I am working on video classification for motion recognition.
I selected 10 frame from video and applied optical flow these sequantial frames.
After that, I found x and y  direction of each flows and stack them together.
Finally, I have 3D matrix with shape H x W x 20.
H = height
W = weight
20 = flows from 10 frame (2 times because of x and y dimension of optical flow).
So, I want to apply 3D convolution layer this matrix above.
When I look at pytorch documentation for 3D convolution, I saw 5 dimensional input like that (N,C,D,H,W).
But my input is 4D dimensional like that (N,C,H,W) with N samples.
So, How can I apply 3D convolutional to my matrix?