Refactor collections and fstrings (#7821)

* Update torch_utils.py

* Additional code refactoring

* tuples to sets

* Cleanup

train.py

@@ -88,7 +88,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # Loggers
     data_dict = None
-    if RANK in [-1, 0]:
+    if RANK in {-1, 0}:
         loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
         if loggers.wandb:
             data_dict = loggers.wandb.data_dict
@@ -181,7 +181,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)
 
     # EMA
-    ema = ModelEMA(model) if RANK in [-1, 0] else None
+    ema = ModelEMA(model) if RANK in {-1, 0} else None
 
     # Resume
     start_epoch, best_fitness = 0, 0.0
@@ -238,7 +238,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
 
     # Process 0
-    if RANK in [-1, 0]:
+    if RANK in {-1, 0}:
         val_loader = create_dataloader(val_path,
                                        imgsz,
                                        batch_size // WORLD_SIZE * 2,
@@ -320,7 +320,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
             train_loader.sampler.set_epoch(epoch)
         pbar = enumerate(train_loader)
         LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
-        if RANK in (-1, 0):
+        if RANK in {-1, 0}:
             pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
         optimizer.zero_grad()
         for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
@@ -369,7 +369,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 last_opt_step = ni
 
             # Log
-            if RANK in (-1, 0):
+            if RANK in {-1, 0}:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                 pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
@@ -383,7 +383,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
         scheduler.step()
 
-        if RANK in (-1, 0):
+        if RANK in {-1, 0}:
             # mAP
             callbacks.run('on_train_epoch_end', epoch=epoch)
             ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
@@ -444,7 +444,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training -----------------------------------------------------------------------------------------------------
-    if RANK in (-1, 0):
+    if RANK in {-1, 0}:
         LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
         for f in last, best:
             if f.exists():
@@ -522,7 +522,7 @@ def parse_opt(known=False):
 
 def main(opt, callbacks=Callbacks()):
     # Checks
-    if RANK in (-1, 0):
+    if RANK in {-1, 0}:
         print_args(vars(opt))
         check_git_status()
         check_requirements(exclude=['thop'])