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May 2, 2023 22:03
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New Generated DataPipe Interface
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# This base template ("datapipe.pyi.in") is generated from mypy stubgen with minimal editing for code injection | |
# The output file will be "datapipe.pyi". This is executed as part of torch/CMakeLists.txt | |
# Note that, for mypy, .pyi file takes precedent over .py file, such that we must define the interface for other | |
# classes/objects here, even though we are not injecting extra code into them at the moment. | |
from typing import Any, Callable, Dict, Generic, Iterator, List, Literal, Optional, TypeVar, Union | |
from torch.utils.data import Dataset, default_collate, IterableDataset | |
from torch.utils.data.datapipes._hook_iterator import _SnapshotState | |
from torch.utils.data.datapipes._typing import _DataPipeMeta, _IterDataPipeMeta | |
T_co = TypeVar("T_co", covariant=True) | |
T = TypeVar("T") | |
UNTRACABLE_DATAFRAME_PIPES: Any | |
class MapDataPipe(Dataset[T_co], metaclass=_DataPipeMeta): | |
functions: Dict[str, Callable] = ... | |
reduce_ex_hook: Optional[Callable] = ... | |
getstate_hook: Optional[Callable] = ... | |
str_hook: Optional[Callable] = ... | |
repr_hook: Optional[Callable] = ... | |
def __getattr__(self, attribute_name: Any): ... | |
@classmethod | |
def register_function(cls, function_name: Any, function: Any) -> None: ... | |
@classmethod | |
def register_datapipe_as_function( | |
cls, | |
function_name: Any, | |
cls_to_register: Any, | |
): ... | |
def __getstate__(self): ... | |
def __reduce_ex__(self, *args: Any, **kwargs: Any): ... | |
@classmethod | |
def set_getstate_hook(cls, hook_fn: Any) -> None: ... | |
@classmethod | |
def set_reduce_ex_hook(cls, hook_fn: Any) -> None: ... | |
# Functional form of 'BatcherMapDataPipe' | |
def batch(self, batch_size: int, drop_last: bool = False, wrapper_class=DataChunk) -> MapDataPipe: | |
r""" | |
Create mini-batches of data (functional name: ``batch``). An outer dimension will be added as | |
``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the | |
last batch if ``drop_last`` is set to ``False``. | |
Args: | |
datapipe: Iterable DataPipe being batched | |
batch_size: The size of each batch | |
drop_last: Option to drop the last batch if it's not full | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.map import SequenceWrapper | |
>>> dp = SequenceWrapper(range(10)) | |
>>> batch_dp = dp.batch(batch_size=2) | |
>>> list(batch_dp) | |
[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]] | |
""" | |
# Functional form of 'ConcaterMapDataPipe' | |
def concat(self, *datapipes: MapDataPipe) -> MapDataPipe: | |
r""" | |
Concatenate multiple Map DataPipes (functional name: ``concat``). | |
The new index of is the cumulative sum of source DataPipes. | |
For example, if there are 2 source DataPipes both with length 5, | |
index 0 to 4 of the resulting `ConcatMapDataPipe` would refer to | |
elements of the first DataPipe, and 5 to 9 would refer to elements | |
of the second DataPipe. | |
Args: | |
datapipes: Map DataPipes being concatenated | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.map import SequenceWrapper | |
>>> dp1 = SequenceWrapper(range(3)) | |
>>> dp2 = SequenceWrapper(range(3)) | |
>>> concat_dp = dp1.concat(dp2) | |
>>> list(concat_dp) | |
[0, 1, 2, 0, 1, 2] | |
""" | |
# Functional form of 'MapperMapDataPipe' | |
def map(self, fn: Callable= ...) -> MapDataPipe: | |
r""" | |
Apply the input function over each item from the source DataPipe (functional name: ``map``). | |
The function can be any regular Python function or partial object. Lambda | |
function is not recommended as it is not supported by pickle. | |
Args: | |
datapipe: Source MapDataPipe | |
fn: Function being applied to each item | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.map import SequenceWrapper, Mapper | |
