If you don’t want to create a “temporal” dimension, you could reshape the activation such that the sequence length would be 1, the batch size would stay the same, and all other activation values would be the features via:
out = ... # assuming it has the shape `[batch_size, nb_filters, height, width]
out = out.view(1, out.size(0), -1) # [1, batch_size, nb_filters*height*width]

However, without a temporal dimension, I’m not sure if an RNN would make much sense.
Could you explain your use case a bit?