PyTorch Lightning

MetricValCallback

class oml.lightning.callbacks.metric.MetricValCallback(metric: IBasicMetric, log_images: bool = False, loader_idx: int = 0)[source]

Bases: Callback

This is a wrapper which allows to use IBasicMetric with PyTorch Lightning.

__init__(metric: IBasicMetric, log_images: bool = False, loader_idx: int = 0)[source]
Parameters

  • metric – Metric

  • log_images – Set True if you want to have visual logging

  • loader_idx – Idx of the loader to calculate metric for

ExtractorModule

class oml.lightning.modules.extractor.ExtractorModule(extractor: IExtractor, criterion: Optional[Module] = None, optimizer: Optional[Optimizer] = None, scheduler: Optional[_LRScheduler] = None, scheduler_interval: str = 'step', scheduler_frequency: int = 1, input_tensors_key: str = 'input_tensors', labels_key: str = 'labels', embeddings_key: str = 'embeddings', scheduler_monitor_metric: Optional[str] = None, freeze_n_epochs: int = 0)[source]

Bases: LightningModule

This is a base module to train your model with Lightning.

__init__(extractor: IExtractor, criterion: Optional[Module] = None, optimizer: Optional[Optimizer] = None, scheduler: Optional[_LRScheduler] = None, scheduler_interval: str = 'step', scheduler_frequency: int = 1, input_tensors_key: str = 'input_tensors', labels_key: str = 'labels', embeddings_key: str = 'embeddings', scheduler_monitor_metric: Optional[str] = None, freeze_n_epochs: int = 0)[source]
Parameters

  • extractor – Extractor to train

  • criterion – Criterion to optimize

  • optimizer – Optimizer

  • scheduler – Learning rate scheduler

  • scheduler_interval – Interval of calling scheduler (must be step or epoch)

  • scheduler_frequency – Frequency of calling scheduler

  • input_tensors_key – Key to get tensors from the batches

  • labels_key – Key to get labels from the batches

  • embeddings_key – Key to get embeddings from the batches

  • scheduler_monitor_metric – Metric to monitor for the schedulers that depend on the metric value

  • freeze_n_epochs – number of epochs to freeze model (for n > 0 model has to be a successor of IFreezable interface). When current_epoch >= freeze_n_epochs model is unfreezed. Note that epochs are starting with 0.

extractor_training_pipeline

oml.lightning.pipelines.train.extractor_training_pipeline(cfg: Union[Dict[str, Any], DictConfig]) → None[source]

This pipeline allows you to train and validate a feature extractor which represents images as feature vectors.

The config can be specified as a dictionary or with hydra: https://hydra.cc/. For more details look at pipelines/features_extraction/README.md

extractor_validation_pipeline

oml.lightning.pipelines.validate.extractor_validation_pipeline(cfg: Union[Dict[str, Any], DictConfig]) → Tuple[Trainer, Dict[str, Any]][source]

This pipeline allows you to validate a feature extractor which represents images as feature vectors.

The config can be specified as a dictionary or with hydra: https://hydra.cc/. For more details look at pipelines/features_extraction/README.md

extractor_prediction_pipeline

oml.lightning.pipelines.predict.extractor_prediction_pipeline(cfg: Union[Dict[str, Any], DictConfig]) → None[source]

This pipeline allows you to save features extracted by a feature extractor.

The config can be specified as a dictionary or with hydra: https://hydra.cc/. For more details look at pipelines/features_extraction/README.md

postprocessor_training_pipeline

oml.lightning.pipelines.train_postprocessor.postprocessor_training_pipeline(cfg: DictConfig) → None[source]

This pipeline allows you to train and validate a pairwise postprocessor which fixes mistakes of a feature extractor in retrieval setup.

The config can be specified as a dictionary or with hydra: https://hydra.cc/. For more details look at pipelines/postprocessing/pairwise_postprocessing/README.md