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https://github.com/enochkan/torch-metrics
Metrics for model evaluation in pytorch
https://github.com/enochkan/torch-metrics
computer-vision deep-learning machine-learning metrics pytorch pytorch-cnn pytorch-implementation torch-metrics
Last synced: about 2 months ago
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Metrics for model evaluation in pytorch
- Host: GitHub
- URL: https://github.com/enochkan/torch-metrics
- Owner: enochkan
- License: mit
- Created: 2020-11-03T19:08:46.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-22T18:02:50.000Z (almost 4 years ago)
- Last Synced: 2024-10-12T22:59:40.098Z (3 months ago)
- Topics: computer-vision, deep-learning, machine-learning, metrics, pytorch, pytorch-cnn, pytorch-implementation, torch-metrics
- Language: Python
- Homepage:
- Size: 42 KB
- Stars: 110
- Watchers: 3
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - torch-metrics
- Awesome-pytorch-list - torch-metrics
README
# Torch-metrics
[](https://badge.fury.io/py/torch-metrics)
[](https://opensource.org/licenses/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](https://github.com/pytorch/pytorch/issues/22439), Pytorch does not have a built-in libary `torch.metrics` for model evaluation metrics. This is similar to the metrics library in [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/latest/metrics.html#class-metrics).
## Usage
- `pip install --upgrade torch-metrics`
```python
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))
```
```python
## 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
- [x] MeanSquaredError class
- [x] RootMeanSquaredError class
- [x] MeanAbsoluteError class
- [x] Precision class
- [x] Recall class
- [x] MeanIoU class
- [x] DSC class (Dice Similarity Coefficient)
- [x] F1Score class
- [x] RSquared class
- [x] Hinge class
- [x] SquaredHinge class
- [x] LogCoshError class
- [x] Accuracy class
- [x] KLDivergence class
- [ ] CosineSimilarity class
- [ ] AUC class
- [ ] BinaryCrossEntropy class
- [ ] CategoricalCrossEntropy class
- [ ] SparseCategoricalCrossentropy class
## Local Development and Testing
To quickly get started with local development, run:
```python
make develop
```
To test, run:
```python
python3 -m pytest
```
### Pre-commit hooks
To run pre-commit against all files:
```python
pre-commit run --all-files
```
## Contributing
Please raise issues or feature requests [here](https://github.com/enochkan/torch-metrics/issues). 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.