torch.nn.functional.fractional_max_pool2d

torch.nn.functional.fractional_max_pool2d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)

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

Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham

The max-pooling operation is applied in $kH \times kW$ regions by a stochastic step size determined by the target output size. The number of output features is equal to the number of input planes.

Parameters:

• kernel_size – the size of the window to take a max over. Can be a single number $k$ (for a square kernel of $k \times k$) or a tuple (kH, kW)

• output_size – the target output size of the image of the form $oH \times oW$. Can be a tuple (oH, oW) or a single number $oH$ for a square image $oH \times oH$

• output_ratio – If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1)

• return_indices – if True, will return the indices along with the outputs. Useful to pass to max_unpool2d().

Examples::

>>> input = torch.randn(20, 16, 50, 32)
>>> # pool of square window of size=3, and target output size 13x12
>>> F.fractional_max_pool2d(input, 3, output_size=(13, 12))
>>> # pool of square window and target output size being half of input image size
>>> F.fractional_max_pool2d(input, 3, output_ratio=(0.5, 0.5))