avg_pool3d — PyTorch 2.7 documentation (original) (raw)

class torch.ao.nn.quantized.functional.avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)[source][source]¶

Applies 3D average-pooling operation in kD timeskH×kWkD \ times kH \times kW regions by step sizesD×sH×sWsD \times 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.

Parameters

• 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 (kD, kH, kW)
• stride – stride of the pooling operation. Can be a single number or a tuple (sD, sH, sW). Default: kernel_size
• padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padD, 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