avg_pool2d

class torch.ao.nn.quantized.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) [source]

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

Note

The input quantization parameters propagate to the output.

See AvgPool2d for details and output shape.

Parameters

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