>>> def add_one(x): | |
... return x + 1 | |
>>> dp = SequenceWrapper(range(10)) | |
>>> map_dp_1 = dp.map(add_one) | |
>>> list(map_dp_1) | |
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
>>> map_dp_2 = Mapper(dp, lambda x: x + 1) | |
>>> list(map_dp_2) | |
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
""" | |
# Functional form of 'ShufflerIterDataPipe' | |
def shuffle(self, *, indices: Optional[List] = None) -> IterDataPipe: | |
r""" | |
Shuffle the input MapDataPipe via its indices (functional name: ``shuffle``). | |
When it is used with :class:`~torch.utils.data.DataLoader`, the methods to | |
set up random seed are different based on :attr:`num_workers`. | |
For single-process mode (:attr:`num_workers == 0`), the random seed is set before | |
the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process | |
mode (:attr:`num_worker > 0`), ``worker_init_fn`` is used to set up a random seed | |
for each worker process. | |
Args: | |
datapipe: MapDataPipe being shuffled | |
indices: a list of indices of the MapDataPipe. If not provided, we assume it uses 0-based indexing | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.map import SequenceWrapper | |
>>> dp = SequenceWrapper(range(10)) | |
>>> shuffle_dp = dp.shuffle().set_seed(0) | |
>>> list(shuffle_dp) | |
[7, 8, 1, 5, 3, 4, 2, 0, 9, 6] | |
>>> list(shuffle_dp) | |
[6, 1, 9, 5, 2, 4, 7, 3, 8, 0] | |
>>> # Reset seed for Shuffler | |
>>> shuffle_dp = shuffle_dp.set_seed(0) | |
>>> list(shuffle_dp) | |
[7, 8, 1, 5, 3, 4, 2, 0, 9, 6] | |
Note: | |
Even thought this ``shuffle`` operation takes a ``MapDataPipe`` as the input, it would return an | |
``IterDataPipe`` rather than a ``MapDataPipe``, because ``MapDataPipe`` should be non-sensitive to | |
the order of data order for the sake of random reads, but ``IterDataPipe`` depends on the order | |
of data during data-processing. | |
""" | |
# Functional form of 'ZipperMapDataPipe' | |
def zip(self, *datapipes: MapDataPipe[T_co]) -> MapDataPipe: | |
r""" | |
Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). | |
This MataPipe is out of bound as soon as the shortest input DataPipe is exhausted. | |
Args: | |
*datapipes: Map DataPipes being aggregated | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.map import SequenceWrapper | |
>>> dp1 = SequenceWrapper(range(3)) | |
>>> dp2 = SequenceWrapper(range(10, 13)) | |
>>> zip_dp = dp1.zip(dp2) | |
>>> list(zip_dp) | |
[(0, 10), (1, 11), (2, 12)] | |
""" | |
class IterDataPipe(IterableDataset[T_co], metaclass=_IterDataPipeMeta): | |
functions: Dict[str, Callable] = ... | |
reduce_ex_hook: Optional[Callable] = ... | |
getstate_hook: Optional[Callable] = ... | |
str_hook: Optional[Callable] = ... | |
repr_hook: Optional[Callable] = ... | |
_number_of_samples_yielded: int = ... | |
_snapshot_state: _SnapshotState = _SnapshotState.Iterating | |
_fast_forward_iterator: Optional[Iterator] = ... | |
def __getattr__(self, attribute_name: Any): ... | |
@classmethod | |
def register_function(cls, function_name: Any, function: Any) -> None: ... | |
@classmethod | |
def register_datapipe_as_function( | |
cls, | |
function_name: Any, | |
cls_to_register: Any, | |
enable_df_api_tracing: bool = ..., | |
): ... | |
def __getstate__(self): ... | |
def __reduce_ex__(self, *args: Any, **kwargs: Any): ... | |
@classmethod | |
def set_getstate_hook(cls, hook_fn: Any) -> None: ... | |
@classmethod | |
def set_reduce_ex_hook(cls, hook_fn: Any) -> None: ... | |
# Functional form of 'BatcherIterDataPipe' | |
def batch(self, batch_size: int, drop_last: bool = False, wrapper_class=DataChunk) -> IterDataPipe: | |
r""" | |
Creates mini-batches of data (functional name: ``batch``). An outer dimension will be added as | |
``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the | |
last batch if ``drop_last`` is set to ``False``. | |
Args: | |
