How to implement SegNet with preserving max-indexes in Keras

Question

I'm trying to implement SegNet in Keras (tf backend) to do semantic segmentation.

The most impressived trick of SgeNet is to pass max-pooling indices to the upsampling layers. However, there are many implementations of SegNet in Keras(e.g.) I find on github just using simple UpSampling (called SegNet-Basic).

I notice that it can be achieved in Tensorflow with " tf.nn.max_pool_with_argmax ". So I want to know is there any similar method to get the max-pooling indices and put them back in upsampling in Keras.

Thanks in advance.

Answer 1

Well, I think I've found the answer.