Pytorch-metrics

This is a repo. for evaluation metrics using Pytorch. The metrics.py is designed for evaluation tasks using two pytorch tensors as input. All implemented metric is compatible with any batch_size and devices(CPU or GPU).

y_pred << 4D tensor in [batch_size, channels, img_rows, img_cols]
y_true << 4D tensor in [batch_size, channels, img_rows, img_cols]

metric = MSE()
acc = metric(y_pred, y_true).item()
print("{} ==> {}".format(repr(metric), acc))

Requirement

python3
pytorch >= 1.
torchvision >= 0.2.0

Implementation

Image similarity
- AE (Average Angular Error)
- MSE (Mean Square Error)
- PSNR (Peak Signal-to-Noise Ratio)
- SSIM (Structural Similarity)
- LPIPS (Learned Perceptual Image Patch Similarity)
Accuray
- OA(Overall Accuracy)
- Precision
- Recall
- F1-score
- Kapp coefficiency
- Jaccard Index

Ongoing

FID(Fréchet Inception Distance)

Acknowledgment

Our implementations are largely inspired by many open-sources codes, repos, as well as papers. Many thanks to the authors.

Richard Zhang, LPIPS(https://github.com/richzhang/PerceptualSimilarity)
Jorge Pessoa, SSIM(https://github.com/jorge-pessoa/pytorch-msssim)

LICENSE