rename fixed weights to pool_weights in AvgPool2dWithConv
@@ -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
