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torchdata is PyTorch oriented library focused on data processing and input pipelines in general.
It extends torch.utils.data.Dataset and equips it with
functionalities known from tensorflow.data
like map or cache (with some additions unavailable in aforementioned).
All of that with minimal interference (single call to super().__init__()) in original
PyTorch's datasets.
- Use
map,apply,reduceorfilter cachedata in RAM or on disk (even partially, say first20%)- Full PyTorch's
DatasetandIterableDatasetsupport (includingtorchvision) - General
torchdata.mapslikeFlattenorSelect - Concrete
torchdata.datasetsdesigned for file reading and other general tasks
- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:
import torchdata
import torchvision
class Images(torchdata.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(torchdata.modifiers.UpToIndex(1000, torchdata.cachers.Memory()))
# Sample from 1000 to the end saved with Pickle on disk
.cache(torchdata.modifiers.FromIndex(1000, torchdata.cachers.Pickle("./cache")))
# You can define your own cachers, modifiers, see docs
)To see what else you can do please check torchdata documentation
pip install --user torchdatapip install --user torchdata-nightlyCPU standalone and various versions of GPU enabled images are available at dockerhub.
For CPU quickstart, issue:
docker pull szymonmaszke/torchdata: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