from .nonadaptivedataloader import NonAdaptiveDSSDataLoader
from cords.selectionstrategies.SSL import CRAIGStrategy
from torch.utils.data import DataLoader
from cords.utils.data.data_utils import WeightedSubset
import time, copy
# CRAIG
[docs]class CRAIGDataLoader(NonAdaptiveDSSDataLoader):
"""
Implements of CRAIGDataLoader that serves as the dataloader for the nonadaptive CRAIG subset selection strategy for semi-supervised learning
and is an adapted version 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 "tea_model" in dss_args.keys(), "'tea_model' is a compulsory argument. Include it as a key in dss_args"
assert "ssl_alg" in dss_args.keys(), "'ssl_alg' 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.reduce:
raise ValueError("Please set 'reduce' of loss function to False 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), copy.deepcopy(dss_args.tea_model),
dss_args.ssl_alg, dss_args.loss, dss_args.device, dss_args.num_classes, dss_args.linear_layer,
False, dss_args.selection_type, logger, dss_args.optimizer)
self.train_model = dss_args.model
self.eta = dss_args.eta
self.num_cls = dss_args.num_classes
self.train_model = dss_args.model
self.teacher_model = dss_args.tea_model
if self.verbose:
print('CRAIG dataloader initialized. ')
self.loss = copy.deepcopy(dss_args.loss)
def _init_subset_loader(self):
"""
Function that initializes the initial subset loader.
"""
# All strategies start with random selection
self.subset_indices, self.subset_weights = self._init_subset_indices()
self.subset_loader = DataLoader(WeightedSubset(self.dataset, self.subset_indices, self.subset_weights),
*self.loader_args, **self.loader_kwargs)
self.batch_wise_indices = list(self.subset_loader.batch_sampler)
self.curr_loader = self.subset_loader
def _init_subset_indices(self):
"""
Function that initializes the initial subset indices by calling the CRAIG 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))
cached_state_dict = copy.deepcopy(self.train_model.state_dict())
clone_dict = copy.deepcopy(self.train_model.state_dict())
if self.teacher_model is not None:
tea_cached_state_dict = copy.deepcopy(self.teacher_model.state_dict())
tea_clone_dict = copy.deepcopy(self.teacher_model.state_dict())
else:
tea_clone_dict = None
subset_indices, subset_weights = self.strategy.select(self.budget, clone_dict, tea_clone_dict)
self.train_model.load_state_dict(cached_state_dict)
if self.teacher_model is not None:
self.teacher_model.load_state_dict(tea_cached_state_dict)
end = time.time()
self.logger.info('Iteration: {0:d}, subset selection finished, takes {1:.2f}. '.format(self.cur_iter, (end - start)))
return subset_indices, subset_weights