Fix a silly bug in Sample version of EvoNorm missing x* part of swish, update EvoNormBatch to accumulated unbiased variance.

timm/models/layers/evo_norm.py

@@ -38,13 +38,15 @@ class EvoNormBatch2d(nn.Module):
         x_type = x.dtype
         if self.training:
             var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
-            self.running_var.copy_(self.momentum * var.detach() + (1 - self.momentum) * self.running_var)
+            n = x.numel() / x.shape[1]
+            self.running_var.copy_(
+                var.detach() * self.momentum * (n / (n - 1)) + self.running_var * (1 - self.momentum))
         else:
             var = self.running_var
 
         if self.apply_act:
             v = self.v.to(dtype=x_type)
-            d = (x * v) + (x.var(dim=(2, 3), unbiased=False, keepdim=True) + self.eps).sqrt().to(dtype=x_type)
+            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))
             x = x / d
         return x * self.weight + self.bias
@@ -74,8 +76,8 @@ class EvoNormSample2d(nn.Module):
         B, C, H, W = x.shape
         assert C % self.groups == 0
         if self.apply_act:
-            n = (x * self.v).sigmoid().reshape(B, self.groups, -1)
+            n = x * (x * self.v).sigmoid()
             x = x.reshape(B, self.groups, -1)
-            x = n / (x.var(dim=-1, unbiased=False, keepdim=True) + self.eps).sqrt()
+            x = n.reshape(B, self.groups, -1) / (x.var(dim=-1, unbiased=False, keepdim=True) + self.eps).sqrt()
             x = x.reshape(B, C, H, W)
         return x * self.weight + self.bias