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/datasets.py

@@ -59,7 +59,7 @@ def exif_size(img):
             s = (s[1], s[0])
         elif rotation == 8:  # rotation 90
             s = (s[1], s[0])
-    except:
+    except Exception:
         pass
 
     return s
@@ -420,7 +420,7 @@ class LoadImagesAndLabels(Dataset):
             cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
             assert cache['version'] == self.cache_version  # same version
             assert cache['hash'] == get_hash(self.label_files + self.img_files)  # same hash
-        except:
+        except Exception:
             cache, exists = self.cache_labels(cache_path, prefix), False  # cache
 
         # Display cache
@@ -514,13 +514,13 @@ class LoadImagesAndLabels(Dataset):
         with Pool(NUM_THREADS) as pool:
             pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
                         desc=desc, total=len(self.img_files))
-            for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
+            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                 nm += nm_f
                 nf += nf_f
                 ne += ne_f
                 nc += nc_f
                 if im_file:
-                    x[im_file] = [l, shape, segments]
+                    x[im_file] = [lb, shape, segments]
                 if msg:
                     msgs.append(msg)
                 pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"
@@ -627,8 +627,8 @@ class LoadImagesAndLabels(Dataset):
     @staticmethod
     def collate_fn(batch):
         img, label, path, shapes = zip(*batch)  # transposed
-        for i, l in enumerate(label):
-            l[:, 0] = i  # add target image index for build_targets()
+        for i, lb in enumerate(label):
+            lb[:, 0] = i  # add target image index for build_targets()
         return torch.stack(img, 0), torch.cat(label, 0), path, shapes
 
     @staticmethod
@@ -645,15 +645,15 @@ class LoadImagesAndLabels(Dataset):
             if random.random() < 0.5:
                 im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[
                     0].type(img[i].type())
-                l = label[i]
+                lb = label[i]
             else:
                 im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
-                l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
+                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
             img4.append(im)
-            label4.append(l)
+            label4.append(lb)
 
-        for i, l in enumerate(label4):
-            l[:, 0] = i  # add target image index for build_targets()
+        for i, lb in enumerate(label4):
+            lb[:, 0] = i  # add target image index for build_targets()
 
         return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
 
@@ -743,6 +743,7 @@ def load_mosaic9(self, index):
     s = self.img_size
     indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
     random.shuffle(indices)
+    hp, wp = -1, -1  # height, width previous
     for i, index in enumerate(indices):
         # Load image
         img, _, (h, w) = load_image(self, index)
@@ -906,30 +907,30 @@ def verify_image_label(args):
         if os.path.isfile(lb_file):
             nf = 1  # label found
             with open(lb_file) as f:
-                l = [x.split() for x in f.read().strip().splitlines() if len(x)]
-                if any([len(x) > 8 for x in l]):  # is segment
-                    classes = np.array([x[0] for x in l], dtype=np.float32)
-                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
-                    l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
-                l = np.array(l, dtype=np.float32)
-            nl = len(l)
+                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
+                if any([len(x) > 8 for x in lb]):  # is segment
+                    classes = np.array([x[0] for x in lb], dtype=np.float32)
+                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
+                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
+                lb = np.array(lb, dtype=np.float32)
+            nl = len(lb)
             if nl:
-                assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected'
-                assert (l >= 0).all(), f'negative label values {l[l < 0]}'
-                assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}'
-                _, i = np.unique(l, axis=0, return_index=True)
+                assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
+                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
+                assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
+                _, i = np.unique(lb, axis=0, return_index=True)
                 if len(i) < nl:  # duplicate row check
-                    l = l[i]  # remove duplicates
+                    lb = lb[i]  # remove duplicates
                     if segments:
                         segments = segments[i]
                     msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
             else:
                 ne = 1  # label empty
-                l = np.zeros((0, 5), dtype=np.float32)
+                lb = np.zeros((0, 5), dtype=np.float32)
         else:
             nm = 1  # label missing
-            l = np.zeros((0, 5), dtype=np.float32)
-        return im_file, l, shape, segments, nm, nf, ne, nc, msg
+            lb = np.zeros((0, 5), dtype=np.float32)
+        return im_file, lb, shape, segments, nm, nf, ne, nc, msg
     except Exception as e:
         nc = 1
         msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'