from torch.utils.data import DataLoader
from ..dssdataloader import DSSDataLoader
from cords.utils.data.datasets.SSL.utils import InfiniteSampler
from cords.utils.data.data_utils import WeightedSubset
[docs]class NonAdaptiveDSSDataLoader(DSSDataLoader):
"""
Implementation of NonAdaptiveDSSDataLoader class which serves as base class for dataloaders of other
nonadaptive subset selection strategies for semi-supervised learning setting.
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
logger: class
Logger for logging the information
"""
def __init__(self, train_loader, val_loader, dss_args,
logger, *args, **kwargs):
"""
Constructor function
"""
super(NonAdaptiveDSSDataLoader, self).__init__(train_loader.dataset, dss_args,
logger, *args, **kwargs)
# Arguments assertion check
assert "device" in dss_args.keys(), "'device' is a compulsory argument. Include it as a key in dss_args"
assert "num_iters" in dss_args.keys(), "'num_iters' is a compulsory argument. Include it as a key in dss_args"
self.train_loader = train_loader
self.val_loader = val_loader
self.initialized = False
self.num_iters = dss_args.num_iters
def __iter__(self):
"""
Iter function that returns the iterator of the data subset loader.
"""
data_sub = WeightedSubset(self.dataset, self.subset_indices, self.subset_weights)
self.curr_loader = DataLoader(data_sub, sampler=InfiniteSampler(len(data_sub),
self.num_iters * self.loader_kwargs['batch_size']),
*self.loader_args, **self.loader_kwargs)
self.batch_wise_indices = list(self.subset_loader.batch_sampler)
return self.curr_loader.__iter__()