- 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 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 (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
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)
return_indices (bool) – if
True, will return the indices along with the outputs. Useful to pass to
Input: or .
Output: or , where or .
>>> # 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)