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.

• 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))

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

To quickly get started with local development, run:

make develop

To test, run:

python3 -m pytest

To run pre-commit against all files:

pre-commit run --all-files

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.