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

connect(**kwargs)[source]

Optionally connect one or multiple loops to this one.

Linked loops should form a tree.

on_load_checkpoint(state_dict)[source]

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

on_run_end()[source]

Returns the outputs of the whole run.

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.

Resets the loop’s internal state.

teardown()[source]

Use to release memory etc.

property done: bool: Returns True if the current iteration count reaches the number of dataloader batches.