pytorch 참고

https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html

CLASS torch.nn.Conv2d(in_channels: int, out_channels: int, kernel_size: Union[T, Tuple[T, T]], stride: Union[T, Tuple[T, T]] = 1, padding: Union[T, Tuple[T, T]] = 0, dilation: Union[T, Tuple[T, T]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros')

• stride : controls the stride for the cross-correlation, a single number or a tuple.
• padding : controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension.
• dilation : controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what dilation does.
• groups : controls the connections between inputs and outputs. in_channels and out_channels must both be divisible by groups. For example,
  • At groups=1, all inputs are convolved to all outputs.
  • At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
  • At groups= in_channels, each input channel is convolved with its own set of filters, of size: \left\lfloor\frac{out_channels}{in_channels}\right\rfloor⌊ in_channels out_channels