How to use 'same' padding for maxpool1d

Question

Tensorflow tf.keras.layers.MaxPool1D has the option to set padding='same' to make the input shape the same as the output shape. Is there something equivalent for torch.nn.MaxPool1d ? I see that torch.nn.Conv1d has the option to set padding='same', but this option seems to be missing from maxpool. What is the current workaround for this?

Answer 1

Note that the output of the keras version is only really the same shape as the input whenever you use it with stride and dilation set to 1, so I'll assume the same parameters in this answer.

For any uneven kernel size, this is quite easily achievable in PyTorch by setting the padding to (kernel_size - 1)/2.

For an even kernel size, both sides of the input need to be padded by a different amount, and this seems not possible in the current implementation of MaxPool1d.