ultralytics 8.0.32 HUB and TensorFlow fixes (#870)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/data/build.py

@@ -61,7 +61,7 @@ def seed_worker(worker_id):
     random.seed(worker_seed)
 
 
-def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_path=None, rank=-1, mode="train"):
+def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, rank=-1, mode="train"):
     assert mode in ["train", "val"]
     shuffle = mode == "train"
     if cfg.rect and shuffle:
@@ -70,9 +70,8 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = YOLODataset(
             img_path=img_path,
-            label_path=label_path,
             imgsz=cfg.imgsz,
-            batch_size=batch_size,
+            batch_size=batch,
             augment=mode == "train",  # augmentation
             hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
             rect=cfg.rect or rect,  # rectangular batches
@@ -82,18 +81,19 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat
             pad=0.0 if mode == "train" else 0.5,
             prefix=colorstr(f"{mode}: "),
             use_segments=cfg.task == "segment",
-            use_keypoints=cfg.task == "keypoint")
+            use_keypoints=cfg.task == "keypoint",
+            names=names)
 
-    batch_size = min(batch_size, len(dataset))
+    batch = min(batch, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
     workers = cfg.workers if mode == "train" else cfg.workers * 2
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
+    nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader  # allow attribute updates
     generator = torch.Generator()
     generator.manual_seed(6148914691236517205 + RANK)
     return loader(dataset=dataset,
-                  batch_size=batch_size,
+                  batch_size=batch,
                   shuffle=shuffle and sampler is None,
                   num_workers=nw,
                   sampler=sampler,