Update modeling_super_linear.py

modeling_super_linear.py

@@ -293,40 +293,40 @@ class SparseNoisyMoE(nn.Module):
             self.gating_network = nn.Linear(input_dim, self.num_experts, bias=True)
 
     def get_periodogram(self, inputs, ker_len=50, con=1, n=10000):
-            if inputs.dim() == 2:
-                x_0 = inputs.unsqueeze(2)
-            else:
-                x_0 = inputs
-            x_0 = x_0 - torch.mean(x_0, dim=1, keepdim=True)
-    
-            v = torch.arange(0, n) / n
-            if con:
-                if ker_len is None:
-                    ker_len = n // 4
-                    ker_len = min(ker_len, 50)
-    
-                x_0 = x_0.permute(0, 2, 1)
-                ker = (torch.ones(1, 1, ker_len) / ker_len).to(x_0.device)
-                x_c = F.conv1d(x_0, ker, padding="same")
-                x_c[:, :, :ker_len // 2] = x_c[:, :, ker_len // 2:ker_len // 2 + 1]
-                x_c[:, :, -ker_len // 2:] = x_c[:, :, -ker_len // 2 - 1:-ker_len // 2]
-                x_0 = x_0 - x_c
-                x_0 = x_0.permute(0, 2, 1)
-    
-            dft = torch.fft.fft(x_0, dim=1, n=n) / np.sqrt(n)
-            dft = dft[:, :n//2, :]
-            I = torch.abs(dft) ** 2
-    
-            I_sum = torch.sum(I, dim=1, keepdim=True)
-            I_sum[I_sum == 0] = 1
-            I = I / I_sum
-    
-            if torch.any(I_sum == 0):
-                print("Zeros in the sum")
-                raise ValueError
-    
-            if inputs.dim() == 2:
-                I = I.squeeze(2)
+        if inputs.dim() == 2:
+            x_0 = inputs.unsqueeze(2)
+        else:
+            x_0 = inputs
+        x_0 = x_0 - torch.mean(x_0, dim=1, keepdim=True)
+
+        v = torch.arange(0, n) / n
+        if con:
+            if ker_len is None:
+                ker_len = n // 4
+                ker_len = min(ker_len, 50)
+
+            x_0 = x_0.permute(0, 2, 1)
+            ker = (torch.ones(1, 1, ker_len) / ker_len).to(x_0.device)
+            x_c = F.conv1d(x_0, ker, padding="same")
+            x_c[:, :, :ker_len // 2] = x_c[:, :, ker_len // 2:ker_len // 2 + 1]
+            x_c[:, :, -ker_len // 2:] = x_c[:, :, -ker_len // 2 - 1:-ker_len // 2]
+            x_0 = x_0 - x_c
+            x_0 = x_0.permute(0, 2, 1)
+
+        dft = torch.fft.fft(x_0, dim=1, n=n) / np.sqrt(n)
+        dft = dft[:, :n//2, :]
+        I = torch.abs(dft) ** 2
+
+        I_sum = torch.sum(I, dim=1, keepdim=True)
+        I_sum[I_sum == 0] = 1
+        I = I / I_sum
+
+        if torch.any(I_sum == 0):
+            print("Zeros in the sum")
+            raise ValueError
+
+        if inputs.dim() == 2:
+            I = I.squeeze(2)
 
 
     def fourier_interp_dim1(self,x, target_len: int = 512):