AdaptiveMaxPool2d

class torch.nn.AdaptiveMaxPool2d(output_size, return_indices=False)

Applies a 2D adaptive max pooling over an input signal composed of several input planes.

The output is of size \(H_{out} \times W_{out}\), for any input size. The number of output features is equal to the number of input planes.

Parameters:

• output_size (Union[int, None, Tuple[Optional[int], Optional[int]]]) – the target output size of the image of the form \(H_{out} \times W_{out}\). Can be a tuple \((H_{out}, W_{out})\) or a single \(H_{out}\) for a square image \(H_{out} \times H_{out}\). \(H_{out}\) and \(W_{out}\) can be either a int, or None which means the size will be the same as that of the input.

• return_indices (bool) – if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool2d. Default: False

Shape:

• Input: \((N, C, H_{in}, W_{in})\) or \((C, H_{in}, W_{in})\).

• Output: \((N, C, H_{out}, W_{out})\) or \((C, H_{out}, W_{out})\), where \((H_{out}, W_{out})=\text{output\_size}\).

Examples

>>> # target output size of 5x7
>>> m = nn.AdaptiveMaxPool2d((5, 7))
>>> input = torch.randn(1, 64, 8, 9)
>>> output = m(input)
>>> # target output size of 7x7 (square)
>>> m = nn.AdaptiveMaxPool2d(7)
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)
>>> # target output size of 10x7
>>> m = nn.AdaptiveMaxPool2d((None, 7))
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)