As summarized in this issue, Pytorch does not have a built-in libary torch.metrics for model evaluation metrics. This python library serves as a custom library to provide common evaluation metrics in Pytorch, similar to tf.keras.metrics. This is similar to the metrics library in PyTorch Lightning.
pip install --upgrade torch-metricsorgit clone https://github.com/enochkan/torch-metrics.git
from torch_metrics import Accuracy
## define metric ##
metric = Accuracy(logits=False)
y_pred = torch.tensor([1, 2, 3, 4])
y_true = torch.tensor([0, 2, 3, 4])
print(metric(y_pred, y_true))from torch_metrics import Accuracy
## define metric ##
metric = Accuracy()
y_true = torch.tensor([0, 2, 3, 4])
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]])
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
- BinaryAccuracy class
- CosineSimilarity class
- AUC class
- BinaryCrossEntropy class
- CategoricalCrossEntropy class
- SparseCategoricalCrossentropy class
Please raise issues or feature requests here.