class dragon.vm.torch.nn.AdaptiveAvgPool3d(output_size)[source]

Apply the 3d adaptive average pooling.

This module excepts the input size $$(N, C, D, H, W)$$, and output size is $$(N, C, D_{\text{out}}, H_{\text{out}}, W_{\text{out}})$$, where $$N$$ is the batch size, $$C$$ is the number of channels, $$D$$, $$H$$ and $$W$$ are the depth, height and width of data.

Examples:

m = torch.nn.AdaptiveAvgPool3d(1)
x = torch.ones(2, 2, 2, 2, 2)
y = m(x)


## __init__¶

AdaptiveAvgPool3d.__init__(output_size)[source]

Create a AdaptiveAvgPool3d module.

Parameters:
• output_size (Union[int, Sequence[int]]) – The target output size.