Multidimensional filling in a N dimensional tensor

[KFrank]

Hi Martin!

@martinferianc

Is there a way, where it would be possible to use a vectorizing operation if we would like to fill a portion of the tensor, given different indices for each sample in a batch B?

# Repalce the for loop with a vectorized implementation
for i in range(B):
    mask[i, :, bby1[i]:bby2[i], bbx1[i]:bbx2[i]] = 1.0

In a reply to a similar question, I showed how to create a “zero-mask” from
a batch of bounding boxes. You should be able to do the same, but convert
the “zero-mask” to a “one-mask” with logical_not().

Here is the relevant post:

Batch indexing to avoid the loop

Hi Hwang! Yes, you can use your bounding boxes, B, to build a mask tensor that you then multiply onto img to zero out the values in the bounding boxes: >>> import torch >>> print (torch.__version__) 1.13.0 >>> >>> _ = torch.manual_seed (2022) >>> >>> nBatch = 256 >>> nChannels = 3 >>> h = 32 >>> w = h >>> >>> img = torch.randn (nBatch, nChannels, h, w) >>> B = torch.rand (nBatch, 4) * h >>> B = B.type (torch.int32) >>> >>> imgB = img.clone() >>> >>> # loop version >>> for i in range (nBatch…

Best.

K. Frank