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LazyConvTranspose2d
class torch.nn.LazyConvTranspose2d(out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None)
[source]-
A
torch.nn.ConvTranspose2d
module with lazy initialization of thein_channels
argument of theConvTranspose2d
that is inferred from theinput.size(1)
. The attributes that will be lazily initialized areweight
andbias
.Check the
torch.nn.modules.lazy.LazyModuleMixin
for further documentation on lazy modules and their limitations.- Parameters
-
- out_channels (int) – Number of channels produced by the convolution
- kernel_size (int or tuple) – Size of the convolving kernel
- stride (int or tuple, optional) – Stride of the convolution. Default: 1
- padding (int or tuple, optional) –
dilation * (kernel_size - 1) - padding
zero-padding will be added to both sides of each dimension in the input. Default: 0 - output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0
- groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
- bias (bool, optional) – If
True
, adds a learnable bias to the output. Default:True
- dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
cls_to_become
-
alias of
ConvTranspose2d
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https://pytorch.org/docs/2.1/generated/torch.nn.LazyConvTranspose2d.html