| Name | unified-focal-loss-pytorch JSON |
| Version |
0.1.2
JSON |
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| home_page | |
| Summary | An implementation of loss functions from "Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation" |
| upload_time | 2023-08-18 23:11:37 |
| maintainer | |
| docs_url | None |
| author | Taylor Denouden |
| requires_python | >=3.9,<4.0 |
| license | MIT |
| keywords |
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No requirements were recorded.
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# Unified Focal Loss PyTorch
An implementation of loss functions
from [“Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation”][1]
Extended for multiclass classification and to allow passing an ignore index.
*Note: This implementation is not tested against the original implementation. It varies
from the original implementation based on my own interpretation of the paper.*
[1]: https://github.com/mlyg/unified-focal-loss
## Installation
```bash
pip install unified-focal-loss-pytorch
```
## Usage
```python
import torch
import torch.nn.functional as F
from unified_focal_loss import AsymmetricUnifiedFocalLoss
loss_fn = AsymmetricUnifiedFocalLoss(
delta=0.7,
gamma=0.5,
ignore_index=2,
)
logits = torch.tensor([
[[0.1000, 0.4000],
[0.2000, 0.5000],
[0.3000, 0.6000]],
[[0.7000, 0.0000],
[0.8000, 0.1000],
[0.9000, 0.2000]]
])
# Shape should be (batch_size, num_classes, ...)
probs = F.softmax(logits, dim=1)
# Shape should be (batch_size, ...). Not one-hot encoded.
targets = torch.tensor([
[0, 1],
[2, 0],
])
loss = loss_fn(probs, targets)
print(loss)
# >>> tensor(0.6737)
```
## Detailed API Reference
See [API docs](docs/api.md).
## License
See [LICENSE](LICENSE).
Raw data
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"description": "# Unified Focal Loss PyTorch\n\nAn implementation of loss functions\nfrom [\u201cUnified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation\u201d][1]\n\nExtended for multiclass classification and to allow passing an ignore index.\n\n*Note: This implementation is not tested against the original implementation. It varies\nfrom the original implementation based on my own interpretation of the paper.*\n\n[1]: https://github.com/mlyg/unified-focal-loss\n\n## Installation\n\n```bash\npip install unified-focal-loss-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nimport torch.nn.functional as F\nfrom unified_focal_loss import AsymmetricUnifiedFocalLoss\n\nloss_fn = AsymmetricUnifiedFocalLoss(\n delta=0.7,\n gamma=0.5,\n ignore_index=2,\n)\n\nlogits = torch.tensor([\n [[0.1000, 0.4000],\n [0.2000, 0.5000],\n [0.3000, 0.6000]],\n\n [[0.7000, 0.0000],\n [0.8000, 0.1000],\n [0.9000, 0.2000]]\n])\n\n# Shape should be (batch_size, num_classes, ...)\nprobs = F.softmax(logits, dim=1)\n# Shape should be (batch_size, ...). Not one-hot encoded.\ntargets = torch.tensor([\n [0, 1],\n [2, 0],\n])\n\nloss = loss_fn(probs, targets)\nprint(loss)\n# >>> tensor(0.6737)\n```\n\n## Detailed API Reference\nSee [API docs](docs/api.md).\n\n## License\nSee [LICENSE](LICENSE).\n",
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