# AvgPool3d¶

class dragon.vm.torch.nn.AvgPool3d(
kernel_size,
stride=1,
ceil_mode=False
)[source]

Apply the 3d 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.AvgPool3d(2, 2)
x = torch.ones(2, 2, 2, 2, 2)
y = m(x)


## __init__¶

AvgPool3d.__init__(
kernel_size,
stride=1,
Create a AvgPool3d module.