Apply a 2D adaptive maximum pooling over an input signal composed of several input planes.

Parameters:
• input (`Union`[`Array`, `NativeArray`]) – Input array. Must have shape (N, C, H_in, W_in) or (C, H_in, W_in) where N is the batch dimension, C is the feature dimension, and H_in and W_in are the 2 spatial dimensions.

• output_size (`Union`[`Sequence`[`int`], `int`]) – Spatial output size.

Returns:

The result of the pooling operation. Will have shape (N, C, S_0, S_1) or (C, S_0, S_1), where S = output_size

Apply a 2D adaptive maximum pooling over an input signal composed of several input planes.

Parameters:
• self (`Array`) – Input array. Must have shape (N, C, H_in, W_in) or (C, H_in, W_in) where N is the batch dimension, C is the feature dimension, and H_in and W_in are the 2 spatial dimensions.

• output_size (`Union`[`Sequence`[`int`], `int`]) – Spatial output size.

Return type:

`Array`

Returns:

• The result of the pooling operation. Will have shape (N, C, S_0, S_1) or

• (C, S_0, S_1), where S = output_size

Container.adaptive_max_pool2d(self, output_size, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Apply a 2D adaptive maximum pooling over an input signal composed of several input planes.

Parameters:
• self (`Container`) – Input container.

• output_size (`Union`[`int`, `Container`]) – Spatial output size.

Return type:

`Container`

Returns:

The result of the pooling operation.