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Online Label Smoothing

Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing.

Introduction

As the abstract states, OLS is a strategy to generates soft labels based on the statistics of the model prediction for the target category. The core idea is that instead of using fixed soft labels for every epoch, we go updating them based on the stats of correct predicted samples.

More details and experiment results can be found in the paper.

Usage

Usage of OnlineLabelSmoothing is pretty straightforward. Just use it as you would use PyTorch CrossEntropyLoss. The only thing that is different is that at the end of the epoch you should call OnlineLabelSmoothing.next_epoch(). It updates the OnlineLabelSmoothing.supervise matrix that will be used in the next epoch for the soft labels.

Standalone

from ols import OnlineLabelSmoothing
import torch

k = 4  # Number of classes
b = 32  # Batch size
criterion = OnlineLabelSmoothing(alpha=0.5, n_classes=k, smoothing=0.1)
logits = torch.randn(b, k)  # Predictions
y = torch.randint(k, (b,))  # Ground truth

loss = criterion(logits, y)

PyTorch

from ols import OnlineLabelSmoothing

criterion = OnlineLabelSmoothing(alpha=..., n_classes=...)
for epoch in range(...):  # loop over the dataset multiple times
    for i, data in enumerate(...):
        inputs, labels = data
        # zero the parameter gradients
        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    print(f'Epoch {epoch} finished!')
    # Update the soft labels for next epoch
    criterion.next_epoch()

Note: criterion.eval() should be called before eval/testing and criterion.train() should be called before training. Otherwise, test/val statistics will be used in step().

PyTorchLightning

With PL you can simply call next_epoch() at the end of the epoch with:

import pytorch_lightning as pl
from ols import OnlineLabelSmoothing


class LitClassification(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.criterion = OnlineLabelSmoothing(alpha=..., n_classes=...)

    def forward(self, x):
        pass

    def configure_optimizers(self):
        pass

    def training_step(self, train_batch, batch_idx):
        pass

    def on_train_epoch_end(self, **kwargs):
        self.criterion.next_epoch()

Note: In this case criterion.eval() and criterion.train() aren't needed since PyTorchLightning handles this automatically.

Installation

pip install -U git+https://github.com/ankandrew/online-label-smoothing-pt.git

Citation

@misc{zhang2020delving,
      title={Delving Deep into Label Smoothing}, 
      author={Chang-Bin Zhang and Peng-Tao Jiang and Qibin Hou and Yunchao Wei and Qi Han and Zhen Li and Ming-Ming Cheng},
      year={2020},
      eprint={2011.12562},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}