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>

export.py

 
         tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
         im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
-        y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
+        _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
         inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
         outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
         keras_model = keras.Model(inputs=inputs, outputs=outputs)
     tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'))  # TensorFlow exports
     file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
 
-    # Checks
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
-    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
-
     # Load PyTorch model
     device = select_device(device)
     assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
     model = attempt_load(weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
     nc, names = model.nc, model.names  # number of classes, class names
 
+    # Checks
+    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
+    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
+    assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
+
     # Input
     gs = int(max(model.stride))  # grid size (max stride)
     imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
 
     for _ in range(2):
         y = model(im)  # dry runs
-    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
+    shape = tuple(y[0].shape)  # model output shape
+    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
 
     # Exports
     f = [''] * 10  # exported filenames

models/tf.py

     # PyTorch model
     im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
     model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
-    y = model(im)  # inference
+    _ = model(im)  # inference
     model.info()
 
     # TensorFlow model
     im = tf.zeros((batch_size, *imgsz, 3))  # BHWC image
     tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-    y = tf_model.predict(im)  # inference
+    _ = tf_model.predict(im)  # inference
 
     # Keras model
     im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)

setup.cfg

     E731  # Do not assign a lambda expression, use a def
     F405  # name may be undefined, or defined from star imports: module
     E402  # module level import not at top of file
-    F841  # local variable name is assigned to but never used
-    E741  # do not use variables named ‘l’, ‘O’, or ‘I’
-    F821  # undefined name name
-    E722  # do not use bare except, specify exception instead
     F401  # module imported but unused
     W504  # line break after binary operator
     E127  # continuation line over-indented for visual indent
     E231  # missing whitespace after ‘,’, ‘;’, or ‘:’
     E501  # line too long
     F403  # ‘from module import *’ used; unable to detect undefined names
-    E302  # expected 2 blank lines, found 0
-    F541  # f-string without any placeholders
 
 
 [isort]

utils/datasets.py

             s = (s[1], s[0])
         elif rotation == 8:  # rotation 90
             s = (s[1], s[0])
-    except:
+    except Exception:
         pass
 
     return s
             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
         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"
     @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
             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
 
     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)
         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}'

utils/downloads.py

             response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
             assets = [x['name'] for x in response['assets']]  # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
             tag = response['tag_name']  # i.e. 'v1.0'
-        except:  # fallback plan
+        except Exception:  # fallback plan
             assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
                       'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
             try:
                 tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
-            except:
+            except Exception:
                 tag = 'v6.0'  # current release
 
         if name in assets:

utils/general.py

     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...")
     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
 
 
 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)

utils/loggers/wandb/wandb_utils.py

             model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
             assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
             modeldir = model_artifact.download()
-            epochs_trained = model_artifact.metadata.get('epochs_trained')
+            # epochs_trained = model_artifact.metadata.get('epochs_trained')
             total_epochs = model_artifact.metadata.get('total_epochs')
             is_finished = total_epochs is None
             assert not is_finished, 'training is finished, can only resume incomplete runs.'

utils/metrics.py

         return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
     return iou  # IoU
 
+
 def box_iou(box1, box2):
     # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
     """

utils/plots.py

     font = font if font.exists() else (CONFIG_DIR / font.name)
     try:
         return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception as e:  # download if missing
+    except Exception:  # download if missing
         check_font(font)
         try:
             return ImageFont.truetype(str(font), size)
     matplotlib.use('svg')  # faster
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195
+    [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195
     ax[0].set_ylabel('instances')
     if 0 < len(names) < 30:
         ax[0].set_xticks(range(len(names)))

utils/torch_utils.py

     s = f'git -C {path} describe --tags --long --always'
     try:
         return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
-    except subprocess.CalledProcessError as e:
+    except subprocess.CalledProcessError:
         return ''  # not a git repository
 
 
     try:
         cmd = 'nvidia-smi -L | wc -l'
         return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
-    except Exception as e:
+    except Exception:
         return 0
 
 
             tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
                 flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
-            except:
+            except Exception:
                 flops = 0
 
             try:
                     try:
                         _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
                         t[2] = time_sync()
-                    except Exception as e:  # no backward method
+                    except Exception:  # no backward method
                         # print(e)  # for debug
                         t[2] = float('nan')
                     tf += (t[1] - t[0]) * 1000 / n  # ms per op forward