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Greetings,
I am attempting to understand the steps you took in your denoising.py (for both versions 1 & 2). I have a few questions:
denoising.py
While adding some noise to the bouding boxes, you have the following operation:
rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 rand_part = torch.rand_like(input_query_bbox) rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask)
As far as I can tell, the final operation is equivalent to running
rand_part = rand_part + negative_gt_mask
Since
$$ (r + 1) \times n + r \times (1-n) = (r \times n + n) + (r - r \times n) = r + n $$
Is there something I missed ? Are you using floating point inaccuracy for non-linearity ?
The text was updated successfully, but these errors were encountered:
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Greetings,
I am attempting to understand the steps you took in your
denoising.py
(for both versions 1 & 2). I have a few questions:While adding some noise to the bouding boxes, you have the following operation:
As far as I can tell, the final operation is equivalent to running
Since
Is there something I missed ? Are you using floating point inaccuracy for non-linearity ?
The text was updated successfully, but these errors were encountered: