annotat unused vars (PL^5017)

* annotate all unused vars

* rank_zero_warn

* Apply suggestions from code review

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* f1 fixed

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
(cherry picked from commit dfbad656c493bd392c59e7d563125006b95d225c)

pytorch_lightning/metrics/classification/f_beta.py

@@ -219,6 +219,9 @@ class F1(FBeta):
         dist_sync_on_step: bool = False,
         process_group: Optional[Any] = None,
     ):
+        if multilabel is not False:
+            rank_zero_warn(f'The `multilabel={multilabel}` parameter is unused and will not have any effect.')
+
         super().__init__(
             num_classes=num_classes,
             beta=1.0,

pytorch_lightning/metrics/functional/classification.py

@@ -188,7 +188,6 @@ def stat_scores_multiple_classes(
 
         tps = torch.zeros((num_classes + 1,), device=pred.device)
         fps = torch.zeros((num_classes + 1,), device=pred.device)
-        tns = torch.zeros((num_classes + 1,), device=pred.device)
         fns = torch.zeros((num_classes + 1,), device=pred.device)
         sups = torch.zeros((num_classes + 1,), device=pred.device)
 

pytorch_lightning/metrics/functional/f_beta.py

@@ -16,6 +16,7 @@ from typing import Tuple
 import torch
 
 from pytorch_lightning.metrics.utils import _input_format_classification_one_hot, class_reduce
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 def _fbeta_update(

pytorch_lightning/metrics/functional/nlp.py

@@ -74,7 +74,6 @@ def bleu_score(
     assert len(translate_corpus) == len(reference_corpus)
     numerator = torch.zeros(n_gram)
     denominator = torch.zeros(n_gram)
-    precision_scores = torch.zeros(n_gram)
     c = 0.0
     r = 0.0
 

pytorch_lightning/metrics/functional/psnr.py

@@ -3,6 +3,8 @@ from typing import Tuple, Optional
 
 import torch
 
+from pytorch_lightning.utilities import rank_zero_warn
+
 
 def _psnr_compute(
     sum_squared_error: torch.Tensor,
@@ -11,6 +13,8 @@ def _psnr_compute(
     base: float = 10.0,
     reduction: str = 'elementwise_mean',
 ) -> torch.Tensor:
+    if reduction != 'elementwise_mean':
+        rank_zero_warn(f'The `reduction={reduction}` parameter is unused and will not have any effect.')
     psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / n_obs)
     psnr = psnr_base_e * (10 / torch.log(torch.tensor(base)))
     return psnr