# What does 'input planes' mean in the phrase 'input signal/image composed of several input planes'?

PyTorch documentation provided the following descriptions to the Convolution layers

nn.Conv1d              Applies a 1D convolution over an input signal composed of several input planes.

nn.Conv2d              Applies a 2D convolution over an input signal composed of several input planes.

nn.Conv3d              Applies a 3D convolution over an input signal composed of several input planes.

nn.ConvTranspose1d     Applies a 1D transposed convolution operator over an input image composed of several input planes.

nn.ConvTranspose2d     Applies a 2D transposed convolution operator over an input image composed of several input planes.

nn.ConvTranspose3d     Applies a 3D transposed convolution operator over an input image composed of several input planes.


If you observe the descriptions on the right side. Each description is of the form "Applies an operation over an input signal/image composed of several input planes." It is not just confined to Convolution layers, same phrase has been used for several other layers including pooling layers and a normalization layer.

I have doubt with the word "input planes" used here.

What is the meaning of the input plane used here? Does it refer geometrical plane or some other?

So, why don't just use channels instead of input planes? Well, initially the major deep learning applications were used for computer vision or image processing approaches. In CV or image processing, each one of the components of the third dimension of an image tensor is called channel, so an image $$I$$ would be $$I:H \times W \times C$$ where $$C$$ is the number of channels (usually: $$C=3$$, RGB, or $$C=4$$, RGBA). So using the traditional terminology the last dimension of a tensor would be called channels. However this terminology is highly coupled to image processing because it assumes the 3D tensor you are processing is an image.