datapipe: Iterable DataPipe being batched | |
batch_size: The size of each batch | |
drop_last: Option to drop the last batch if it's not full | |
wrapper_class: wrapper to apply onto each batch (type ``List``) before yielding, | |
defaults to ``DataChunk`` | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> dp = IterableWrapper(range(10)) | |
>>> dp = dp.batch(batch_size=3, drop_last=True) | |
>>> list(dp) | |
[[0, 1, 2], [3, 4, 5], [6, 7, 8]] | |
""" | |
# Functional form of 'CollatorIterDataPipe' | |
def collate(self, conversion: Optional[Union[Callable[..., Any],Dict[Union[str, Any], Union[Callable, Any]],]] = default_collate, collate_fn: Optional[Callable] = None) -> IterDataPipe: | |
r""" | |
Collates samples from DataPipe to Tensor(s) by a custom collate function (functional name: ``collate``). | |
By default, it uses :func:`torch.utils.data.default_collate`. | |
.. note:: | |
While writing a custom collate function, you can import :func:`torch.utils.data.default_collate` for the | |
default behavior and `functools.partial` to specify any additional arguments. | |
Args: | |
datapipe: Iterable DataPipe being collated | |
collate_fn: Customized collate function to collect and combine data or a batch of data. | |
Default function collates to Tensor(s) based on data type. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> # Convert integer data to float Tensor | |
>>> class MyIterDataPipe(torch.utils.data.IterDataPipe): | |
... def __init__(self, start, end): | |
... super(MyIterDataPipe).__init__() | |
... assert end > start, "this example code only works with end >= start" | |
... self.start = start | |
... self.end = end | |
... | |
... def __iter__(self): | |
... return iter(range(self.start, self.end)) | |
... | |
... def __len__(self): | |
... return self.end - self.start | |
... | |
>>> ds = MyIterDataPipe(start=3, end=7) | |
>>> print(list(ds)) | |
[3, 4, 5, 6] | |
>>> def collate_fn(batch): | |
... return torch.tensor(batch, dtype=torch.float) | |
... | |
>>> collated_ds = CollateIterDataPipe(ds, collate_fn=collate_fn) | |
>>> print(list(collated_ds)) | |
[tensor(3.), tensor(4.), tensor(5.), tensor(6.)] | |
""" | |
# Functional form of 'ConcaterIterDataPipe' | |
def concat(self, *datapipes: IterDataPipe) -> IterDataPipe: | |
r""" | |
Concatenates multiple Iterable DataPipes (functional name: ``concat``). The resulting DataPipe will | |
yield all the elements from the first input DataPipe, before yielding from the subsequent ones. | |
Args: | |
datapipes: Iterable DataPipes being concatenated | |
Example: | |
>>> # xdoctest: +REQUIRES(module:torchdata) | |
>>> import random | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> dp1 = IterableWrapper(range(3)) | |
>>> dp2 = IterableWrapper(range(5)) | |
>>> list(dp1.concat(dp2)) | |
[0, 1, 2, 0, 1, 2, 3, 4] | |
""" | |
# Functional form of 'DemultiplexerIterDataPipe' | |
def demux(self, num_instances: int, classifier_fn: Callable[[T_co], Optional[int]], drop_none: bool = False, buffer_size: int = 1000) -> List[IterDataPipe]: | |
r""" | |
Splits the input DataPipe into multiple child DataPipes, using the given | |
classification function (functional name: ``demux``). A list of the child DataPipes is returned from this operation. | |
Args: | |
datapipe: Iterable DataPipe being filtered | |
num_instances: number of instances of the DataPipe to create | |
classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` | |
drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` | |
buffer_size: this defines the maximum number of inputs that the buffer can hold across all child | |
DataPipes while waiting for their values to be yielded. | |
Defaults to ``1000``. Use ``-1`` for the unlimited buffer. | |
Examples: | |
>>> # xdoctest: +REQUIRES(module:torchdata) | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> def odd_or_even(n): | |
... return n % 2 | |
>>> source_dp = IterableWrapper(range(5)) | |
