Conv2d¶
- class
dragon.vm.torch.nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True
)[source]¶ Apply the 2d convolution.
The spatial output dimension is computed as:
\[\begin{cases} \text{DK}_{size} = dilation * (\text{K}_{size} - 1) + 1 \\ \text{Dim}_{out} = (\text{Dim}_{in} + 2 * pad - \text{DK}_{size}) / stride + 1 \end{cases} \]Examples:
m = torch.nn.Conv2d(2, 3, 3, padding=1) x = torch.ones(2, 2, 4, 4) y = m(x)
__init__¶
Conv2d.__init__(
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True
)[source]¶Create a
Conv2dmodule.- 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 kernel.
- stride (Union[int, Sequence[int]], optional, default=1) – The stride of sliding window.
- padding (Union[int, Sequence[int]], optional, default=0) – The zero-padding size.
- dilation (Union[int, Sequence[int]], optional, default=1) – The rate of dilated convolution kernel.
- groups (int, optional, default=1) – The number of groups to split input channels.
- bias (bool, optional, default=True) – True to add a bias on the output.