Ejemplo n.º 1
0
def test_no_neptune_client(no_site_packages):

    with pytest.raises(
            RuntimeError,
            match=
            r"This contrib module requires neptune-client to be installed."):
        NeptuneLogger()
Ejemplo n.º 2
0
def test_integration_as_context_manager():
    n_epochs = 5
    data = list(range(50))

    losses = torch.rand(n_epochs * len(data))
    losses_iter = iter(losses)

    def update_fn(engine, batch):
        return next(losses_iter)

    with NeptuneLogger(offline_mode=True) as npt_logger:
        trainer = Engine(update_fn)

        def dummy_handler(engine, logger, event_name):
            global_step = engine.state.get_event_attrib_value(event_name)
            logger.log_metric("test_value", global_step, global_step)

        npt_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED)

        trainer.run(data, max_epochs=n_epochs)
def run(train_batch_size, val_batch_size, epochs, lr, momentum):
    train_loader, val_loader = get_data_loaders(train_batch_size,
                                                val_batch_size)
    model = Net()
    device = "cpu"

    if torch.cuda.is_available():
        device = "cuda"

    model.to(device)  # Move model before creating optimizer
    optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
    criterion = nn.CrossEntropyLoss()
    trainer = create_supervised_trainer(model,
                                        optimizer,
                                        criterion,
                                        device=device)
    trainer.logger = setup_logger("Trainer")

    metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)}

    train_evaluator = create_supervised_evaluator(model,
                                                  metrics=metrics,
                                                  device=device)
    train_evaluator.logger = setup_logger("Train Evaluator")
    validation_evaluator = create_supervised_evaluator(model,
                                                       metrics=metrics,
                                                       device=device)
    validation_evaluator.logger = setup_logger("Val Evaluator")

    @trainer.on(Events.EPOCH_COMPLETED)
    def compute_metrics(engine):
        train_evaluator.run(train_loader)
        validation_evaluator.run(val_loader)

    npt_logger = NeptuneLogger(
        api_token="ANONYMOUS",
        project_name="shared/pytorch-ignite-integration",
        name="ignite-mnist-example",
        params={
            "train_batch_size": train_batch_size,
            "val_batch_size": val_batch_size,
            "epochs": epochs,
            "lr": lr,
            "momentum": momentum,
        },
    )

    npt_logger.attach_output_handler(
        trainer,
        event_name=Events.ITERATION_COMPLETED(every=100),
        tag="training",
        output_transform=lambda loss: {"batchloss": loss},
    )

    for tag, evaluator in [("training", train_evaluator),
                           ("validation", validation_evaluator)]:
        npt_logger.attach_output_handler(
            evaluator,
            event_name=Events.EPOCH_COMPLETED,
            tag=tag,
            metric_names=["loss", "accuracy"],
            global_step_transform=global_step_from_engine(trainer),
        )

    npt_logger.attach_opt_params_handler(
        trainer,
        event_name=Events.ITERATION_COMPLETED(every=100),
        optimizer=optimizer)

    npt_logger.attach(trainer,
                      log_handler=WeightsScalarHandler(model),
                      event_name=Events.ITERATION_COMPLETED(every=100))

    npt_logger.attach(trainer,
                      log_handler=GradsScalarHandler(model),
                      event_name=Events.ITERATION_COMPLETED(every=100))

    def score_function(engine):
        return engine.state.metrics["accuracy"]

    handler = Checkpoint(
        {"model": model},
        NeptuneSaver(npt_logger),
        n_saved=2,
        filename_prefix="best",
        score_function=score_function,
        score_name="validation_accuracy",
        global_step_transform=global_step_from_engine(trainer),
    )
    validation_evaluator.add_event_handler(Events.COMPLETED, handler)

    # kick everything off
    trainer.run(train_loader, max_epochs=epochs)

    npt_logger.close()