Added small eps to dice and iou to avoid NaN

[bernardomig]

The code fixes a bug calculating both the IoU and the Dice metrics, which resulted in a nan in the calculation. This was due to not including a small eps in the division on both these metrics. I have added a eps (equal to 1e-15), which successfully prevents this NaN calculation.

Example

Previous Code (with bug)

import torch
from pytorch_lightning.metrics.functional import iou

y = torch.tensor([0, 1, 2, 3, 3])
y_true = torch.tensor([0, 1, 2, 2, 2])

# reduction = 'none' so we can see the iou for each class
out = iou(y, y_true, num_classes=5, reduction='none')

print(out)
>>> tensor([1.0000, 1.0000, 0.3333, 0.0000,    nan])

The fifth class has a IoU of nan, not 0.

Fixed code

import torch
from pytorch_lightning.metrics.functional import iou

y = torch.tensor([0, 1, 2, 3, 3])
y_true = torch.tensor([0, 1, 2, 2, 2])

# reduction = 'none' so we can see the iou for each class
out = iou(y, y_true, num_classes=5, reduction='none')

print(out)
>>> tensor([1.0000, 1.0000, 0.3333, 0.0000, 0.0000])

[codecov]

Codecov Report

Merging #2545 into master will not change coverage.
The diff coverage is 100%.

@@          Coverage Diff           @@
##           master   #2545   +/-   ##
======================================
  Coverage      90%     90%           
======================================
  Files          69      69           
  Lines        5669    5669           
======================================
  Hits         5077    5077           
  Misses        592     592           

[Borda]

I would rather add if tp + fp + fn if greater then 0

[Borda]

in fact

1 / (1 + 1e-15)
0.9999999999999989

cc: @justusschock @SkafteNicki