torch.stack

torch.stack(tensors, dim=0, *, out=None) → Tensor

Concatenates a sequence of tensors along a new dimension.

All tensors need to be of the same size.

See also

torch.cat() concatenates the given sequence along an existing dimension.

Parameters

• tensors (sequence of Tensors) – sequence of tensors to concatenate

• dim (int, optional) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive). Default: 0

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.3367,  0.1288,  0.2345],
        [ 0.2303, -1.1229, -0.1863]])
>>> x = torch.stack((x, x)) # same as torch.stack((x, x), dim=0)
>>> x
tensor([[[ 0.3367,  0.1288,  0.2345],
         [ 0.2303, -1.1229, -0.1863]],

        [[ 0.3367,  0.1288,  0.2345],
         [ 0.2303, -1.1229, -0.1863]]])
>>> x.size()
torch.Size([2, 2, 3])
>>> x = torch.stack((x, x), dim=1)
tensor([[[ 0.3367,  0.1288,  0.2345],
         [ 0.3367,  0.1288,  0.2345]],

        [[ 0.2303, -1.1229, -0.1863],
         [ 0.2303, -1.1229, -0.1863]]])
>>> x = torch.stack((x, x), dim=2)
tensor([[[ 0.3367,  0.3367],
         [ 0.1288,  0.1288],
         [ 0.2345,  0.2345]],

        [[ 0.2303,  0.2303],
         [-1.1229, -1.1229],
         [-0.1863, -0.1863]]])
>>> x = torch.stack((x, x), dim=-1)
tensor([[[ 0.3367,  0.3367],
         [ 0.1288,  0.1288],
         [ 0.2345,  0.2345]],

        [[ 0.2303,  0.2303],
         [-1.1229, -1.1229],
         [-0.1863, -0.1863]]])