Utilize unified seed configuration in reproducability

fltk/nets/util/reproducability.py

@@ -25,7 +25,7 @@ def cuda_reproducible_backend(cuda: bool) -> None:
         torch.backends.cudnn.deterministic = False
 
 
-def init_reproducibility(config: Optional[ExecutionConfig] = None, test_seed: int = 42) -> None:
+def init_reproducibility(config: Optional[ExecutionConfig] = None, seed: Optional[int] = None) -> None:
     """
     Function to pre-set all seeds for libraries used during training. Allows for re-producible network initialization,
     and non-deterministic number generation. Allows to prevent 'lucky' draws in network initialization.
@@ -34,21 +34,17 @@ def init_reproducibility(config: Optional[ExecutionConfig] = None, test_seed: in
     @return: None
     @rtype: None
     """
-    if config:
-        random_seed = config.reproducibility.arrival_seed
-        torch_seed = config.reproducibility.torch_seed
-        cuda = config.cuda
-    else:
-        random_seed = test_seed
-        torch_seed = test_seed
-        cuda = torch.cuda.is_available()
+    torch_seed, rand_seed = seed, seed
+    if not seed:
+        torch_seed, rand_seed = config.reproducibility.seeds[0], config.reproducibility.seeds[0]
+
 
     torch.manual_seed(torch_seed)
-    if cuda:
+    if config.cuda:
         torch.cuda.manual_seed_all(torch_seed)
         cuda_reproducible_backend(True)
-    np.random.seed(random_seed)
-    os.environ['PYTHONHASHSEED'] = str(random_seed)
+    np.random.seed(rand_seed)
+    os.environ['PYTHONHASHSEED'] = str(rand_seed)