Tensor manipulation - creating a positional tensor from a given tensor

Question

I have a input tensor which has zero padding at the start and then a sequence of values. So something like:

x = torch.tensor([[0, 2, 8, 12],
                  [0, 0, 6, 3]])

What I need is another tensor having same shape and retaining 0's for the padding and an increasing sequence for the rest of the numbers. So my output tensor should be:

y = ([[0, 1, 2, 3],
      [0, 0, 1, 2]])

I tried something like:

MAX_SEQ=4
seq_start = np.nonzero(x)
start = seq_start[0][0]
pos_id = torch.cat((torch.from_numpy(np.zeros(start, dtype=int)).to(device), torch.arange(1, MAX_SEQ-start+1).to(device)), 0)
print(pos_id)

This works if the tensor is 1 dimensional but needs additional logic to handle it for 2-D shape. This can be done as np.nonzeros returns a tuple and we could probably loop thru' those tuples updating a counter or something. However I am sure there must be a simple tensor operation which should do this in 1-2 lines of code and also perhaps more effectively.

Help appreciated

Ivan · Accepted Answer · 2020-12-27 11:03:48Z

A possible solution in three small steps:

Find the index of the first non zero element for each row. This can be done with a trick explained here (adapted here for non-binary tensors).

> idx = torch.arange(x.shape[1], 0, -1)
tensor([4, 3, 2, 1])

> xbin = torch.where(x == 0, 0, 1)
tensor([[0, 1, 1, 1],
        [0, 0, 1, 1]])

> xbin*idx
tensor([[0, 3, 2, 1],
        [0, 0, 2, 1]])

> indices = torch.argmax(xbin*idx, dim=1, keepdim=True)
tensor([[1],
        [2]])

Create an arangement for the resulting tensor (without padding). This can be done by applying torch.repeat and torch.view on a torch.arange call:
```
> rows, cols = x.shape
> seq = torch.arange(1, cols+1).repeat(1, rows).view(-1, cols)
tensor([[1, 2, 3, 4],
        [1, 2, 3, 4]])
```

Lastly - here's the trick! - we substract the index of the first non-zero element with the arangement, for each row. Then we mask the padding values and replace them with zeros:

> pos_id = seq - indices
tensor([[ 0,  1,  2,  3],
        [-1,  0,  1,  2]])

> mask = indices > seq - 1
tensor([[ True, False, False, False],
        [ True,  True, False, False]])

> pos_id[mask] = 0
tensor([[0, 1, 2, 3],
        [0, 0, 1, 2]])

halfer · Accepted Answer · 2020-12-31 11:49:49Z

0

Expanding Ivan's nice answer to include batch size as my model had that. This 'seems' to work. This is just for a reference in case more than 2D to be considered

x = torch.tensor([[[ 0,  0,  2,  3,  4,  5,  6,  7,  8,  9],
                [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]],

               [[0, 0, 0, 0, 0, 0, 26, 27, 28, 29],
                [0, 31, 32, 33, 34, 35, 36, 37, 38, 39]],

               [[0, 0, 42, 43, 44, 45, 46, 47, 48, 49],
                [0, 0, 0, 53, 0, 55, 56, 57, 58, 59]]])

bs, rows, cols = x.shape
seq = torch.arange(1, cols+1).repeat(1, rows).repeat(1, bs).view(bs, rows, cols)

idx = torch.arange(x.shape[-1], 0, -1)
xbin = torch.where(x == 0, 0, 1)
indices = torch.argmax(xbin*idx, dim=2, keepdim=True)

pos_id = seq - indices
mask = indices > seq - 1
pos_id[mask] = 0
print(pos_id)

edited Dec 31, 2020 at 11:49

halfer

20.2k20 gold badges110 silver badges207 bronze badges

answered Dec 27, 2020 at 13:01

Allohvk

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1 Comment

Ivan Over a year ago

Nice! Glad I could help. You can pull it off by calling torch.repeat once, with torch.arange(1, cols+1).repeat(1, bs*rows).view(bs, rows, cols), but that's a minor detail ;)

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