torch.nn.functional.fractional_max_pool3d¶
- torch.nn.functional.fractional_max_pool3d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)¶
Applies 3D 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 \(kT \times kH \times kW\) 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 \(k \times k \times k\)) or a tuple (kT, kH, kW)
output_size – the target output size of the form \(oT \times oH \times oW\). Can be a tuple (oT, oH, oW) or a single number \(oH\) for a cubic output \(oH \times oH \times oH\)
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 tomax_unpool3d().
- Shape:
Input: \((N, C, T_{in}, H_{in}, W_{in})\) or \((C, T_{in}, H_{in}, W_{in})\).
Output: \((N, C, T_{out}, H_{out}, W_{out})\) or \((C, T_{out}, H_{out}, W_{out})\), where \((T_{out}, H_{out}, W_{out})=\text{output\_size}\) or \((T_{out}, H_{out}, W_{out})=\text{output\_ratio} \times (T_{in}, H_{in}, W_{in})\)
- Examples::
>>> input = torch.randn(20, 16, 50, 32, 16) >>> # pool of cubic window of size=3, and target output size 13x12x11 >>> F.fractional_max_pool3d(input, 3, output_size=(13, 12, 11)) >>> # pool of cubic window and target output size being half of input size >>> F.fractional_max_pool3d(input, 3, output_ratio=(0.5, 0.5, 0.5))
