torch.cat¶
- torch.cat(tensors, dim=0, *, out=None) Tensor ¶
Concatenates the given sequence of
seq
tensors in the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be a 1-D empty tensor with size(0,)
.torch.cat()
can be seen as an inverse operation fortorch.split()
andtorch.chunk()
.torch.cat()
can be best understood via examples.See also
torch.stack()
concatenates the given sequence along a new dimension.- Parameters
tensors (sequence of Tensors) – any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.
dim (int, optional) – the dimension over which the tensors are concatenated
- Keyword Arguments
out (Tensor, optional) – the output tensor.
Example:
>>> x = torch.randn(2, 3) >>> x tensor([[ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497]]) >>> torch.cat((x, x, x), 0) tensor([[ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497], [ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497], [ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497]]) >>> torch.cat((x, x, x), 1) tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497]])