# TorchMetrics Extension
[](https://pypi.org/project/torchmetrics-ext/)
[](https://badge.fury.io/py/torchmetrics-ext)
[](https://github.com/eamonn-zh/torchmetrics_ext/blob/master/LICENSE)
## Installation
Simple installation from PyPI
```bash
pip install torchmetrics-ext
```
## What is TorchMetrics Extension
It is an extension of [torchmetrics](https://lightning.ai/docs/torchmetrics/) containing more metrics for machine learning tasks. Currently, it offers metrics for:
- 3D Visual Grounding
- [ScanRefer](https://daveredrum.github.io/ScanRefer/)
## Using TorchMetrics Extension
Here are examples for using the metrics in TorchMetrics Extension:
### ScanRefer
It measures the thresholded accuracy Acc@kIoU, where the positive predictions have higher intersection over union (IoU) with the ground truths than the thresholds. The metric is based on the [ScanRefer](https://daveredrum.github.io/ScanRefer/).
```python
import torch
from torchmetrics_ext.visual_grounding import ScanReferMetric
metric = ScanReferMetric()
pred_aabbs = torch.tensor([[[0., 0., 0.], [1., 1., 1.]], [[0., 0., 0.], [2., 2., 2.]]], dtype=torch.float32)
gt_aabbs = torch.tensor([[[0., 0., 0.], [1., 1., 1.]], [[0., 0., 0.], [1.5, 1.5, 1.5]]], dtype=torch.float32)
gt_eval_types = ("unique", "multiple")
results = metric(pred_aabbs, gt_aabbs, gt_eval_types)
```
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