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https://github.com/jeffreyksmithjr/onnxs
ONNX interop for Elixir
https://github.com/jeffreyksmithjr/onnxs
deep-learning deep-neural-networks elixir neural-network onnx
Last synced: 2 months ago
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ONNX interop for Elixir
- Host: GitHub
- URL: https://github.com/jeffreyksmithjr/onnxs
- Owner: jeffreyksmithjr
- License: mit
- Created: 2018-04-07T20:42:30.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-04-08T15:52:09.000Z (over 6 years ago)
- Last Synced: 2024-09-23T12:46:13.832Z (3 months ago)
- Topics: deep-learning, deep-neural-networks, elixir, neural-network, onnx
- Language: Elixir
- Size: 4.32 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ONNXS
[](https://hex.pm/packages/onnxs)
[](https://travis-ci.org/jeffreyksmithjr/onnxs)
_ONNXS_
_/ON-eks-ESS/_
_noun_
_1. ONNX interop for Elixir_
_2. The twenty-first century's yesterday_
## Overview
The [Open Neural Network eXchange format](https://onnx.ai/)(ONNX) is an open format for representing deep learning models.
Because it's an open format [supported by various deep learning frameworks](https://onnx.ai/supported-tools), it enables greater interoperation between different toolchains.
ONNXS allows you to decode trained neural network models produced by deep learning frameworks, modify them, and encode them back into the standard ONNX format.
## Installation
The package can be installed by adding onnxs to your list of dependencies in mix.exs:
```
def deps do
[
{:onnxs, "~> 0.2.0"}
]
end
```
## Usage
ONNXS allows you to decode saved ONNX models into Elixir structs.
```
iex(1)> {:ok, mnist_data} = File.read "./test/examples/mnist.onnx"
{:ok,
<<8, 3, 18, 4, 67, 78, 84, 75, 26, 3, 50, 46, 52, 40, 1, 58, 227, 206, 1, 10,
199, 80, 18, 12, 80, 97, 114, 97, 109, 101, 116, 101, 114, 49, 57, 51, 26,
12, 80, 97, 114, 97, 109, 101, 116, 101, 114, 49, ...>>}
iex(2)> mnist_struct = Onnx.ModelProto.decode(mnist_data)
%Onnx.ModelProto{
doc_string: nil,
domain: nil,
graph: %Onnx.GraphProto{
doc_string: nil,
initializer: [],
input: [
%Onnx.ValueInfoProto{
doc_string: nil,
name: "Input3",
type: %Onnx.TypeProto{
value: {:tensor_type,
%Onnx.TypeProto.Tensor{
elem_type: 1,
shape: %Onnx.TensorShapeProto
...
ir_version: 3,
metadata_props: [],
model_version: 1,
opset_import: [%Onnx.OperatorSetIdProto{domain: "", version: 1}],
producer_name: "CNTK",
producer_version: "2.4"
}
```
Once converted to structs, the models can be modified like any other Elixir struct.
```
iex(3)> mnist_updated = %{mnist_struct | model_version: 2}
```
Finally, ONNXS allows you define Elixir data as a struct that can be encoded to valid ONNX.
```
iex(4)> mnist_map = Map.from_struct(mnist_updated)
%{
doc_string: nil,
domain: nil,
...
model_version: 2,
opset_import: [%Onnx.OperatorSetIdProto{domain: "", version: 1}],
producer_name: "CNTK",
producer_version: "2.4"
}
iex(5)> new_mnist_struct = Onnx.ModelProto.new(mnist_map)
%Onnx.ModelProto{
doc_string: nil,
domain: nil,
...
producer_name: "CNTK",
producer_version: "2.4"
}
iex(6)> encoded_mnist = Onnx.ModelProto.encode(new_mnist_struct)
<<8, 3, 18, 4, 67, 78, 84, 75, 26, 3, 50, 46, 52, 40, 2, 58, 227, 206, 1, 10,
199, 80, 18, 12, 80, 97, 114, 97, 109, 101, 116, 101, 114, 49, 57, 51, 26, 12,
80, 97, 114, 97, 109, 101, 116, 101, 114, 49, 57, 51, ...>>
iex(7)> {:ok, file} = File.open "/tmp/mnist_v2.proto", [:write]
{:ok, #PID<0.229.0>}
iex(8)> IO.binwrite file, encoded_mnist
:ok
iex(9)> File.close file
:ok
```
## Implementation
This implementation uses [Bing Tony Han](https://github.com/tony612/protobuf-elixir)'s [protobuf-elixir](https://github.com/tony612/protobuf-elixir) library to generate Elixir code from the [ONNX proto file](https://github.com/onnx/onnx/blob/master/onnx/onnx.proto).