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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.
maporapplyarbitrary functions to datasetcacheallows you to cache data in memory or on disk (even partially, say first20%)- Full
torch.utils.data.IterableDatasetandtorch.utils.data.Datasetsupport - Easy to create custom methods of caching, choosing elements to cache, maps and datasets
- Concrete and base classes designed for file reading and other general tasks
- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:
# Imports assumed
# Example dataset return all 1 labels
class Labels(torchdata.Dataset):
def __init__(self, length):
self.length = length
super().__init__()
def __getitem__(self, _):
return 1
def __len__(self):
return len(length)
# Convenience class based on torchdata.Dataset
class ImageDataset(torchdata.Files):
def __getitem__(self, index):
return Image.open(self.files[index])
images = (
ImageDataset.from_folder("./data").map(torchvision.transforms.ToTensor()).cache()
)
smoothed_labels = Labels(len(images)).map(lambda label: label - 0.1)
# That's how you concatenate sample-wise
for image, label in images | smoothed_labels:
pass- 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(torchdata.cachers.Memory(), 1000))
# Sample from 1000 to the end saved with Pickle on disk
.cache(torchdata.modifiers.FromIndex(torchdata.cachers.Pickle("./cache"), 1000))
# 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 something which one can done to help this project, see Roadmap