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stl10_datamodule.py
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stl10_datamodule.py
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import os
from warnings import warn
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, random_split
from pl_bolts.datamodules.concat_dataset import ConcatDataset
from pl_bolts.transforms.dataset_normalizations import stl10_normalization
try:
from torchvision import transforms as transform_lib
from torchvision.datasets import STL10
except ImportError:
warn('You want to use `torchvision` which is not installed yet,' # pragma: no-cover
' install it with `pip install torchvision`.')
_TORCHVISION_AVAILABLE = False
else:
_TORCHVISION_AVAILABLE = True
class STL10DataModule(LightningDataModule): # pragma: no cover
name = 'stl10'
def __init__(
self,
data_dir: str = None,
unlabeled_val_split: int = 5000,
train_val_split: int = 500,
num_workers: int = 16,
batch_size: int = 32,
seed: int = 42,
*args,
**kwargs,
):
"""
.. figure:: https://samyzaf.com/ML/cifar10/cifar1.jpg
:width: 400
:alt: STL-10
Specs:
- 10 classes (1 per type)
- Each image is (3 x 96 x 96)
Standard STL-10, train, val, test splits and transforms.
STL-10 has support for doing validation splits on the labeled or unlabeled splits
Transforms::
mnist_transforms = transform_lib.Compose([
transform_lib.ToTensor(),
transforms.Normalize(
mean=(0.43, 0.42, 0.39),
std=(0.27, 0.26, 0.27)
)
])
Example::
from pl_bolts.datamodules import STL10DataModule
dm = STL10DataModule(PATH)
model = LitModel()
Trainer().fit(model, dm)
Args:
data_dir: where to save/load the data
unlabeled_val_split: how many images from the unlabeled training split to use for validation
train_val_split: how many images from the labeled training split to use for validation
num_workers: how many workers to use for loading data
batch_size: the batch size
"""
super().__init__(*args, **kwargs)
if not _TORCHVISION_AVAILABLE:
raise ImportError('You want to use STL10 dataset loaded from `torchvision` which is not installed yet.')
self.dims = (3, 96, 96)
self.data_dir = data_dir if data_dir is not None else os.getcwd()
self.unlabeled_val_split = unlabeled_val_split
self.train_val_split = train_val_split
self.num_workers = num_workers
self.batch_size = batch_size
self.seed = seed
self.num_unlabeled_samples = 100000 - unlabeled_val_split
self.labeled_val_split = 200
@property
def num_classes(self):
return 10
def prepare_data(self):
"""
Downloads the unlabeled, train and test split
"""
STL10(self.data_dir, split='unlabeled', download=True, transform=transform_lib.ToTensor())
STL10(self.data_dir, split='train', download=True, transform=transform_lib.ToTensor())
STL10(self.data_dir, split='test', download=True, transform=transform_lib.ToTensor())
def train_dataloader(self):
"""
Loads the 'unlabeled' split minus a portion set aside for validation via `unlabeled_val_split`.
"""
transforms = self.default_transforms() if self.train_transforms is None else self.train_transforms
dataset = STL10(self.data_dir, split='unlabeled', download=False, transform=transforms)
train_length = len(dataset)
dataset_train, _ = random_split(dataset,
[train_length - self.unlabeled_val_split, self.unlabeled_val_split],
generator=torch.Generator().manual_seed(self.seed))
loader = DataLoader(
dataset_train,
batch_size=self.batch_size,
shuffle=True,
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
def val_dataloader(self):
"""
Loads a portion of the 'unlabeled' training data set aside for validation
The val dataset = (unlabeled - train_val_split)
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
dataset = STL10(self.data_dir, split='unlabeled', download=False, transform=transforms)
train_length = len(dataset)
_, dataset_val = random_split(dataset,
[train_length - self.unlabeled_val_split, self.unlabeled_val_split],
generator=torch.Generator().manual_seed(self.seed))
loader = DataLoader(
dataset_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
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 test_dataloader(self):
"""
Loads the test split of STL10
Args:
batch_size: the batch size
transforms: the transforms
"""
transforms = self.default_transforms() if self.test_transforms is None else self.test_transforms
dataset = STL10(self.data_dir, split='test', download=False, transform=transforms)
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_labeled(self):
transforms = self.default_transforms() if self.val_transforms is None else self.val_transforms
dataset = STL10(self.data_dir, split='train', download=False, transform=transforms)
train_length = len(dataset)
dataset_train, _ = random_split(dataset,
[train_length - self.labeled_val_split, self.labeled_val_split],
generator=torch.Generator().manual_seed(self.seed))
loader = DataLoader(
dataset_train,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=True
)
return loader
def val_dataloader_labeled(self):
transforms = self.default_transforms() if self.val_transforms is None else self.val_transforms
dataset = STL10(self.data_dir,
split='train',
download=False,
transform=transforms)
labeled_length = len(dataset)
_, labeled_val = random_split(dataset,
[labeled_length - self.labeled_val_split, self.labeled_val_split],
generator=torch.Generator().manual_seed(self.seed))
loader = DataLoader(
labeled_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True
)
return loader
def default_transforms(self):
data_transforms = transform_lib.Compose([
transform_lib.ToTensor(),
stl10_normalization()
])
return data_transforms