Merge branch 'master_temp' into indoor_pipeline

tools/train.py

@@ -8,7 +8,7 @@ import time
 
 import mmcv
 import torch
-from mmcv import Config
+from mmcv import Config, DictAction
 from mmcv.runner import init_dist
 
 from mmdet3d import __version__
@@ -26,9 +26,9 @@ def parse_args():
     parser.add_argument(
         '--resume-from', help='the checkpoint file to resume from')
     parser.add_argument(
-        '--validate',
+        '--no-validate',
         action='store_true',
-        help='whether to evaluate the checkpoint during training')
+        help='whether not to evaluate the checkpoint during training')
     group_gpus = parser.add_mutually_exclusive_group()
     group_gpus.add_argument(
         '--gpus',
@@ -46,6 +46,8 @@ def parse_args():
         '--deterministic',
         action='store_true',
         help='whether to set deterministic options for CUDNN backend.')
+    parser.add_argument(
+        '--options', nargs='+', action=DictAction, help='arguments in dict')
     parser.add_argument(
         '--launcher',
         choices=['none', 'pytorch', 'slurm', 'mpi'],
@@ -67,6 +69,9 @@ def main():
     args = parse_args()
 
     cfg = Config.fromfile(args.config)
+    if args.options is not None:
+        cfg.merge_from_dict(args.options)
+
     # set cudnn_benchmark
     if cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True
@@ -101,7 +106,7 @@ def main():
     mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
     # init the logger before other steps
     timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
-    log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
+    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
     logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
 
     # add a logging filter
@@ -113,28 +118,27 @@ def main():
     meta = dict()
     # log env info
     env_info_dict = collect_env()
-    env_info = '\n'.join([('{}: {}'.format(k, v))
-                          for k, v in env_info_dict.items()])
+    env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
     dash_line = '-' * 60 + '\n'
     logger.info('Environment info:\n' + dash_line + env_info + '\n' +
                 dash_line)
     meta['env_info'] = env_info
 
     # log some basic info
-    logger.info('Distributed training: {}'.format(distributed))
-    logger.info('Config:\n{}'.format(cfg.text))
+    logger.info(f'Distributed training: {distributed}')
+    logger.info(f'Config:\n{cfg.pretty_text}')
 
     # set random seeds
     if args.seed is not None:
-        logger.info('Set random seed to {}, deterministic: {}'.format(
-            args.seed, args.deterministic))
+        logger.info(f'Set random seed to {args.seed}, '
+                    f'deterministic: {args.deterministic}')
         set_random_seed(args.seed, deterministic=args.deterministic)
     cfg.seed = args.seed
     meta['seed'] = args.seed
 
     model = build_detector(
         cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
-    logger.info('Model:\n{}'.format(model))
+    logger.info(f'Model:\n{model}')
     datasets = [build_dataset(cfg.data.train)]
     if len(cfg.workflow) == 2:
         val_dataset = copy.deepcopy(cfg.data.val)
@@ -145,7 +149,7 @@ def main():
         # checkpoints as meta data
         cfg.checkpoint_config.meta = dict(
             mmdet_version=__version__,
-            config=cfg.text,
+            config=cfg.pretty_text,
             CLASSES=datasets[0].CLASSES)
     # add an attribute for visualization convenience
     model.CLASSES = datasets[0].CLASSES
@@ -154,7 +158,7 @@ def main():
         datasets,
         cfg,
         distributed=distributed,
-        validate=args.validate,
+        validate=(not args.no_validate),
         timestamp=timestamp,
         meta=meta)