torchdata-nightly 1638404477
PyTorch based library focused on data processing and input pipelines in general.
License: MIT
Requires Python: >=3.6
Keywords: pytorch torch data datasets map cache memory disk apply database

<img align="left" width="256" height="256" src="https://github.com/szymonmaszke/torchdata/blob/master/assets/logos/medium.png">

* 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`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) and [`IterableDataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset>) support
* General `torchdata.maps` like `Flatten` or `Select`
* Extensible interface (your own cache methods, cache modifiers, maps etc.)
* Useful `torchdata.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 |
|---------|------|-------|----------|-------|------|--------|---------|--------|---------|
| [![Version](https://img.shields.io/static/v1?label=&message=0.2.0&color=377EF0&style=for-the-badge)](https://github.com/szymonmaszke/torchdata/releases) | [![Documentation](https://img.shields.io/static/v1?label=&message=docs&color=EE4C2C&style=for-the-badge)](https://szymonmaszke.github.io/torchdata/)  | ![Tests](https://github.com/szymonmaszke/torchdata/workflows/test/badge.svg) | ![Coverage](https://img.shields.io/codecov/c/github/szymonmaszke/torchdata?label=%20&logo=codecov&style=for-the-badge) | [![codebeat](https://img.shields.io/static/v1?label=&message=CB&color=27A8E0&style=for-the-badge)](https://codebeat.co/projects/github-com-szymonmaszke-torchdata-master) | [![PyPI](https://img.shields.io/static/v1?label=&message=PyPI&color=377EF0&style=for-the-badge)](https://pypi.org/project/torchdata/) | [![Python](https://img.shields.io/static/v1?label=&message=3.6&color=377EF0&style=for-the-badge&logo=python&logoColor=F8C63D)](https://www.python.org/) | [![PyTorch](https://img.shields.io/static/v1?label=&message=>=1.2.0&color=EE4C2C&style=for-the-badge)](https://pytorch.org/) | [![Docker](https://img.shields.io/static/v1?label=&message=docker&color=309cef&style=for-the-badge)](https://hub.docker.com/r/szymonmaszke/torchdata) | [![Roadmap](https://img.shields.io/static/v1?label=&message=roadmap&color=009688&style=for-the-badge)](https://github.com/szymonmaszke/torchdata/blob/master/ROADMAP.md) |

# :bulb: Examples

__Check documentation here:__
[https://szymonmaszke.github.io/torchdata](https://szymonmaszke.github.io/torchdata)

## General example

- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:

```python
import torchdata 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:

```python
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`:

```python
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 [**torchdata documentation**](https://szymonmaszke.github.io/torchdata/)

## Integration with `torchvision`

Using `torchdata` you can easily split `torchvision` datasets and apply augmentation
only to the training part of data without any troubles:

```python
import torchvision

import torchdata 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!

# :wrench: Installation

## :snake: [pip](<https://pypi.org/project/torchdata/>)

### Latest release:

```shell
pip install --user torchdata
```

### Nightly:

```shell
pip install --user torchdata-nightly
```

## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchdata)

__CPU standalone__ and various versions of __GPU enabled__ images are available
at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchdata/tags).

For CPU quickstart, issue:

```shell
docker pull szymonmaszke/torchdata: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](https://github.com/NVIDIA/nvidia-docker) installed and it's runtime set.

# :question: Contributing

If you find any issue or you think some functionality may be useful to others and fits this library, please [open new Issue](https://help.github.com/en/articles/creating-an-issue) or [create Pull Request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).

To get an overview of thins one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchdata/blob/master/ROADMAP.md)