Calculate coherence for non-gensim topic model

I've built a topic model, with:

• Input: list of tokenized lists
• Output: a m x t matrix (with each cell indicating the probability of word i appearing in topic k).
• Output: a k x n matrix (with each cell indicating the probability of topic k in document j).

To find the optimal number of topics, I want to calculate the coherence for a model. However, I am only aware of Gensim's Coherencemodel, which seems to require a Gensim model as input.

Are there any other packages/implementations that I could use to calculate the coherence of a computed topic model? Or, if it is indeed possible to use the Coherencemodel without inputting a LDAmodel, could someone show me how to do that?

Actually, you can do this with the Gensim package.

input_data = list of list with tokenized texts

topics = list with top N words per topic

import gensim.corpora as corpora
from gensim.models.coherencemodel import CoherenceModel

id2word = corpora.Dictionary(input_data)
corpus = [id2word.doc2bow(text) for text in input_data]

cm = CoherenceModel(
    topics=topics,
    texts=input_data,
    corpus=corpus,
    dictionary=id2word,
    coherence='c_v')
coherence = cm.get_coherence()