>>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) | |
>>> list(dp1) | |
[0, 2, 4] | |
>>> list(dp2) | |
[1, 3] | |
>>> # It can also filter out any element that gets `None` from the `classifier_fn` | |
>>> def odd_or_even_no_zero(n): | |
... return n % 2 if n != 0 else None | |
>>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True) | |
>>> list(dp1) | |
[2, 4] | |
>>> list(dp2) | |
[1, 3] | |
""" | |
# Functional form of 'FilterIterDataPipe' | |
def filter(self, filter_fn: Callable, input_col=None) -> IterDataPipe: | |
r""" | |
Filters out elements from the source datapipe according to input ``filter_fn`` (functional name: ``filter``). | |
Args: | |
datapipe: Iterable DataPipe being filtered | |
filter_fn: Customized function mapping an element to a boolean. | |
input_col: Index or indices of data which ``filter_fn`` is applied, such as: | |
- ``None`` as default to apply ``filter_fn`` to the data directly. | |
- Integer(s) is used for list/tuple. | |
- Key(s) is used for dict. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> def is_even(n): | |
... return n % 2 == 0 | |
>>> dp = IterableWrapper(range(5)) | |
>>> filter_dp = dp.filter(filter_fn=is_even) | |
>>> list(filter_dp) | |
[0, 2, 4] | |
""" | |
# Functional form of 'ForkerIterDataPipe' | |
def fork(self, num_instances: int, buffer_size: int = 1000, copy: Optional[Literal["shallow", "deep"]] = None) -> List[IterDataPipe]: | |
r""" | |
Creates multiple instances of the same Iterable DataPipe (functional name: ``fork``). | |
Args: | |
datapipe: Iterable DataPipe being copied | |
num_instances: number of instances of the datapipe to create | |
buffer_size: this restricts how far ahead the leading child DataPipe | |
can read relative to the slowest child DataPipe. | |
Defaults to ``1000``. Use ``-1`` for the unlimited buffer. | |
copy: copy strategy to use for items yielded by each branch. Supported | |
options are ``None`` for no copying, ``"shallow"`` for shallow object | |
copies, and ``"deep"`` for deep object copies. Defaults to ``None``. | |
Note: | |
All branches of the forked pipeline return the identical object unless | |
the copy parameter is supplied. If the object is mutable or contains | |
mutable objects, changing them in one branch will affect all others. | |
Example: | |
>>> # xdoctest: +REQUIRES(module:torchdata) | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> source_dp = IterableWrapper(range(5)) | |
>>> dp1, dp2 = source_dp.fork(num_instances=2) | |
>>> list(dp1) | |
[0, 1, 2, 3, 4] | |
>>> list(dp2) | |
[0, 1, 2, 3, 4] | |
""" | |
# Functional form of 'GrouperIterDataPipe' | |
def groupby(self, group_key_fn: Callable[[T_co], Any], *, keep_key: bool = False, buffer_size: int = 10000, group_size: Optional[int] = None, guaranteed_group_size: Optional[int] = None, drop_remaining: bool = False) -> IterDataPipe: | |
r""" | |
Groups data from input IterDataPipe by keys which are generated from ``group_key_fn``, | |
and yields a ``DataChunk`` with batch size up to ``group_size`` if defined (functional name: ``groupby``). | |
The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group | |
will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, | |
the DataPipe will yield the largest batch with the same key, provided that its size is larger | |
than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. | |
After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity | |
will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. | |
Args: | |
datapipe: Iterable datapipe to be grouped | |
group_key_fn: Function used to generate group key from the data of the source datapipe | |
keep_key: Option to yield the matching key along with the items in a tuple, | |
resulting in `(key, [items])` otherwise returning [items] | |
buffer_size: The size of buffer for ungrouped data | |
group_size: The max size of each group, a batch is yielded as soon as it reaches this size | |
guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full | |
drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer | |
when the buffer is full | |
Example: | |
>>> import os | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> def group_fn(file): | |
... return os.path.basename(file).split(".")[0] | |
>>> source_dp = IterableWrapper(["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"]) | |
>>> dp0 = source_dp.groupby(group_key_fn=group_fn) | |
>>> list(dp0) | |
[['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] | |
>>> # A group is yielded as soon as its size equals to `group_size` | |
>>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) | |
>>> list(dp1) | |
[['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] | |
>>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` | |
>>> dp2 = source_dp.groupby(group_key_fn=group_fn, buffer_size=3, group_size=3, guaranteed_group_size=2) | |
>>> list(dp2) | |
[['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] | |
""" | |
# Functional form of 'FileListerIterDataPipe' | |
def list_files(self, masks: Union[str, List[str]] = '', *, recursive: bool = False, abspath: bool = False, non_deterministic: bool = False, length: int = -1) -> IterDataPipe: | |
r""" | |
Given path(s) to the root directory, yields file pathname(s) (path + filename) of files within the root directory. | |
Multiple root directories can be provided (functional name: ``list_files``). | |
Args: | |
root: Root directory or a sequence of root directories | |
masks: Unix style filter string or string list for filtering file name(s) | |
recursive: Whether to return pathname from nested directories or not | |
abspath: Whether to return relative pathname or absolute pathname | |
non_deterministic: Whether to return pathname in sorted order or not. | |
If ``False``, the results yielded from each root directory will be sorted | |
length: Nominal length of the datapipe | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import FileLister | |
>>> dp = FileLister(root=".", recursive=True) | |
>>> list(dp) | |
['example.py', './data/data.tar'] | |
""" | |
# Functional form of 'MapperIterDataPipe' | |
def map(self, fn: Callable, input_col=None, output_col=None) -> IterDataPipe: | |
r""" | |
Applies a function over each item from the source DataPipe (functional name: ``map``). | |
The function can be any regular Python function or partial object. Lambda | |
function is not recommended as it is not supported by pickle. | |
Args: | |
datapipe: Source Iterable DataPipe | |
fn: Function being applied over each item | |
input_col: Index or indices of data which ``fn`` is applied, such as: | |
- ``None`` as default to apply ``fn`` to the data directly. | |
- Integer(s) is used for list/tuple. | |
- Key(s) is used for dict. | |
output_col: Index of data where result of ``fn`` is placed. ``output_col`` can be specified | |
only when ``input_col`` is not ``None`` | |
- ``None`` as default to replace the index that ``input_col`` specified; For ``input_col`` with | |
multiple indices, the left-most one is used, and other indices will be removed. | |
- Integer is used for list/tuple. ``-1`` represents to append result at the end. | |
- Key is used for dict. New key is acceptable. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper, Mapper | |
>>> def add_one(x): | |
... return x + 1 | |
>>> dp = IterableWrapper(range(10)) | |
>>> map_dp_1 = dp.map(add_one) # Invocation via functional form is preferred | |
>>> list(map_dp_1) | |
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
>>> # We discourage the usage of `lambda` functions as they are not serializable with `pickle` | |
>>> # Use `functools.partial` or explicitly define the function instead | |
>>> map_dp_2 = Mapper(dp, lambda x: x + 1) | |
>>> list(map_dp_2) | |
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
""" | |
# Functional form of 'MultiplexerIterDataPipe' | |
def mux(self, *datapipes) -> IterDataPipe: | |
