
def
attr_shape_value1
():
out
=
fluid
.
layers
.
crop_tensor
(
input1
,
shape
=
[
2
,
-
2
,
dim
,
3
])
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
-
2
,
dim
,
3
])
def
attr_shape_value2
():
out
=
fluid
.
layers
.
crop_tensor
(
input1
,
shape
=
[
2
,
0
,
dim
,
3
])
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
0
,
dim
,
3
])
def
attr_offsets_type
():
out
=
fluid
.
layers
.
crop_tensor
(
input1
,
shape
=
[
2
,
2
,
3
,
3
],
offsets
=
0
)
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
2
,
3
,
3
],
offsets
=
0
)
def
attr_offsets_dtype
():
out
=
fluid
.
layers
.
crop_tensor
(
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
2
,
3
,
3
],
offsets
=
[
0
,
1.0
,
0
,
0
])
def
attr_offsets_value
():
out
=
fluid
.
layers
.
crop_tensor
(
out
=
paddle
.
crop
(
input1
,
shape
=
[
2
,
2
,
3
,
3
],
offsets
=
[
0
,
-
1
,
offset
,
0
])
def
input_dtype
():
out
=
fluid
.
layers
.
crop_tensor
(
input2
,
shape
=
[
2
,
2
,
3
,
3
])
out
=
paddle
.
crop
(
input2
,
shape
=
[
2
,
2
,
3
,
3
])
self
.
assertRaises
(
TypeError
,
attr_shape_type
)
self
.
assertRaises
(
TypeError
,
attr_shape_dtype
)
...
...
python/paddle/fluid/tests/unittests/test_slice_op.py
浏览文件 @
c239f15a
...
...
@@ -534,13 +534,13 @@ class TestSliceAPI(unittest.TestCase):
# value_int64 is greater than 2147483647 which is the max of int32
value_int64
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
2147483648
)
out_1
=
fluid
.
layers
.
slice
(
out_1
=
paddle
.
slice
(
x
,
axes
=
[
0
,
1
,
2
],
starts
=
[
-
3
,
0
,
2
],
ends
=
[
value_int64
,
100
,
-
1
])
out_2
=
fluid
.
layers
.
slice
(
out_2
=
paddle
.
slice
(
x
,
axes
=
[
0
,
1
,
3
],
starts
=
[
minus_3
,
0
,
2
],
ends
=
[
3
,
100
,
-
1
])
out_3
=
fluid
.
layers
.
slice
(
out_3
=
paddle
.
slice
(
x
,
axes
=
[
0
,
1
,
3
],
starts
=
[
minus_3
,
0
,
2
],
ends
=
[
3
,
100
,
minus_1
])
out_4
=
fluid
.
layers
.
slice
(
x
,
axes
=
[
0
,
1
,
2
],
starts
=
starts
,
ends
=
ends
)
out_4
=
paddle
.
slice
(
x
,
axes
=
[
0
,
1
,
2
],
starts
=
starts
,
ends
=
ends
)
out_5
=
x
[
-
3
:
3
,
0
:
100
,
2
:
-
1
]
out_6
=
x
[
minus_3
:
3
,
0
:
100
,
:,
2
:
-
1
]
...
...
python/paddle/fluid/tests/unittests/test_strided_slice_op.py
浏览文件 @
c239f15a
...
...
@@ -534,25 +534,25 @@ class TestStridedSliceAPI(unittest.TestCase):
shape
=
[
3
,
4
,
5
,
6
],
append_batch_size
=
False
,
dtype
=
"float64"
)
out_1
=
fluid
.
layers
.
strided_slice
(
out_1
=
paddle
.
strided_slice
(
x
,
axes
=
[
0
,
1
,
2
],
starts
=
[
-
3
,
0
,
2
],
ends
=
[
3
,
100
,
-
1
],
strides
=
[
1
,
1
,
1
])
out_2
=
fluid
.
layers
.
strided_slice
(
out_2
=
paddle
.
