How can use combine

To combine machine learning models using Core ML, you can utilize the new support for multifunction models introduced at WWDC 2024. This feature allows you to merge multiple models into a single Core ML model, which can perform multiple tasks using a shared feature extractor. Here’s a step-by-step guide based on the sessions:

1. Convert Models to Core ML: First, convert your individual models to Core ML models using ct convert and save them as individual ML packages.

2. Create a Multifunction Descriptor: Use Core ML tools to create a multifunction descriptor. This descriptor specifies which models to merge and the new function names in the merged model.

3. Merge Models: Use the save multifunctionutil to produce a merged multifunction Core ML model. During this process, Core ML tools will deduplicate shared weights by calculating the hash values of weights.

4. Load and Use the Multifunction Model: When loading the multifunction model via the Core ML tools Python API, you can specify the function name to load the specific function and then perform predictions as usual.

Here is a sample code snippet for merging models:

import coremltools as ct

# Convert individual models
model1 = ct.convert('model1.onnx')
model2 = ct.convert('model2.onnx')

# Save models as ML packages
model1.save('Model1.mlpackage')
model2.save('Model2.mlpackage')

# Create a multifunction descriptor
descriptor = ct.models.MLModelDescriptor(
    models=['Model1.mlpackage', 'Model2.mlpackage'],
    function_names=['function1', 'function2']
)

# Merge models
multifunction_model = ct.models.MLModel(descriptor)
multifunction_model.save('MultifunctionModel.mlpackage')

# Load and use the multifunction model
loaded_model = ct.models.MLModel('MultifunctionModel.mlpackage')
prediction = loaded_model.predict({'input': input_data}, function_name='function1')

For more detailed information, you can refer to the session Bring your machine learning and AI models to Apple silicon (26:31).

Additionally, the session Deploy machine learning and AI models on-device with Core ML (14:15) demonstrates a practical example of using multifunction models in an app, showcasing how different functions can be used to generate different styles of images.

Relevant Sessions:

1. Bring your machine learning and AI models to Apple silicon
2. Deploy machine learning and AI models on-device with Core ML