Validate with 2x --workers (ultralytics#6658)

train.py

@@ -232,7 +232,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     if RANK in [-1, 0]:
         val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
                                        hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
-                                       workers=workers, pad=0.5,
+                                       workers=workers * 2, pad=0.5,
                                        prefix=colorstr('val: '))[0]
 
         if not resume:

utils/datasets.py

@@ -110,7 +110,7 @@ def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=Non
 
     batch_size = min(batch_size, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
+    nw = min([2 * os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
     return loader(dataset,