Add support for loading args from yaml file (and saving them with each experiment)

requirements.txt

@@ -1,2 +1,3 @@
 torch>=1.1.0
 torchvision>=0.3.0
+pyyaml

train.py

@@ -2,6 +2,7 @@
 import argparse
 import time
 import logging
+import yaml
 from datetime import datetime
 
 try:
@@ -26,6 +27,14 @@ import torchvision.utils
 
 torch.backends.cudnn.benchmark = True
 
+
+# The first arg parser parses out only the --config argument, this argument is used to
+# load a yaml file containing key-values that override the defaults for the main parser below
+config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
+parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
+                    help='YAML config file specifying default arguments')
+
+
 parser = argparse.ArgumentParser(description='Training')
 # Dataset / Model parameters
 parser.add_argument('data', metavar='DIR',
@@ -145,9 +154,27 @@ parser.add_argument('--tta', type=int, default=0, metavar='N',
 parser.add_argument("--local_rank", default=0, type=int)
 
 
+def _parse_args():
+    # Do we have a config file to parse?
+    args_config, remaining = config_parser.parse_known_args()
+    if args_config.config:
+        with open(args_config.config, 'r') as f:
+            cfg = yaml.safe_load(f)
+            parser.set_defaults(**cfg)
+
+    # The main arg parser parses the rest of the args, the usual
+    # defaults will have been overridden if config file specified.
+    args = parser.parse_args(remaining)
+
+    # Cache the args as a text string to save them in the output dir later
+    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
+    return args, args_text
+
+
 def main():
     setup_default_logging()
-    args = parser.parse_args()
+    args, args_text = _parse_args()
+
     args.prefetcher = not args.no_prefetcher
     args.distributed = False
     if 'WORLD_SIZE' in os.environ:
@@ -345,6 +372,8 @@ def main():
         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)
+        with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
+            f.write(args_text)
 
     try:
         for epoch in range(start_epoch, num_epochs):