Default to img_size in model default_cfg, defer output folder creation until later in the init sequence

Author: rwightman

train.py

@@ -42,8 +42,8 @@
                     help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
 parser.add_argument('--pretrained', action='store_true', default=False,
                     help='Start with pretrained version of specified network (if avail)')
-parser.add_argument('--img-size', type=int, default=224, metavar='N',
-                    help='Image patch size (default: 224)')
+parser.add_argument('--img-size', type=int, default=None, metavar='N',
+                    help='Image patch size (default: None => model default)')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                     help='Override mean pixel value of dataset')
 parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
@@ -159,15 +159,6 @@ def main():
 
     torch.manual_seed(args.seed + args.rank)
 
-    output_dir = ''
-    if args.local_rank == 0:
-        output_base = args.output if args.output else './output'
-        exp_name = '-'.join([
-            datetime.now().strftime("%Y%m%d-%H%M%S"),
-            args.model,
-            str(args.img_size)])
-        output_dir = get_outdir(output_base, 'train', exp_name)
-
     model = create_model(
         args.model,
         pretrained=args.pretrained,
@@ -291,13 +282,21 @@ def main():
         validate_loss_fn = train_loss_fn
 
     eval_metric = args.eval_metric
+    best_metric = None
+    best_epoch = None
     saver = None
-    if output_dir:
-        # only set if process is rank 0
+    output_dir = ''
+    if args.local_rank == 0:
+        output_base = args.output if args.output else './output'
+        exp_name = '-'.join([
+            datetime.now().strftime("%Y%m%d-%H%M%S"),
+            args.model,
+            str(data_config['input_size'][-1])
+        ])
+        output_dir = get_outdir(output_base, 'train', exp_name)
         decreasing = True if eval_metric == 'loss' else False
         saver = CheckpointSaver(checkpoint_dir=output_dir, decreasing=decreasing)
-    best_metric = None
-    best_epoch = None
+
     try:
         for epoch in range(start_epoch, num_epochs):
             if args.distributed:

utils.py

@@ -253,9 +253,9 @@ def _load_checkpoint(self, checkpoint_path):
                     name = k
                 new_state_dict[name] = v
             self.ema.load_state_dict(new_state_dict)
-            print("=> loaded state_dict_ema")
+            print("=> Loaded state_dict_ema")
         else:
-            print("=> failed to find state_dict_ema, starting from loaded model weights)")
+            print("=> Failed to find state_dict_ema, starting from loaded model weights")
 
     def update(self, model):
         # correct a mismatch in state dict keys