Training reproducibility improvements

train.py

@@ -101,7 +101,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     # Config
     plots = not evolve and not opt.noplots  # create plots
     cuda = device.type != 'cpu'
-    init_seeds(1 + RANK)
+    init_seeds(opt.seed + 1 + RANK, deterministic=True)
     with torch_distributed_zero_first(LOCAL_RANK):
         data_dict = data_dict or check_dataset(data)  # check if None
     train_path, val_path = data_dict['train'], data_dict['val']
@@ -504,6 +504,7 @@ def parse_opt(known=False):
     parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
     parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
     parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
+    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
     parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
 
     # Weights & Biases arguments

utils/general.py

@@ -195,14 +195,22 @@ def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
     LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
 
 
-def init_seeds(seed=0):
+def init_seeds(seed=0, deterministic=False):
     # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
     # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
     import torch.backends.cudnn as cudnn
+
+    if deterministic and check_version(torch.__version__, '1.12.0'):  # https://github.com/ultralytics/yolov5/pull/8213
+        torch.use_deterministic_algorithms(True)
+        os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
+        # os.environ['PYTHONHASHSEED'] = str(seed)
+
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
     cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
+    # torch.cuda.manual_seed(seed)
+    # torch.cuda.manual_seed_all(seed)  # for multi GPU, exception safe
 
 
 def intersect_dicts(da, db, exclude=()):