update

ultralytics/yolo/engine/trainer.py

@@ -28,16 +28,19 @@ from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT
 from ultralytics.yolo.utils.checks import print_args
 from ultralytics.yolo.utils.files import increment_path, save_yaml
 from ultralytics.yolo.utils.modeling import get_model
-from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, one_cycle
+from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, one_cycle, init_seeds
 
 DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
+RANK = int(os.getenv('RANK', -1))
 
 
 class BaseTrainer:
 
     def __init__(self, config=DEFAULT_CONFIG, overrides={}):
-        self.console = LOGGER
         self.args = self._get_config(config, overrides)
+
+        init_seeds(self.args.seed + 1 + RANK, deterministic=True)
+        self.console = LOGGER
         self.validator = None
         self.model = None
         self.callbacks = defaultdict(list)

ultralytics/yolo/engine/validator.py

@@ -41,7 +41,6 @@ class BaseValidator:
         else:  # TODO: handle this when detectMultiBackend is supported
             assert model is not None, "Either trainer or model is needed for validation"
             # model = DetectMultiBacked(model)
-            # TODO: implement init_model_attributes()
 
         model.eval()
         dt = Profile(), Profile(), Profile(), Profile()

ultralytics/yolo/utils/torch_utils.py

@@ -2,10 +2,12 @@ import math
 import os
 import platform
 import time
+import random
 from contextlib import contextmanager
 from copy import deepcopy
 from pathlib import Path
 
+import numpy as np
 import thop
 import torch
 import torch.distributed as dist
@@ -198,6 +200,20 @@ def one_cycle(y1=0.0, y2=1.0, steps=100):
     # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
     return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
 
+def init_seeds(seed=0, deterministic=False):
+    # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe
+    # torch.backends.cudnn.benchmark = True  # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
+    if deterministic and check_version(torch.__version__, '1.12.0'):  # https://github.com/ultralytics/yolov5/pull/8213
+        torch.use_deterministic_algorithms(True)
+        torch.backends.cudnn.deterministic = True
+        os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
+        os.environ['PYTHONHASHSEED'] = str(seed)
+
 
 class ModelEMA:
     """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models