Update train.py

train.py

     # Create model
     model = Model(opt.cfg).to(device)
     assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
-   
 
     # Image sizes
     gs = int(max(model.stride))  # grid size (max stride)
             with open(results_file, 'w') as file:
                 file.write(ckpt['training_results'])  # write results.txt
 
+        # epochs
         start_epoch = ckpt['epoch'] + 1
+        if epochs < start_epoch:
+            print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
+                  (opt.weights, ckpt['epoch'], epochs))
+            epochs += ckpt['epoch']  # finetune additional epochs
+
         del ckpt
 
     # Mixed precision training https://github.com/NVIDIA/apex
     # 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)
 
     # Testloader
     testloader = create_dataloader(test_path, imgsz_test, batch_size, gs, opt,
-                                            hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0]
+                                   hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0]
 
     # Model parameters
     hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
     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)
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     opt = parser.parse_args()
-    opt.weights = last if opt.resume else opt.weights
+    opt.weights = last if opt.resume and not opt.weights else opt.weights
     opt.cfg = check_file(opt.cfg)  # check file
     opt.data = check_file(opt.data)  # check file
     print(opt)