Blur filter no longer a buffer

timm/models/layers/blurpool.py

@@ -40,18 +40,16 @@ class BlurPool2d(nn.Module):
 
         blur_matrix = (np.poly1d((0.5, 0.5)) ** (blur_filter_size - 1)).coeffs
         blur_filter = torch.Tensor(blur_matrix[:, None] * blur_matrix[None, :])
-        # FIXME figure a clean hack to prevent the filter from getting saved in weights, but still
-        # plays nice with recursive module apply for fn like .cuda(), .type(), etc -RW
-        self.register_buffer('blur_filter', blur_filter[None, None, :, :].repeat((self.channels, 1, 1, 1)))
+        self.blur_filter = blur_filter[None, None, :, :]
+
+    def _apply(self, fn):
+        # override nn.Module _apply to prevent need for blur_filter to be registered as a buffer,
+        # this keeps it out of state dict, but allows .cuda(), .type(), etc to work as expected
+        super(BlurPool2d, self)._apply(fn)
+        self.blur_filter = fn(self.blur_filter)
 
     def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:  # type: ignore
-        if not torch.is_tensor(input_tensor):
-            raise TypeError("Input input type is not a torch.Tensor. Got {}".format(type(input_tensor)))
-        if not len(input_tensor.shape) == 4:
-            raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}".format(input_tensor.shape))
-        # apply blur_filter on input
         return F.conv2d(
             self.padding(input_tensor),
-            self.blur_filter.type(input_tensor.dtype),
-            stride=self.stride,
-            groups=input_tensor.shape[1])
+            self.blur_filter.type(input_tensor.dtype).expand(C, -1, -1, -1),
+            stride=self.stride, groups=input_tensor.shape[1])