Converting PyTorch & TensorFlow Models Into Apple Core ML Using CoreMLTools

Core ML is an Apple framework that allows developers to integrate machine learning/deep learning models into their applications. However, it does not support model creation and training, i.e., you first need to create the model in a framework like TensorFlow or PyTorch, then you can convert and use it. There are two ways you can convert your machine learning model from the framework of your choice to the Core ML model format: through an intermediary model format like ONNX or by using Apple’s own CoreMLTools Python library. 

Although ONNX works just fine for the conversion, CoreMLTools offers other useful functionalities like model optimization. Also, you’ll need to use CoreMLTools for the final conversion from ONNX format to Core ML format anyway. Currently, it supports the conversion of models created using the following libraries:

• PyTorch
• TensorFlow 1.x & 2.x
• TensorFlow’s Keras APIs
• scikit-learn
• XGBoost
• LibSVM

Installation 

Install CoreMLTools from PyPI.

The package is only available for macOS (10.13+) and Linux.

pip install coremltools

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You can learn more about the other methods of the installation here.

Converting models into Core ML model using CoreMLTools

From PyTorch

For conversion from PyTorch, you can either use the TorchScript object or TorchScript object saved as a .pt file. 

1. Create and train or load a pre-trained model and set it to evaluation mode.

 import torch
 import torchvision
 import coremltools as ct
 # load a pre-trained MobileNetV2 model
 torch_model = torchvision.models.mobilenet_v2(pretrained=True)

 # set it to evaluation mode
 torch_model.eval() 

2. Create TorchScript object using the torch.jit.trace and random data

 random_input = torch.rand(1, 3, 224, 224) 
 traced_model = torch.jit.trace(torch_model, random_input) 

3. Convert the TorchScript object to Core ML using the CoreMLTools convert() method and save it.

 # Convert to Core ML using the Unified Conversion API
 model = ct.convert(
     traced_model,
     inputs=[ct.ImageType(name="input_1", shape=random_input.shape)]
 ) 

4. Make predictions on the converted model using the predict() method. 

Note: This is only supported on macOS version 10.13 or later.

 from PIL import Image
 example_image = Image.open("test.jpg").resize((224, 224))
 out_dict = model.predict({"input_1": example_image}) 

From TensorFlow

For conversion from TensorFlow 2.x you can use tf.keras.Model object, HDF5 .H5 file, SavedModel path or concrete functions. For TensorFlow 1.x, CoreMLTools also supports frozen grpah (tf.Graph) objects and .pb file path.

1. Create and train or load a pre-trained model and set it to evaluation mode. 

 import tensorflow as tf
 import coremltools as ct
 model = tf.keras.Sequential(
     [
         tf.keras.layers.Flatten(input_shape=(28, 28)),
         tf.keras.layers.Dense(128, activation=tf.nn.relu),
         tf.keras.layers.Dense(10, activation=tf.nn.softmax),
     ]
 )
 # get the data
 (X_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 # fit the model
 model.fit(X_train, y_train, batch_size=32) 

2. Convert the TensorFlow model object using the convert() method.

mlmodel = ct.convert(model)

3. Make a prediction using the predict() method. 

Note: This is only supported on macOS version 10.13 or later.

  import numpy as np
  from IPython.display import display
  from PIL import Image
  img = Image.open('two.png').resize((28, 28)).convert('L')
  display(img)
  output =  mlmodel.predict({"input_1": example_img})
  print(output) 

Colab Notebook of the above implementation.

References

For a more comprehensive understanding of CoreMLTools, refer to the following resources:

• GitHub
• Docs
• Core ML