How can I convert a tensor with the shape of [1, 3, 64, 64] to [1, 4, 64, 64] with the newly added layer being the same as the previous?
Question:
I have a PyTorch tensor with the shape of [1, 3, 64, 64], and I want to convert it to the shape [1, 4, 64, 64] while setting the value of the newly added layer to be the same as the previous layer in the same dimension (eg newtensor[0][3] = oldtensor[0][2])
Note that my tensor has requires_grad=True, so I cannot use resize_()
How can I do this?
Answers:
Get a slice from the old tensor, and concatenate it to the new tensor along dimension 1.
tslice = old[:,-1:,:,:]
new = torch.cat((old,tslice), dim = 1)
This will work perfectly. @DerekG code had an error in -1, but his idea is correct.
tensor is your tensor data.
new = torch.cat((tensor, tensor[:, 0:1, :, :]), dim=1)
I have a PyTorch tensor with the shape of [1, 3, 64, 64], and I want to convert it to the shape [1, 4, 64, 64] while setting the value of the newly added layer to be the same as the previous layer in the same dimension (eg newtensor[0][3] = oldtensor[0][2])
Note that my tensor has requires_grad=True, so I cannot use resize_()
How can I do this?
Get a slice from the old tensor, and concatenate it to the new tensor along dimension 1.
tslice = old[:,-1:,:,:]
new = torch.cat((old,tslice), dim = 1)
This will work perfectly. @DerekG code had an error in -1, but his idea is correct.
tensor is your tensor data.
new = torch.cat((tensor, tensor[:, 0:1, :, :]), dim=1)