:py:mod:`tonic` =============== .. py:module:: tonic Subpackages ----------- .. toctree:: :titlesonly: :maxdepth: 3 datasets/index.rst functional/index.rst Submodules ---------- .. toctree:: :titlesonly: :maxdepth: 1 audio_augmentations/index.rst audio_transforms/index.rst cached_dataset/index.rst collation/index.rst dataset/index.rst download_utils/index.rst io/index.rst sliced_dataset/index.rst slicers/index.rst transforms/index.rst utils/index.rst Package Contents ---------------- Classes ~~~~~~~ .. autoapisummary:: tonic.Aug_DiskCachedDataset tonic.CachedDataset tonic.DiskCachedDataset tonic.MemoryCachedDataset tonic.Dataset tonic.SlicedDataset Attributes ~~~~~~~~~~ .. autoapisummary:: tonic.__version__ .. py:class:: Aug_DiskCachedDataset Bases: :py:obj:`DiskCachedDataset` Aug_DiskCachedDataset is a child class from DiskCachedDataset with further customizations to handle augmented copies of a sample. The goal of this customization is to map the indices of cached files (copy) to augmentation parameters. This is useful in a category of augmentations where the range of parameter is rather disceret and non probabilistic, for instance an audio sample is being augmented with noise and SNR can take only N=5 values. Passing copy_index to augmentation Class as an init argument ensures that each copy will be a a distinct augmented sample with a trackable parameter. 'generate_all' method generates all augmented vesions of a sample. 'generate_copy' method generates the missing variant (augmented version) Therefore all transforms applied to the dataset are categorized by the keys: "pre_aug", "augmentations" and "post_aug". Args: 'all_transforms' is a dictionarty passed to this class containing information about all transforms. .. py:attribute:: all_transforms :type: dict | None .. py:method:: __post_init__() .. py:method:: generate_all(item) .. py:method:: generate_copy(item, copy) .. py:method:: __getitem__(item) -> tuple[object, object] .. py:class:: CachedDataset(*args, **kwargs) Bases: :py:obj:`DiskCachedDataset` Deprecated class that points to DiskCachedDataset for now but will be removed in a future release. Please use MemoryCachedDataset or DiskCachedDataset in the future. .. py:class:: DiskCachedDataset DiskCachedDataset caches the data samples to the hard drive for subsequent reads, thereby potentially improving data loading speeds. If dataset is None, then the length of this dataset will be inferred from the number of files in the caching folder. Pay attention to the cache path you're providing, as DiskCachedDataset will simply check if there is a file present with the index that it is looking for. When using train/test splits, it is wise to also take that into account in the cache path. .. note:: When you change the transform that is applied before caching, DiskCachedDataset cannot know about this and will present you with an old file. To avoid this you either have to clear your cache folder manually when needed, incorporate all transformation parameters into the cache path which creates a tree of cache files or use reset_cache=True. .. note:: Caching Pytorch tensors will write numpy arrays to disk, so be careful when loading the sample and you expect a tensor. The recommendation is to defer the transform to tensor as late as possible. :param dataset: Dataset to be cached to disk. Can be None, if only files in cache_path should be used. :param cache_path: The preferred path where the cache will be written to and read from. :param reset_cache: When True, will clear out the cache path during initialisation. Default is False :param transform: Transforms to be applied on the data :param target_transform: Transforms to be applied on the label/targets :param transforms: A callable of transforms that is applied to both data and labels at the same time. :param num_copies: Number of copies of each sample to be cached. This is a useful parameter if the dataset is being augmented with slow, random transforms. :param compress: Whether to apply lightweight lzf compression, default is True. .. py:attribute:: dataset :type: collections.abc.Iterable .. py:attribute:: cache_path :type: str .. py:attribute:: reset_cache :type: bool :value: False .. py:attribute:: transform :type: collections.abc.Callable | None .. py:attribute:: target_transform :type: collections.abc.Callable | None .. py:attribute:: transforms :type: collections.abc.Callable | None .. py:attribute:: num_copies :type: int :value: 1 .. py:attribute:: compress :type: bool :value: True .. py:method:: __post_init__() .. py:method:: __getitem__(item) -> tuple[object, object] .. py:method:: __len__() .. py:class:: MemoryCachedDataset MemoryCachedDataset caches the samples to memory to substantially improve data loading speeds. However you have to keep a close eye on memory consumption while loading your samples, which can increase rapidly when converting events to rasters/frames. If your transformed dataset doesn't fit into memory, yet you still want to cache samples to speed up training, consider using `DiskCachedDataset` instead. :param dataset: Dataset to be cached to memory. :param device: Device to cache to. This is preferably a torch device. Will cache to CPU memory if None (default). :param transform: Transforms to be applied on the data :param target_transform: Transforms to be applied on the label/targets :param transforms: A callable of transforms that is applied to both data and labels at the same time. .. py:attribute:: dataset :type: collections.abc.Iterable .. py:attribute:: device :type: str | None .. py:attribute:: transform :type: collections.abc.Callable | None .. py:attribute:: target_transform :type: collections.abc.Callable | None .. py:attribute:: transforms :type: collections.abc.Callable | None .. py:attribute:: samples_dict :type: dict .. py:method:: __getitem__(index) .. py:method:: __len__() .. py:class:: Dataset(save_to: str, transform: collections.abc.Callable | None = None, target_transform: collections.abc.Callable | None = None, transforms: collections.abc.Callable | None = None) Base class for Tonic datasets which download public data. Contains a few helper function to reduce duplicated code. .. py:method:: __repr__() Return repr(self). .. py:method:: download() -> None Downloads from a given url, places into target folder and verifies the file hash. .. py:class:: SlicedDataset The primary use case for a SlicedDataset is to cut existing examples in a dataset into smaller chunks. For that it takes an iterable dataset and a slicing method as input. It then generates metadata about the slices and where to find them in each original sample. The new dataset length will be the sum of all slices across samples. :param dataset: a dataset object which implements __getitem__ and __len__ methods. :param slicer: a function which implements the tonic.slicers.Slicer protocol, meaning that it doesn't have to inherit from it but implement all its methods. :param metadata_path: filepath where slice metadata should be stored, so that it does not have to be recomputed the next time. If None, will be recomputed every time. :param transform: Transforms to be applied on the data :param target_transform: Transforms to be applied on the label/targets :param transforms: A callable of transforms that is applied to both data and labels at the same time. .. py:attribute:: dataset :type: collections.abc.Iterable .. py:attribute:: slicer :type: tonic.slicers.Slicer .. py:attribute:: metadata_path :type: str | None .. py:attribute:: transform :type: collections.abc.Callable | None .. py:attribute:: target_transform :type: collections.abc.Callable | None .. py:attribute:: transforms :type: collections.abc.Callable | None .. py:method:: __post_init__() Will try to read metadata from disk to know where slices start and stop for each sample. If no metadata_path is provided or no file slice_metadata.h5 is found in that path, metadata will be generated from scratch. .. py:method:: generate_metadata() Slices every sample in the wrapped dataset and returns start and stop metadata for each slice. .. py:method:: __getitem__(item) -> Any .. py:method:: __len__() .. py:data:: __version__