Lightning-AI / torchmetrics

Torchmetrics - Machine learning metrics for distributed, scalable PyTorch applications.
https://lightning.ai/docs/torchmetrics/
Apache License 2.0
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AUROC reported wrong value when called differently #1989

Open SinChee opened 11 months ago

SinChee commented 11 months ago

🐛 Bug

AUROC calculation is different when being called differently.

To Reproduce

Copy the following code and run.

Note that when .update() is called, values that are greater than 1.0 will be scaled using the sigmoid activation, values that is less than 1.0 will not be scaled. This result in the compute step to compute with the wrong input values. However, directly calling the auroc will not rescale the value

from torchmetrics import AUROC
from torch import tensor

pp = tensor([tensor([1.2]), tensor([0.9678]), tensor([1.7])])
tt = tensor([tensor([1]), tensor([0]), tensor([1])])

auroc = AUROC(num_classes=1, task="binary")
print("Forward:", auroc(pp, tt))

for p, t in zip(pp, tt):
    auroc.update(p, t)
    print(auroc.preds, auroc.target)

print("Update and Compute:", auroc.compute())

Expected behavior

The reported AUROC metrics for both cases should be the same.

Environment

github-actions[bot] commented 11 months ago

Hi! thanks for your contribution!, great first issue!