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# ConvTranspose3d

class torch.ao.nn.quantized.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None)

  Applies a 3D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see `ConvTranspose3d`.

  Note

  Currently only the FBGEMM engine is implemented. Please, set the torch.backends.quantized.engine = ‘fbgemm’

  For special notes, please, see `Conv3d`

  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 `ConvTranspose3d` for other attributes.

  Examples:

      >>> torch.backends.quantized.engine = 'fbgemm'
      >>> from torch.ao.nn import quantized as nnq
      >>> # With cubic kernels and equal stride
      >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
      >>> # non-cubic kernels and unequal stride and with padding
      >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
      >>> input = torch.randn(20, 16, 50, 100, 100)
      >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
      >>> output = m(q_input)
      >>> # exact output size can be also specified as an argument
      >>> input = torch.randn(1, 16, 12, 12, 12)
      >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
      >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
      >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
      >>> h = downsample(q_input)
      >>> h.size()
      torch.Size([1, 16, 6, 6, 6])
      >>> output = upsample(h, output_size=input.size())
      >>> output.size()
      torch.Size([1, 16, 12, 12, 12])

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