class, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)[source]

Applies 2D average-pooling operation in kH×kWkH \times kW regions by step size sH×sWsH \times sW steps. The number of output features is equal to the number of input planes.


The input quantization parameters propagate to the output.

See AvgPool2d for details and output shape.

  • input – quantized input tensor (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW)

  • kernel_size – size of the pooling region. Can be a single number or a tuple (kH, kW)

  • stride – stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default: kernel_size

  • padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0

  • ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape. Default: False

  • count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: True

  • divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None


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