diff --git a/codes/train.py b/codes/train.py
index 58803dc6..d2e2b973 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -43,7 +43,6 @@ class Trainer:
             device_id = torch.cuda.current_device()
             resume_state = torch.load(opt['path']['resume_state'],
                                       map_location=lambda storage, loc: storage.cuda(device_id))
-            option.check_resume(opt, resume_state['iter'])  # check resume options
         else:
             resume_state = None
 
@@ -64,18 +63,15 @@ class Trainer:
             # tensorboard logger
             if opt['use_tb_logger'] and 'debug' not in opt['name']:
                 self.tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger')
-                version = float(torch.__version__[0:3])
-                if version >= 1.1:  # PyTorch 1.1
-                    from torch.utils.tensorboard import SummaryWriter
-                else:
-                    self.self.logger.info(
-                        'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
-                    from tensorboardX import SummaryWriter
+                from torch.utils.tensorboard import SummaryWriter
                 self.tb_logger = SummaryWriter(log_dir=self.tb_logger_path)
         else:
             util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
             self.logger = logging.getLogger('base')
 
+        if resume_state is not None:
+            option.check_resume(opt, resume_state['iter'])  # check resume options
+
         # convert to NoneDict, which returns None for missing keys
         opt = option.dict_to_nonedict(opt)
         self.opt = opt
@@ -284,7 +280,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mass_distill.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_noisy_audio_clips_classifier.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()
diff --git a/codes/utils/options.py b/codes/utils/options.py
index 5b152c00..613cb145 100644
--- a/codes/utils/options.py
+++ b/codes/utils/options.py
@@ -103,7 +103,20 @@ def check_resume(opt, resume_iter):
         # Automatically fill in the network paths for a given resume iteration.
         for k in opt['networks'].keys():
             pt_key = 'pretrain_model_%s' % (k,)
-            assert pt_key not in opt['path'].keys()  # There's no real reason to load from a training_state AND a model.
-            opt['path'][pt_key] = osp.join(opt['path']['models'],
-                                      '{}_{}.pth'.format(resume_iter, k))
-            logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))
+            if pt_key in opt['path'].keys():
+                # This is a dicey, error prone situation that has bitten me in both ways it can be handled. Opt for
+                # a big, verbose error message.
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!WARNING!! YOU SPECIFIED A PRETRAINED MODEL PATH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!AND A RESUME STATE PATH. THERE IS NO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!GOOD WAY TO HANDLE THIS SO WE JUST IGNORE!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!THE MODEL PATH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+            else:
+                opt['path'][pt_key] = osp.join(opt['path']['models'],
+                                          '{}_{}.pth'.format(resume_iter, k))
+                logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))