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EvaluationEpochLoop

class pytorch_lightning.loops.epoch.EvaluationEpochLoop[source]

Bases: abc.ABC, Generic[pytorch_lightning.loops.base.T]

This is the loop performing the evaluation.

It mainly loops over the given dataloader and runs the validation or test step (depending on the trainer’s current state).

advance(data_fetcher, dataloader_idx, dl_max_batches, num_dataloaders)[source]

Calls the evaluation step with the corresponding hooks and updates the logger connector.

Parameters
  • data_fetcher (AbstractDataFetcher) – iterator over the dataloader

  • dataloader_idx (int) – index of the current dataloader

  • dl_max_batches (int) – maximum number of batches the dataloader can produce

  • num_dataloaders (int) – the total number of dataloaders

Raises

StopIteration – If the current batch is None

Return type

None

on_load_checkpoint(state_dict)[source]

Called when loading a model checkpoint, use to reload loop state.

Return type

None

on_run_end()[source]

Returns the outputs of the whole run.

Return type

List[Union[Tensor, Dict[str, Any]]]

on_run_start(data_fetcher, dataloader_idx, dl_max_batches, num_dataloaders)[source]

Adds the passed arguments to the loop’s state if necessary.

Parameters
  • data_fetcher (AbstractDataFetcher) – the current data_fetcher wrapping the dataloader

  • dataloader_idx (int) – index of the current dataloader

  • dl_max_batches (int) – maximum number of batches the dataloader can produce

  • num_dataloaders (int) – the total number of dataloaders

Return type

None

on_save_checkpoint()[source]

Called when saving a model checkpoint, use to persist loop state.

Return type

Dict

Returns

The current loop state.

reset()[source]

Resets the loop’s internal state.

Return type

None

teardown()[source]

Use to release memory etc.

Return type

None

property done: bool

Returns True if the current iteration count reaches the number of dataloader batches.

Return type

bool

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