strided_slice
(
x
,
axes
=
[
0
,
1
,
3
],
starts
=
[
minus_3
,
0
,
2
],
ends
=
[
3
,
100
,
-
1
],
strides
=
[
1
,
1
,
1
])
out_3
=
fluid
.
layers
.
strided_slice
(
out_3
=
paddle
.
strided_slice
(
x
,
axes
=
[
0
,
1
,
3
],
starts
=
[
minus_3
,
0
,
2
],
ends
=
[
3
,
100
,
minus_1
],
strides
=
[
1
,
1
,
1
])
out_4
=
fluid
.
layers
.
strided_slice
(
out_4
=
paddle
.
strided_slice
(
x
,
axes
=
[
0
,
1
,
2
],
starts
=
starts
,
ends
=
ends
,
strides
=
strides
)
out_5
=
x
[
-
3
:
3
,
0
:
100
:
2
,
-
1
:
2
:
-
1
]
...
...
python/paddle/tensor/attribute.py
浏览文件 @
c239f15a
...
...
@@ -14,37 +14,128 @@
from
__future__
import
print_function
from
..framework
import
core
from
..f
luid.layer_helper
import
LayerHelper
from
..framework
import
core
,
_non_static_mode
from
..f
ramework
import
LayerHelper
from
..fluid.data_feeder
import
check_variable_and_dtype
from
..fluid.data_feeder
import
check_type
from
.creation
import
assign
from
.creation
import
_complex_to_real_dtype
# TODO: define functions to get tensor attributes
from
..fluid.layers
import
rank
# noqa: F401
from
..fluid.layers
import
shape
# noqa: F401
import
paddle
from
paddle
import
_C_ops
from
paddle
.static
import
Variable
from
.
.static
import
Variable
from
..fluid.framework
import
_in_legacy_dygraph
,
in_dygraph_mode
import
numpy
as
np
__all__
=
[]
def
_complex_to_real_dtype
(
dtype
):
if
dtype
==
core
.
VarDesc
.
VarType
.
COMPLEX64
:
return
core
.
VarDesc
.
VarType
.
FP32
elif
dtype
==
core
.
VarDesc
.
VarType
.
COMPLEX128
:
return
core
.
VarDesc
.
VarType
.
FP64
else
:
return
dtype
def
rank
(
input
):
"""
The OP returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
Args:
input (Tensor): The input N-D tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary.
Returns:
Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor.
Examples:
.. code-block:: python
import paddle
input = paddle.rand((3, 100, 100))
rank = paddle.rank(input)
print(rank)
# 3
"""
check_type
(
input
,
'input'
,
(
Variable
),
'input'
)
ndims
=
len
(
input
.
shape
)
out
=
assign
(
np
.
array
(
ndims
,
'int32'
))
return
out
def
shape
(
input
):
"""
:alias_main: paddle.shape
:alias: paddle.shape,paddle.tensor.shape,paddle.tensor.attribute.shape
:old_api: paddle.fluid.layers.shape
**Shape Layer**
Get the shape of the input.
.. code-block:: text
Case1:
Given N-D Tensor:
input = [ [1, 2, 3, 4], [5, 6, 7, 8] ]
Then:
input.shape = [2, 4]
Case2:
Given SelectedRows:
input.rows = [0, 4, 19]
input.height = 20
input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor
Then:
input.shape = [3, 2]
Args:
input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64.
If input variable is type of SelectedRows, returns the shape of it's inner tensor.
Returns:
Variable (Tensor): The shape of the input variable.
Examples:
.. code-block:: python
def
_real_to_complex_dtype
(
dtype
):
if
dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
return
core
.
VarDesc
.
VarType
.
COMPLEX64
elif
dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
return
core
.
VarDesc
.
VarType
.
COMPLEX128
else
:
return
dtype
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32")
output = fluid.layers.shape(inputs)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
img = np.ones((3, 100, 100)).astype(np.float32)
res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output])
print(res) # [array([ 3, 100, 100], dtype=int32)]
"""
if
in_dygraph_mode
():
out
=
_C_ops
.
final_state_shape
(
input
)
out
.
stop_gradient
=
True
return
out
if
_in_legacy_dygraph
():
out
=
_C_ops
.
shape
(
input
)
out
.
stop_gradient
=
True
return
out
check_variable_and_dtype
(
input
,
'input'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
,
'complex64'
,
'complex128'
],
'shape'
)
helper
=
LayerHelper
(
'shape'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
'shape'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
out
},
stop_gradient
=
True
)
return
out
def
is_complex
(
x
):
...
...
python/paddle/tensor/creation.py
浏览文件 @
c239f15a
...
...
@@ -14,27 +14,138 @@
from
__future__
import
print_function
import
numpy
as
np
import
math
from
paddle.common_ops_import
import
fill_constant
from
..fluid.layers
import
utils
from
..fluid.layers
import
tensor
from
..static
import
Variable
,
device_guard
from
..framework
import
_current_expected_place
,
_get_paddle_place
from
..framework
import
dygraph_only
from
..framework
import
core
from
..fluid.layer_helper
import
LayerHelper
from
..framework
import
in_dygraph_mode
,
_non_static_mode
from
..framework
import
LayerHelper
from
..fluid.data_feeder
import
check_variable_and_dtype
,
check_type
,
check_dtype
,
convert_dtype
from
..framework
import
convert_np_dtype_to_dtype_
,
_varbase_creator
,
OpProtoHolder
from
paddle.tensor.attribute
import
_complex_to_real_dtype
,
_real_to_complex_dtype
# TODO: define functions to get create a tensor
from
..fluid.layers
import
linspace
# noqa: F401
import
paddle
from
paddle
import
_C_ops
from
..fluid.framework
import
_in_legacy_dygraph
,
in_dygraph_mode
,
_in_eager_without_dygraph_check
from
..fluid.framework
import
_in_legacy_dygraph
,
_in_eager_without_dygraph_check
import
warnings
__all__
=
[]
def
_complex_to_real_dtype
(
dtype
):
if
dtype
==
core
.
VarDesc
.
VarType
.
COMPLEX64
:
return
core
.
VarDesc
.
VarType
.
FP32
elif
dtype
==
core
.
VarDesc
.
VarType
.
COMPLEX128
:
return
core
.
VarDesc
.
VarType
.
FP64
else
:
return
dtype
def
_real_to_complex_dtype
(
dtype
):
if
dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
return
core
.
VarDesc
.
VarType
.
COMPLEX64
elif
dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
return
core
.
VarDesc
.
VarType
.
COMPLEX128
else
:
return
dtype
def
linspace
(
start
,
stop
,
num
,
dtype
=
None
,
name
=
None
):
r
"""
This OP return fixed number of evenly spaced values within a given interval.
Args:
start(int|float|Tensor): The input :attr:`start` is start variable of range. It is a scalar, \
or a Tensor of shape [1] with input data type int32, int64, float32 or float64.
stop(int|float|Tensor): The input :attr:`stop` is start variable of range. It is a scalar, \
or a Tensor of shape [1] with input data type int32, int64, float32 or float64.
num(int|Tensor): The input :attr:`num` is given num of the sequence. It is an int scalar, \
or a Tensor of shape [1] with data type int32.
dtype(np.dtype|str, optional): The data type of output tensor, it could be
int32, int64, float32 and float64. Default: if None, the data type is float32.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.Default: None.
Returns:
Tensor: the output data type will be float32, float64. The 1-D tensor with fixed number of evenly spaced values, \
the data shape of this tensor is :math:`[num]` . If the :attr:`num` is set 1, the output tensor just has \
the value with input :attr:`start`.
