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]]])