def __init__(self, start, limit=None, delta=1): super(Range, self).__init__() validator.check_value_type("start", start, [int, float], self.cls_name) validator.check_value_type("delta", delta, [int, float], self.cls_name) if delta == 0: raise ValueError("The input of `delta` can not be equal to zero.") if limit is not None: validator.check_value_type("limit", limit, [int, float], self.cls_name) if isinstance(start, int) and isinstance(limit, int) and isinstance(delta, int): self.dtype = mstype.int32 else: self.dtype = mstype.float32 else: if isinstance(start, int) and isinstance(delta, int): self.dtype = mstype.int32 else: self.dtype = mstype.float32 if isinstance(start, int): start = float(start) if isinstance(limit, int): limit = float(limit) if isinstance(delta, int): delta = float(delta) self.range_x = inner.Range(start, limit, delta) if limit is None: length_input = math.ceil(start / delta) else: length_input = math.ceil((limit - start) / delta) self.input_tensor = Tensor(list(range(length_input)), self.dtype)
def __init__(self): super(RangeNet, self).__init__() self.range_ops = inner.Range(1.0, 8.0, 2.0)