I want to have a random bit mask that has some specified percent of 0
s. The function I devised is:
def create_mask(shape, rate): """ The idea is, you take a random permutations of numbers. You then mod then mod it by the [number of entries in the bitmask] / [percent of 0s you want]. The number of zeros will be exactly the rate of zeros need. You can clamp the values for a bitmask. """ mask = torch.randperm(reduce(operator.mul, shape, 1)).float().cuda() # Mod it by the percent to get an even dist of 0s. mask = torch.fmod(mask, reduce(operator.mul, shape, 1) / rate) # Anything not zero should be put to 1 mask = torch.clamp(mask, 0, 1) return mask.view(shape)
To illustrate:
>>> x = create_mask((10, 10), 10) >>> x 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 [torch.cuda.FloatTensor of size 10x10 (GPU 0)]
The main issue I have with this method is it requires the rate
to divide the shape
. I want a function that accepts an arbitrary decimal and gives approximately rate
percent of 0s in the bitmask. Furthermore, I am trying to find a relatively efficient way of doing so. Hence, I would rather not move a numpy
array from the CPU to the GPU. Is there an effiecient way of doing so that allows for a decimal rate
?
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Answer
For anyone running into this, this will create a bitmask with approximately 80% zero’s directly on GPU. (PyTorch 0.3)
torch.cuda.FloatTensor(10, 10).uniform_() > 0.8