Examples:
.. code-block:: python
import paddle
data = paddle.linspace(0, 10, 5, 'float32') # [0.0, 2.5, 5.0, 7.5, 10.0]
data = paddle.linspace(0, 10, 1, 'float32') # [0.0]
"""
if
dtype
is
None
:
dtype
=
'float32'
tensor_num
=
num
tensor_start
=
start
tensor_stop
=
stop
if
not
isinstance
(
num
,
Variable
):
check_type
(
num
,
'num'
,
(
int
),
'linspace'
)
if
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
not
isinstance
(
start
,
Variable
):
with
device_guard
(
"cpu"
):
tensor_start
=
fill_constant
([
1
],
dtype
,
start
)
if
not
isinstance
(
stop
,
Variable
):
with
device_guard
(
"cpu"
):
tensor_stop
=
fill_constant
([
1
],
dtype
,
stop
)
if
not
isinstance
(
num
,
Variable
):
with
device_guard
(
"cpu"
):
tensor_num
=
fill_constant
([
1
],
'int32'
,
num
)
if
_non_static_mode
():
return
_C_ops
.
linspace
(
tensor_start
,
tensor_stop
,
tensor_num
,
'dtype'
,
dtype
)
helper
=
LayerHelper
(
"linspace"
,
**
locals
())
start_dtype
=
convert_dtype
(
tensor_start
.
dtype
)
stop_dtype
=
convert_dtype
(
tensor_stop
.
dtype
)
out_dtype
=
convert_dtype
(
dtype
)
if
isinstance
(
start
,
Variable
):
check_dtype
(
start
.
dtype
,
'start'
,
[
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'linspace'
)
else
:
check_type
(
start
,
'start'
,
(
int
,
float
),
'linspace'
)
if
isinstance
(
stop
,
Variable
):
check_dtype
(
stop
.
dtype
,
'stop'
,
[
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'linspace'
)
else
:
check_type
(
stop
,
'stop'
,
(
int
,
float
),
'linspace'
)
if
isinstance
(
num
,
Variable
):
check_dtype
(
num
.
dtype
,
'num'
,
[
'int32'
],
'linspace'
)
check_dtype
(
dtype
,
'dtype'
,
[
'int32'
,
'int64'
,
'float32'
,
'float64'
],
'linspace'
)
if
((
stop_dtype
==
"float64"
or
start_dtype
==
"float64"
)
and
out_dtype
in
[
"float32"
,
"int32"
])
or
((
stop_dtype
==
"int64"
or
start_dtype
==
"int64"
)
and
out_dtype
==
"int32"
):
raise
ValueError
(
"The dtype of start/stop is {}/{} but the attr(dtype) of linspace is {}, "
"which may cause data type overflows. Please reset attr(dtype) of linspace."
.
format
(
start_dtype
,
stop_dtype
,
dtype
))
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
helper
.
append_op
(
type
=
'linspace'
,
inputs
=
{
'Start'
:
tensor_start
,
'Stop'
:
tensor_stop
,
'Num'
:
tensor_num
},
attrs
=
{
'dtype'
:
dtype
},
outputs
=
{
'Out'
:
[
out
]})
if
isinstance
(
num
,
int
):
out
.
desc
.
set_shape
((
num
,
))
return
out
@
dygraph_only
def
to_tensor
(
data
,
dtype
=
None
,
place
=
None
,
stop_gradient
=
True
):
r
"""
...
...
@@ -60,7 +171,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
Tensor: A Tensor constructed from ``data`` .
Raises:
TypeError: If the data type of ``data`` is not scalar, list, tuple, n
umpy
.ndarray, paddle.Tensor
TypeError: If the data type of ``data`` is not scalar, list, tuple, n
p
.ndarray, paddle.Tensor
ValueError: If ``data`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]
TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
...
...
@@ -152,7 +263,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
return
data
else
:
raise
TypeError
(
"Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|n
umpy
.ndarray|paddle.Tensor"
.
"Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|n
p
.ndarray|paddle.Tensor"
.
format
(
type
(
data
)))
if
not
dtype
:
if
data
.
dtype
in
[
...
...
