- Use
map,apply,reduceorfilterdirectly onDatasetobjects -
cachedata in RAM/disk or via your own method (partial caching supported) - Full PyTorch's
DatasetandIterableDatasetsupport - General
torchdatasets.mapslikeFlattenorSelect - Extensible interface (your own cache methods, cache modifiers, maps etc.)
- Useful
torchdatasets.datasetsclasses designed for general tasks (e.g. file reading) - Support for
torchvisiondatasets (e.g.ImageFolder,MNIST,CIFAR10) viatd.datasets.WrapDataset - Minimal overhead (single call to
super().__init__())
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Check documentation here: https://szymonmaszke.github.io/torchdatasets
- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:
import torchdatasets as td
import torchvision
class Images(td.Dataset): # Different inheritance
def __init__(self, path: str):
super().__init__() # This is the only change
self.files = [file for file in pathlib.Path(path).glob("*")]
def __getitem__(self, index):
return Image.open(self.files[index])
def __len__(self):
return len(self.files)
images = Images("./data").map(torchvision.transforms.ToTensor()).cache()You can concatenate above dataset with another (say labels) and iterate over them as per usual:
for data, label in images | labels:
# Do whatever you want with your data- Cache first
1000samples in memory, save the rest on disk in folder./cache:
images = (
ImageDataset.from_folder("./data").map(torchvision.transforms.ToTensor())
# First 1000 samples in memory
.cache(td.modifiers.UpToIndex(1000, td.cachers.Memory()))
# Sample from 1000 to the end saved with Pickle on disk
.cache(td.modifiers.FromIndex(1000, td.cachers.Pickle("./cache")))
# You can define your own cachers, modifiers, see docs
)To see what else you can do please check torchdatasets documentation
Using torchdatasets you can easily split torchvision datasets and apply augmentation
only to the training part of data without any troubles:
import torchvision
import torchdatasets as td
# Wrap torchvision dataset with WrapDataset
dataset = td.datasets.WrapDataset(torchvision.datasets.ImageFolder("./images"))
# Split dataset
train_dataset, validation_dataset, test_dataset = torch.utils.data.random_split(
model_dataset,
(int(0.6 * len(dataset)), int(0.2 * len(dataset)), int(0.2 * len(dataset))),
)
# Apply torchvision mappings ONLY to train dataset
train_dataset.map(
td.maps.To(
torchvision.transforms.Compose(
[
torchvision.transforms.RandomResizedCrop(224),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
),
# Apply this transformation to zeroth sample
# First sample is the label
0,
)Please notice you can use td.datasets.WrapDataset with any existing torch.utils.data.Dataset
instance to give it additional caching and mapping powers!
๐ pip
pip install --user torchdatasetspip install --user torchdatasets-nightly๐ Docker
CPU standalone and various versions of GPU enabled images are available at dockerhub.
For CPU quickstart, issue:
docker pull szymonmaszke/torchdatasets:18.04Nightly builds are also available, just prefix tag with nightly_. If you are going for GPU image make sure you have
nvidia/docker installed and it's runtime set.
If you find any issue or you think some functionality may be useful to others and fits this library, please open new Issue or create Pull Request.
To get an overview of thins one can do to help this project, see Roadmap
