FractionalMaxPool2d

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

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 \(kH \times kW\) 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 \(kH + oH - 1 <= H_{in}\) and \(kW + oW - 1 <= W_{in}\)

• 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 \(kH + (output\_ratio\_H * H_{in}) - 1 <= H_{in}\) and \(kW + (output\_ratio\_W * W_{in}) - 1 <= W_{in}\)

• return_indices (bool) – if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool2d(). Default: False

Shape:

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

• Output: \((N, C, H_{out}, W_{out})\) or \((C, H_{out}, W_{out})\), where \((H_{out}, W_{out})=\text{output\_size}\) or \((H_{out}, W_{out})=\text{output\_ratio} \times (H_{in}, W_{in})\).

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)