Is there a common format for neural networks
ONNX (Open Neural Network Exchange)
ONNX is an open-source AI ecosystem that provides a common format for neural networks.
It helps converting a deep learning model to another.
Generally, model conversion takes weeks/months without ONNX:
ONNX offers simpler and faster conversion process:
For all supported conversions, see here.
It makes deployment easier, models stored in a much preferable way:
In the above image, ONNX acts as a data ingestion layer, transforms each input model to the same format. Otherwise, all models would be like a bunch of puzzle pieces that do not fit each other.
How to Use ONNX - Keras Conversion Example
Let's say you have your Keras model and you want to transform it to ONNX:
model = load_model("keras_model.hdf5") # h5 is also OK!
onnx_model = keras2onnx.convert_keras(model, model.name)
onnx_model_file = 'output_model.onnx'
onnx.save_model(onnx_model, onnx_model_file)
Then load & run saved model :
onnx_model = onnx.load_model('output_model.onnx')
content = onnx_model.SerializeToString()
sess = onnxruntime.InferenceSession(content)
x = x if isinstance(x, list) else [x]
feed = dict([(input.name, x[n]) for n, input in enumerate(sess.get_inputs())])
# Do inference
pred_onnx = sess.run(None, feed)
This example uses keras2onnx to convert the Keras model and onnxruntime to do inference.
Note: There are also lots of pre-trained models in the ONNX format. Check this out!
References:
1. https://towardsdatascience.com/onnx-made-easy-957e60d16e94
2. https://blog.codecentric.de/en/2019/08/portability-deep-learning-frameworks-onnx/
3. http://on-demand.gputechconf.com/gtc/2018/presentation/s8818-onnx-interoperable-deep-learning-presented-by-facebook.pdf
4. https://devblogs.nvidia.com/tensorrt-3-faster-tensorflow-inference/
Is there a preferred (shared?) format to store neural networks?
Each library / framework has its own serialization, e.g. Caffe uses Protocol Buffers, Torch has a built-in serialization scheme and Theano objects can be serialized with pickle.
In some cases like OverFeat or darknet the weights and biases are stored on-disk in binary format via plain fwrite-s of the corresponding float(or double) contiguous arrays (see this answer for more details). Note that this does not cover the architecture of the network / model which has to be known or represented separately (like declared explicitly at load time).
Also: a library like libccv stores the structure and the weights in a SQLite database.
Is there a service or library that can help handle different types of networks / or transform one type into another?
I don't think there is a single (meta) library that claims to do so. But it exists distinct projects that provide convenient converters.
Some examples (non exhaustive):
- Caffe -> Torch: https://github.com/szagoruyko/loadcaffe
- Torch -> Caffe: https://github.com/facebook/fb-caffe-exts
- Caffe -> TensorFlow: https://github.com/ethereon/caffe-tensorflow
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UPDATE (2017-09): two noticeable initiatives are:
(1) the ONNX format (a.k.a. Open Neural Network Exchange):
[...] a standard for representing deep learning models that enables models to be transferred between frameworks
See these blog posts.
(2) the CoreML format introduced by Apple:
[...] a public file format (
.mlmodel) for a broad set of ML methods [...] Models in this format can be directly integrated into apps through Xcode.