thank you @ptrblck

i found the link above only can show the results of Cov2d (H * W) , and my case is Cov1d.

of course, here is my use case:

i have a representation with size (20, 1, 4 * 200) **(batch_size, in_channels, in_length)**, and i want to convolute it with the out_channels 256, kernel size 2, stride 200, and *dilation should be 2 * 200 (i know the error happens here)*, the output tensorâ€™s size should be (20, 256, 2) **(batch_size, out_channels, out_length)**

if change the dilation to be 2 * 200

F.conv1d((20, 1, 4*200), (256, 1, 2)), stride=200, padding=0, *dilation=2 * 200*)

if got the tensor with size (20, 256, 2), i think this should be same with

F.conv1d((20, 200, 4), (256, 200, 2), stride=1, padding=0, dilation=2)

right ?