def on_evaluation_batch_start(self, batch, dataloader_idx,
num_dataloaders):
model = self.trainer.lightning_module
# set dataloader_idx only if multiple ones
model._current_dataloader_idx = dataloader_idx if num_dataloaders > 1 else None
# track batch_size
self.cached_results._batch_size = Result.extract_batch_size(batch)
def on_evaluation_batch_start(self, testing, batch, dataloader_idx, num_dataloaders):
# Todo: required argument `testing` is not used
model = self.trainer.lightning_module
# set dataloader_idx only if multiple ones
model._current_dataloader_idx = dataloader_idx if num_dataloaders > 1 else None
# track batch_size
self.cached_results._batch_size = Result.extract_batch_size(batch)
def on_evaluation_batch_start(self, testing, batch, dataloader_idx,
num_dataloaders):
# reset the result of the PL module
model = self.trainer.get_model()
model._current_dataloader_idx = dataloader_idx if num_dataloaders > 1 else None
# track batch_size
self.cached_results._batch_size = Result.extract_batch_size(batch)
def on_train_split_start(self, split_idx: int, opt_idx: int, split_batch) -> None:
self.cached_results._split_idx = split_idx
self.cached_results._opt_idx = opt_idx
self.cached_results._batch_size = Result.extract_batch_size(split_batch)