@@ -439,11 +550,39 @@ def eye(num_rows, num_columns=None, dtype=None, name=None):
dtype
=
'float32'
if
num_columns
is
None
:
num_columns
=
num_rows
return
paddle
.
fluid
.
layers
.
eye
(
num_rows
=
num_rows
,
num_columns
=
num_columns
,
batch_shape
=
None
,
dtype
=
dtype
,
name
=
name
)
if
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
num_columns
is
not
None
:
if
not
isinstance
(
num_columns
,
int
)
or
num_columns
<
0
:
raise
TypeError
(
"num_columns should be a non-negative int"
)
else
:
num_columns
=
num_rows
if
_non_static_mode
():
out
=
_C_ops
.
eye
(
'dtype'
,
dtype
,
'num_rows'
,
num_rows
,
'num_columns'
,
num_columns
)
else
:
helper
=
LayerHelper
(
"eye"
,
**
locals
())
check_dtype
(
dtype
,
'dtype'
,
[
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'eye'
)
if
not
isinstance
(
num_rows
,
int
)
or
num_rows
<
0
:
raise
TypeError
(
"num_rows should be a non-negative int"
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
helper
.
append_op
(
type
=
'eye'
,
inputs
=
{},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'num_rows'
:
num_rows
,
'num_columns'
:
num_columns
,
'dtype'
:
dtype
},
stop_gradient
=
True
)
out
.
stop_gradient
=
True
return
out
def
full
(
shape
,
fill_value
,
dtype
=
None
,
name
=
None
):
...
...
@@ -564,7 +703,53 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
end
=
start
start
=
0
return
paddle
.
fluid
.
layers
.
range
(
start
,
end
,
step
,
dtype
,
name
)
if
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
not
isinstance
(
start
,
Variable
):
with
device_guard
(
"cpu"
):
start
=
fill_constant
([
1
],
dtype
,
start
,
force_cpu
=
True
)
elif
start
.
dtype
!=
dtype
:
start
=
paddle
.
cast
(
start
,
dtype
)
if
not
isinstance
(
end
,
Variable
):
with
device_guard
(
"cpu"
):
end
=
fill_constant
([
1
],
dtype
,
end
,
force_cpu
=
True
)
elif
end
.
dtype
!=
dtype
:
end
=
paddle
.
cast
(
end
,
dtype
)
if
not
isinstance
(
step
,
Variable
):
with
device_guard
(
"cpu"
):
step
=
fill_constant
([
1
],
dtype
,
step
,
force_cpu
=
True
)
elif
step
.
dtype
!=
dtype
:
step
=
paddle
.
cast
(
step
,
dtype
)
if
in_dygraph_mode
():
return
_C_ops
.
final_state_arange
(
start
,
end
,
step
,
dtype
,
_current_expected_place
())
if
_in_legacy_dygraph
():
out
=
_C_ops
.
range
(
start
,
end
,
step
)
out
.
stop_gradient
=
True
return
out
out_shape
=
None
if
not
isinstance
(
start
,
Variable
)
and
not
isinstance
(
end
,
Variable
)
and
not
isinstance
(
step
,
Variable
):
out_shape
=
[
int
(
math
.
ceil
((
end
-
start
)
/
step
))]
check_dtype
(
dtype
,
'dtype'
,
[
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'range/arange'
)
helper
=
LayerHelper
(
'range'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
,
shape
=
out_shape
)
helper
.
append_op
(
type
=
'range'
,
inputs
=
{
'Start'
:
start
,
'End'
:
end
,
'Step'
:
step
},
outputs
=
{
'Out'
:
out
})
out
.
stop_gradient
=
True
return
out
def
_tril_triu_op
(
helper
):
...
...
@@ -1187,7 +1372,7 @@ def assign(x, output=None):
The OP copies the :attr:`x` to the :attr:`output`.
Parameters:
x (Tensor|n
umpy
.ndarray|list|tuple|scalar): A tensor, numpy ndarray, tuple/list of scalar,
x (Tensor|n
p
.ndarray|list|tuple|scalar): A tensor, numpy ndarray, tuple/list of scalar,
or scalar. Its data type supports float16, float32, float64, int32, int64, and bool.
Note: the float64 data will be converted to float32 because of current platform protobuf
data limitation.
...
