ConvTranspose1d

class torch.ao.nn.quantized.ConvTranspose1d(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 1D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see ConvTranspose1d.

Note

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

For special notes, please, see Conv1d

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 ConvTranspose2d for other attributes.

Examples:

>>> torch.backends.quantized.engine = 'qnnpack'
>>> from torch.ao.nn import quantized as nnq
>>> # With square kernels and equal stride
>>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> input = torch.randn(20, 16, 50)
>>> 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)
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1)
>>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
>>> h = downsample(q_input)
>>> h.size()
torch.Size([1, 16, 6])
>>> output = upsample(h, output_size=input.size())
>>> output.size()
torch.Size([1, 16, 12])