Remove `opt` from `create_dataloader()`` (#3552)

test.py

@@ -88,7 +88,7 @@ def test(data,
         if device.type != 'cpu':
             model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
         task = opt.task if opt.task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
-        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
+        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=0.5, rect=True,
                                        prefix=colorstr(f'{task}: '))[0]
 
     seen = 0

train.py

@@ -41,8 +41,9 @@ logger = logging.getLogger(__name__)
 
 def train(hyp, opt, device, tb_writer=None):
     logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
-    save_dir, epochs, batch_size, total_batch_size, weights, rank = \
-        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
+    save_dir, epochs, batch_size, total_batch_size, weights, rank, single_cls = \
+        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, \
+        opt.single_cls
 
     # Directories
     wdir = save_dir / 'weights'
@@ -75,8 +76,8 @@ def train(hyp, opt, device, tb_writer=None):
         if wandb_logger.wandb:
             weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
 
-    nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
-    names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
+    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
+    names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
     is_coco = opt.data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
@@ -187,7 +188,7 @@ def train(hyp, opt, device, tb_writer=None):
         logger.info('Using SyncBatchNorm()')
 
     # Trainloader
-    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
+    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, single_cls,
                                             hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
                                             world_size=opt.world_size, workers=opt.workers,
                                             image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
@@ -197,7 +198,7 @@ def train(hyp, opt, device, tb_writer=None):
 
     # Process 0
     if rank in [-1, 0]:
-        testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt,  # testloader
+        testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, single_cls,
                                        hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
                                        world_size=opt.world_size, workers=opt.workers,
                                        pad=0.5, prefix=colorstr('val: '))[0]
@@ -357,7 +358,7 @@ def train(hyp, opt, device, tb_writer=None):
                                                  batch_size=batch_size * 2,
                                                  imgsz=imgsz_test,
                                                  model=ema.ema,
-                                                 single_cls=opt.single_cls,
+                                                 single_cls=single_cls,
                                                  dataloader=testloader,
                                                  save_dir=save_dir,
                                                  save_json=is_coco and final_epoch,
@@ -429,7 +430,7 @@ def train(hyp, opt, device, tb_writer=None):
                                               conf_thres=0.001,
                                               iou_thres=0.7,
                                               model=attempt_load(m, device).half(),
-                                              single_cls=opt.single_cls,
+                                              single_cls=single_cls,
                                               dataloader=testloader,
                                               save_dir=save_dir,
                                               save_json=True,

utils/datasets.py

@@ -62,8 +62,8 @@ def exif_size(img):
     return s
 
 
-def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
-                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
+def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0,
+                      rect=False, rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
     # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
     with torch_distributed_zero_first(rank):
         dataset = LoadImagesAndLabels(path, imgsz, batch_size,
@@ -71,7 +71,7 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                       hyp=hyp,  # augmentation hyperparameters
                                       rect=rect,  # rectangular training
                                       cache_images=cache,
-                                      single_cls=opt.single_cls,
+                                      single_cls=single_cls,
                                       stride=int(stride),
                                       pad=pad,
                                       image_weights=image_weights,