Remove commented code, add more consistent seed fn

timm/models/byobnet.py

@@ -515,52 +515,6 @@ def create_block(block: Union[str, nn.Module], **kwargs):
     return _block_registry[block](**kwargs)
 
 
-# class Stem(nn.Module):
-#
-#     def __init__(self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool',
-#                  num_rep=3, num_act=None, chs_decay=0.5, layers: LayerFn = None):
-#         super().__init__()
-#         assert stride in (2, 4)
-#         if pool:
-#             assert stride == 4
-#         layers = layers or LayerFn()
-#
-#         if isinstance(out_chs, (list, tuple)):
-#             num_rep = len(out_chs)
-#             stem_chs = out_chs
-#         else:
-#             stem_chs = [round(out_chs * chs_decay ** i) for i in range(num_rep)][::-1]
-#
-#         self.stride = stride
-#         stem_strides = [2] + [1] * (num_rep - 1)
-#         if stride == 4 and not pool:
-#             # set last conv in stack to be strided if stride == 4 and no pooling layer
-#             stem_strides[-1] = 2
-#
-#         num_act = num_rep if num_act is None else num_act
-#         # if num_act < num_rep, first convs in stack won't have bn + act
-#         stem_norm_acts = [False] * (num_rep - num_act) + [True] * num_act
-#         prev_chs = in_chs
-#         convs = []
-#         for i, (ch, s, na) in enumerate(zip(stem_chs, stem_strides, stem_norm_acts)):
-#             layer_fn = layers.conv_norm_act if na else create_conv2d
-#             convs.append(layer_fn(prev_chs, ch, kernel_size=kernel_size, stride=s))
-#             prev_chs = ch
-#         self.conv = nn.Sequential(*convs) if len(convs) > 1 else convs[0]
-#
-#         if not pool:
-#             self.pool = nn.Identity()
-#         elif 'max' in pool.lower():
-#             self.pool = nn.MaxPool2d(3, 2, 1) if pool else nn.Identity()
-#         else:
-#             assert False, "Unknown pooling type"
-#
-#     def forward(self, x):
-#         x = self.conv(x)
-#         x = self.pool(x)
-#         return x
-
-
 class Stem(nn.Sequential):
 
     def __init__(self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool',

timm/utils/__init__.py

@@ -9,4 +9,5 @@ from .metrics import AverageMeter, accuracy
 from .misc import natural_key, add_bool_arg
 from .model import unwrap_model, get_state_dict
 from .model_ema import ModelEma, ModelEmaV2
+from .random import random_seed
 from .summary import update_summary, get_outdir

timm/utils/random.py

@@ -0,0 +1,9 @@
+import random
+import numpy as np
+import torch
+
+
+def random_seed(seed=42, rank=0):
+    torch.manual_seed(seed + rank)
+    np.random.seed(seed + rank)
+    random.seed(seed + rank)

train.py

@@ -329,7 +329,7 @@ def main():
         _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
                         "Install NVIDA apex or upgrade to PyTorch 1.6")
 
-    torch.manual_seed(args.seed + args.rank)
+    random_seed(args.seed, args.rank)
 
     model = create_model(
         args.model,