Update train.py

train.py

@@ -147,15 +147,6 @@ def train(hyp):
     # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
     # plot_lr_scheduler(optimizer, scheduler, epochs)
 
-    # Initialize distributed training
-    if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
-        dist.init_process_group(backend='nccl',  # distributed backend
-                                init_method='tcp://127.0.0.1:9999',  # init method
-                                world_size=1,  # number of nodes
-                                rank=0)  # node rank
-        model = torch.nn.parallel.DistributedDataParallel(model)
-        # pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
-
     # Trainloader
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                             hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect)
@@ -173,6 +164,15 @@ def train(hyp):
     model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
     model.names = data_dict['names']
+    
+    # Initialize distributed training
+    if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
+        dist.init_process_group(backend='nccl',  # distributed backend
+                                init_method='tcp://127.0.0.1:9999',  # init method
+                                world_size=1,  # number of nodes
+                                rank=0)  # node rank
+        model = torch.nn.parallel.DistributedDataParallel(model)
+        # pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
 
     # Class frequency
     labels = np.concatenate(dataset.labels, 0)
@@ -289,7 +289,7 @@ def train(hyp):
                                              batch_size=batch_size,
                                              imgsz=imgsz_test,
                                              save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
-                                             model=ema.ema,
+                                             model=ema.ema.module if hasattr(model, 'module') else ema.ema,
                                              single_cls=opt.single_cls,
                                              dataloader=testloader)
 
@@ -315,14 +315,6 @@ def train(hyp):
         # Save model
         save = (not opt.nosave) or (final_epoch and not opt.evolve)
         if save:
-            if hasattr(model, 'module'):
-                # Duplicate Model parameters for Multi-GPU save
-                ema.ema.module.nc = model.nc  # attach number of classes to model
-                ema.ema.module.hyp = model.hyp  # attach hyperparameters to model
-                ema.ema.module.gr = model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
-                ema.ema.module.class_weights = model.class_weights # attach class weights
-                ema.ema.module.names = data_dict['names']
-                
             with open(results_file, 'r') as f:  # create checkpoint
                 ckpt = {'epoch': epoch,
                         'best_fitness': best_fitness,