Package renamed to torchdatasets!
- 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__())
| 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
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
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
1 maintainer
Metadata
1 maintainer
Release files for torchdatasets-nightly 1706400212
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| torchdatasets-nightly-1706400212.tar.gz | 25.0 kB | Without provenance | Details for torchdatasets-nightly-1706400212.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| torchdatasets_nightly-1706400212-py3-none-any.whl | Python 3 | none | any | Details for torchdatasets_nightly-1706400212-py3-none-any.whl |
Total release size: 54.6 kB
Release files / torchdatasets-nightly-1706400212.tar.gz
| Download URL | torchdatasets-nightly-1706400212.tar.gz |
|---|---|
| Size | 25.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
34a8368c7e4e48ee8199df19dcca1b5aabfbf4925aef83470077c035d76c81dc
|
|
BLAKE2b-256 checksum How to use checksums |
03718a7fa9c19fc7efd5cb7d0df2fa15bc0d06ff2427c3640266ea72a6dfbe23
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
Release files / torchdatasets_nightly-1706400212-py3-none-any.whl
| Download URL | torchdatasets_nightly-1706400212-py3-none-any.whl |
|---|---|
| Size | 29.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a43a99a564504e73bc0978b4c56adb67c1c935f99fbc6e255f11ae55876bea70
|
|
BLAKE2b-256 checksum How to use checksums |
be7196fa63de8573c9cc1d80edecea6f4e89f70b6faab5a0878a218b2802f962
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
Security for torchdatasets-nightly 1706400212
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.
