cleanup

utils/layers.py

@@ -42,7 +42,7 @@ class WeightedFeatureFusion(nn.Module):  # weighted sum of 2 or more layers http
         self.weight = weight  # apply weights boolean
         self.n = len(layers) + 1  # number of layers
         if weight:
-            self.w = torch.nn.Parameter(torch.zeros(self.n), requires_grad=True)  # layer weights
+            self.w = nn.Parameter(torch.zeros(self.n), requires_grad=True)  # layer weights
 
     def forward(self, x, outputs):
         # Weights
@@ -83,7 +83,7 @@ class MixConv2d(nn.Module):  # MixConv: Mixed Depthwise Convolutional Kernels ht
             a[0] = 1
             ch = np.linalg.lstsq(a, b, rcond=None)[0].round().astype(int)  # solve for equal weight indices, ax = b
 
-        self.m = nn.ModuleList([torch.nn.Conv2d(in_channels=in_ch,
+        self.m = nn.ModuleList([nn.Conv2d(in_channels=in_ch,
                                           out_channels=ch[g],
                                           kernel_size=k[g],
                                           stride=stride,