Using ENOT Lite

ENOT Lite provides a unified interface for running neural network inference with various technologies.

To run neural network inference using ENOT Lite all you need to do is:

  1. Create Backend instance by using create() method of BackendFactory.

  2. Pass your input data into created Backend instance by using __call__() method to obtain prediction.

Here is an example which fully covers the basic usage of ENOT Lite:

1from enot_lite.backend import BackendFactory
2from enot_lite.type import BackendType
3
4backend = BackendFactory().create('path/to/model.onnx', BackendType.ORT_CPU)
5prediction = backend(inputs)

  • At line 1 in example above we import BackendFactory which will be used to create an instance of Backend.

  • At line 2 we import BackendType which allows to easily choose among various backends.

  • At line 4 we create Backend instance by using create() method of BackendFactory. Created backend is a wrap for your model which provides an easy-to-use interface for inference.

  • And finally, at line 5 inference is done by passing inputs (it can be images, text or something else) into backend and the results are stored in prediction variable.

BackendType allows you to choose among various inference technologies, so you don’t need to do anything special, just create Backend instance by BackendFactory and use it for inference.

To refine Backend setting, see BackendType, ModelType.

class ModelType(value)

Model type.

Currently supported model types:

  • YOLO_V5 - YOLOv5 model type.

class Device(value)

Device type.

  • CPU

  • GPU

class Backend

Interface for running inference.

All backends implemented in ENOT Lite framework follow this interface.

__call__(inputs, **kwargs)

Computes the predictions for given inputs.

Parameters

inputs (Any) – Model input. There are several ways to pass data into this function, see examples.

Returns

Prediction.

Return type

Any

Examples

>>> backend(input_0)  # For models with only one input.
>>> backend([input_0, input_1, ..., input_n])  # For models with several inputs.
>>> backend((input_0, input_1, ..., input_n))  # Is equivalent to previous one.
>>> backend({
...     'input_name_0': input_0,  # Explicitly specifying mapping between
...     'input_name_1': input_1,  # input names and input data.
...     ...
...     'input_name_n': input_n,
... })