Merge branch 'add_micro_average_IOU' of https://github.com/razmikmeli…

…kbekyan/metrics into add_micro_average_IOU

.github/workflows/docs-check.yml

@@ -26,8 +26,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -r requirements.txt -U -f https://download.pytorch.org/whl/cpu/torch_stable.html -q --use-feature=2020-resolver
-          pip install -r requirements/docs.txt --use-feature=2020-resolver
+          pip install -r requirements.txt -U -f https://download.pytorch.org/whl/cpu/torch_stable.html -q
+          pip install -r requirements/docs.txt
           python --version ; pip --version ; pip list
         shell: bash
 
@@ -62,8 +62,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -r requirements.txt -U -f https://download.pytorch.org/whl/torch_stable.html -q --use-feature=2020-resolver
-          pip install -r requirements/docs.txt --use-feature=2020-resolver
+          pip install -r requirements.txt -U -f https://download.pytorch.org/whl/torch_stable.html -q
+          pip install -r requirements/docs.txt
           # install Texlive, see https://linuxconfig.org/how-to-install-latex-on-ubuntu-20-04-focal-fossa-linux
           sudo apt-get update
           sudo apt-get install -y texlive-latex-extra dvipng texlive-pictures

torchmetrics/functional/classification/jaccard.py

@@ -72,21 +72,23 @@ def _jaccard_from_confmat(
                 ]
             )
         return scores
-    elif average == "macro":
+
+    if average == "macro":
         scores = _jaccard_from_confmat(
             confmat, num_classes, average="none", ignore_index=ignore_index, absent_score=absent_score
         )
         return torch.mean(scores)
-    elif average == "micro":
+
+    if average == "micro":
         intersection = torch.sum(torch.diag(confmat))
         union = torch.sum(torch.sum(confmat, dim=1) + torch.sum(confmat, dim=0) - torch.diag(confmat))
         return intersection.float() / union.float()
-    else:
-        weights = torch.sum(confmat, dim=1).float() / torch.sum(confmat).float()
-        scores = _jaccard_from_confmat(
-            confmat, num_classes, average="none", ignore_index=ignore_index, absent_score=absent_score
-        )
-        return torch.sum(weights * scores)
+
+    weights = torch.sum(confmat, dim=1).float() / torch.sum(confmat).float()
+    scores = _jaccard_from_confmat(
+        confmat, num_classes, average="none", ignore_index=ignore_index, absent_score=absent_score
+    )
+    return torch.sum(weights * scores)
 
 
 def jaccard_index(