# Copyright 2022 MosaicML Streaming authors
# SPDX-License-Identifier: Apache-2.0
"""ADE20K Semantic segmentation and scene parsing dataset.
Please refer to the `ADE20K dataset <https://groups.csail.mit.edu/vision/datasets/ADE20K/>`_ for more details about this
dataset.
"""
from typing import Any, Callable, Optional, Tuple
from streaming.base import StreamingDataset
__all__ = ['StreamingADE20K']
[docs]class StreamingADE20K(StreamingDataset):
"""Implementation of the ADE20K 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``.
joint_transform (callable, optional): A function/transforms that takes in an image and a
target and returns the transformed versions of both. Defaults to ``None``.
transform (callable, optional): A function/transform that takes in an image and returns a
transformed version. Defaults to ``None``.
target_transform (callable, optional): A function/transform that takes in the target and
transforms it. 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,
joint_transform: Optional[Callable] = None,
transform: Optional[Callable] = None,
target_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.joint_transform = joint_transform
self.transform = transform
self.target_transform = target_transform
def __getitem__(self, idx: int) -> Tuple[Any, Any]:
"""Get sample by global index, blocking to load its shard if missing.
Args:
idx (int): Sample index.
Returns:
Tuple[Any, Any]: Sample data and label.
"""
obj = super().__getitem__(idx)
x = obj['x']
y = obj['y']
if self.joint_transform:
x, y = self.joint_transform((x, y))
if self.transform:
x = self.transform(x)
if self.target_transform:
y = self.target_transform(y)
return x, y