FractionalMaxPool2d — PyTorch 2.7 documentation (original) (raw)

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

Note

Exactly one of output_size or output_ratio must be defined.

Parameters

• kernel_size (Union[_int,_ tuple[_int,_ int] ]) – 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 (Union[_int,_ tuple[_int,_ int] ]) – 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. Note that we must have kH+oH−1<=HinkH + oH - 1 <= H_{in} and kW+oW−1<=WinkW + oW - 1 <= W_{in}
• output_ratio (Union[_float,_ tuple[_float,_ float] ]) – 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). Note that we must have kH+(output_ratio_H∗Hin)−1<=HinkH + (output\_ratio\_H * H_{in}) - 1 <= H_{in}and kW+(output_ratio_W∗Win)−1<=WinkW + (output\_ratio\_W * W_{in}) - 1 <= W_{in}
• return_indices (bool) – if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool2d(). Default: False

Shape:

• 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}).

Examples

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)