TorchCriterion

torchcriterion is a modular, extensible library of PyTorch-compatible loss functions ("criteria") for classification, regression, segmentation, and metric learning tasks. It offers a curated collection of both standard and custom loss functions, built with flexibility and composability in mind.

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🚀 Features

• 🧱 Modular architecture for clean API and extension
• 🧪 Ready-to-use implementations of popular losses
• 🧩 Supports multi-loss composition and custom scheduling
• ⚡ Fully compatible with PyTorch’s autograd and GPU acceleration

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📦 Installation

pip install torchcriterion

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🧰 Supported Losses

Classification

• CrossEntropyLoss
• FocalLoss

Regression

• MSELoss
• HuberLoss

Segmentation

• DiceLoss
• TverskyLoss

Metric Learning

• TripletLoss
• ContrastiveLoss

NLP Metrics (new)

Under torchcriterion.nlp we provide common NLP evaluation metrics.

Example usage:

fromtorchcriterion.nlp.bleuimportbleu_scorefromtorchcriterion.nlp.rougeimportrouge_l_batchfromtorchcriterion.nlp.perplexityimportperplexitypreds= ["the cat sat on mat"]
refs= ["the cat is on the mat"]
print("BLEU:", bleu_score(preds, refs))
print("ROUGE-L:", rouge_l_batch(preds, refs))
# LM perplexity examplelogits=model_out# (batch, seq_len, vocab)targets=labels# (batch, seq_len)print("Perplexity:", perplexity(logits, targets))

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🧪 Example Usage

from torchcriterion import FocalLoss
criterion = FocalLoss(gamma=2.0, alpha=0.25)
loss = criterion(predictions, targets)

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📁 Project Structure

torchcriterion/
├── classification/
│ ├── cross_entropy.py
│ ├── focal.py
├── regression/
│ ├── mse.py
│ ├── huber.py
├── segmentation/
│ ├── dice.py
│ ├── tversky.py
├── metric_learning/
│ ├── triplet.py
│ ├── contrastive.py
├── nlp/
│ ├── bleu.py
│ ├── perplexity.py
│ ├── rouge.py
├── base.py
├── __init__.py

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🙌 Contributing

Pull requests, ideas, and issues are welcome! Feel free to open a PR or start a discussion.

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👤 Author

Developed by Liodon AI

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⭐️ Star the Repo

If you find this library useful, please consider starring it to show your support!

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🔗 Related Projects

• torchmetrics — for evaluation metrics
• timm — for models with built-in loss support

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📚 Citation

If you use TorchCriterion in your research or project, please consider citing it:

@misc{torchcriterion2025,
title = {TorchCriterion: Advanced Loss Functions for PyTorch},
year = {2025},
publisher = {GitHub},
howpublished = {\url{https://github.com/Liodon-AI/TorchCriterion}},
note = {GitHub repository},
}

Made with ❤️ and PyTorch