r""" | |
Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). As in, | |
one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, | |
and so on. It ends when the shortest input DataPipe is exhausted. | |
Args: | |
datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted | |
Example: | |
>>> # xdoctest: +REQUIRES(module:torchdata) | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25)) | |
>>> list(dp1.mux(dp2, dp3)) | |
[0, 10, 20, 1, 11, 21, 2, 12, 22] | |
""" | |
# Functional form of 'FileOpenerIterDataPipe' | |
def open_files(self, mode: str = 'r', encoding: Optional[str] = None, length: int = -1) -> IterDataPipe: | |
r""" | |
Given pathnames, opens files and yield pathname and file stream | |
in a tuple (functional name: ``open_files``). | |
Args: | |
datapipe: Iterable datapipe that provides pathnames | |
mode: An optional string that specifies the mode in which | |
the file is opened by ``open()``. It defaults to ``r``, other options are | |
``b`` for reading in binary mode and ``t`` for text mode. | |
encoding: An optional string that specifies the encoding of the | |
underlying file. It defaults to ``None`` to match the default encoding of ``open``. | |
length: Nominal length of the datapipe | |
Note: | |
The opened file handles will be closed by Python's GC periodically. Users can choose | |
to close them explicitly. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import FileLister, FileOpener, StreamReader | |
>>> dp = FileLister(root=".").filter(lambda fname: fname.endswith('.txt')) | |
>>> dp = FileOpener(dp) | |
>>> dp = StreamReader(dp) | |
>>> list(dp) | |
[('./abc.txt', 'abc')] | |
""" | |
# Functional form of 'StreamReaderIterDataPipe' | |
def read_from_stream(self, chunk=None) -> IterDataPipe: | |
r""" | |
Given IO streams and their label names, yields bytes with label | |
name in a tuple (functional name: ``read_from_stream``). | |
Args: | |
datapipe: Iterable DataPipe provides label/URL and byte stream | |
chunk: Number of bytes to be read from stream per iteration. | |
If ``None``, all bytes will be read until the EOF. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper, StreamReader | |
>>> from io import StringIO | |
>>> dp = IterableWrapper([("alphabet", StringIO("abcde"))]) | |
>>> list(StreamReader(dp, chunk=1)) | |
[('alphabet', 'a'), ('alphabet', 'b'), ('alphabet', 'c'), ('alphabet', 'd'), ('alphabet', 'e')] | |
""" | |
# Functional form of 'RoutedDecoderIterDataPipe' | |
def routed_decode(self, *handlers: Callable, key_fn: Callable= ...) -> IterDataPipe: | |
r""" | |
Decodes binary streams from input DataPipe, yields pathname and decoded data | |
in a tuple (functional name: ``routed_decode``). | |
Args: | |
datapipe: Iterable datapipe that provides pathname and binary stream in tuples | |
handlers: Optional user defined decoder handlers. If ``None``, basic and image decoder | |
handlers will be set as default. If multiple handles are provided, the priority | |
order follows the order of handlers (the first handler has the top priority) | |
key_fn: Function for decoder to extract key from pathname to dispatch handlers. | |
Default is set to extract file extension from pathname | |
Note: | |
When ``key_fn`` is specified returning anything other than extension, the default | |
handler will not work and users need to specify custom handler. Custom handler | |
could use regex to determine the eligibility to handle data. | |
""" | |
# Functional form of 'ShardingFilterIterDataPipe' | |
def sharding_filter(self, sharding_group_filter=None) -> IterDataPipe: | |
r""" | |
Wrapper that allows DataPipe to be sharded (functional name: ``sharding_filter``). After ``apply_sharding`` is | |
called, each instance of the DataPipe (on different workers) will have every `n`-th element of the | |
original DataPipe, where `n` equals to the number of instances. | |
Args: | |
