AdaptiveAvgPool2d#

classtorch.nn.AdaptiveAvgPool2d(output_size)[source]#

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 aint, orNone which means the size willbe the same as that of the input.

Shape:

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

• Output:$(N,C,S_0,S_1)$(N,C,S0​,S1​) or$(C,S_0,S_1)$(C,S0​,S1​), where$S=\text{output\_size}$S=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)[source]#

Runs the forward pass.

Return type

Tensor