class torch.nn.FractionalMaxPool2d(kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)[source]

Applies a 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×kWkH \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.

  • kernel_size – the size of the window to take a max over. Can be a single number k (for a square kernel of k x k) or a tuple (kh, kw)

  • output_size – the target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x 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 nn.MaxUnpool2d(). Default: False

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

  • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) or (C,Hout,Wout)(C, H_{out}, W_{out}), where (Hout,Wout)=output_size(H_{out}, W_{out})=\text{output\_size} or (Hout,Wout)=output_ratio×(Hin,Win)(H_{out}, W_{out})=\text{output\_ratio} \times (H_{in}, W_{in}).


>>> # pool of square window of size=3, and target output size 13x12
>>> m = nn.FractionalMaxPool2d(3, output_size=(13, 12))
>>> # pool of square window and target output size being half of input image size
>>> m = nn.FractionalMaxPool2d(3, output_ratio=(0.5, 0.5))
>>> input = torch.randn(20, 16, 50, 32)
>>> output = m(input)


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