Implementation of some unbalanced loss for NLP task like focal_loss, dice_loss, DSC Loss, GHM Loss


Here is a loss implementation repository included unbalanced loss

Loss Name paper Notes
Weighted CE Loss UNet Architectures in Multiplanar Volumetric Segmentation — Validated on Three Knee MRI Cohorts
Focal Loss Focal Loss for Dense Object Detection
Dice Loss V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
DSC Loss Dice Loss for Data-imbalanced NLP Tasks
GHM Loss Gradient Harmonized Single-stage Detector

How to use?

You can find all the loss usage information in

Here is a simple demo of usage:

import torch
from unbalanced_loss.focal_loss import MultiFocalLoss

batch_size, num_class = 64, 10
Loss_Func = MultiFocalLoss(num_class=num_class, gamma=2.0, reduction='mean')

logits = torch.rand(batch_size, num_class, requires_grad=True)  # (batch_size, num_classes)
targets = torch.randint(0, num_class, size=(batch_size, ))  # (batch_size, )

loss = Loss_Func(logits, targets)


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