prevent testloader caching on --notest

train.py

@@ -165,8 +165,8 @@ def train(hyp, opt, device, tb_writer=None):
 
     # Trainloader
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
-                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
-                                            world_size=opt.world_size, workers=opt.workers)
+                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,
+                                            rank=rank, world_size=opt.world_size, workers=opt.workers)
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
     nb = len(dataloader)  # number of batches
     assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
@@ -175,8 +175,8 @@ def train(hyp, opt, device, tb_writer=None):
     if rank in [-1, 0]:
         ema.updates = start_epoch * nb // accumulate  # set EMA updates
         testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
-                                       hyp=hyp, augment=False, cache=opt.cache_images, rect=True, rank=-1,
-                                       world_size=opt.world_size, workers=opt.workers)[0]  # testloader
+                                       hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
+                                       rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader
 
         if not opt.resume:
             labels = np.concatenate(dataset.labels, 0)