This operation applies 2D adaptive max pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus on the output size.

\begin{align}\begin{aligned}hstart &= floor(i * H_{in} / H_{out})\\hend &= ceil((i + 1) * H_{in} / H_{out})\\wstart &= floor(j * W_{in} / W_{out})\\wend &= ceil((j + 1) * W_{in} / W_{out})\\Output(i ,j) &= max(Input[hstart:hend, wstart:wend])\end{aligned}\end{align}
Parameters
• output_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.

• return_mask (bool, optional) – If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.

• name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Shape:
• x(Tensor): The input tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type can be float32, float64.

• output(Tensor): The output tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type is same as input x.

Returns

Examples

>>> # adaptive max pool2d
>>> # suppose input data in shape of [N, C, H, W], output_size is [m, n],
>>> # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
>>> # of input data into m * n grids averagely and performs poolings in each
>>> # grid to get output.
>>> # adaptive max pool performs calculations as follow:
>>> #
>>> #     for i in range(m):
>>> #         for j in range(n):
>>> #             hstart = floor(i * H / m)
>>> #             hend = ceil((i + 1) * H / m)
>>> #             wstart = floor(i * W / n)
>>> #             wend = ceil((i + 1) * W / n)
>>> #             output[:, :, i, j] = max(input[:, :, hstart: hend, wstart: wend])
>>> #

>>> x = paddle.rand([2, 3, 32, 32])

>>> pool_out, indices = adaptive_max_pool(x = x)
>>> print(pool_out.shape)
[2, 3, 3, 3]
>>> print(indices.shape)
[2, 3, 3, 3]

forward ( x )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

extra_repr ( )

Extra representation of this layer, you can have custom implementation of your own layer.