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24 lines
862 B
24 lines
862 B
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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class DenseBinaryCrossEntropy(nn.Module):
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""" BCE using one-hot from dense targets w/ label smoothing
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NOTE for experiments comparing CE to BCE /w label smoothing, may remove
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"""
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def __init__(self, smoothing=0.1):
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super(DenseBinaryCrossEntropy, self).__init__()
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assert 0. <= smoothing < 1.0
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self.smoothing = smoothing
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self.bce = nn.BCEWithLogitsLoss()
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def forward(self, x, target):
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num_classes = x.shape[-1]
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off_value = self.smoothing / num_classes
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on_value = 1. - self.smoothing + off_value
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target = target.long().view(-1, 1)
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target = torch.full(
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(target.size()[0], num_classes), off_value, device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
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return self.bce(x, target)
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