Add synchronized random seed to training

map2map/args.py

@@ -64,7 +64,7 @@ def add_train_args(parser):
 #            help='weight decay')
     parser.add_argument('--dist-backend', default='nccl', type=str,
             choices=['gloo', 'nccl'], help='distributed backend')
-    parser.add_argument('--seed', default=42, type=int,
+    parser.add_argument('--seed', type=int,
             help='seed for initializing training')
     parser.add_argument('--log-interval', default=20, type=int,
             help='interval between logging training loss')

map2map/train.py

@@ -1,5 +1,6 @@
 import os
 import shutil
+import random
 import torch
 from torch.multiprocessing import spawn
 from torch.distributed import init_process_group, destroy_process_group, all_reduce
@@ -13,6 +14,8 @@ from .models import UNet, narrow_like
 
 
 def node_worker(args):
+    if args.seed is None:
+        args.seed = random.randint(0, 65535)
     torch.manual_seed(args.seed)  # NOTE: why here not in gpu_worker?
     #torch.backends.cudnn.deterministic = True  # NOTE: test perf