source_datapipe: Iterable DataPipe that will be sharded | |
""" | |
# Functional form of 'ShufflerIterDataPipe' | |
def shuffle(self, *, buffer_size: int = 10000, unbatch_level: int = 0) -> IterDataPipe: | |
r""" | |
Shuffles the input DataPipe with a buffer (functional name: ``shuffle``). The buffer | |
with ``buffer_size`` is filled with elements from the datapipe first. Then, | |
each item will be yielded from the buffer by reservoir sampling via iterator. | |
``buffer_size`` is required to be larger than ``0``. For ``buffer_size == 1``, the | |
datapipe is not shuffled. In order to fully shuffle all elements from datapipe, | |
``buffer_size`` is required to be greater than or equal to the size of datapipe. | |
When it is used with :class:`torch.utils.data.DataLoader`, the methods to | |
set up random seed are different based on :attr:`num_workers`. | |
For single-process mode (:attr:`num_workers == 0`), the random seed is set before | |
the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process | |
mode (:attr:`num_worker > 0`), `worker_init_fn` is used to set up a random seed | |
for each worker process. | |
Args: | |
datapipe: The IterDataPipe being shuffled | |
buffer_size: The buffer size for shuffling (default to ``10000``) | |
unbatch_level: Specifies if it is necessary to unbatch source data before | |
applying the shuffle | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> dp = IterableWrapper(range(10)) | |
>>> shuffle_dp = dp.shuffle() | |
>>> list(shuffle_dp) | |
[0, 4, 1, 6, 3, 2, 9, 5, 7, 8] | |
""" | |
# Functional form of 'UnBatcherIterDataPipe' | |
def unbatch(self, unbatch_level: int = 1) -> IterDataPipe: | |
r""" | |
Undoes batching of data (functional name: ``unbatch``). In other words, it flattens the data up to the specified level | |
within a batched DataPipe. | |
Args: | |
datapipe: Iterable DataPipe being un-batched | |
unbatch_level: Defaults to ``1`` (only flattening the top level). If set to ``2``, | |
it will flatten the top two levels, and ``-1`` will flatten the entire DataPipe. | |
Example: | |
>>> # xdoctest: +SKIP | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]]) | |
>>> dp1 = source_dp.unbatch() | |
>>> list(dp1) | |
[[0, 1], [2], [3, 4], [5], [6]] | |
>>> dp2 = source_dp.unbatch(unbatch_level=2) | |
>>> list(dp2) | |
[0, 1, 2, 3, 4, 5, 6] | |
""" | |
# Functional form of 'ZipperIterDataPipe' | |
def zip(self, *datapipes: IterDataPipe) -> IterDataPipe: | |
r""" | |
Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). | |
The output is stopped as soon as the shortest input DataPipe is exhausted. | |
Args: | |
*datapipes: Iterable DataPipes being aggregated | |
Example: | |
>>> # xdoctest: +REQUIRES(module:torchdata) | |
>>> from torchdata.datapipes.iter import IterableWrapper | |
>>> dp1, dp2, dp3 = IterableWrapper(range(5)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25)) | |
>>> list(dp1.zip(dp2, dp3)) | |
[(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] | |
""" | |
class DFIterDataPipe(IterDataPipe): | |
def _is_dfpipe(self): ... | |
class _DataPipeSerializationWrapper: | |
def __init__(self, datapipe): ... | |
def __getstate__(self): ... | |
def __setstate__(self, state): ... | |
def __len__(self): ... | |
class _IterDataPipeSerializationWrapper(_DataPipeSerializationWrapper, IterDataPipe): | |
def __iter__(self): ... | |
class _MapDataPipeSerializationWrapper(_DataPipeSerializationWrapper, MapDataPipe): | |
def __getitem__(self, idx): ... | |
class DataChunk(list, Generic[T]): | |
def __init__(self, items): | |
super().__init__(items) | |
self.items = items | |
def as_str(self, indent: str = "") -> str: | |
res = indent + "[" + ", ".join(str(i) for i in iter(self)) + "]" | |
return res | |
def __iter__(self) -> Iterator[T]: | |
yield from super().__iter__() | |
def raw_iterator(self) -> T: # type: ignore[misc] | |
yield from self.items |
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