forked from mrq/DL-Art-School
56 lines
2.2 KiB
Python
56 lines
2.2 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, discrete_bins, dim, expected_variance, store_past=0):
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super().__init__()
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self.discrete_bins = discrete_bins
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self.dim = dim
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self.dist = torch.distributions.Normal(0, scale=expected_variance)
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if store_past > 0:
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self.record_past = True
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self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins))
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else:
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self.record_past = False
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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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if self.record_past:
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acc_count = self.accumulator.shape[0]
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avg = averaged.detach().clone()
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if self.accumulator_filled > 0:
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averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \
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averaged / acc_count
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# Also push averaged into the accumulator.
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self.accumulator[self.accumulator_index] = avg
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self.accumulator_index += 1
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if self.accumulator_index >= acc_count:
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self.accumulator_index *= 0
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if self.accumulator_filled <= 0:
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self.accumulator_filled += 1
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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(1024, 1, 1e-6, store_past=20)
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for _ in range(500):
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v = torch.randn(16, 1024, 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)) |