burn-train 0.23.0-pre.1

Training crate for the Burn framework
Documentation 
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mod async_wrapper;
mod base;
mod error;
mod full;
mod metrics;
mod minimal;
#[cfg(feature = "rl")]
mod rl_metrics;
#[cfg(feature = "rl")]
mod rl_processor;

pub use base::*;
pub use error::*;
pub(crate) use full::*;
pub(crate) use metrics::*;
#[cfg(feature = "rl")]
pub(crate) use rl_metrics::*;
#[cfg(feature = "rl")]
pub use rl_processor::*;

#[cfg(test)]
pub(crate) use minimal::*;

pub use async_wrapper::{AsyncProcessorEvaluation, AsyncProcessorTraining};

#[cfg(test)]
pub(crate) mod test_utils {
    use crate::metric::{
        Adaptor, LossInput,
        processor::{EventProcessorTraining, LearnerEvent, MinimalEventProcessor, TrainingItem},
    };
    use burn_core::{data::dataloader::Progress, tensor::Tensor};

    use super::ItemLazy;

    impl ItemLazy for f64 {
        fn sync(self) -> Result<Self, burn_std::ExecutionError> {
            Ok(self)
        }
    }

    impl Adaptor<LossInput> for f64 {
        fn adapt(&self) -> LossInput {
            LossInput::new(Tensor::from_data([*self], &Default::default()))
        }
    }

    pub(crate) fn process_train(
        processor: &mut MinimalEventProcessor<f64, f64>,
        value: f64,
        epoch: usize,
    ) {
        let dummy_progress =
            burn_core::data::dataloader::Progress::new(epoch, 3, Some("items".to_string()));
        let dummy_iteration = Some(1);

        processor
            .process_train(LearnerEvent::ProcessedItem(TrainingItem::new(
                value,
                dummy_progress,
                dummy_iteration,
                None,
            )))
            .unwrap();
    }

    pub(crate) fn start_epoch(
        processor: &mut MinimalEventProcessor<f64, f64>,
        epoch: usize,
        num_items: usize,
    ) {
        processor
            .process_train(LearnerEvent::StartSplit {
                epoch_number: epoch,
                progress: Progress::new(0, num_items, Some("items".to_string())),
            })
            .unwrap();
        processor
            .process_valid(LearnerEvent::StartSplit {
                epoch_number: epoch,
                progress: Progress::new(0, num_items, Some("items".to_string())),
            })
            .unwrap();
    }

    pub(crate) fn end_epoch(processor: &mut MinimalEventProcessor<f64, f64>, epoch: usize) {
        processor
            .process_train(LearnerEvent::EndSplit {
                epoch,
                progress: Progress::new(1, 1, Some("items".to_string())),
            })
            .unwrap();
        processor
            .process_valid(LearnerEvent::EndSplit {
                epoch,
                progress: Progress::new(1, 1, Some("items".to_string())),
            })
            .unwrap();
    }
}