TorchMetrics Extension

Installation

Simple installation from PyPI

pip install torchmetrics-ext

What is TorchMetrics Extension

It is an extension of torchmetrics containing more metrics for machine learning tasks. Currently, it offers metrics for:

• 3D Visual Grounding
  • ScanRefer
  • ReferIt3D

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.

import torch
from torchmetrics_ext.visual_grounding import ScanReferMetric
metric = ScanReferMetric()

# min max bounds of 3D axis-aligned bounding boxes (B, 2, 3)
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)

ReferIt3D

It measures the accuracy of selecting the target object from the candidates. The metric is based on the ReferIt3D.

import torch
from torchmetrics_ext.visual_grounding import ReferIt3DMetric
metric = ReferIt3DMetric()

# indices of predicted and ground truth objects (B, )
pred_indices = torch.tensor([5, 2, 0, 0], dtype=torch.uint8)
gt_indices = torch.tensor([5, 5, 1, 0], dtype=torch.uint8)

gt_eval_types = (("easy", "view_dep"), ("easy", "view_indep"), ("hard", "view_dep"), ("hard", "view_dep"))
results = metric(pred_indices, gt_indices, gt_eval_types)