AdaptiveAvgPool3d#

classtorch.nn.AdaptiveAvgPool3d(output_size)[source]#

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 (Union[int,None,tuple[Optional[int],Optional[int],Optional[int]]]) – 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 aint, orNone which means the size willbe the same as that of the input.

Shape:

• Input:$(N,C,D_{in},H_{in},W_{in})$(N,C,Din​,Hin​,Win​) or$(C,D_{in},H_{in},W_{in})$(C,Din​,Hin​,Win​).

• Output:$(N,C,S_0,S_1,S_2)$(N,C,S0​,S1​,S2​) or$(C,S_0,S_1,S_2)$(C,S0​,S1​,S2​),where$S=\text{output\_size}$S=output_size.

Examples

>>># target output size of 5x7x9>>>m=nn.AdaptiveAvgPool3d((5,7,9))>>>input=torch.randn(1,64,8,9,10)>>>output=m(input)>>># target output size of 7x7x7 (cube)>>>m=nn.AdaptiveAvgPool3d(7)>>>input=torch.randn(1,64,10,9,8)>>>output=m(input)>>># target output size of 7x9x8>>>m=nn.AdaptiveAvgPool3d((7,None,None))>>>input=torch.randn(1,64,10,9,8)>>>output=m(input)

forward(input)[source]#

Runs the forward pass.

Return type

Tensor