Conv3d¶

class dragon.vm.torch.nn.Conv3d( in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True )[source]¶

Apply the 3d convolution.

This module excepts the input size \((N, C_{\text{in}}, D, H, W)\), and output size is \((N, C_{\text{out}}, D_{\text{out}}, H_{\text{out}}, W_{\text{out}})\), where \(N\) is the batch size, \(C\) is the number of channels, \(D\), \(H\) and \(W\) are the depth, height and width of data.

Examples:

m = torch.nn.Conv3d(2, 3, 3, padding=1)
x = torch.ones(2, 2, 2, 2, 2)
y = m(x)

init¶

Conv3d.__init__( in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True )[source]¶

Create a Conv3d 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.
dilation (Union[int, Sequence[int]], optional, default=1) – The rate of dilated convolution.
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.

Conv3d¶

__init__¶

init¶