Hi,
I have pruned convolutional weights groupwise like in condenseNet. Right now, the convolutional weights in each of the layer of my model are [[0.5, 0, 0, 0], [0,0.7,0,0], [0,0,0.25,0], [0,0,0,0.8]](groups = 4 is applied). I want to store the group convolutions weight as [[0.5, 0.7, 0.25, 0.8]] and store it in model.weight.dict(). So that i can deploy  these weights back in normal convolutional model architecture with groups=4 to validate my pruning model. How to implement this storage operation on all convolutional layers of the model if i am using nn.sequential to store the features?