11.09.2020       Выпуск 351 (07.09.2020 - 13.09.2020)       Статьи

PyTorch-Ignite: training and evaluating neural networks flexibly and transparently

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Экспериментальная функция:

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This allows the construction of training logic from the simplest to the most complicated scenarios.

The type of output of the process functions (i.e. loss or y_pred, y in the above examples) is not restricted. These functions can return everything the user wants. Output is set to an engine's internal object engine.state.output and can be used further for any type of processing.

Events and Handers

To improve the engine’s flexibility, a configurable event system is introduced to facilitate the interaction on each step of the run. Namely, Engine allows to add handlers on various Events that are triggered during the run. When an event is triggered, attached handlers (named functions, lambdas, class functions) are executed. Here is a schema for when built-in events are triggered by default:

fire_event(Events.STARTED)
while epoch < max_epochs:
    fire_event(Events.EPOCH_STARTED)
    # run once on data
    for batch in data:
        fire_event(Events.ITERATION_STARTED)

        output = process_function(batch)

        fire_event(Events.ITERATION_COMPLETED)
    fire_event(Events.EPOCH_COMPLETED)
fire_event(Events.COMPLETED)

Note that each engine (i.e. trainer and evaluator) has its own event system which allows to define its own engine's process logic.

Using Events and handlers, it is possible to completely customize the engine's runs in a very intuitive way:






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