I’m glad it worked! 
Did you compare the outputs of your Lasagne and Pytorch model? It should be approx. the same up to a tolerance. Just sample a random input with Numpy and call the forward passes on both models.
After you transfer the weights to Pytorch and run a simple prediction, what is the accuracy? Is it random, e.g. ~0.1 for 10 classes?
When you call model.parameters(), do you see all your layers?
Did you try a really low learning rate? Since the network is already pre-trained, the weights should be in a “good” state, so that the lr should be quite low. Could you try lowering the lr by factor 10 or 100 and run it again?
How similar is your new data set to the pre-trained data? Is it sampled from the same distribution (e.g. both are natural images)?