MaxPool2d

class dragon.vm.torch.nn.MaxPool2d(
  kernel_size,
  stride=1,
  padding=0,
  ceil_mode=False
)[source]

Apply the 2d max pooling.

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

Examples:

m = torch.nn.MaxPool2d(2, 2)
x = torch.ones(2, 2, 2, 2)
y = m(x)

__init__

MaxPool2d.__init__(
  kernel_size,
  stride=1,
  padding=0,
  ceil_mode=False
)[source]

Create a MaxPool2d module.

Parameters:
  • kernel_size (Union[int, Sequence[int]]) – The size of pooling window.
  • stride (Union[int, Sequence[int]], optional, default=1) – The stride of pooling window.
  • padding (Union[int, Sequence[int]], optional, default=0) – The zero padding size.
  • ceil_mode (bool, optional, default=False) – Ceil or floor the boundary.