ConvTranspose2d¶
- class
dragon.vm.torch.nn.
ConvTranspose2d
(
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1
)[source]¶ Apply the 2d deconvolution.
Examples:
m = torch.nn.ConvTranspose2d(2, 3, 2, stride=2) x = torch.ones(2, 2, 1, 1) y = m(x)
__init__¶
ConvTranspose2d.
__init__
(
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1
)[source]¶Create a
ConvTranspose2d
module.- Parameters:
- in_channels (int) – The number of input channels.
- out_channels (int) – The number of output channels.
- kernel_size (Union[int, Sequence[int]]) – The size of convolution window.
- stride (Union[int, Sequence[int]], optional, default=1) – The stride of convolution window.
- padding (Union[int, Sequence[int]], optional, default=0) – The zero padding size.
- output_padding (int, optional, default=1) – The additional size added to the output shape.
- groups (int, optional, default=1) – The number of groups to split channels into.
- bias (bool, optional, default=True) – Add a bias tensor to output or not.
- dilation (Union[int, Sequence[int]], optional, default=1) – The rate of dilated convolution.