3D depthwise separable convolution

Hi all,

I try to implement a depthwise separable convolution as described in the Xception paper for 3D input data (batch size, channels, x, y, z). Is the following class correct or am I missing something?

class DepthwiseSeparableConv(nn.Module):
  def __init__(self, in_channels, out_channels):
    super(DepthwiseSeparableConv, self).__init__()
    self.depthwise = nn.Conv3d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=in_channels, bias=False, padding_mode='zeros')
    self.pointwise = nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=False)
    self.bn = nn.BatchNorm3d(num_features=out_channels)

  def forward(self, x):
    out = self.depthwise(x)
    out = self.pointwise(out)
    return self.bn(out)

I think it might be necessary to additionally add a parameter for the number of kernels according to this example. But what would the number of kernels be then? I could not read something about this in the paper.