def on_epoch_start(self, runner: IRunner) -> None:
"""
Set loaders for current epoch.
If validation is not required then the first loader
from loaders used in current epoch will be used
as validation loader.
Metrics from the latest epoch with true
validation loader will be used
in the epochs where this loader is missing.
Args:
runner (IRunner): current runner
Raises:
ValueError: if there are no loaders in epoch
"""
epoch_num = runner.epoch
# loaders to use in current epoch
epoch_loaders = OrderedDict()
for name, loader in self.loaders.items():
period = self.loader_periods.get(name, 1)
# ignore loaders where period - 0
if period > 0 and epoch_num % period == 0:
epoch_loaders[name] = loader
if len(epoch_loaders) == 0:
raise ValueError(f"There is no loaders in epoch {epoch_num}!")
first_loader = next(iter(epoch_loaders.keys()))
runner.valid_loader = (self.valid_loader if self.valid_loader
in epoch_loaders else first_loader)
runner.loaders = epoch_loaders
def on_epoch_end(self, runner: IRunner):
"""
Unwraps loaders for current epoch.
Args:
runner (IRunner): current runner
"""
runner.loaders = {
key: value.origin
if isinstance(value, BatchLimitLoaderWrapper) else value
for key, value in runner.loaders.items()
}
def on_epoch_start(self, runner: IRunner) -> None:
"""
Wraps loaders for current epoch.
If number-of-batches for loader is not provided then the first batch
from loader will be used for overfitting.
Args:
runner: current runner
"""
epoch_loaders = OrderedDict()
for name, loader in runner.loaders.items():
num_batches = self.loader_batches.get(name, 1)
if isinstance(num_batches, float):
num_batches = int(len(loader) * num_batches)
epoch_loaders[name] = BatchLimitLoaderWrapper(
loader=loader, num_batches=num_batches,
)
runner.loaders = epoch_loaders