Do some refactors and improvements

Author: sadra-barikbin

ignite/metrics/precision.py

@@ -188,7 +188,7 @@ class Precision(_BasePrecisionRecall):
             weighted_metric = Precision(average='weighted')
             metric.attach(default_evaluator, "precision")
             weighted_metric.attach(default_evaluator, "weighted precision")
-            y_true = torch.Tensor([1, 0, 1, 1, 0, 1]).long()
+            y_true = torch.tensor([1, 0, 1, 1, 0, 1])
             y_pred = torch.Tensor([1, 0, 1, 0, 1, 1])
             state = default_evaluator.run([[y_pred, y_true]])
             print(f"Precision: {state.metrics['precision']}")
@@ -211,7 +211,7 @@ class Precision(_BasePrecisionRecall):
             macro_metric.attach(default_evaluator, "macro precision")
             weighted_metric.attach(default_evaluator, "weighted precision")
 
-            y_true = torch.Tensor([2, 0, 2, 1, 0]).long()
+            y_true = torch.tensor([2, 0, 2, 1, 0])
             y_pred = torch.Tensor([
                 [0.0266, 0.1719, 0.3055],
                 [0.6886, 0.3978, 0.8176],
@@ -286,14 +286,14 @@ def thresholded_output_transform(output):
 
             metric = Precision(output_transform=thresholded_output_transform)
             metric.attach(default_evaluator, "precision")
-            y_true = torch.Tensor([1, 0, 1, 1, 0, 1]).long()
+            y_true = torch.tensor([1, 0, 1, 1, 0, 1])
             y_pred = torch.Tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics["precision"])
 
         .. testoutput:: 4
 
-            [0.5, 0.75]
+            tensor([0.5000, 0.7500], dtype=torch.float64)
 
 
     .. versionchanged:: 0.5.0
@@ -306,23 +306,19 @@ def update(self, output: Sequence[torch.Tensor]) -> None:
         self._check_type(output)
         y_pred, y = output[0].detach(), output[1].detach()
 
-        if self._type == "binary":
+        if self._type == "binary" or self._type == "multiclass":
 
-            y = to_onehot(y.view(-1), num_classes=2)
-            y_pred = to_onehot(y_pred.view(-1).long(), num_classes=2)
-        elif self._type == "multiclass":
-
-            num_classes = y_pred.size(1)
-            if y.max() + 1 > num_classes:
+            num_classes = 2 if self._type == "binary" else y_pred.size(1)
+            if self._type == "multiclass" and y.max() + 1 > num_classes:
                 raise ValueError(
                     f"y_pred contains less classes than y. Number of predicted classes is {num_classes}"
                     f" and element in y has invalid class = {y.max().item() + 1}."
                 )
             y = to_onehot(y.view(-1), num_classes=num_classes)
-            indices = torch.argmax(y_pred, dim=1).view(-1)
-            y_pred = to_onehot(indices, num_classes=num_classes)
+            indices = torch.argmax(y_pred, dim=1) if self._type == "multiclass" else y_pred.long()
+            y_pred = to_onehot(indices.view(-1), num_classes=num_classes)
         elif self._type == "multilabel":
-
+            # if y, y_pred shape is (N, C, ...) -> (N * ..., C)
             num_labels = y_pred.size(1)
             y_pred = torch.transpose(y_pred, 1, -1).reshape(-1, num_labels)
             y = torch.transpose(y, 1, -1).reshape(-1, num_labels)

ignite/metrics/recall.py

@@ -216,23 +216,19 @@ def update(self, output: Sequence[torch.Tensor]) -> None:
         self._check_type(output)
         y_pred, y = output[0].detach(), output[1].detach()
 
-        if self._type == "binary":
+        if self._type == "binary" or self._type == "multiclass":
 
-            y = to_onehot(y.view(-1), num_classes=2)
-            y_pred = to_onehot(y_pred.view(-1).long(), num_classes=2)
-        elif self._type == "multiclass":
-
-            num_classes = y_pred.size(1)
-            if y.max() + 1 > num_classes:
+            num_classes = 2 if self._type == "binary" else y_pred.size(1)
+            if self._type == "multiclass" and y.max() + 1 > num_classes:
                 raise ValueError(
                     f"y_pred contains less classes than y. Number of predicted classes is {num_classes}"
                     f" and element in y has invalid class = {y.max().item() + 1}."
                 )
             y = to_onehot(y.view(-1), num_classes=num_classes)
-            indices = torch.argmax(y_pred, dim=1).view(-1)
-            y_pred = to_onehot(indices, num_classes=num_classes)
+            indices = torch.argmax(y_pred, dim=1) if self._type == "multiclass" else y_pred.long()
+            y_pred = to_onehot(indices.view(-1), num_classes=num_classes)
         elif self._type == "multilabel":
-
+            # if y, y_pred shape is (N, C, ...) -> (N * ..., C)
             num_labels = y_pred.size(1)
             y_pred = torch.transpose(y_pred, 1, -1).reshape(-1, num_labels)
             y = torch.transpose(y, 1, -1).reshape(-1, num_labels)

tests/ignite/metrics/test_precision.py

@@ -169,7 +169,7 @@ def test_multiclass_wrong_inputs():
         pr.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long()))
     assert pr._updated is False
 
-    pr = Precision()
+    pr = Precision(average=True)
     assert pr._updated is False
 
     with pytest.raises(ValueError):
@@ -184,7 +184,7 @@ def test_multiclass_wrong_inputs():
         pr.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long()))
     assert pr._updated is True
 
-    pr = Precision()
+    pr = Precision(average=False)
     assert pr._updated is False
 
     with pytest.raises(ValueError):

tests/ignite/metrics/test_recall.py

@@ -171,7 +171,7 @@ def test_multiclass_wrong_inputs():
         re.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long()))
     assert re._updated is False
 
-    re = Recall()
+    re = Recall(average=True)
     assert re._updated is False
 
     with pytest.raises(ValueError):
@@ -186,7 +186,7 @@ def test_multiclass_wrong_inputs():
         re.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long()))
     assert re._updated is True
 
-    re = Recall()
+    re = Recall(average=False)
     assert re._updated is False
 
     with pytest.raises(ValueError):