<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 |
|---------|------|-------|----------|-------|------|--------|---------|--------|---------|
| [](https://github.com/szymonmaszke/torchdata/releases) | [](https://szymonmaszke.github.io/torchdata/) |  |  | [](https://codebeat.co/projects/github-com-szymonmaszke-torchdata-master) | [](https://pypi.org/project/torchdata/) | [](https://www.python.org/) | [](https://pytorch.org/) | [](https://hub.docker.com/r/szymonmaszke/torchdata) | [](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)
torchdata-nightly-1620519056.tar.gz （28个子文件） 
torchdata-nightly-1620519056 
setup.cfg 38B README.md 6KB tests datasets.py 843B modifiers_test.py 1KB utils.py 1KB __init__.py 0B samplers_test.py 982B datasets_test.py 805B maps_test.py 3KB torchdata_test.py 3KB cachers_test.py 3KB PKG-INFO 9KB torchdata _base.py 5KB _dev_utils.py 507B maps.py 11KB datasets.py 23KB _name.py 28B _version.py 27B cachers.py 10KB __init__.py 2KB modifiers.py 8KB samplers.py 5KB torchdata_nightly.egg-info 
dependency_links.txt 1B PKG-INFO 9KB SOURCES.txt 620B top_level.txt 16B requires.txt 13B setup.py 2KB资源评论
