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调整 logspace 的位置
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@BrilliantYuKaimin
BrilliantYuKaimin committed Apr 16, 2022
1 parent 7cc079f commit 3530acf
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Showing 2 changed files with 123 additions and 126 deletions.
125 changes: 0 additions & 125 deletions python/paddle/fluid/layers/tensor.py
View file
Expand Up @@ -56,7 +56,6 @@
'isfinite',
'range',
'linspace',
'logspace',
'zeros_like',
'ones_like',
'diag',
Expand Down Expand Up @@ -1615,130 +1614,6 @@ def linspace(start, stop, num, dtype=None, name=None):
return out


def logspace(start, stop, num, base=10.0, dtype=None, name=None):
r"""
Return fixed number of logarithmical-evenly spaced values within the interval \
:math:`[base^{start}, base^{stop}]`.
Notes:
This API does not compute the gradient.
Args:
start(int|float|Tensor): The input :attr:`start` is exponent of first entry in \
the sequence. 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 exponent of last entry in the \
sequence. 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 number of items in the sequence. \
It is an int scalar, or a Tensor of shape [1] with data type int32.
base(int|float|Tensor): The input :attr:`base` is base of the logarithm function. \
It is a scalar, or a Tensor of shape [1] with input data type int32, int64, \
float32 or float64.
dtype(np.dtype|str, optional): The data type of output tensor, it could be \
int32, int64, float32 or 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 logarithmical-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 exponential of :attr:`start` with base :attr:`base`.
Examples:
.. code-block:: python
:name: logspace-example
import paddle
data = paddle.logspace(0, 10, 5, 2, 'float32')
# [1. , 5.65685415 , 32. , 181.01933289, 1024. ]
data = paddle.logspace(0, 10, 1, 2, 'float32')
# [1.]
"""
if dtype is None:
dtype = 'float32'
tensor_num = num
tensor_start = start
tensor_stop = stop
tensor_base = base
if not isinstance(num, Variable):
check_type(num, 'num', (int), 'logspace')
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 not isinstance(base, Variable):
with device_guard("cpu"):
tensor_base = fill_constant([1], dtype, base)
if _non_static_mode():
return _C_ops.logspace(tensor_start, tensor_stop, tensor_num,
tensor_base, 'dtype', dtype)

helper = LayerHelper("logspace", **locals())

start_dtype = convert_dtype(tensor_start.dtype)
stop_dtype = convert_dtype(tensor_stop.dtype)
base_dtype = convert_dtype(tensor_base.dtype)
out_dtype = convert_dtype(dtype)
if isinstance(start, Variable):
check_dtype(start.dtype, 'start',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(start, 'start', (int, float), 'logspace')

if isinstance(stop, Variable):
check_dtype(stop.dtype, 'stop',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(stop, 'stop', (int, float), 'logspace')

if isinstance(num, Variable):
check_dtype(num.dtype, 'num', ['int32'], 'logspace')

if isinstance(base, Variable):
check_dtype(base.dtype, 'base',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(base, 'base', (int, float), 'logspace')

check_dtype(dtype, 'dtype', ['int32', 'int64', 'float32', 'float64'],
'logspace')
if ((stop_dtype == "float64" or start_dtype == "float64"
or base_dtype == "float64")
and out_dtype in ["float32", "int32"]) or \
((stop_dtype == "int64" or start_dtype == "int64"
or base_dtype == "int64")
and out_dtype == "int32"):
raise ValueError(
"The dtype of start/stop/base is {}/{}/{} but the attr(dtype) of logspace is {}, "
"which may cause data type overflows. Please reset attr(dtype) of logspace."
.format(start_dtype, stop_dtype, base_dtype, dtype))

out = helper.create_variable_for_type_inference(dtype=dtype)

helper.append_op(
type='logspace',
inputs={
'Start': tensor_start,
'Stop': tensor_stop,
'Num': tensor_num,
'Base': tensor_base
},
attrs={'dtype': dtype},
outputs={'Out': [out]})
if isinstance(num, int):
out.desc.set_shape((num, ))
return out


def zeros_like(x, out=None):
"""
This OP creates a zeros tensor which has identical shape and dtype
Expand Down
124 changes: 123 additions & 1 deletion python/paddle/tensor/creation.py
View file
Expand Up @@ -26,7 +26,6 @@
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
# TODO: define functions to get create a tensor
from ..fluid.layers import logspace # noqa: F401
import paddle
from paddle import _C_ops
from ..fluid.framework import _in_legacy_dygraph, _in_eager_without_dygraph_check
Expand Down Expand Up @@ -146,6 +145,129 @@ def linspace(start, stop, num, dtype=None, name=None):
out.desc.set_shape((num, ))
return out

def logspace(start, stop, num, base=10.0, dtype=None, name=None):
r"""
Return fixed number of logarithmical-evenly spaced values within the interval \
:math:`[base^{start}, base^{stop}]`.
Notes:
This API does not compute the gradient.
Args:
start(int|float|Tensor): The input :attr:`start` is exponent of first entry in \
the sequence. 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 exponent of last entry in the \
sequence. 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 number of items in the sequence. \
It is an int scalar, or a Tensor of shape [1] with data type int32.
base(int|float|Tensor): The input :attr:`base` is base of the logarithm function. \
It is a scalar, or a Tensor of shape [1] with input data type int32, int64, \
float32 or float64.
dtype(np.dtype|str, optional): The data type of output tensor, it could be \
int32, int64, float32 or 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 logarithmical-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 exponential of :attr:`start` with base :attr:`base`.
Examples:
.. code-block:: python
:name: logspace-example
import paddle
data = paddle.logspace(0, 10, 5, 2, 'float32')
# [1. , 5.65685415 , 32. , 181.01933289, 1024. ]
data = paddle.logspace(0, 10, 1, 2, 'float32')
# [1.]
"""
if dtype is None:
dtype = 'float32'
tensor_num = num
tensor_start = start
tensor_stop = stop
tensor_base = base
if not isinstance(num, Variable):
check_type(num, 'num', (int), 'logspace')
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 not isinstance(base, Variable):
with device_guard("cpu"):
tensor_base = fill_constant([1], dtype, base)
if _non_static_mode():
return _C_ops.logspace(tensor_start, tensor_stop, tensor_num,
tensor_base, 'dtype', dtype)

helper = LayerHelper("logspace", **locals())

start_dtype = convert_dtype(tensor_start.dtype)
stop_dtype = convert_dtype(tensor_stop.dtype)
base_dtype = convert_dtype(tensor_base.dtype)
out_dtype = convert_dtype(dtype)
if isinstance(start, Variable):
check_dtype(start.dtype, 'start',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(start, 'start', (int, float), 'logspace')

if isinstance(stop, Variable):
check_dtype(stop.dtype, 'stop',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(stop, 'stop', (int, float), 'logspace')

if isinstance(num, Variable):
check_dtype(num.dtype, 'num', ['int32'], 'logspace')

if isinstance(base, Variable):
check_dtype(base.dtype, 'base',
['float32', 'float64', 'int32', 'int64'], 'logspace')
else:
check_type(base, 'base', (int, float), 'logspace')

check_dtype(dtype, 'dtype', ['int32', 'int64', 'float32', 'float64'],
'logspace')
if ((stop_dtype == "float64" or start_dtype == "float64"
or base_dtype == "float64")
and out_dtype in ["float32", "int32"]) or \
((stop_dtype == "int64" or start_dtype == "int64"
or base_dtype == "int64")
and out_dtype == "int32"):
raise ValueError(
"The dtype of start/stop/base is {}/{}/{} but the attr(dtype) of logspace is {}, "
"which may cause data type overflows. Please reset attr(dtype) of logspace."
.format(start_dtype, stop_dtype, base_dtype, dtype))

out = helper.create_variable_for_type_inference(dtype=dtype)

helper.append_op(
type='logspace',
inputs={
'Start': tensor_start,
'Stop': tensor_stop,
'Num': tensor_num,
'Base': tensor_base
},
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):
Expand Down

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