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Convert Torch7 models into Apple CoreML format.

Project description

This tool helps convert Torch7 models into Apple CoreML format which can then be run on Apple devices.


pip install -U torch2coreml

In order to use this tool you need to have these installed: * Xcode 9 * python 2.7

If you want to run tests, you need MacOS High Sierra 10.13 installed.


  • coremltools (0.6.2+)

  • PyTorch

How to use

Using this library you can implement converter for your own model types. An example of such a converter is located at “example/fast-neural-style/”. To implement converters you should use single function “convert” from torch2coreml:

from torch2coreml import convert

This function is simple enough to be self-describing:

def convert(model,


model: Torch7 model (loaded with PyTorch) | str
A trained Torch7 model loaded in python using PyTorch or path to file with model (*.t7).
input_shape: tuple
Shape of the input tensor.
mode: str (‘classifier’, ‘regressor’ or None)
Mode of the converted coreml model:
‘classifier’, a NeuralNetworkClassifier spec will be constructed.
‘regressor’, a NeuralNetworkRegressor spec will be constructed.
preprocessing_args: dict
‘is_bgr’, ‘red_bias’, ‘green_bias’, ‘blue_bias’, ‘gray_bias’, ‘image_scale’ keys with the same meaning as
deprocessing_args: dict
Same as ‘preprocessing_args’ but for deprocessing.
class_labels: A string or list of strings.
As a string it represents the name of the file which contains the classification labels (one per line). As a list of strings it represents a list of categories that map the index of the output of a neural network to labels in a classifier.
predicted_feature_name: str
Name of the output feature for the class labels exposed in the Core ML model (applies to classifiers only). Defaults to ‘classLabel’


model: A coreml model.

Currently supported


Only Torch7 “nn” module is supported now.


List of Torch7 layers that can be converted into their CoreML equivalent:

  1. Sequential

  2. ConcatTable

  3. SpatialConvolution

  4. ELU

  5. ReLU

  6. SpatialBatchNormalization

  7. Identity

  8. CAddTable

  9. SpatialFullConvolution

  10. SpatialSoftMax

  11. SpatialMaxPooling

  12. SpatialAveragePooling

  13. View

  14. Linear

  15. Tanh

  16. MulConstant

  17. SpatialZeroPadding

  18. SpatialReflectionPadding

  19. Narrow


Copyright (c) 2017 Prisma Labs, Inc. All rights reserved.

Use of this source code is governed by the MIT License that can be found in the LICENSE.txt file.

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