Make evonorm variables 1d to match other PyTorch norm layers, will break weight compat for any existing use (likely minimal, easy to fix).

timm/models/layers/evo_norm.py

@@ -21,12 +21,10 @@ class EvoNormBatch2d(nn.Module):
         self.apply_act = apply_act  # apply activation (non-linearity)
         self.momentum = momentum
         self.eps = eps
-        param_shape = (1, num_features, 1, 1)
-        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
-        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
-        if apply_act:
-            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
-        self.register_buffer('running_var', torch.ones(1, num_features, 1, 1))
+        self.weight = nn.Parameter(torch.ones(num_features), requires_grad=True)
+        self.bias = nn.Parameter(torch.zeros(num_features), requires_grad=True)
+        self.v = nn.Parameter(torch.ones(num_features), requires_grad=True) if apply_act else None
+        self.register_buffer('running_var', torch.ones(num_features))
         self.reset_parameters()
 
     def reset_parameters(self):
@@ -38,20 +36,21 @@ class EvoNormBatch2d(nn.Module):
     def forward(self, x):
         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]
-            self.running_var.copy_(
-                var.detach() * self.momentum * (n / (n - 1)) + self.running_var * (1 - self.momentum))
+            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 = self.running_var
+            var = running_var
 
-        if self.apply_act:
-            v = self.v.to(dtype=x_type)
+        if self.v is not None:
+            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))
             x = x / d
-        return x * self.weight + self.bias
+        return x * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)
 
 
 class EvoNormSample2d(nn.Module):
@@ -60,11 +59,9 @@ class EvoNormSample2d(nn.Module):
         self.apply_act = apply_act  # apply activation (non-linearity)
         self.groups = groups
         self.eps = eps
-        param_shape = (1, num_features, 1, 1)
-        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
-        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
-        if apply_act:
-            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
+        self.weight = nn.Parameter(torch.ones(num_features), requires_grad=True)
+        self.bias = nn.Parameter(torch.zeros(num_features), requires_grad=True)
+        self.v = nn.Parameter(torch.ones(num_features), requires_grad=True) if apply_act else None
         self.reset_parameters()
 
     def reset_parameters(self):
@@ -77,9 +74,9 @@ class EvoNormSample2d(nn.Module):
         _assert(x.dim() == 4, 'expected 4D input')
         B, C, H, W = x.shape
         _assert(C % self.groups == 0, '')
-        if self.apply_act:
-            n = x * (x * self.v).sigmoid()
+        if self.v is not None:
+            n = x * (x * self.v.view(1, -1, 1, 1)).sigmoid()
             x = x.reshape(B, self.groups, -1)
             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
+        return x * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)