tm

torch-metrics

Metrics for model evaluation in pytorch

Showing:

Popularity

Downloads/wk

0

GitHub Stars

93

Maintenance

Last Commit

7mos ago

Contributors

5

Package

Dependencies

0

License

Categories

Readme

Torch-metrics

PyPI version License: MIT

Model evaluation metrics for PyTorch

Torch-metrics serves as a custom library to provide common ML evaluation metrics in Pytorch, similar to tf.keras.metrics.

As summarized in this issue, Pytorch does not have a built-in libary torch.metrics for model evaluation metrics. This is similar to the metrics library in PyTorch Lightning.

Usage

  • pip install --upgrade torch-metrics
from torch_metrics import Accuracy

## define metric ##
metric = Accuracy(from_logits=False)
y_pred = torch.tensor([1, 2, 3, 4])
y_true = torch.tensor([0, 2, 3, 4])

print(metric(y_pred, y_true))

## define metric ##
metric = Accuracy()
y_pred = torch.tensor([[0.2, 0.6, 0.1, 0.05, 0.05],
                       [0.2, 0.1, 0.6, 0.05, 0.05],
                       [0.2, 0.05, 0.1, 0.6, 0.05],
                       [0.2, 0.05, 0.05, 0.05, 0.65]])
y_true = torch.tensor([0, 2, 3, 4])

print(metric(y_pred, y_true))

Implementation

Metrics from tf.keras.metrics and other metrics that are already implemented vs to-do

  • MeanSquaredError class
  • RootMeanSquaredError class
  • MeanAbsoluteError class
  • Precision class
  • Recall class
  • MeanIoU class
  • DSC class (Dice Similarity Coefficient)
  • F1Score class
  • RSquared class
  • Hinge class
  • SquaredHinge class
  • LogCoshError class
  • Accuracy class
  • KLDivergence class
  • CosineSimilarity class
  • AUC class
  • BinaryCrossEntropy class
  • CategoricalCrossEntropy class
  • SparseCategoricalCrossentropy class

Local Development and Testing

To quickly get started with local development, run:

make develop

To test, run:

python3 -m pytest

Pre-commit hooks

To run pre-commit against all files:

pre-commit run --all-files

Contributing

Please raise issues or feature requests here. It will be extremely helpful if you comment on a specific issue before working on it. This provides visibility for others who also intend to work on the same issue. Reference any pull requests to their original issues.

Rate & Review

Great Documentation0
Easy to Use0
Performant0
Highly Customizable0
Bleeding Edge0
Responsive Maintainers0
Poor Documentation0
Hard to Use0
Slow0
Buggy0
Abandoned0
Unwelcoming Community0
100
No reviews found
Be the first to rate

Alternatives

No alternatives found

Tutorials

No tutorials found
Add a tutorial