Currently, AvgPool1d doesn’t support dilation at all. While MaxPool1d do support dilation, it’s limited by:
In [6]: MaxPool1d(2,stride=1,padding=2,dilation=2)(torch.rand(2,4,8))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 MaxPool1d(2,stride=1,padding=2,dilation=2)(torch.rand(2,4,8))
File ~/p10/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File ~/p10/lib/python3.10/site-packages/torch/nn/modules/pooling.py:92, in MaxPool1d.forward(self, input)
91 def forward(self, input: Tensor):
---> 92 return F.max_pool1d(input, self.kernel_size, self.stride,
93 self.padding, self.dilation, ceil_mode=self.ceil_mode,
94 return_indices=self.return_indices)
File ~/p10/lib/python3.10/site-packages/torch/_jit_internal.py:484, in boolean_dispatch.<locals>.fn(*args, **kwargs)
482 return if_true(*args, **kwargs)
483 else:
--> 484 return if_false(*args, **kwargs)
File ~/p10/lib/python3.10/site-packages/torch/nn/functional.py:696, in _max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)
694 if stride is None:
695 stride = torch.jit.annotate(List[int], [])
--> 696 return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)
RuntimeError: max_pool1d() padding should be at most half of kernel size, but got padding=2 and kernel_size=2
Which I think do makes sense since adding dilation will “eat” more output, which should allow more padding as compensation.
BTW, it would even be better to allow one sided padding (instead of both sided).