def train_dataloader_mixed(self, batch_size, transforms=None):
if transforms is None:
transforms = self._default_transforms()
unlabeled_dataset = STL10(self.save_path,
split='unlabeled',
download=False,
transform=transforms)
unlabeled_length = len(unlabeled_dataset)
unlabeled_dataset, _ = random_split(unlabeled_dataset, [
unlabeled_length - self.unlabeled_val_split,
self.unlabeled_val_split
])
labeled_dataset = STL10(self.save_path,
split='train',
download=False,
transform=transforms)
labeled_length = len(labeled_dataset)
labeled_dataset, _ = random_split(
labeled_dataset,
[labeled_length - self.train_val_split, self.train_val_split])
dataset = ConcatDataset(unlabeled_dataset, labeled_dataset)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader
def val_dataloader_mixed(self):
"""
Loads a portion of the 'unlabeled' training data set aside for validation along with
the portion of the 'train' dataset to be used for validation
unlabeled_val = (unlabeled - train_val_split)
labeled_val = (train- train_val_split)
full_val = unlabeled_val + labeled_val
Args:
batch_size: the batch size
transforms: a sequence of transforms
"""
transforms = self.default_transforms(
) if self.val_transforms is None else self.val_transforms
unlabeled_dataset = STL10(self.data_dir,
split='unlabeled',
download=False,
transform=transforms)
unlabeled_length = len(unlabeled_dataset)
_, unlabeled_dataset = random_split(
unlabeled_dataset, [
unlabeled_length - self.unlabeled_val_split,
self.unlabeled_val_split
],
generator=torch.Generator().manual_seed(self.seed))
labeled_dataset = STL10(self.data_dir,
split='train',
download=False,
transform=transforms)
labeled_length = len(labeled_dataset)
_, labeled_dataset = random_split(
labeled_dataset,
[labeled_length - self.train_val_split, self.train_val_split],
generator=torch.Generator().manual_seed(self.seed))
dataset = ConcatDataset(unlabeled_dataset, labeled_dataset)
loader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader
def train_dataloader_mixed(self):
"""
Loads a portion of the 'unlabeled' training data and 'train' (labeled) data.
both portions have a subset removed for validation via `unlabeled_val_split` and `train_val_split`
Args:
batch_size: the batch size
transforms: a sequence of transforms
"""
transforms = self.default_transforms(
) if self.train_transforms is None else self.train_transforms
unlabeled_dataset = STL10(self.data_dir,
split='unlabeled',
download=False,
transform=transforms)
unlabeled_length = len(unlabeled_dataset)
unlabeled_dataset, _ = random_split(
unlabeled_dataset, [
unlabeled_length - self.unlabeled_val_split,
self.unlabeled_val_split
],
generator=torch.Generator().manual_seed(self.seed))
labeled_dataset = STL10(self.data_dir,
split='train',
download=False,
transform=transforms)
labeled_length = len(labeled_dataset)
labeled_dataset, _ = random_split(
labeled_dataset,
[labeled_length - self.train_val_split, self.train_val_split],
generator=torch.Generator().manual_seed(self.seed))
dataset = ConcatDataset(unlabeled_dataset, labeled_dataset)
loader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader