delete nn.LinSpace interface

mindspore/nn/layer/math.py

@@ -27,7 +27,6 @@ from ..._checkparam import Validator as validator
 
 __all__ = ['ReduceLogSumExp',
            'Range',
-           'LinSpace',
            'LGamma',
            'DiGamma',
            'IGamma',
@@ -157,57 +156,6 @@ class Range(Cell):
         return range_out
 
 
-class LinSpace(Cell):
-    r"""
-    Generates values in an interval.
-
-    Args:
-        start (Union[int, float]): The start of interval. With shape of 0-D.
-        stop (Union[int, float]): The end of interval. With shape of 0-D.
-        num (int): ticks number in the interval, the ticks include start and stop value. With shape of 0-D.
-
-    Outputs:
-        Tensor, With type same as `start`. The shape is 1-D with length of `num`.
-
-    Supported Platforms:
-        ``Ascend``
-
-    Examples:
-        >>> linspace = nn.LinSpace(1, 10, 5)
-        >>> output = linspace()
-        >>> print(output)
-        [ 1.    3.25  5.5   7.75 10.  ]
-    """
-
-    def __init__(self, start, stop, num):
-        super(LinSpace, self).__init__()
-        validator.check_value_type("start", start, [int, float], self.cls_name)
-        validator.check_value_type("stop", stop, [int, float], self.cls_name)
-        validator.check_value_type("num", num, [int], self.cls_name)
-        validator.check_positive_int(num, "num", self.cls_name)
-
-        self.is_single = bool(num == 1)
-        self.lin_space = P.LinSpace()
-        self.start = Tensor(start, mstype.float32)
-        self.stop = Tensor(stop, mstype.float32)
-        self.num = num
-        self.start_array = Tensor([start], mstype.float32)
-
-    def construct(self):
-        if self.is_single:
-            return self.start_array
-
-        lin_space_out = self.lin_space(self.start, self.stop, self.num)
-        return lin_space_out
-
-@constexpr
-def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
-    """Check tensors data type same."""
-    if data_dtype in value_dtype:
-        return True
-    raise TypeError(f"For '{op_name}', the value data type '{value_dtype}' "
-                    f"is not consistent with assigned tensor data type {data_dtype}.")
-
 class LGamma(Cell):
     r"""
     Calculate LGamma using Lanczos' approximation refering to "A Precision Approximationof the Gamma Function".