Absent class miou fix [WIP] #2892
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Isalia20
requested review from
SkafteNicki,
Borda,
justusschock and
stancld
as code owners
Case 1: Perfect predictionimport torch
from torchmetrics.segmentation import MeanIoU
from torchmetrics.functional.segmentation.mean_iou import mean_iou
num_classes = 3
metric_one = MeanIoU(num_classes=num_classes, per_class=False, input_format='index')
metric_two = MeanIoU(num_classes=num_classes, per_class=True, input_format='index')
target = torch.tensor([
[0, 1], # Ground truth: class 0, class 1
[1, 0], # Ground truth: class 1, class 0
[2, 2], # Ground truth: class 2, class 2
])
preds = torch.tensor([
[0, 1], # Predictions: class 0, class 1
[1, 0], # Predictions: class 1, class 0
[2, 2], # Predictions: class 2, class 2
])
metric_one.update(preds, target)
miou_per_class_one = metric_one.compute()
metric_two.update(preds, target)
miou_per_class_two = metric_two.compute()
print(miou_per_class_one)
print(miou_per_class_two)Returns:tensor(1.)
tensor([1., 1., 1.])Case 2 Perfect prediction but one completely absent classimport torch
from torchmetrics.segmentation import MeanIoU
from torchmetrics.functional.segmentation.mean_iou import mean_iou
num_classes = 4
metric_one = MeanIoU(num_classes=num_classes, per_class=False, input_format='index')
metric_two = MeanIoU(num_classes=num_classes, per_class=True, input_format='index')
target = torch.tensor([
[0, 1], # Ground truth: class 0, class 1
[1, 0], # Ground truth: class 1, class 0
[2, 2], # Ground truth: class 2, class 2
])
preds = torch.tensor([
[0, 1], # Predictions: class 0, class 1
[1, 0], # Predictions: class 1, class 0
[2, 2], # Predictions: class 2, class 2
])
metric_one.update(preds, target)
miou_per_class_one = metric_one.compute()
metric_two.update(preds, target)
miou_per_class_two = metric_two.compute()
print(miou_per_class_one)
print(miou_per_class_two)Returns:tensor(nan)
tensor([1., 1., 1., nan])Case 3: Completely wrong predictions(Probably same as the old one)import torch
from torchmetrics.segmentation import MeanIoU
num_classes = 3
metric_one = MeanIoU(num_classes=num_classes, per_class=False, input_format='index')
metric_two = MeanIoU(num_classes=num_classes, per_class=True, input_format='index')
target = torch.tensor([
[0, 1], # Ground truth: class 0, class 1
[1, 0], # Ground truth: class 1, class 0
[2, 2], # Ground truth: class 2, class 2
])
preds = torch.tensor([
[1, 2], # Predictions: all wrong
[2, 1], # Predictions: all wrong
[0, 1], # Predictions: all wrong
])
metric_one.update(preds, target)
miou_per_class_one = metric_one.compute()
metric_two.update(preds, target)
miou_per_class_two = metric_two.compute()
print(miou_per_class_one)
print(miou_per_class_two)Returnstensor(0.)
tensor([0., 0., 0.]) |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #2892 +/- ##
========================================
- Coverage 69% 35% -34%
========================================
Files 332 332
Lines 18966 18969 +3
========================================
- Hits 13055 6598 -6457
- Misses 5911 12371 +6460 |
|
While it's marked WIP I still need comments from you @Borda . I posted some cases and wonder how I should proceed |
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What does this PR do?
Fixes #2866
This is not the final version. I wanted to discuss the expected outputs for different edge cases and how to deal with them. I haven't included tests for these edge cases because monAI deals with such cases differently than what is proposed in this PR/issue, therefore adding a test and checking it against the monAI value would lead to failing tests. I'll post different inputs and what kind of outputs we should expect in the followup comment.
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📚 Documentation preview 📚: https://torchmetrics--2892.org.readthedocs.build/en/2892/