MaxPool2d¶
- class continual.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, temporal_fill='zeros')[source]¶
Applies a Continual 2D max pooling over an input signal composed of several input planes.
In the simplest case, the output value of the layer with input size , output and
kernel_size
can be precisely described as:The pooling over the dimension is continual (progressively cached) and the other is regular. If
padding
is non-zero, then the input is implicitly padded with negative infinity on both sides forpadding
number of points.dilation
controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of whatdilation
does.Note
When stride > 1, the forward_step will only produce non-None values every stride steps.
Note
When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.
The parameters
kernel_size
,stride
,padding
,dilation
can either be:a single
int
– in which case the same value is used for the height and width dimensiona
tuple
of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
- Parameters:
kernel_size (Union[int, Tuple[int, int]]) – the size of the window to take a max over
stride (Union[int, Tuple[int, int]]) – the stride of the window. Default value is
kernel_size
padding (Union[int, Tuple[int, int]]) – implicit zero padding to be added on both sides
dilation (Union[int, Tuple[int, int]]) – a parameter that controls the stride of elements in the window
ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape
temporal_fill (PaddingMode) – How temporal states are initialized.
- Shape:
Input:
Output: , where
Examples:
m = MaxPool2d(3, stride=2) x = torch.randn(20, 16, 50, 32) assert torch.allclose(m.forward(x), m.forward_steps(x))