torch.div¶

torch.div(input, other, *, rounding_mode=None, out=None) → Tensor¶

Divides each element of the input input by the corresponding element of other.

$$\text{out}_i = \frac{\text{input}_i}{\text{other}_i}$$

Note

By default, this performs a “true” division like Python 3. See the rounding_mode argument for floor division.

Supports broadcasting to a common shape, type promotion, and integer, float, and complex inputs. Always promotes integer types to the default scalar type.

Parameters

• input (Tensor) – the dividend

• other (Tensor or Number) – the divisor

Keyword Arguments

• rounding_mode (str, optional) –

  Type of rounding applied to the result:

  • None - default behavior. Performs no rounding and, if both input and other are integer types, promotes the inputs to the default scalar type. Equivalent to true division in Python (the / operator) and NumPy’s np.true_divide.

  • "trunc" - rounds the results of the division towards zero. Equivalent to C-style integer division.

  • "floor" - rounds the results of the division down. Equivalent to floor division in Python (the // operator) and NumPy’s np.floor_divide.

• out (Tensor, optional) – the output tensor.

Examples:

>>> x = torch.tensor([ 0.3810,  1.2774, -0.2972, -0.3719,  0.4637])
>>> torch.div(x, 0.5)
tensor([ 0.7620,  2.5548, -0.5944, -0.7438,  0.9274])

>>> a = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
...                   [ 0.1815, -1.0111,  0.9805, -1.5923],
...                   [ 0.1062,  1.4581,  0.7759, -1.2344],
...                   [-0.1830, -0.0313,  1.1908, -1.4757]])
>>> b = torch.tensor([ 0.8032,  0.2930, -0.8113, -0.2308])
>>> torch.div(a, b)
tensor([[-0.4620, -6.6051,  0.5676,  1.2639],
        [ 0.2260, -3.4509, -1.2086,  6.8990],
        [ 0.1322,  4.9764, -0.9564,  5.3484],
        [-0.2278, -0.1068, -1.4678,  6.3938]])

>>> torch.div(a, b, rounding_mode='trunc')
tensor([[-0., -6.,  0.,  1.],
        [ 0., -3., -1.,  6.],
        [ 0.,  4., -0.,  5.],
        [-0., -0., -1.,  6.]])

>>> torch.div(a, b, rounding_mode='floor')
tensor([[-1., -7.,  0.,  1.],
        [ 0., -4., -2.,  6.],
        [ 0.,  4., -1.,  5.],
        [-1., -1., -2.,  6.]])