timm/loss/jsd.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from .cross_entropy import LabelSmoothingCrossEntropy


class JsdCrossEntropy(nn.Module):
    """ Jensen-Shannon Divergence + Cross-Entropy Loss

    Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
    From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
    https://arxiv.org/abs/1912.02781

    Hacked together by / Copyright 2020 Ross Wightman
    """
    def __init__(self, num_splits=3, alpha=12, smoothing=0.1):
        super().__init__()
        self.num_splits = num_splits
        self.alpha = alpha
        if smoothing is not None and smoothing > 0:
            self.cross_entropy_loss = LabelSmoothingCrossEntropy(smoothing)
        else:
            self.cross_entropy_loss = torch.nn.CrossEntropyLoss()

    def __call__(self, output, target):
        split_size = output.shape[0] // self.num_splits
        assert split_size * self.num_splits == output.shape[0]
        logits_split = torch.split(output, split_size)

        # Cross-entropy is only computed on clean images
        loss = self.cross_entropy_loss(logits_split[0], target[:split_size])
        probs = [F.softmax(logits, dim=1) for logits in logits_split]

        # Clamp mixture distribution to avoid exploding KL divergence
        logp_mixture = torch.clamp(torch.stack(probs).mean(axis=0), 1e-7, 1).log()
        loss += self.alpha * sum([F.kl_div(
            logp_mixture, p_split, reduction='batchmean') for p_split in probs]) / len(probs)
        return loss