slightly changed some setup order in main/train.py to achieve writing 'train.log' to output directory by default

train.py

@@ -364,7 +364,7 @@ def _parse_args():
 
 
 def main():
-    utils.setup_default_logging()
+    
     args, args_text = _parse_args()
 
     if torch.cuda.is_available():
@@ -373,8 +373,53 @@ def main():
 
     if args.data and not args.data_dir:
         args.data_dir = args.data
+    
     args.prefetcher = not args.no_prefetcher
     device = utils.init_distributed_device(args)
+    
+    # setup model based on args
+    in_chans = 3
+    if args.in_chans is not None:
+        in_chans = args.in_chans
+    elif args.input_size is not None:
+        in_chans = args.input_size[0]
+        
+    model = create_model(
+        args.model,
+        pretrained=args.pretrained,
+        in_chans=in_chans,
+        num_classes=args.num_classes,
+        drop_rate=args.drop,
+        drop_path_rate=args.drop_path,
+        drop_block_rate=args.drop_block,
+        global_pool=args.gp,
+        bn_momentum=args.bn_momentum,
+        bn_eps=args.bn_eps,
+        scriptable=args.torchscript,
+        checkpoint_path=args.initial_checkpoint,
+    )
+    if args.num_classes is None:
+        assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
+        args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly
+
+    if args.grad_checkpointing:
+        model.set_grad_checkpointing(enable=True)        
+        
+    # initialize data config 
+    data_config = resolve_data_config(vars(args), model=model, verbose=utils.is_primary(args))
+    output_dir = None
+    if args.experiment:
+        exp_name = args.experiment
+    else:
+        exp_name = '-'.join([
+            datetime.now().strftime("%Y%m%d-%H%M%S"),
+            safe_model_name(args.model),
+            str(data_config['input_size'][-1])
+        ])
+    # confirm output directory & write 'train.log' to this directory by default
+    output_dir = utils.get_outdir(args.output if args.output else './output/train', exp_name)
+    utils.setup_default_logging(log_path=os.path.join(output_dir, 'train.log'))
+  
     if args.distributed:
         _logger.info(
             'Training in distributed mode with multiple processes, 1 device per process.'
@@ -390,7 +435,7 @@ def main():
             _logger.warning(
                 "You've requested to log metrics to wandb but package not found. "
                 "Metrics not being logged to wandb, try `pip install wandb`")
-
+              
     # resolve AMP arguments based on PyTorch / Apex availability
     use_amp = None
     amp_dtype = torch.float16
@@ -413,38 +458,11 @@ def main():
     if args.fast_norm:
         set_fast_norm()
 
-    in_chans = 3
-    if args.in_chans is not None:
-        in_chans = args.in_chans
-    elif args.input_size is not None:
-        in_chans = args.input_size[0]
-
-    model = create_model(
-        args.model,
-        pretrained=args.pretrained,
-        in_chans=in_chans,
-        num_classes=args.num_classes,
-        drop_rate=args.drop,
-        drop_path_rate=args.drop_path,
-        drop_block_rate=args.drop_block,
-        global_pool=args.gp,
-        bn_momentum=args.bn_momentum,
-        bn_eps=args.bn_eps,
-        scriptable=args.torchscript,
-        checkpoint_path=args.initial_checkpoint,
-    )
-    if args.num_classes is None:
-        assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
-        args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly
-
-    if args.grad_checkpointing:
-        model.set_grad_checkpointing(enable=True)
-
     if utils.is_primary(args):
         _logger.info(
             f'Model {safe_model_name(args.model)} created, param count:{sum([m.numel() for m in model.parameters()])}')
 
-    data_config = resolve_data_config(vars(args), model=model, verbose=utils.is_primary(args))
+    
 
     # setup augmentation batch splits for contrastive loss or split bn
     num_aug_splits = 0
@@ -686,17 +704,8 @@ def main():
     best_metric = None
     best_epoch = None
     saver = None
-    output_dir = None
     if utils.is_primary(args):
-        if args.experiment:
-            exp_name = args.experiment
-        else:
-            exp_name = '-'.join([
-                datetime.now().strftime("%Y%m%d-%H%M%S"),
-                safe_model_name(args.model),
-                str(data_config['input_size'][-1])
-            ])
-        output_dir = utils.get_outdir(args.output if args.output else './output/train', exp_name)
+
         decreasing = True if eval_metric == 'loss' else False
         saver = utils.CheckpointSaver(
             model=model,