Feature maps from network for multiple images all the same

[Martin]

I'm trying to extract the feature maps from a network, similar to what is done here, just for a list of images instead of just one. My code looks something like this:

net = caffe.Net(prototxt, weights, caffe.TEST)
# ...
for img_idx, img_path in enumerate(list_of_image_paths):
    img = caffe.io.load_image(img_path)
    net.blobs['data'].data[...] = transformer.preprocess('data', img)
    net.forward()

    #extract filters and features
    for layer, _ in net.blobs.iteritems():
        if img_idx == 0:
            filters = net.params[l][0].data.copy()
            save_filters(filters, layer) #saves filters for all units
        features = net.blobs[l].data.copy()
        save_features(features, layer, img_idx) #saves featuremaps for all units

This works however the feature maps remain the same for every image. Do I need to somehow reset the network inbetween or how do I accomplish this? 

I've also asked this on Stackoverflow (link)