Diff between linear vs con2d

Can anyone explain the diff between nn.Linear(in_features, out_features) vs nn.conv2d(in_channels, out_channels, kernel_size)

I believe the kernel_size is easy to understand as the size of the kernel window to compute content for next layer.

is the in_features same as in_channels, and out_features same as out_channels?

Thank you

From a high level point of view you could probably say so.
However, the operations in a convolution and linear layer are different regarding the connectivity etc.

Have a look at CS231n - Convolution for more information about conv layers.