Add support for different normalization layers (ultralytics#7377)

* Add support for different normalization layers.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Cleanup

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

train.py

@@ -151,10 +151,11 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
 
     g = [], [], []  # optimizer parameter groups
+    bn = nn.BatchNorm2d, nn.LazyBatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d, nn.LazyInstanceNorm2d, nn.LayerNorm
     for v in model.modules():
         if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias
             g[2].append(v.bias)
-        if isinstance(v, nn.BatchNorm2d):  # weight (no decay)
+        if isinstance(v, bn):  # weight (no decay)
             g[1].append(v.weight)
         elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
             g[0].append(v.weight)