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https://github.com/prisma-ai/torch2coreml

Torch7 -> CoreML
https://github.com/prisma-ai/torch2coreml

ai coreml deep-learning ios ios11 neural-style torch

Last synced: 2 months ago
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Torch7 -> CoreML

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README 

        
# Convert Torch7 models into Apple CoreML format.



[Short tutorial](https://blog.prismalabs.ai/diy-prisma-app-with-coreml-6b4994cc99e1)



This tool helps convert Torch7 models into [Apple CoreML](https://developer.apple.com/documentation/coreml) format which can then be run on Apple devices.



![fast-neural-style example app screenshot](https://github.com/prisma-ai/torch2coreml/raw/master/screenshot.jpg "fast-neural-style example app")



## Installation

```bash

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.



## Dependencies



* 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/convert-fast-neural-style.py".

To implement converters you should use single function "convert" from torch2coreml:



```python

from torch2coreml import convert

```



This function is simple enough to be self-describing:



```python

def convert(model,

            input_shapes,

            input_names=['input'],

            output_names=['output'],

            mode=None,

            image_input_names=[],

            preprocessing_args={},

            image_output_names=[],

            deprocessing_args={},

            class_labels=None,

            predicted_feature_name='classLabel',

            unknown_layer_converter_fn=None)

```



### Parameters

__model__: Torch7 model (loaded with PyTorch) | str  

    A trained Torch7 model loaded in python using PyTorch or path to file

    with model (*.t7).



__input_shapes__: list of tuples

    Shapes of the input tensors.



__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

    https://apple.github.io/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder.set_pre_processing_parameters



__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'



__unknown_layer_converter_fn__: function with signature:  

    (builder, name, layer, input_names, output_names)  

        builder: object - instance of NeuralNetworkBuilder class  

        name: str - generated layer name  

        layer: object - PyTorch (python) object for corresponding layer  

        input_names: list of strings  

        output_names: list of strings  

        Returns: list of strings for layer output names  

    Callback function to handle unknown for torch2coreml layers



### Returns

model: A coreml model.



## Currently supported

### Models

Only Torch7 "nn" module is supported now.



### Layers

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

20. SpatialUpSamplingNearest

21. SplitTable



## License



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



Use of this source code is governed by the [MIT License](https://opensource.org/licenses/MIT) that can be found in the LICENSE.txt file.