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40 lines
1.5 KiB
40 lines
1.5 KiB
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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from .cross_entropy import LabelSmoothingCrossEntropy
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class JsdCrossEntropy(nn.Module):
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""" Jensen-Shannon Divergence + Cross-Entropy Loss
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Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
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From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
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https://arxiv.org/abs/1912.02781
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Hacked together by Ross Wightman
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"""
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def __init__(self, num_splits=3, alpha=12, smoothing=0.1):
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super().__init__()
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self.num_splits = num_splits
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self.alpha = alpha
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if smoothing is not None and smoothing > 0:
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self.cross_entropy_loss = LabelSmoothingCrossEntropy(smoothing)
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else:
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self.cross_entropy_loss = torch.nn.CrossEntropyLoss()
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def __call__(self, output, target):
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split_size = output.shape[0] // self.num_splits
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assert split_size * self.num_splits == output.shape[0]
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logits_split = torch.split(output, split_size)
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# Cross-entropy is only computed on clean images
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loss = self.cross_entropy_loss(logits_split[0], target[:split_size])
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probs = [F.softmax(logits, dim=1) for logits in logits_split]
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# Clamp mixture distribution to avoid exploding KL divergence
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logp_mixture = torch.clamp(torch.stack(probs).mean(axis=0), 1e-7, 1).log()
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loss += self.alpha * sum([F.kl_div(
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logp_mixture, p_split, reduction='batchmean') for p_split in probs]) / len(probs)
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return loss
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