# Copyright 2023 MosaicML Streaming authors
# SPDX-License-Identifier: Apache-2.0
"""COCO (Common Objects in Context) dataset.
COCO is a large-scale object detection, segmentation, and captioning dataset. Please refer to the
`COCO dataset <https://cocodataset.org>`_ for more details.
"""
from typing import Any, Callable, Optional
from streaming.base import StreamingDataset
__all__ = ['StreamingCOCO']
[docs]class StreamingCOCO(StreamingDataset):
"""Implementation of the COCO dataset using StreamingDataset.
Args:
local (str): Local dataset directory where shards are cached by split.
remote (str, optional): Download shards from this remote path or directory. If None, this
rank and worker's partition of the dataset must all exist locally. Defaults to
``None``.
split (str, optional): Which dataset split to use, if any. Defaults to ``None``.
shuffle (bool): Whether to iterate over the samples in randomized order. Defaults to
``False``.
transform (callable, optional): A function/transform that takes in an image and bboxes and
returns a transformed version. Defaults to ``None``.
predownload (int, optional): Target number of samples ahead to download the shards of while
iterating. Defaults to ``100_000``.
keep_zip (bool, optional): Whether to keep or delete the compressed file when
decompressing downloaded shards. If set to None, keep iff remote is local. Defaults to
``None``.
download_retry (int): Number of download re-attempts before giving up. Defaults to ``2``.
download_timeout (float): Number of seconds to wait for a shard to download before raising
an exception. Defaults to ``60``.
validate_hash (str, optional): Optional hash or checksum algorithm to use to validate
shards. Defaults to ``None``.
shuffle_seed (int): Seed for Deterministic data shuffling. Defaults to ``9176``.
num_canonical_nodes (int, optional): Canonical number of nodes for shuffling with resumption.
Defaults to ``None``, which is interpreted as the number of nodes of the initial run.
batch_size (int, optional): Batch size of its DataLoader, which affects how the dataset is
partitioned over the workers. Defaults to ``None``.
"""
def __init__(self,
local: str,
remote: Optional[str] = None,
split: Optional[str] = None,
shuffle: bool = False,
transform: Optional[Callable] = None,
predownload: Optional[int] = 100_000,
keep_zip: Optional[bool] = None,
download_retry: int = 2,
download_timeout: float = 60,
validate_hash: Optional[str] = None,
shuffle_seed: int = 9176,
num_canonical_nodes: Optional[int] = None,
batch_size: Optional[int] = None):
super().__init__(local, remote, split, shuffle, predownload, keep_zip, download_retry,
download_timeout, validate_hash, shuffle_seed, num_canonical_nodes,
batch_size)
self.transform = transform
def __getitem__(self, idx: int) -> Any:
"""Get sample by global index, blocking to load its shard if missing.
Args:
idx (int): Sample index.
Returns:
Any: Sample data.
"""
x = super().__getitem__(idx)
img = x['img'].convert('RGB')
img_id = x['img_id']
htot = x['htot']
wtot = x['wtot']
bbox_sizes = x['bbox_sizes']
bbox_labels = x['bbox_labels']
if self.transform:
img, (htot,
wtot), bbox_sizes, bbox_labels = self.transform(img, (htot, wtot), bbox_sizes,
bbox_labels)
return img, img_id, (htot, wtot), bbox_sizes, bbox_labels