AdaptiveAvgPool2d

class torch.nn.AdaptiveAvgPool2d(output_size)

Applies a 2D adaptive average pooling over an input signal composed of several input planes.

The output is of size 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]]]) – the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input.

Shape:

• Input: $(N,C,H_{in},W_{in})$ or $(C,H_{in},W_{in})$.

• Output: $(N,C,S_0,S_1)$ or $(C,S_0,S_1)$, where $S=\text{output\_size}$.

Examples

>>> # target output size of 5x7
>>> m = nn.AdaptiveAvgPool2d((5, 7))
>>> input = torch.randn(1, 64, 8, 9)
>>> output = m(input)
>>> # target output size of 7x7 (square)
>>> m = nn.AdaptiveAvgPool2d(7)
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)
>>> # target output size of 10x7
>>> m = nn.AdaptiveAvgPool2d((None, 7))
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)

forward(input)

Runs the forward pass.

Return type

Tensor