--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py +++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py @@ -16,10 +16,6 @@ If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides for :attr:`padding` number of points. :attr:`dilation` controls the spacing between the kernel points. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. - - Note: - When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding - or the input. Sliding windows that would start in the right padded region are ignored. The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be: @@ -61,12 +57,8 @@ https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ - kernel_size: _size_2_t - stride: _size_2_t - padding: _size_2_t - dilation: _size_2_t - - def forward(self, input: Tensor) -> Tensor: + @weak_script_method + def forward(self, input): return F.max_pool2d(input, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices)