ConvTranspose3d

class torch.ao.nn.quantized.dynamic.modules.conv.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)

A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.

For details on input arguments, parameters, and implementation see ConvTranspose3d.

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:

>>> # 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))
>>> output = m(input)
>>> # exact output size can be also specified as an argument
>>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
>>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
>>> h = downsample(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])