31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import random
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from math import prod
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# Fits a soft-discretized input to a normal-PDF across the specified dimension.
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# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete
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# values with the specified expected variance.
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class DiscretizationLoss(nn.Module):
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def __init__(self, dim, expected_variance):
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super().__init__()
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self.dim = dim
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self.dist = torch.distributions.Normal(0, scale=expected_variance)
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def forward(self, x):
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other_dims = set(range(len(x.shape)))-set([self.dim])
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averaged = x.sum(dim=tuple(other_dims)) / x.sum()
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averaged = averaged - averaged.mean()
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return torch.sum(-self.dist.log_prob(averaged))
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if __name__ == '__main__':
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d = DiscretizationLoss(1, 1e-6)
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v = torch.randn(16, 8192, 500)
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#for k in range(5):
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# v[:, random.randint(0,8192), :] += random.random()*100
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v = F.softmax(v, 1)
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print(d(v)) |