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"description": "![](\"https://github.com/szymonmaszke/torchdata/blob/master/assets/logos/medium.png\")
\n\n* Use `map`, `apply`, `reduce` or `filter` directly on `Dataset` objects\n* `cache` data in RAM/disk or via your own method (partial caching supported)\n* 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\n* General `torchdata.maps` like `Flatten` or `Select`\n* Extensible interface (your own cache methods, cache modifiers, maps etc.)\n* Useful `torchdata.datasets` classes designed for general tasks (e.g. file reading)\n* Support for `torchvision` datasets (e.g. `ImageFolder`, `MNIST`, `CIFAR10`) via `td.datasets.WrapDataset`\n* Minimal overhead (single call to `super().__init__()`)\n\n| Version | Docs | Tests | Coverage | Style | PyPI | Python | PyTorch | Docker | Roadmap |\n|---------|------|-------|----------|-------|------|--------|---------|--------|---------|\n| [](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) |\n\n# :bulb: Examples\n\n__Check documentation here:__\n[https://szymonmaszke.github.io/torchdata](https://szymonmaszke.github.io/torchdata)\n\n## General example\n\n- Create image dataset, convert it to Tensors, cache and concatenate with smoothed labels:\n\n```python\nimport torchdata as td\nimport torchvision\n\nclass Images(td.Dataset): # Different inheritance\n def __init__(self, path: str):\n super().__init__() # This is the only change\n self.files = [file for file in pathlib.Path(path).glob(\"*\")]\n\n def __getitem__(self, index):\n return Image.open(self.files[index])\n\n def __len__(self):\n return len(self.files)\n\n\nimages = Images(\"./data\").map(torchvision.transforms.ToTensor()).cache()\n```\n\nYou can concatenate above dataset with another (say `labels`) and iterate over them as per usual:\n\n```python\nfor data, label in images | labels:\n # Do whatever you want with your data\n```\n\n- Cache first `1000` samples in memory, save the rest on disk in folder `./cache`:\n\n```python\nimages = (\n ImageDataset.from_folder(\"./data\").map(torchvision.transforms.ToTensor())\n # First 1000 samples in memory\n .cache(td.modifiers.UpToIndex(1000, td.cachers.Memory()))\n # Sample from 1000 to the end saved with Pickle on disk\n .cache(td.modifiers.FromIndex(1000, td.cachers.Pickle(\"./cache\")))\n # You can define your own cachers, modifiers, see docs\n)\n```\nTo see what else you can do please check [**torchdata documentation**](https://szymonmaszke.github.io/torchdata/)\n\n## Integration with `torchvision`\n\nUsing `torchdata` you can easily split `torchvision` datasets and apply augmentation\nonly to the training part of data without any troubles:\n\n```python\nimport torchvision\n\nimport torchdata as td\n\n# Wrap torchvision dataset with WrapDataset\ndataset = td.datasets.WrapDataset(torchvision.datasets.ImageFolder(\"./images\"))\n\n# Split dataset\ntrain_dataset, validation_dataset, test_dataset = torch.utils.data.random_split(\n model_dataset,\n (int(0.6 * len(dataset)), int(0.2 * len(dataset)), int(0.2 * len(dataset))),\n)\n\n# Apply torchvision mappings ONLY to train dataset\ntrain_dataset.map(\n td.maps.To(\n torchvision.transforms.Compose(\n [\n torchvision.transforms.RandomResizedCrop(224),\n torchvision.transforms.RandomHorizontalFlip(),\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize(\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n ),\n ]\n )\n ),\n # Apply this transformation to zeroth sample\n # First sample is the label\n 0,\n)\n```\n\nPlease notice you can use `td.datasets.WrapDataset` with any existing `torch.utils.data.Dataset`\ninstance to give it additional `caching` and `mapping` powers!\n\n# :wrench: Installation\n\n## :snake: [pip]()\n\n### Latest release:\n\n```shell\npip install --user torchdata\n```\n\n### Nightly:\n\n```shell\npip install --user torchdata-nightly\n```\n\n## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchdata)\n\n__CPU standalone__ and various versions of __GPU enabled__ images are available\nat [dockerhub](https://hub.docker.com/r/szymonmaszke/torchdata/tags).\n\nFor CPU quickstart, issue:\n\n```shell\ndocker pull szymonmaszke/torchdata:18.04\n```\n\nNightly builds are also available, just prefix tag with `nightly_`. If you are going for `GPU` image make sure you have\n[nvidia/docker](https://github.com/NVIDIA/nvidia-docker) installed and it's runtime set.\n\n# :question: Contributing\n\nIf 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).\n\nTo get an overview of thins one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchdata/blob/master/ROADMAP.md)\n\n\n",
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