...
@@ -1211,9 +1396,91 @@ def assign(x, output=None):
result2 = paddle.assign(data) # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
"""
check_type
(
x
,
'x'
,
(
Variable
,
np
.
ndarray
,
list
,
tuple
,
float
,
int
,
bool
),
'assign'
)
return
tensor
.
assign
(
x
,
output
)
input
=
x
helper
=
LayerHelper
(
'assign'
,
**
locals
())
check_type
(
input
,
'input'
,
(
Variable
,
np
.
ndarray
,
list
,
tuple
,
float
,
int
,
bool
),
'assign'
)
is_inplace
=
True
if
output
is
not
None
else
False
if
np
.
isscalar
(
input
)
and
not
isinstance
(
input
,
str
):
input
=
np
.
array
([
input
])
elif
isinstance
(
input
,
(
list
,
tuple
)):
input
=
np
.
array
(
input
)
# NOTE(Aurelius84): Why we judge core.VarBase?
# In case of @to_static, a VarBase can be as input of `assign`,
# but _non_static_mode()==False under @to_static, which means
# isinstance(VarBase, Variable) == False. It will cause return None
# after this api.
if
isinstance
(
input
,
(
Variable
,
core
.
VarBase
)):
if
_non_static_mode
():
if
output
is
None
:
if
_in_legacy_dygraph
():
output
=
core
.
VarBase
()
else
:
output
=
core
.
eager
.
Tensor
()
_C_ops
.
assign
(
input
,
output
)
else
:
check_dtype
(
input
.
dtype
,
'input'
,
[
'float16'
,
'uint16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
,
'uint8'
,
'bool'
],
'assign'
,
'(When the type of input in assign is Variable.)'
)
if
output
is
None
:
output
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
'assign'
,
inputs
=
{
'X'
:
[
input
]},
outputs
=
{
'Out'
:
[
output
]})
elif
isinstance
(
input
,
np
.
ndarray
):
# Not support [var, var, ...] currently.
if
len
(
input
.
shape
)
>
0
and
any
(
isinstance
(
x
,
Variable
)
for
x
in
input
):
raise
TypeError
(
"Required type(input) numpy.ndarray, but found `list(Variable)` in input."
)
dtype
=
convert_np_dtype_to_dtype_
(
input
.
dtype
)
if
dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
# Setting FP64 numpy data is not supported in Paddle, so we
# use FP32 here
warnings
.
warn
(
"paddle.assign doesn't support float64 input now due "
"to current platform protobuf data limitation, we convert "
"it to float32"
)
dtype
=
core
.
VarDesc
.
VarType
.
FP32
if
dtype
==
core
.
VarDesc
.
VarType
.
BOOL
:
value_name
=
"bool_values"
values
=
[
int
(
v
)
for
v
in
input
.
flat
]
elif
dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
value_name
=
"fp32_values"
values
=
[
float
(
v
)
for
v
in
input
.
flat
]
elif
dtype
==
core
.
VarDesc
.
VarType
.
INT32
:
value_name
=
"int32_values"
values
=
[
int
(
v
)
for
v
in
input
.
flat
]
elif
dtype
==
core
.
VarDesc
.
VarType
.
INT64
:
value_name
=
"int64_values"
values
=
[
int
(
v
)
for
v
in
input
.
flat
]
else
:
raise
TypeError
(
"When the type of 'input' in assign is numpy.ndarray, "
"the data type of 'input' must be bool, float32, int32 or int64, but "
"received %s."
%
convert_dtype
(
dtype
))
if
input
.
size
>
1024
*
1024
:
raise
ValueError
(
"The size of input is too big. Please consider "
"saving it to file and 'load_op' to load it"
)
if
output
is
None
:
output
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
'assign_value'
,
outputs
=
{
'Out'
:
[
output
]},
attrs
=
{
'dtype'
:
dtype
,
'shape'
:
list
(
input
.
shape
),
value_name
:
values
})
if
is_inplace
and
_non_static_mode
():
output
.
