oneflow.div¶
-
oneflow.
div
(x, y, *, rounding_mode=None)¶ Computes the division of input by other for each element, scalar and broadcast promotation are supported. The formula is:
\[out = \frac{input}{other}\]- Parameters
input (Union[int, float, oneflow.Tensor]) – input.
other (Union[int, float, oneflow.Tensor]) – other.
- Keyword Arguments
rounding_mode (str, optional) – It can be set as
"floor"
(roudning the results down) or"trunc"
(rounding the results towards zero). None for default (no rounding).
For example:
>>> import numpy as np >>> import oneflow as flow # element-wise divide >>> input = flow.tensor(np.random.randn(2,3), dtype=flow.float32) >>> other = flow.tensor(np.random.randn(2,3), dtype=flow.float32) >>> out = flow.div(input,other).numpy() >>> out.shape (2, 3) # scalar divide >>> input = 5 >>> other = flow.tensor(np.random.randn(2,3), dtype=flow.float32) >>> out = flow.div(input,other).numpy() >>> out.shape (2, 3) # broadcast divide >>> input = flow.tensor(np.random.randn(1,1), dtype=flow.float32) >>> other = flow.tensor(np.random.randn(2,3), dtype=flow.float32) >>> out = flow.div(input,other).numpy() >>> out.shape (2, 3) # rounding_mode >>> x = flow.tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637]) >>> flow.div(x, 0.5) tensor([ 0.7620, 2.5548, -0.5944, -0.7438, 0.9274], dtype=oneflow.float32) >>> flow.div(x, 0.5, rounding_mode="floor") tensor([ 0., 2., -1., -1., 0.], dtype=oneflow.float32) >>> flow.div(x, 0.5, rounding_mode="trunc") tensor([0., 2., -0., -0., 0.], dtype=oneflow.float32)