drop duplicated metric helper (PL^5366)

* drop duplicated metric helper

* .

* fix tests

Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>
(cherry picked from commit 220dfaf7dcd12ad230ea2938d8d67a119a22e143)

pytorch_lightning/metrics/functional/confusion_matrix.py

@@ -15,7 +15,7 @@ from typing import Optional
 
 import torch
 
-from pytorch_lightning.metrics.utils import _input_format_classification
+from pytorch_lightning.metrics.classification.helpers import _input_format_classification
 from pytorch_lightning.utilities import rank_zero_warn
 
 
@@ -23,7 +23,10 @@ def _confusion_matrix_update(preds: torch.Tensor,
                              target: torch.Tensor,
                              num_classes: int,
                              threshold: float = 0.5) -> torch.Tensor:
-    preds, target = _input_format_classification(preds, target, threshold)
+    preds, target, mode = _input_format_classification(preds, target, threshold)
+    if mode not in ('binary', 'multi-label'):
+        preds = preds.argmax(dim=1)
+        target = target.argmax(dim=1)
     unique_mapping = (target.view(-1) * num_classes + preds.view(-1)).to(torch.long)
     bins = torch.bincount(unique_mapping, minlength=num_classes ** 2)
     confmat = bins.reshape(num_classes, num_classes)

pytorch_lightning/metrics/utils.py

@@ -43,34 +43,6 @@ def _check_same_shape(pred: torch.Tensor, target: torch.Tensor):
         raise RuntimeError("Predictions and targets are expected to have the same shape")
 
 
-def _input_format_classification(
-    preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Convert preds and target tensors into label tensors
-
-    Args:
-        preds: either tensor with labels, tensor with probabilities/logits or
-            multilabel tensor
-        target: tensor with ground true labels
-        threshold: float used for thresholding multilabel input
-
-    Returns:
-        preds: tensor with labels
-        target: tensor with labels
-    """
-    if not (preds.ndim == target.ndim or preds.ndim == target.ndim + 1):
-        raise ValueError("preds and target must have same number of dimensions, or one additional dimension for preds")
-
-    if preds.ndim == target.ndim + 1:
-        # multi class probabilites
-        preds = torch.argmax(preds, dim=1)
-
-    if preds.ndim == target.ndim and preds.is_floating_point():
-        # binary or multilabel probablities
-        preds = (preds >= threshold).long()
-    return preds, target
-
-
 def _input_format_classification_one_hot(
     num_classes: int, preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5, multilabel: bool = False
 ) -> Tuple[torch.Tensor, torch.Tensor]: