Package renamed to torchdatasets!

Use map, apply, reduce or filter directly on Dataset objects
cache data in RAM/disk or via your own method (partial caching supported)
Full PyTorch's Dataset and IterableDataset support
General torchdatasets.maps like Flatten or Select
Extensible interface (your own cache methods, cache modifiers, maps etc.)
Useful torchdatasets.datasets classes designed for general tasks (e.g. file reading)
Support for torchvision datasets (e.g. ImageFolder, MNIST, CIFAR10) via td.datasets.WrapDataset
Minimal overhead (single call to super().__init__())

Version	Docs	Tests	Coverage	Style	PyPI	Python	PyTorch	Docker	Roadmap

💡 Examples

Check documentation here: https://szymonmaszke.github.io/torchdatasets

General example

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 1000 samples 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

Integration with `torchvision`

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!

🔧 Installation

🐍 pip

Latest release:

pip install --user torchdatasets

Nightly:

pip install --user torchdatasets-nightly

🐋 Docker

CPU standalone and various versions of GPU enabled images are available at dockerhub.

For CPU quickstart, issue:

docker pull szymonmaszke/torchdatasets:18.04

Nightly 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.

❓ Contributing

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

Metadata

Release files for torchdatasets-nightly 1649032258

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for torchdatasets-nightly 1649032258
File	Size	Uploaded	Provenance	Actions
torchdatasets-nightly-1649032258.tar.gz	25.0 kB	Apr 4, 2022	Without provenance	Details for torchdatasets-nightly-1649032258.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for torchdatasets-nightly 1649032258
File	Interpreter	ABI	Platform	Actions Reset
torchdatasets_nightly-1649032258-py3-none-any.whl 29.7 kB Apr 4, 2022 Without provenance	Python 3	none	any	Details for torchdatasets_nightly-1649032258-py3-none-any.whl

Total release size: 54.6 kB

Release files / torchdatasets-nightly-1649032258.tar.gz

Download URL	torchdatasets-nightly-1649032258.tar.gz
Size	25.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`a2f48fab909471abf1d7a29913cd3f5ddf4e2e16a1a1a40af2deb9f21a6704c1`
BLAKE2b-256 checksum How to use checksums	`85f32b7f78c024ee141ed719698ce88258f7aa8db566582b347aa9bc503d74c6`
Upload date	Apr 4, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.0 CPython/3.9.12`

Release files / torchdatasets_nightly-1649032258-py3-none-any.whl

Download URL	torchdatasets_nightly-1649032258-py3-none-any.whl
Size	29.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`94db4b6528e1de3ce706774357726d76b92aaed9b515b2807aa32efd695ec8b8`
BLAKE2b-256 checksum How to use checksums	`2b0539b0f4d15a9017f4b59268d00db64c30edb016280c3c8cd1225c5e6b4077`
Upload date	Apr 4, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.0 CPython/3.9.12`

Security for torchdatasets-nightly 1649032258

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

1649032258 This release

Apr 4, 2022 2 release files No provenance

torchdatasets-nightly 1649032258