rename fixed weights to pool_weights in AvgPool2dWithConv

braindecode/torch_ext/modules.py

@@ -52,7 +52,10 @@ class AvgPool2dWithConv(torch.nn.Module):
         self.kernel_size = kernel_size
         self.stride = stride
         self.dilation = dilation
-        self.weights = None
+        # don't name them "weights" to
+        # make sure these are not accidentally used by some procedure
+        # that initializes parameters or something
+        self._pool_weights = None
 
     def forward(self, x):
         # Create weights for the convolution on demand:
@@ -60,11 +63,11 @@ class AvgPool2dWithConv(torch.nn.Module):
         in_channels = x.size()[1]
         weight_shape = (in_channels, 1,
                         self.kernel_size[0], self.kernel_size[1])
-        if self.weights is None or (
-                (tuple(self.weights.size()) != tuple(weight_shape)) or (
-                  self.weights.is_cuda != x.is_cuda
+        if self._pool_weights is None or (
+                (tuple(self._pool_weights.size()) != tuple(weight_shape)) or (
+                  self._pool_weights.is_cuda != x.is_cuda
                 ) or (
-                    self.weights.data.type() != x.data.type()
+                    self._pool_weights.data.type() != x.data.type()
                 )):
             n_pool = np.prod(self.kernel_size)
             weights = np_to_var(
@@ -72,9 +75,9 @@ class AvgPool2dWithConv(torch.nn.Module):
             weights = weights.type_as(x)
             if x.is_cuda:
                 weights = weights.cuda()
-            self.weights = weights
+            self._pool_weights = weights
 
-        pooled = F.conv2d(x, self.weights, bias=None, stride=self.stride,
+        pooled = F.conv2d(x, self._pool_weights, bias=None, stride=self.stride,
                           dilation=self.dilation,
                           groups=in_channels,)
         return pooled