[Enchance] Set a random seed when the user does not set a seed. (#1072)

mmdet3d/apis/__init__.py

@@ -4,10 +4,11 @@ from .inference import (convert_SyncBN, inference_detector,
                         inference_multi_modality_detector, inference_segmentor,
                         init_model, show_result_meshlab)
 from .test import single_gpu_test
-from .train import train_model
+from .train import init_random_seed, train_model
 
 __all__ = [
     'inference_detector', 'init_model', 'single_gpu_test',
     'inference_mono_3d_detector', 'show_result_meshlab', 'convert_SyncBN',
-    'train_model', 'inference_multi_modality_detector', 'inference_segmentor'
+    'train_model', 'inference_multi_modality_detector', 'inference_segmentor',
+    'init_random_seed'
 ]

mmdet3d/apis/train.py

 # Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from mmcv.runner import get_dist_info
+from torch import distributed as dist
+
 from mmdet.apis import train_detector
 from mmseg.apis import train_segmentor
 
 
+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, optional): 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 train_model(model,
                 dataset,
                 cfg,

tools/train.py

@@ -14,7 +14,7 @@ from os import path as osp
 
 from mmdet import __version__ as mmdet_version
 from mmdet3d import __version__ as mmdet3d_version
-from mmdet3d.apis import train_model
+from mmdet3d.apis import init_random_seed, train_model
 from mmdet3d.datasets import build_dataset
 from mmdet3d.models import build_model
 from mmdet3d.utils import collect_env, get_root_logger
@@ -169,12 +169,12 @@ 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}, '
-                    f'deterministic: {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_model(