torch.where

torch.where(condition, input, other, *, out=None) → Tensor

Return a tensor of elements selected from either input or other, depending on condition.

The operation is defined as:

$$\text{out}_i = \begin{cases} \text{input}_i & \text{if } \text{condition}_i \\ \text{other}_i & \text{otherwise} \\ \end{cases}$$

Note

The tensors condition, input, other must be broadcastable.

Parameters

• condition (BoolTensor) – When True (nonzero), yield input, otherwise yield other
• input (Tensor or Scalar) – value (if input is a scalar) or values selected at indices where condition is True
• other (Tensor or Scalar) – value (if other is a scalar) or values selected at indices where condition is False

Keyword Arguments

out (Tensor, optional) – the output tensor.

Returns

A tensor of shape equal to the broadcasted shape of condition, input, other

Return type

Tensor

Example:

>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620,  0.3139],
        [ 0.3898, -0.7197],
        [ 0.0478, -0.1657]])
>>> torch.where(x > 0, 1.0, 0.0)
tensor([[0., 1.],
        [1., 0.],
        [1., 0.]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000,  0.3139],
        [ 0.3898,  1.0000],
        [ 0.0478,  1.0000]])
>>> x = torch.randn(2, 2, dtype=torch.double)
>>> x
tensor([[ 1.0779,  0.0383],
        [-0.8785, -1.1089]], dtype=torch.float64)
>>> torch.where(x > 0, x, 0.)
tensor([[1.0779, 0.0383],
        [0.0000, 0.0000]], dtype=torch.float64)

torch.where(condition) → tuple of LongTensor

torch.where(condition) is identical to torch.nonzero(condition, as_tuple=True).

Note

See also torch.nonzero().