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https://github.com/szymonmaszke/torchdatasets
PyTorch dataset extended with map, cache etc. (tensorflow.data like)
https://github.com/szymonmaszke/torchdatasets
cache concatenate dataset disk filter library map pytorch tensorflow tf-data torch
Last synced: 9 days ago
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PyTorch dataset extended with map, cache etc. (tensorflow.data like)
- Host: GitHub
- URL: https://github.com/szymonmaszke/torchdatasets
- Owner: szymonmaszke
- License: mit
- Created: 2019-09-16T00:42:36.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-06-13T18:47:23.000Z (over 2 years ago)
- Last Synced: 2024-10-12T04:49:05.883Z (30 days ago)
- Topics: cache, concatenate, dataset, disk, filter, library, map, pytorch, tensorflow, tf-data, torch
- Language: Python
- Homepage:
- Size: 1.46 MB
- Stars: 328
- Watchers: 7
- Forks: 19
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
## 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`](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 `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 |
|---------|------|-------|----------|-------|------|--------|---------|--------|---------|
| [](https://github.com/szymonmaszke/torchdatasets/releases) | [](https://szymonmaszke.github.io/torchdatasets/) |  |  | [](https://codebeat.co/projects/github-com-szymonmaszke-torchdatasets-master) | [](https://pypi.org/project/torchdatasets/) | [](https://www.python.org/) | [](https://pytorch.org/) | [](https://hub.docker.com/r/szymonmaszke/torchdatasets) | [](https://github.com/szymonmaszke/torchdatasets/blob/master/ROADMAP.md) |
# :bulb: Examples
__Check documentation here:__
[https://szymonmaszke.github.io/torchdatasets](https://szymonmaszke.github.io/torchdatasets)
## General example
- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:
```python
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:
```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 [**torchdatasets documentation**](https://szymonmaszke.github.io/torchdatasets/)
## Integration with `torchvision`
Using `torchdatasets` you can easily split `torchvision` datasets and apply augmentation
only to the training part of data without any troubles:
```python
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!
# :wrench: Installation
## :snake: [pip]()
### Latest release:
```shell
pip install --user torchdatasets
```
### Nightly:
```shell
pip install --user torchdatasets-nightly
```
## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchdatasets)
__CPU standalone__ and various versions of __GPU enabled__ images are available
at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchdatasets/tags).
For CPU quickstart, issue:
```shell
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](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/torchdatasets/blob/master/ROADMAP.md)