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# Conv3d

class torch.ao.nn.quantized.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)

  Applies a 3D convolution over a quantized input signal composed of several quantized input planes.

  For details on input arguments, parameters, and implementation see `Conv3d`.

  Note

  Only zeros is supported for the `padding_mode` argument.

  Note

  Only torch.quint8 is supported for the input data type.

  Variables:

    - weight (Tensor) – packed tensor derived from the learnable weight parameter.

    - scale (Tensor) – scalar for the output scale

    - zero_point (Tensor) – scalar for the output zero point

  See `Conv3d` for other attributes.

  Examples:

      >>> # With square kernels and equal stride
      >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
      >>> # non-square kernels and unequal stride and with padding
      >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
      >>> # non-square kernels and unequal stride and with padding and dilation
      >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
      >>> input = torch.randn(20, 16, 56, 56, 56)
      >>> # quantize input to quint8
      >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
      >>> output = m(q_input)

  classmethod from_float(mod)

    Creates a quantized module from a float module or qparams_dict.

    Parameters:

      mod (Module) – a float module, either produced by torch.ao.quantization utilities or provided by the user

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