Update .pre-commit-config.yaml (#2019)

* Update .pre-commit-config.yaml

* Update __init__.py

* Update .pre-commit-config.yaml

* Precommit updates

classify/predict.py

@@ -179,7 +179,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms')
 
     # Print results
     t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
@@ -221,6 +221,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

classify/train.py

@@ -220,11 +220,11 @@ def train(opt, device):
 
             # Log
             metrics = {
-                "train/loss": tloss,
-                f"{val}/loss": vloss,
-                "metrics/accuracy_top1": top1,
-                "metrics/accuracy_top5": top5,
-                "lr/0": optimizer.param_groups[0]['lr']}  # learning rate
+                'train/loss': tloss,
+                f'{val}/loss': vloss,
+                'metrics/accuracy_top1': top1,
+                'metrics/accuracy_top5': top5,
+                'lr/0': optimizer.param_groups[0]['lr']}  # learning rate
             logger.log_metrics(metrics, epoch)
 
             # Save model
@@ -251,11 +251,11 @@ def train(opt, device):
     if RANK in {-1, 0} and final_epoch:
         LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
                     f"\nResults saved to {colorstr('bold', save_dir)}"
-                    f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
-                    f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
-                    f"\nExport:          python export.py --weights {best} --include onnx"
+                    f'\nPredict:         python classify/predict.py --weights {best} --source im.jpg'
+                    f'\nValidate:        python classify/val.py --weights {best} --data {data_dir}'
+                    f'\nExport:          python export.py --weights {best} --include onnx'
                     f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
-                    f"\nVisualize:       https://netron.app\n")
+                    f'\nVisualize:       https://netron.app\n')
 
         # Plot examples
         images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
@@ -263,7 +263,7 @@ def train(opt, device):
         file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
 
         # Log results
-        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
+        meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()}
         logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
         logger.log_model(best, epochs, metadata=meta)
 
@@ -310,7 +310,7 @@ def main(opt):
         assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
         torch.cuda.set_device(LOCAL_RANK)
         device = torch.device('cuda', LOCAL_RANK)
-        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
+        dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
 
     # Parameters
     opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
@@ -328,6 +328,6 @@ def run(**kwargs):
     return opt
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

classify/tutorial.ipynb

@@ -1477,4 +1477,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

classify/val.py

@@ -100,7 +100,7 @@ def run(
     pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
     n = len(dataloader)  # number of batches
     action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
-    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}'
     bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
     with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
         for images, labels in bar:
@@ -123,14 +123,14 @@ def run(
     top1, top5 = acc.mean(0).tolist()
 
     if pbar:
-        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+        pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}'
     if verbose:  # all classes
         LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
         LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
         for i, c in model.names.items():
             acc_i = acc[targets == i]
             top1i, top5i = acc_i.mean(0).tolist()
-            LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+            LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}')
 
         # Print results
         t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image
@@ -165,6 +165,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)