[Enchance] Set a random seed when the user does not set a seed. (#1039)

mmseg/apis/__init__.py

@@ -1,10 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .inference import inference_segmentor, init_segmentor, show_result_pyplot
 from .test import multi_gpu_test, single_gpu_test
-from .train import get_root_logger, set_random_seed, train_segmentor
+from .train import (get_root_logger, init_random_seed, set_random_seed,
+                    train_segmentor)
 
 __all__ = [
     'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
     'inference_segmentor', 'multi_gpu_test', 'single_gpu_test',
-    'show_result_pyplot'
+    'show_result_pyplot', 'init_random_seed'
 ]

mmseg/apis/train.py

@@ -4,8 +4,9 @@ import warnings
 
 import numpy as np
 import torch
+import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import HOOKS, build_optimizer, build_runner
+from mmcv.runner import HOOKS, build_optimizer, build_runner, get_dist_info
 from mmcv.utils import build_from_cfg
 
 from mmseg.core import DistEvalHook, EvalHook
@@ -13,6 +14,37 @@ from mmseg.datasets import build_dataloader, build_dataset
 from mmseg.utils import get_root_logger
 
 
+def init_random_seed(seed=None, device='cuda'):
+    """Initialize random seed.
+
+    If the seed is not set, the seed will be automatically randomized,
+    and then broadcast to all processes to prevent some potential bugs.
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+    Returns:
+        int: Seed to be used.
+    """
+    if seed is not None:
+        return seed
+
+    # Make sure all ranks share the same random seed to prevent
+    # some potential bugs. Please refer to
+    # https://github.com/open-mmlab/mmdetection/issues/6339
+    rank, world_size = get_dist_info()
+    seed = np.random.randint(2**31)
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+    dist.broadcast(random_num, src=0)
+    return random_num.item()
+
+
 def set_random_seed(seed, deterministic=False):
     """Set random seed.
 

tools/train.py

@@ -13,7 +13,7 @@ from mmcv.runner import get_dist_info, init_dist
 from mmcv.utils import Config, DictAction, get_git_hash
 
 from mmseg import __version__
-from mmseg.apis import set_random_seed, train_segmentor
+from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
 from mmseg.datasets import build_dataset
 from mmseg.models import build_segmentor
 from mmseg.utils import collect_env, get_root_logger
@@ -125,12 +125,12 @@ def main():
     logger.info(f'Config:\n{cfg.pretty_text}')
 
     # set random seeds
-    if args.seed is not None:
-        logger.info(f'Set random seed to {args.seed}, deterministic: '
-                    f'{args.deterministic}')
-        set_random_seed(args.seed, deterministic=args.deterministic)
-    cfg.seed = args.seed
-    meta['seed'] = args.seed
+    seed = init_random_seed(args.seed)
+    logger.info(f'Set random seed to {seed}, '
+                f'deterministic: {args.deterministic}')
+    set_random_seed(seed, deterministic=args.deterministic)
+    cfg.seed = seed
+    meta['seed'] = seed
     meta['exp_name'] = osp.basename(args.config)
 
     model = build_segmentor(