from .adaptivedataloader import AdaptiveDSSDataLoader
from cords.selectionstrategies.SL import CRAIGStrategy
import time, copy
# CRAIG
[docs]class CRAIGDataLoader(AdaptiveDSSDataLoader):
"""
Implements of CRAIGDataLoader that serves as the dataloader for the adaptive CRAIG subset selection strategy from the paper :footcite:`pmlr-v119-mirzasoleiman20a`.
Parameters
-----------
train_loader: torch.utils.data.DataLoader class
Dataloader of the training dataset
val_loader: torch.utils.data.DataLoader class
Dataloader of the validation dataset
dss_args: dict
Data subset selection arguments dictionary required for CRAIG subset selection strategy
logger: class
Logger for logging the information
"""
def __init__(self, train_loader, val_loader, dss_args, logger, *args, **kwargs):
"""
Constructor function
"""
# Arguments assertion check
assert "model" in dss_args.keys(), "'model' is a compulsory argument. Include it as a key in dss_args"
assert "loss" in dss_args.keys(), "'loss' is a compulsory argument. Include it as a key in dss_args"
if dss_args.loss.reduction != "none":
raise ValueError("Please set 'reduction' of loss function to 'none' for adaptive subset selection strategies")
assert "num_classes" in dss_args.keys(), "'num_classes' is a compulsory argument for CRAIG. Include it as a key in dss_args"
assert "linear_layer" in dss_args.keys(), "'linear_layer' is a compulsory argument for CRAIG. Include it as a key in dss_args"
assert "selection_type" in dss_args.keys(), "'selection_type' is a compulsory argument for CRAIG. Include it as a key in dss_args"
assert "optimizer" in dss_args.keys(), "'optimizer' is a compulsory argument for CRAIG. Include it as a key in dss_args"
super(CRAIGDataLoader, self).__init__(train_loader, val_loader, dss_args,
logger, *args, **kwargs)
self.strategy = CRAIGStrategy(train_loader, val_loader, copy.deepcopy(dss_args.model), dss_args.loss,
dss_args.device, dss_args.num_classes, dss_args.linear_layer,
True, dss_args.selection_type, logger, dss_args.optimizer)
self.train_model = dss_args.model
self.logger.info('CRAIG dataloader initialized. ')
def _resample_subset_indices(self):
"""
Function that calls the CRAIG subset selection strategy to sample new subset indices and the corresponding subset weights.
"""
start = time.time()
self.logger.info('Epoch: {0:d}, requires subset selection. '.format(self.cur_epoch))
cached_state_dict = copy.deepcopy(self.train_model.state_dict())
clone_dict = copy.deepcopy(self.train_model.state_dict())
subset_indices, subset_weights = self.strategy.select(self.budget, clone_dict)
self.train_model.load_state_dict(cached_state_dict)
end = time.time()
self.logger.info('Epoch: {0:d}, subset selection finished, takes {1:.4f}. '.format(self.cur_epoch, (end - start)))
return subset_indices, subset_weights