How to make combined mlmodel based on seperate mlmodels? adding if-else control flow

[xueyingxin]

❓Question

I want to use coremltools to help accomplish combining some separate coreml models to form a bigger one. For example, I have models: main.mlmodel, if_branch.mlmodel, else_branch.mlmodel, each have so many layers. I want to make another mlmodel based on those three. And the flow should be like this: Inputs include input to "main" and a condition input "cond". Input feed to "main", then output of it will go to either "if_branch" or "else_branch" based on the other input "cond".

I tried using "NeuralNetworkBuilder" to construct the bigger model. But when constructing the if layer, what kind of method(api) should I use to move all layers in "if_branch.mlmodel" to the "ifbranch" of the if layer instead of rewriting each layer into "ifbranch"?

[TobyRoseman]

It sounds like you should be using more than one model. It sounds like you want to create a model pipeline.

Model pipelines allow you to create an MLModel that consists of other MLModels. Models in the pipeline can take as input earlier Model's output.

Model Pipeline Resources

• User Guide Doc for Building a Pipeline Classifier
• API Docs for make_pipeline
• Protobuf for Pipelines