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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: 3 months ago
JSON representation
Torch7 -> CoreML
- Host: GitHub
- URL: https://github.com/prisma-ai/torch2coreml
- Owner: prisma-ai
- License: mit
- Archived: true
- Created: 2017-08-16T19:36:23.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-04T21:37:19.000Z (almost 7 years ago)
- Last Synced: 2024-04-24T01:20:32.425Z (6 months ago)
- Topics: ai, coreml, deep-learning, ios, ios11, neural-style, torch
- Language: Python
- Homepage:
- Size: 3.12 MB
- Stars: 382
- Watchers: 16
- Forks: 56
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - torch2coreml - > CoreML，该工具可将Torch7模型转换为[Apple CoreML](https://developer.apple.com/documentation/coreml)格式以便在Apple设备上运行。 (Pytorch & related libraries｜Pytorch & 相关库 / Other libraries｜其他库:)
- Awesome-pytorch-list - torch2coreml - > CoreML (Pytorch & related libraries / Other libraries:)
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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.
## 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.