_bump_inplace_version
()
return
output
def
clone
(
x
,
name
=
None
):
...
...
python/paddle/tensor/linalg.py
浏览文件 @
c239f15a
...
...
@@ -13,14 +13,16 @@
# limitations under the License.
import
numpy
as
np
from
..f
luid.layer_helper
import
LayerHelper
from
..f
ramework
import
LayerHelper
from
..framework
import
_varbase_creator
,
_dygraph_tracer
,
in_dygraph_mode
,
_non_static_mode
from
..fluid.data_feeder
import
check_variable_and_dtype
,
check_type
,
check_dtype
from
..static
import
Variable
from
..fluid.framework
import
_in_legacy_dygraph
from
.manipulation
import
cast
from
.math
import
multiply
,
add
from
.logic
import
logical_not
from
.creation
import
full
from
..fluid
import
layers
import
paddle
from
paddle.common_ops_import
import
core
from
paddle.common_ops_import
import
VarDesc
...
...
@@ -2532,11 +2534,11 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
y
=
paddle
.
to_tensor
(
y
,
dtype
=
x
.
dtype
)
condition
=
s
>
cutoff
cond_int
=
layers
.
cast
(
condition
,
s
.
dtype
)
cond_not_int
=
layers
.
cast
(
layers
.
logical_not
(
condition
),
s
.
dtype
)
out1
=
layers
.
elementwise_mul
(
1
/
s
,
cond_int
)
out2
=
layers
.
elementwise_mul
(
1
/
y
,
cond_not_int
)
singular
=
layers
.
elementwise_
add
(
out1
,
out2
)
cond_int
=
cast
(
condition
,
s
.
dtype
)
cond_not_int
=
cast
(
logical_not
(
condition
),
s
.
dtype
)
out1
=
multiply
(
1
/
s
,
cond_int
)
out2
=
multiply
(
1
/
y
,
cond_not_int
)
singular
=
add
(
out1
,
out2
)
st
,
_
=
_C_ops
.
unsqueeze2
(
singular
,
'axes'
,
[
-
2
])
dims
=
list
(
range
(
len
(
vt
.
shape
)))
...
...
@@ -2559,11 +2561,11 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
y
=
paddle
.
to_tensor
(
y
,
dtype
=
s
.
dtype
)
condition
=
s_abs
>
cutoff
cond_int
=
layers
.
cast
(
condition
,
s
.
dtype
)
cond_not_int
=
layers
.
cast
(
layers
.
logical_not
(
condition
),
s
.
dtype
)
out1
=
layers
.
elementwise_mul
(
1
/
s
,
cond_int
)
out2
=
layers
.
elementwise_mul
(
1
/
y
,
cond_not_int
)
singular
=
layers
.
elementwise_
add
(
out1
,
out2
)
cond_int
=
cast
(
condition
,
s
.
dtype
)
cond_not_int
=
cast
(
logical_not
(
condition
),
s
.
dtype
)
out1
=
multiply
(
1
/
s
,
cond_int
)
out2
=
multiply
(
1
/
y
,
cond_not_int
)
singular
=
add
(
out1
,
out2
)
st
,
_
=
_C_ops
.
unsqueeze2
(
singular
,
'axes'
,
[
-
2
])
out_1
=
u
*
st
...
...
@@ -2597,17 +2599,17 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
'keep_dim'
:
True
,
'reduce_all'
:
False
})
rcond
=
layers
.
fill_constant
(
shape
=
[
1
],
value
=
rcond
,
dtype
=
dtype
)
rcond
=
full
(
shape
=
[
1
],
fill_
value
=
rcond
,
dtype
=
dtype
)
cutoff
=
rcond
*
max_singular_val
y
=
float
(
'inf'
)
y
=
layers
.
fill_constant
(
shape
=
[
1
],
value
=
y
,
dtype
=
dtype
)
y
=
full
(
shape
=
[
1
],
fill_
value
=
y
,
dtype
=
dtype
)
condition
=
s
>
cutoff
cond_int
=
layers
.
