Fix 6 Flake8 issues (#6541)

* F541

* F821

* F841

* E741

* E302

* E722

* Apply suggestions from code review

* Update general.py

* Update datasets.py

* Update export.py

* Update plots.py

* Update plots.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/general.py

@@ -295,7 +295,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
     for r in requirements:
         try:
             pkg.require(r)
-        except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
+        except Exception:  # DistributionNotFound or VersionConflict if requirements not met
             s = f"{prefix} {r} not found and is required by YOLOv5"
             if install:
                 LOGGER.info(f"{s}, attempting auto-update...")
@@ -699,16 +699,16 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
     output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
     for xi, x in enumerate(prediction):  # image index, image inference
         # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
         x = x[xc[xi]]  # confidence
 
         # Cat apriori labels if autolabelling
         if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
+            lb = labels[xi]
+            v = torch.zeros((len(lb), nc + 5), device=x.device)
+            v[:, :4] = lb[:, 1:5]  # box
             v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
             x = torch.cat((x, v), 0)
 
         # If none remain process next image
@@ -783,7 +783,8 @@ def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_op
 
 
 def print_mutation(results, hyp, save_dir, bucket):
-    evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml'
+    evolve_csv = save_dir / 'evolve.csv'
+    evolve_yaml = save_dir / 'hyp_evolve.yaml'
     keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
             'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys())  # [results + hyps]
     keys = tuple(x.strip() for x in keys)