def training_step(self, batch, *args):
if SemiSupervisedIterator.is_labeled(batch):
return self.supervised_training_step(
SemiSupervisedIterator.get_batch(batch), *args)
else:
return self.unsupervised_training_step(
SemiSupervisedIterator.get_batch(batch), *args)
def train_dataloader(self) -> SemiSupervisedIterator:
return SemiSupervisedIterator(self.active_dataset,
self.hparams.batch_size,
num_steps=self.hparams.num_steps,
p=self.hparams.p,
num_workers=self.hparams.workers,
shuffle=True)
def test_epoch(self):
labeled_data = []
unlabeled_data = []
for batch_idx, batch in enumerate(self.ss_iterator):
if SemiSupervisedIterator.is_labeled(batch):
batch = SemiSupervisedIterator.get_batch(batch)
labeled_data.extend(batch)
else:
batch = SemiSupervisedIterator.get_batch(batch)
unlabeled_data.extend(batch)
labeled_data = torch.tensor(labeled_data)
unlabeled_data = torch.tensor(unlabeled_data)
assert len(labeled_data) == len(unlabeled_data)
assert torch.all(labeled_data % 2 == 0)
assert torch.all(unlabeled_data % 2 != 0)
def test_p(self):
ss_iterator = SemiSupervisedIterator(self.al_dataset,
p=0.1,
num_steps=None,
batch_size=10)
labeled_data = []
unlabeled_data = []
for batch_idx, batch in enumerate(ss_iterator):
if SemiSupervisedIterator.is_labeled(batch):
batch = SemiSupervisedIterator.get_batch(batch)
labeled_data.extend(batch)
else:
batch = SemiSupervisedIterator.get_batch(batch)
unlabeled_data.extend(batch)
total = len(labeled_data) + len(unlabeled_data)
l_ratio = len(labeled_data) / total
u_ratio = len(unlabeled_data) / total
assert l_ratio < .15
assert u_ratio > 0.85
def train_dataloader(self) -> SemiSupervisedIterator:
"""SemiSupervisedIterator for train set.
Returns:
SemiSupervisedIterator on the train set + pool set.
"""
return SemiSupervisedIterator(
self.active_dataset,
self.hparams.batch_size,
num_steps=self.hparams.num_steps,
p=self.hparams.p,
num_workers=self.hparams.workers,
shuffle=True,
)
def test_no_pool(self):
d1 = SSLTestDataset(labeled=True, length=100)
al_dataset = ActiveLearningDataset(d1)
al_dataset.label_randomly(100)
ss_iterator = SemiSupervisedIterator(al_dataset,
p=0.1,
num_steps=None,
batch_size=10)
labeled_data = []
unlabeled_data = []
for batch_idx, batch in enumerate(ss_iterator):
if SemiSupervisedIterator.is_labeled(batch):
batch = SemiSupervisedIterator.get_batch(batch)
labeled_data.extend(batch)
else:
batch = SemiSupervisedIterator.get_batch(batch)
unlabeled_data.extend(batch)
total = len(labeled_data) + len(unlabeled_data)
l_ratio = len(labeled_data) / total
u_ratio = len(unlabeled_data) / total
assert l_ratio == 1
assert u_ratio == 0
def setUp(self):
d1_len = 100
d2_len = 1000
d1 = SSLTestDataset(labeled=True, length=d1_len)
d2 = SSLTestDataset(labeled=False, length=d2_len)
dataset = ConcatDataset([d1, d2])
print(len(dataset))
self.al_dataset = ActiveLearningDataset(dataset)
self.al_dataset.label(list(
range(d1_len))) # Label data from d1 (even numbers)
self.ss_iterator = SemiSupervisedIterator(self.al_dataset,
p=None,
num_steps=None,
batch_size=10)