Adding world_size

Reduce calls to torch.distributed. For use in create_dataloader.

train.py

@@ -294,7 +294,7 @@ def train(hyp, tb_writer, opt, device):
             loss, loss_items = compute_loss(pred, targets.to(device), model)
             # loss is scaled with batch size in func compute_loss. But in DDP mode, gradient is averaged between devices.
             if local_rank != -1:
-                loss *= dist.get_world_size()
+                loss *= opt.world_size
             if not torch.isfinite(loss):
                 print('WARNING: non-finite loss, ending training ', loss_items)
                 return results
@@ -451,15 +451,17 @@ def train(hyp, tb_writer, opt, device):
     opt.total_batch_size = opt.batch_size
     if device.type == 'cpu':
         mixed_precision = False
+        opt.world_size = 1
     elif opt.local_rank != -1:
         # DDP mode
         assert torch.cuda.device_count() > opt.local_rank
         torch.cuda.set_device(opt.local_rank)
         device = torch.device("cuda", opt.local_rank)
         dist.init_process_group(backend='nccl', init_method='env://')  # distributed backend
-
-        assert opt.batch_size % dist.get_world_size() == 0
-        opt.batch_size = opt.total_batch_size // dist.get_world_size()
+
+        opt.world_size = dist.get_world_size()
+        assert opt.batch_size % opt.world_size == 0
+        opt.batch_size = opt.total_batch_size // opt.world_size
     print(opt)
 
     # Train