from .adaptivedataloader import AdaptiveDSSDataLoader
from cords.selectionstrategies.SL import RandomStrategy
import time, copy, logging
[docs]class RandomDataLoader(AdaptiveDSSDataLoader):
"""
Implements of RandomDataLoader that serves as the dataloader for the non-adaptive Random subset selection strategy.
Parameters
-----------
train_loader: torch.utils.data.DataLoader class
Dataloader of the training dataset
dss_args: dict
Data subset selection arguments dictionary required for Random subset selection strategy
logger: class
Logger for logging the information
"""
def __init__(self, train_loader, dss_args, logger, *args, **kwargs):
"""
Constructor function
"""
super(RandomDataLoader, self).__init__(train_loader, train_loader, dss_args,
logger, *args, **kwargs)
self.strategy = RandomStrategy(train_loader, online=False)
self.logger.debug('Random dataloader initialized. ')
def _resample_subset_indices(self):
"""
Function that calls the Random subset selection strategy to sample new subset indices and the corresponding subset weights.
"""
start = time.time()
self.logger.debug("Iteration: {0:d}, requires subset selection. ".format(self.cur_iter))
logging.debug("Random budget: %d", self.budget)
subset_indices, _ = self.strategy.select(self.budget)
end = time.time()
self.logger.info("Iteration: {0:d}, subset selection finished, takes {1:.2f}. ".format(self.cur_iter, (end - start)))
return subset_indices