diff --git a/timm/models/layers/evo_norm.py b/timm/models/layers/evo_norm.py
index 8c08e49f..6ef0c881 100644
--- a/timm/models/layers/evo_norm.py
+++ b/timm/models/layers/evo_norm.py
@@ -34,18 +34,17 @@ class EvoNormBatch2d(nn.Module):
             nn.init.ones_(self.v)
 
     def forward(self, x):
-        assert x.dim() == 4, 'expected 4D input'
+        _assert(x.dim() == 4, 'expected 4D input')
         x_type = x.dtype
-        running_var = self.running_var.view(1, -1, 1, 1)
-        if self.training:
-            var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
-            n = x.numel() / x.shape[1]
-            running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
-            self.running_var.copy_(running_var.view(self.running_var.shape))
-        else:
-            var = running_var
-
         if self.v is not None:
+            running_var = self.running_var.view(1, -1, 1, 1)
+            if self.training:
+                var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
+                n = x.numel() / x.shape[1]
+                running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
+                self.running_var.copy_(running_var.view(self.running_var.shape))
+            else:
+                var = running_var
             v = self.v.to(dtype=x_type).reshape(1, -1, 1, 1)
             d = x * v + (x.var(dim=(2, 3), unbiased=False, keepdim=True) + self.eps).sqrt().to(dtype=x_type)
             d = d.max((var + self.eps).sqrt().to(dtype=x_type))