oneflow.nn.AdaptiveAvgPool3d

class oneflow.nn.AdaptiveAvgPool3d(output_size)

Applies a 3D adaptive average pooling over an input signal composed of several input planes.

The output is of size D x H x W, for any input size. The number of output features is equal to the number of input planes.

Parameters

output_size – the target output size of the form D x H x W. Can be a tuple (D, H, W) or a single number D for a cube D x D x D. D, H and W can be either a int, or None which means the size will be the same as that of the input.

For example:

>>> import numpy as np
>>> import oneflow as flow
>>> import oneflow.nn as nn

>>> m = nn.AdaptiveAvgPool3d((5,7,9))
>>> input = flow.Tensor(np.random.randn(1, 64, 8, 9, 10))
>>> output = m(input)
>>> output.size()
oneflow.Size([1, 64, 5, 7, 9])

>>> m = nn.AdaptiveAvgPool3d(7)
>>> input = flow.Tensor(np.random.randn(1, 64, 10, 9, 8))
>>> output = m(input)
>>> output.size()
oneflow.Size([1, 64, 7, 7, 7])

>>> m = nn.AdaptiveAvgPool3d((7, None, None))
>>> input = flow.Tensor(np.random.randn(1, 64, 10, 9, 8))
>>> output = m(input)
>>> output.size()
oneflow.Size([1, 64, 7, 9, 8])