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

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 – 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 – 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