cast
(
condition
,
dtype
)
cond_not_int
=
layers
.
cast
(
layers
.
logical_not
(
condition
),
dtype
)
out1
=
layers
.
elementwise_mul
(
1
/
s
,
cond_int
)
out2
=
layers
.
elementwise_mul
(
1
/
y
,
cond_not_int
)
singular
=
layers
.
elementwise_
add
(
out1
,
out2
)
cond_int
=
cast
(
condition
,
dtype
)
cond_not_int
=
cast
(
logical_not
(
condition
),
dtype
)
out1
=
multiply
(
1
/
s
,
cond_int
)
out2
=
multiply
(
1
/
y
,
cond_not_int
)
singular
=
add
(
out1
,
out2
)
st
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
st_shape
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
...
...
@@ -2682,17 +2684,17 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
'keep_dim'
:
True
,
'reduce_all'
:
False
})
rcond
=
layers
.
fill_constant
(
shape
=
[
1
],
value
=
rcond
,
dtype
=
s_type
)
rcond
=
full
(
shape
=
[
1
],
fill_
value
=
rcond
,
dtype
=
s_type
)
cutoff
=
rcond
*
max_singular_val
y
=
float
(
'inf'
)
y
=
layers
.
fill_constant
(
shape
=
[
1
],
value
=
y
,
dtype
=
s_type
)
y
=
full
(
shape
=
[
1
],
fill_
value
=
y
,
dtype
=
s_type
)
condition
=
s_abs
>
cutoff
cond_int
=
layers
.
cast
(
condition
,
s_type
)
cond_not_int
=
layers
.
cast
(
layers
.
logical_not
(
condition
),
s_type
)
out1
=
layers
.
elementwise_mul
(
1
/
s
,
cond_int
)
out2
=
layers
.
elementwise_mul
(
1
/
y
,
cond_not_int
)
singular
=
layers
.
elementwise_
add
(
out1
,
out2
)
cond_int
=
cast
(
condition
,
s_type
)
cond_not_int
=
cast
(
logical_not
(
condition
),
s_type
)
out1
=
multiply
(
1
/
s
,
cond_int
)
out2
=
multiply
(
1
/
y
,
cond_not_int
)
singular
=
add
(
out1
,
out2
)
st
=
helper
.
create_variable_for_type_inference
(
dtype
=
s_type
)
st_shape
=
helper
.
create_variable_for_type_inference
(
dtype
=
s_type
)
...
...
python/paddle/tensor/manipulation.py
浏览文件 @
c239f15a
此差异已折叠。
点击以展开。
python/paddle/tensor/random.py
浏览文件 @
c239f15a
...
...
@@ -16,7 +16,7 @@
from
..framework
import
core
from
..framework
import
convert_np_dtype_to_dtype_
,
dygraph_only
from
..f
luid.layer_helper
import
LayerHelper
from
..f
ramework
import
LayerHelper
from
..fluid.data_feeder
import
check_variable_and_dtype
,
check_type
,
check_dtype
,
check_shape
from
..fluid.layers
import
utils
import
paddle
...
...
python/paddle/tensor/search.py
浏览文件 @
c239f15a
...
...
@@ -14,7 +14,7 @@
from
__future__
import
print_function
import
numpy
as
np
import
paddle
from
..f
luid.layer_helper
import
LayerHelper
from
..f
ramework
import
LayerHelper
from
..fluid.data_feeder
import
check_variable_and_dtype
,
check_type
,
check_dtype
from
..fluid
import
layers
from
..framework
import
core
,
in_dygraph_mode
,
_non_static_mode
...
...
python/paddle/tensor/stat.py
浏览文件 @
c239f15a
...
...
@@ -16,7 +16,7 @@
import
numpy
as
np
from
..static
import
Variable
from
..f
luid.layer_helper
import
LayerHelper
from
..f
ramework
import
LayerHelper
from
..framework
import
core
from
paddle.fluid.framework
import
_in_legacy_dygraph
,
in_dygraph_mode
from
.search
import
where
...
...
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